Assessment of spongy moth infestation impacts on forest productivity and carbon loss using the Sentinel-2 satellite remote sensing and eddy covariance flux data

Background

Deciduous forests in eastern North America experienced a widespread and intense spongy moth (Lymantria dispar) infestation in 2021. This study quantified the impact of this spongy moth infestation on carbon (C) cycle in forests across the Great Lakes region in Canada, utilizing high-resolution (10 × 10 m2) Sentinel-2 satellite remote sensing images and eddy covariance (EC) flux data. Study results showed a significant reduction in leaf area index (LAI) and gross primary productivity (GPP) values in deciduous and mixed forests in the region in 2021.

Results

Remote sensing derived, growing season mean LAI values of deciduous (mixed) forests were 3.66 (3.18), 2.74 (2.64), and 3.53 (2.94) m2 m-2 in 2020, 2021 and 2022, respectively, indicating about 24 (14)% reduction in LAI, as compared to pre- and post-infestation years. Similarly, growing season GPP values in deciduous (mixed) forests were 1338 (1208), 868 (932), and 1367 (1175) g C m-2, respectively in 2020, 2021 and 2022, showing about 35 (22)% reduction in GPP in 2021 as compared to pre- and post-infestation years. This infestation induced reduction in GPP of deciduous and mixed forests, when upscaled to whole study area (178,000 km2), resulted in 21.1 (21.4) Mt of C loss as compared to 2020 (2022), respectively. It shows the large scale of C losses caused by this infestation in Canadian Great Lakes region.

Conclusions

The methods developed in this study offer valuable tools to assess and quantify natural disturbance impacts on the regional C balance of forest ecosystems by integrating field observations, high-resolution remote sensing data and models. Study results will also help in developing sustainable forest management practices to achieve net-zero C emission goals through nature-based climate change solutions.

Optimization of leaf area index measurement method and correction of green plot ratio formula based on regional plant characteristics-a study in Chongqing, China

The quantification of green space green plot ratio (GPR) is mostly based on estimation formulas, and the leaf area index (LAI) estimation values in these estimation formulas have not been well verified by measured LAI values, resulting in errors and uncertainties in GPR quantification results. This study aims to address this gap by measuring the LAI of 113 regional plants in Chongqing, China, following a standardized measurement path for digital hemispherical photography (DHP). The results indicate that the optimal relative exposure value (REV) was - 1 under overcast conditions and - 2 under sunny and cloudy conditions. Among the threshold algorithms for hemispherical images, the Intermodes algorithm in ImageJ was the best. The LAI of regional plants is highest in summer, followed by spring and autumn, and lowest in winter. Tree height (h) and crown width (w) are key factors affecting LAI, but the LAI also varies with plant species. Overall, the LAI of evergreen trees is higher than that of deciduous trees. The LAI of evergreen trees and shrubs with a height shorter than 5 m is the largest, and that of deciduous trees and shrubs with a crown width larger than 8 m is the largest. The study further verified that the existing GPR estimation formula exhibited large errors in Chongqing, while there was a strong correlation (R2 = 0.973) between the GPR estimation value and the measured value. A conversion formula was developed to reduce estimation biases, and the corrected formula is capable of estimating GPR values more accurately when actual LAI measurements are insufficient. Overall, this study verifies the significance of measuring localized LAI values, promotes the understanding of LAI suitability for GPR calculations, and provides an empirical formula for GPR estimation in Chongqing, China.

Divergent trajectories of future global gross primary productivity and evapotranspiration of terrestrial vegetation in Shared Socioeconomic Pathways

Understanding the future trends of carbon and water fluxes between terrestrial ecosystems and the atmosphere is crucial for predicting Earth's climate dynamics. This study employs an advanced numerical approach to project global gross primary productivity (GPP) and evapotranspiration (ET) from 2001 to 2100 under various climate scenarios based on Shared Socioeconomic Pathways (SSPs). To improve predictions of vegetation dynamics, we introduce a novel model (CoLM-PVPM), an enhancement of the Common Land Model version 2014 (CoLM2014), incorporating a prognostic vegetation phenology model (PVPM). Compared to CoLM2014 that relies on satellite-based leaf area index (LAI) inputs, CoLM-PVPM predicts LAI time series using climate variables. Model validation using historical data from 2001 to 2010 demonstrates PVPM in capturing spatiotemporal variations in satellite LAI. Our modeling results indicate that annual averaged LAI and total GPP increase under SSP1-2.6 but decrease under SSP2-4.5, SSP3-7.0, and SSP5-8.5 by 2100. By comparison, annual total ET consistently increases under all SSP scenarios by 2100. Global annual averaged LAI is highly correlated with annual total GPP in all scenarios, while its correlation with annual total ET weakens in SSP2-4.5, SSP3-7.0, and SSP5-8.5. Global annual total vapor pressure deficit (VPD) and precipitation are highly correlated with annual total ET in all scenarios. As emission levels increase, the negative correlation between annual total VPD and GPP strengthens, while the correlation between annual total precipitation and GPP weakens. This research presents an improved model for predicting terrestrial vegetation processes and underscores the importance of low carbon emission scenarios in maintaining carbon-water balances in specific regions.

A new digital technology to reduce fungicide use in vineyards

There is a growing demand for technologies able to decrease the environmental impact of agricultural activities without penalizing quali-quantitative characteristics of productions. In the case of viticulture, one of the key problems is represented by the spray drift during fungicide treatments. The diffusion in operational farming contexts of technologies based on variable-rate and recycling tunnel sprayers is often limited by their cost and, for the latter, by their size and lower maneuverability, representing clear disadvantages especially in case of small farms or in hilly and mountain areas. We present a new digital technology implemented in a mobile app that supports the reduction of both the number of treatments and the amount of fungicide distributed per treatment. The technology is based (i) on an alert system that prevents unneeded treatments in case of no risk of infection and (ii) on the quantification of the optimal amounts of active ingredients and dilution water based on the sprayer type/settings and on leaf area index values estimated with a common smartphone. An internal database allows to adjust (in case of need) the active ingredient dose to assure full compliance with product's legal requirements. In case of heterogeneity in leaf area index values inside the vineyard, prescription maps are generated. Results from a 2-year case study in a vineyard in northern Italy are shown, where the system allowed to reduce by 26.4 % and 27.4 % (mean of two years), respectively, the seasonal amounts of fungicides and dilution water, and by 43.8 % the copper content in must. The high usability of the technology proposed (just a common smartphone is needed) and the fact that it does not require updating the farm machine park highlights the suitability of the proposed solution for operational farming conditions, including premium wine production districts often characterized by small farms in hilly areas.

Vegetation canopy structure mediates the response of gross primary production to environmental drivers across multiple temporal scales

Gross primary production (GPP) is a critical component of the global carbon cycle and plays a significant role in the terrestrial carbon budget. The impact of environmental factors on GPP can occur through both direct (by influencing photosynthetic efficiency) and indirect (through the modulation of vegetation structure) pathways, but the extent to which these mechanisms contribute has been seldom quantified. In this study, we used structural equation modeling and observations from the FLUXNET network to investigate the direct and indirect effects of environmental factors on terrestrial ecosystem GPP at multiple temporal scales. We found that canopy structure, represented by leaf area index (LAI), is a crucial intermediate factor in the GPP response to environmental drivers. Environmental factors affect GPP indirectly by altering canopy structure, and the relative proportion of indirect effects decreased with increasing LAI. The study also identified different effects of environmental factors on GPP across time scales. At the half-hourly time scale, radiation was the primary driver of GPP. In contrast, the influences of temperature and vapor pressure deficit took on greater prominence at longer time scales. About half of the total effect of temperature on GPP was indirect through the regulation of canopy structure, and the indirect effect increased with increasing time scale (GPPNT-based models: 0.135 (half-hourly) vs. 0.171 (daily) vs. 0.189 (weekly) vs. 0.217 (monthly); GPPDT-based models: 0.139 vs. 0.170 vs. 0.187 vs. 0.215; all values were reported in gC m-2 d-1 °C-1, P < 0.001); while the indirect effect of radiation on GPP was comparatively lower, accounting for less than a quarter of the total effect. Furthermore, we observed a direct, negative-to-positive impact of precipitation on GPP across timescales. These findings provide crucial information on the interplay between environmental factors and LAI on GPP and enable a deeper understanding of the driving mechanisms of GPP.

The influence of stand composition and season on canopy structure and understory light environment in different subtropical montane Pinus massoniana forests

Canopy structure and understory light have important effects on forest productivity and the growth and distribution of the understory. However, the effects of stand composition and season on canopy structure and understory light environment (ULE) in the subtropical mountain Pinus massoniana forest system are poorly understood. In this study, the natural secondary P. massoniana-Castanopsis eyrei mixed forest (MF) and P. massoniana plantation forest (PF) were investigated. The study utilized Gap Light Analyzer 2.0 software to process photographs, extracting two key canopy parameters, canopy openness (CO) and leaf area index (LAI). Additionally, data on the transmitted direct (Tdir), diffuse (Tdif), and total (Ttot) radiation in the light environment were obtained. Seasonal variations in canopy structure, the ULE, and spatial heterogeneity were analyzed in the two P. massoniana forest stands. The results showed highly significant (P < 0.01) differences in canopy structure and ULE indices among different P. massoniana forest types and seasons. CO and ULE indices (Tdir, Tdif, and Ttot) were significantly lower in the MF than in the PF, while LAI was notably higher in the MF than in the PF. CO was lower in summer than in winter, and both LAI and ULE indices were markedly higher in summer than in winter. In addition, canopy structure and ULE indices varied significantly among different types of P. massoniana stands. The LAI heterogeneity was lower in the MF than in the PF, and Tdir heterogeneity was higher in summer than in winter. Meanwhile, canopy structure and ULE indices were predominantly influenced by structural factors, with spatial correlations at the 10 m scale. Our results revealed that forest type and season were important factors affecting canopy structure, ULE characteristics, and heterogeneity of P. massoniana forests in subtropical mountains.

Satellite-observed increasing coupling between vegetation productivity and greenness in the semiarid Loess Plateau of China is not captured by process-based models

Global vegetation has experienced notable changes in greenness and productivity since the early 1980s. However, the changes in the relationship between productivity and greenness, i.e., the coupling, and its underlying mechanisms, are poorly understood. The Loess Plateau (LP) is one of China's most significant areas for vegetation greening. Yet, it remains poorly documented what changes in the coupling between productivity and greenness are and how environmental and anthropogenic factors affect this coupling in the LP over the past four decades. We investigated the interannual trend of coupling between Gross Primary Productivity (GPP) and Leaf Area Index (LAI), i.e., the GPP-LAI coupling, and its response to climate factors and afforestation in the LP using long-term remote-sensed LAI, GPP and Solar-induced Chlorophyll Fluorescence (SIF). We found a monotonically increasing trend in the GPP-LAI coupling in the LP from 1982 to 2018 (0.0043 yr-1, p < 0.05), in which the significant trend in the northwest LP was driven by increasing soil water and landcover change, e.g., increased grassland and afforestation. An ensemble of 11 state-of-the-art ecosystem models from the TRENDY project failed to capture the observed monotonically increasing trend of the GPP-LAI coupling in the LP. The consistent projection of a decreasing GPP-LAI coupling in LP during 2019-2100 by 22 Earth System Models (ESMs) under various future scenarios should be treated with caution due to the identified inherent uncertainties in the ecosystem component in ESMs and the notable biases in the simulation of future climate conditions. Our study highlights the need to enhance the key mechanisms that regulate the coupling relationships between photosynthesis and canopy structure in indigenized ecosystem models to accurately estimate the ecosystem change in drylands under global climate change.

Ecohydrologic model with satellite-based data for predicting streamflow in ungauged basins

Information on water availability in basins can be crucial for making decisions for effective water resource management in basins. As the operation of hydrometric stations in Korea is mainly focused on flood season and large rivers, most basins have lack or no observed data. Consequently, this complicates water resource planning and management. Remote sensing data is emerging as a powerful alternative to hydrological information in ungauged basins. This study investigated the applicability of Satellite-Remote Sensed Data (SRSD) as a source for model calibration in Prediction in Ungauged Basins (PUB) through modeling. Remote sensed leaf area index (LAI), actual evapotranspiration, and soil moisture data were used. Each SRSD was used alone to calibrate a hydrologic model to predict the daily streamflow for 28 basins in Korea. A vegetation module was added to the existing hydrologic model to use LAI. Among the SRSDs tested, the model calibrated with LAI had the most robust performance, predicting streamflow with acceptable accuracy compared to the traditional calibration based on streamflow. In particular, since the model account for vegetation actively interacting with evapotranspiration and soil moisture in the season of low flow, the LAI-calibrated model showed an advantage in improving the flow prediction performance. Although further research is required to utilize evapotranspiration and soil moisture data, the overall results of the LAI-based calibration were promising for predicting streamflow in ungauged basins where observations are scarce or absent, given that the satellite-derived LAI data were used alone without any preprocessing such as a bias correction. However, the prediction performance of the LAI-calibrated model was found to have a statistically significant relationship with local conditions. Therefore, by evaluating and improving the potential of SRSD in different region and climatic conditions, it is expected that the application of the SRSD-only calibration method can be extended to various ungauged basins.

Yield and lodging response of tef [Eragrostis tef (Zucc) trotter] varieties to nitrogen and silicon application rates

Lodging, poor crop varieties and nitrogen management are among the main tef cultivation problems in acidic soils of northwestern Ethiopia. Though Si has been shown to improve crop yield and lodging resistance, knowledge of its effect on tef, along genotypes and nitrogen, is yet to be uncovered. Therefore, a 4 × 2 × 2 factorial field experiment was conducted on fixed experimental plot at the Koga irrigation scheme to assess yield and lodging responses of tef varieties to nitrogen and silicon fertilizer rates during two consecutive years of 2021 and 2022. The experiment comprised four nitrogen levels: 0 (N1), 23 (N2), 46 (N3), and 92 kg N ha-1(N4), two Si levels: 0 (Si1) and 485 (Si2) kg ha-1, and two improved varieties: Hiber-1 (V1) and Quncho (V2) treatment combinations, which were replicated four times. Results showed that regardless of silicon supply and variety, nitrogen had a significant effect (p < .0001) on agronomic attributes of tef grain yield, biomass yield, harvest index, chlorophyll content, plant height, panicle length, leaf area index, and the number of plants m-2 over the two years. Application of N4, N3, and N2 improved grain yield by 166.9, 126.2, and 75.2 % over N1, respectively. The harvest index showed a declining trend with nitrogen rates, which ranged from 36.1 to 26.5 %. Hiber-1 showed a significantly (p < .01) higher panicle length than Quncho. The interaction of nitrogen, silicon, and variety significantly (p < .001) affected lodging index, with a minimum lodging index of 0 % from V1Si1N1 and a maximum lodging index (71.9 %) from V2Si1N4. Maximum net return (2552.6 USD) was obtained from V1Si1N4, while the marginal rate of return (6961.7 %) from V1Si1N3. Therefore, it can be concluded that genotype and optimum nitrogen can maximize yield and lodging resistance of tef, while silicon in the form of carbonized rice husk results no significant effect on tef lodging.

A study of the thermal comfort in urban mountain parks and its physical influencing factors

Urban thermal comforts are increasingly holding people's attention due to global warming and urban heat islands. Urban parks can absorb sunlight radiation, which reduces air temperature, improving urban microclimates. Various factors in the park are confirmed to be effective in heat mitigation. However, there are few studies on thermal comfort in urban mountain parks, and mountain areas might cause peculiar climatic conditions owing to their particular landforms. To fill this gap in the research, this study explored thermal comfort in mountain parks and the environmental factors that would affect thermal comfort. A field measurement in the summertime (July & August) of 2018, it was found that trees, the river, and the area of parks could adjust the thermal comforts of mountain parks. Their effects varied throughout the day, and the impacts of trees were most pronounced at noon and late afternoon, while the influence of rivers and park areas was most pronounced at noon. Increasing the leaf area index by 1 point could result in decreases in physiological equivalent temperature, land surface temperature, and solar radiation level by 3.90 °C, 2.69 °C, and 270.10 W/m2, respectively. The findings have practical implications for future urban mountain park design works.

Seasonal evaluation and mapping of aboveground biomass in natural rangelands using Sentinel-1 and Sentinel-2 data

Rangelands play a vital role in developing countries' biodiversity conservation and economic growth, since most people depend on rangelands for their livelihood. Aboveground-biomass (AGB) is an ecological indicator of the health and productivity of rangeland and provides an estimate of the amount of carbon stored in the vegetation. Thus, monitoring seasonal AGB is important for understanding and managing rangelands' status and resilience. This study assesses the impact of seasonal dynamics and fire on biophysical parameters using Sentinel-1 (S1) and Sentinel-2 (S2) image data in the mesic rangeland of Limpopo, South Africa. Six sites were selected (3/area), with homogenous vegetation (10 plots/site of 30m2). The seasonal measurements of LAI and biomass were undertaken in the early summer (December 2020), winter (July-August 2021), and late summer (March 2022). Two regression approaches, random forest (RF) and stepwise multiple linear regression (SMLR), were used to estimate seasonal AGB. The results show a significant difference (p < 0.05) in AGB seasonal distribution and occurrence between the fire (ranging from 0.26 to 0.39 kg/m2) and non-fire areas (0.24-0.35 kg/m2). In addition, the seasonal predictive models derived from random forest regression (RF) are fit to predict disturbance and seasonal variations in mesic tropical rangelands. The S1 variables were excluded from all models due to high moisture content. Hence, this study analyzed the time series to evaluate the correlation between seasonal estimated and field AGB in mesic tropical rangelands. A significant correlation between backscattering, AGB and ecological parameters was observed. Therefore, using S1 and S2 data provides sufficient data to obtain the seasonal changes of biophysical parameters in mesic tropical rangelands after disturbance (fire) and enhanced assessments of critical phenology stages.

Combined influence of ENSO and North Atlantic Oscillation (NAO) on Eurasian Steppe during 1982-2018

As the most influential atmospheric oscillation on Earth, the El Niño/Southern Oscillation (ENSO) can significantly change the surface climate of the tropics and subtropics and affect the high latitudes of northern hemisphere areas through atmospheric teleconnection. The North Atlantic Oscillation (NAO) is the dominant pattern of low-frequency variability in the Northern Hemisphere. As the dominant oscillations in the Northern Hemisphere, the ENSO and NAO have been affecting the giant grassland belt in the world, the Eurasian Steppe (EAS), in recent decades. In this study, the spatio-temporal anomaly patterns of grassland growth in the EAS and their correlations with the ENSO and NAO were investigated using four long-term leaf area index (LAI) and one normalized difference vegetation index (NDVI) remote sensing products from 1982 to 2018. The driving forces of meteorological factors under the ENSO and NAO were analyzed. The results showed that grassland in the EAS has been turning green over the past 36 years. Warm ENSO events or positive NAO events accompanied by increased temperature and slightly more precipitation promoted grassland growth, and cold ENSO events or negative NAO events with cooling effects over the whole EAS and uneven precipitation decreased deteriorated the EAS grassland. During the combination of warm ENSO and positive NAO events, a more severe warming effect caused more significant grassland greening. Moreover, the co-occurrence of positive NAO with cold ENSO or warm ENSO with negative NAO kept the characteristic of the decreased temperature and rainfall in cold ENSO or negative NAO events, and deteriorate the grassland more severely.

Divergent sensitivity of vegetation to aridity between drylands and humid regions

The land surface has been drying over recent decades, which is inconsistent with the greening of the Earth. The extent and spatial variation in the sensitivity of vegetation to aridity changes in drylands and humid regions remain unclear. In this study, satellite observation and reanalysis data were used to analyze the relationship between vegetation growth and atmospheric aridity changes in different climatological regions on a global scale. Our results showed that the leaf area index (LAI) increased at a rate of 0.032/decade from 1982 to 2014, while the aridity index (AI) increased slightly at a rate of 0.005/decade. Over the past three decades, the sensitivity of the LAI to AI has decreased in drylands and increased in humid regions. Thus, the LAI and AI were decoupled in drylands, whereas the effect of aridity on vegetation was enhanced in humid regions during the study period. The physical and physiological effects of increasing CO₂ concentration are responsible for the divergent responses of vegetation sensitivity to aridity in drylands and humid regions. The results of the structural equation models showed that the effect of increasing CO₂ concentration via LAI and temperature, with respect to decreasing AI, enhanced the negative relationship between LAI and AI in humid regions. The greenhouse effect of increasing CO₂ concentration resulted in an increase in temperature and a reduction in aridity, whereas the fertilization effect of CO₂ increased LAI, thus creating an inconsistent trend with LAI and AI in drylands.

Multi-phase recovery of carabid assemblages during 19 years of secondary succession in forest stands disturbed by windstorm without salvage logging in northern Poland

Windstorms impact the functioning and structure of forests and cause economic losses. For this reason, various potential methods of regenerating windthrown stands are investigated. Some of these studies use invertebrates, such as carabid beetles (Col., Carabidae). Salvage logging is used to recoup some of the economic ecosystem losses but increases the environmental impact of windthrow. I sampled ground beetles annually over 19 years (2003-2021y) in stands without salvage logging to test the effect of three varying levels of disturbance (severely, moderately and least disturbed stands with canopy cover of 10-30 %, 40-60 % and 70-90 %, respectively) on the regeneration of carabid assemblages and to determine its association with changes in the soil environment and in the recovering stands. Increased disturbance severity increased the abundance (up to 0.4 ind/trap/day) and species richness of ground beetles (up to 16.4) and proportion of beetles associated with early successional habitats (up to 53.5 %). Recovery of carabid assemblages and the environment was slowest in the severely disturbed stands, where at high soil pH nitrification initially increased the pool of nitrogen in the soil (up to 0.3), which was exploited by nitrophilous grasses taking over the space (up to 37,5 %), limiting the occurrence of forest species (decrease from 82.2 % to 51.4 %) and delaying the development of natural regeneration. Carabid recovery and ecosystem regeneration were associated with forest mosses surviving (84.1 % coverage) in patches with a high leaf area index (up to 1.9) and with the presence of Vaccinium vitis-idaea (up to 53.3 % coverage) in the moderately and least disturbed stands. The study indicated advanced successional development of carabid assemblages in less disturbed stands which can regenerate naturally. Natural recovery of carabids and regeneration of the most disturbed stands, rapidly taken over by nitrophilous grasses, was impeded; therefore, such stands should be regenerated traditionally.

Is stunting in children under five associated with the state of vegetation in the Democratic Republic of the Congo? Secondary analysis of Demographic Health Survey data and the satellite-derived leaf area index

Background

The prevalence of stunting in the Democratic Republic of the Congo (DRC) is one of the highest globally. However, only a few studies have attempted to measure the association between stunting and vegetation, which is an important food source. The leaf area index (LAI) is an excellent measure for the vegetation state.

Objective

This paper intended to measure the association between the LAI and stunting among children under five years of age in the DRC. Its aim was to better understand the boundary conditions of stunting and explore potential links to climate and environmental change.

Methods

This paper adopts a secondary data analysis approach. We used data on 5241 children from the DRC Demographic Health Survey (DHS) 2013-2014, which was collected from a nationally representative cross-sectional survey. We used the satellite-derived LAI as a measure for the state of vegetation and created a 10-km buffer to extract each DHS cluster centroid's corresponding mean leaf-area value. We used a generalised mixed-effect logistic regression to measure the association between LAI and stunting, adjusting the model for mother's education, occupation and birth interval, as well as child's age and national wealth quintile. A height-for-age Z-score (HAZ) was calculated and classified according to WHO guidelines.

Results

Children in communities surrounded by high LAI values have lower odds of being stunted (OR [odds ratio] = 0.63; 95% CI [confidence interval] = 0.47-0.86) than those exposed to low LAI values. The association still holds when the exposure is analysed as a continuous variable (OR = 0.84; 95% CI = 0.74-0.95).When stratified in rural and urban areas, a significant association was only observed in rural areas (OR = 0.6; 95% CI = 0.39-0.81), but not in urban areas (OR = 0.9; 95% CI = 0.5-0.5). Furthermore, the study showed that these associations were robust to LAI buffer variations under 25 km.

Conclusions

Good vegetation conditions have a protective effect against stunting in children under five years of age. Further advanced study designs are needed to confirm these findings.

Will future maize improvement programs leverage the canopy light-interception, photosynthetic, and biomass capacities of traditional accessions?

Maize germplasm has greater latent potential to address the global food and feed crisis because of its high radiation, water and nutrient efficiencies. Photosynthetic and canopy architectural traits in maize are important in determining yield. The present study aimed to screen a subset of local maize accessions in Sri Lanka to evaluate their photosynthetic, biomass and yield related traits and to identify resource efficient germplasm. Experiments were carried out in the Ampara district of Sri Lanka. Eight maize accessions viz; SEU2, SEU6, SEU9, SEU10, SEU14, SEU15, SEU17 and SEU17 and two elite F₁ cultivars (cv. Pacific-999 and cv. Bhadra) were analyzed under field conditions. Our results showed that maize genotypes produced a lower leaf area index (LAI) at the third and tenth week after field planting (WAP). However, the LAI was significantly increased in six WAP by Pacific-999, SEU2, SEU9, and SEU15. A similar trend was observed for percentage of light interception at three WAP (47%), six WAP (>64%), and decreased at 10 WAP. In addition, LAI maximum values were between 3.0 and 3.5, allowing 80% of the incident light to be intercepted by maize canopies. The estimated light extinction coefficient (k) remained lower (<0.5), suggesting that maize leaves are eractophilic canopies. Although fractional interception (f) varies, SEU2 and SEU9 had the highest values (0.57), and quantum yields of PSII (>0.73) in dark-adapted leaves. In addition, Pacific-999, SEU2, SEU9, and SEU17 had significantly higher rates of photosynthesis with minimal stomatal conductance and transpiration rates. As a result, they outperformed the control plants in terms of biomass, cob weight and grain yield. This suggests that native maize germplasm could be introduced as novel, less resource-intensive cultivars to sustain global food security.

Hybrid AI model for estimating the canopy photosynthesis of eggplants

Modern models for estimating canopy photosynthetic rates (A_c) can be broadly classified into two categories, namely, process-based mechanistic models and artificial intelligence (AI) models, each category having unique strengths (i.e., process-based models have generalizability to a wide range of situations, and AI models can reproduce a complex process using data without prior knowledge about the underlying mechanism). To exploit the strengths of both categories of models, a novel "hybrid" canopy photosynthesis model that combines process-based models with an AI model was proposed. In the proposed hybrid model, process-based models for single-leaf photosynthesis and image analysis first transform raw inputs (environmental data and canopy images) into the single-leaf photosynthetic rate (A_L) and effective leaf area index (L_c)), after which A_L and L_c are fed into an artificial neural network (ANN) model to predict A_c. The hybrid model successfully predicted the diurnal cycles of A_c of an eggplant canopy even with a small training dataset and successfully reproduced a typical A_c response to changes in the CO₂ concentration outside the range of the training data. The proposed hybrid AI model can provide an effective means to estimate A_c in actual crop fields, where obtaining a large amount of training data is difficult.

Estimation of cotton canopy parameters based on unmanned aerial vehicle (UAV) oblique photography

BACKGROUND

The technology of cotton defoliation is essential for mechanical cotton harvesting. Agricultural unmanned aerial vehicle (UAV) spraying has the advantages of low cost, high efficiency and no mechanical damage to cotton and has been favored and widely used by cotton planters in China. However, there are also some problems of low cotton defoliation rates and high impurity rates caused by unclear spraying amounts of cotton defoliants. The chemical rate recommendation and application should be based upon crop canopy volume rather than on land area. Plant height and leaf area index (LAI) is directly connected to plant canopy structure. Accurate dynamic monitoring of plant height and LAI provides important information for evaluating cotton growth and production. The traditional method to obtain plant height and LAI was s a time-consuming and labor-intensive task. It is very difficult and unrealistic to use the traditional measurement method to make the temporal and spatial variation map of plant height and LAI of large cotton fields. With the application of UAV in agriculture, remote sensing by UAV is currently regarded as an effective technology for monitoring and estimating plant height and LAI.

RESULTS

In this paper, we used UAV RGB photos to build dense point clouds to estimate cotton plant height and LAI following cotton defoliant spraying. The results indicate that the proposed method was able to dynamically monitor the changes in the LAI of cotton at different times. At 3 days after defoliant spraying, the correlation between the plant height estimated based on the constructed dense point cloud and the measured plant height was strong, with [Formula: see text] and RMSE values of 0.962 and 0.913, respectively. At 10 days after defoliant spraying, the correlation became weaker over time, with [Formula: see text] and RMSE values of 0.018 and 0.027, respectively. Comparing the actual manually measured LAI with the estimated LAI based on the dense point cloud, the [Formula: see text] and RMSE were 0.872 and 0.814 and 0.132 and 0.173 at 3 and 10 days after defoliant spraying, respectively.

CONCLUSIONS

Dense point cloud construction based on UAV remote sensing is a potential alternative to plant height and LAI estimation. The accuracy of LAI estimation can be improved by considering both plant height and planting density.

Reconstruction and application of the temperature-vegetation-precipitation drought index in mainland China based on remote sensing datasets and a spatial distance model

In recent years, remote sensing drought monitoring indices have been gradually developed and have been widely used for global or regional drought monitoring due to their strong drought-monitoring capabilities and easy implementation advantages. However, some defects of remote sensing drought indices stand to be improved due to certain errors in the inversion of surface characteristics by remote sensing datasets. The temperature-vegetation-precipitation drought index (TVPDI) was taken as the research object, and the leaf area index (LAI), the difference between the land surface temperature (LST) and monthly average temperature, and Global Precipitation Measurement (GPM) precipitation data were selected instead of the normalized difference vegetation index (NDVI), LST and tropical rainfall measuring mission (TRMM) data to improve TVPDI. The improved remote sensing drought index was named the improved temperature-vegetation-precipitation drought index (iTVPDI). The drought-monitoring accuracy of iTVPDI was verified by the gross primary productivity (GPP), soil moisture, and crop yield per unit. The drought-monitoring ability of iTVPDI was verified with traditional drought indices, including the standardized precipitation index (SPI), standardized precipitation evapotranspiration index (SPEI), Palmer drought severity index (PDSI), temperature-vegetation drought index (TVDI), drought severity index (DSI) and crop water stress index (CWSI). The drought-monitoring accuracy of iTVPDI was verified by selecting sample areas. iTVPDI was applied to monitor drought in mainland China over the 2001-2020 period and obtained four main results. First, the correlation analyses of iTVPDI and TVPDI with GPP, crop yield per unit area, and soil moisture showed that iTVPDI had a stronger monitoring ability in Northeast, North, and Southwest China; the R² value obtained with soil moisture was 0.62 (p < 0.05), and this value was higher than that of TVPDI. Then, the correlation analyses of iTVPDI and TVPDI with SPI, SPEI, PDSI, CWSI, DSI and TVDI showed that the correlation coefficients of iTVPDI and TVPDI with these six indicators were basically consistent, which indicated that the drought-monitoring capability of iTVPDI was consistent with that of TVPDI. In local areas such as the Qinghai-Tibet Plateau in China, the monitoring ability of iTVPDI was stronger than that of TVPDI. Third, through the sample area analysis, iTVPDI was found to moderate the NDVI-characterized vegetation factors in TVPDI in low-vegetation-cover areas affected by soil disturbances and in high-vegetation-cover areas affected by oversaturation. Finally, the results obtained from the application of iTVPDI in mainland China showed that during the warm-dry to warm-wet climate transition between 2001 and 2021, in 2010 and 2018, and in other special drought years, iTVPDI had the best response.

Hyperspectral response of agronomic variables to background optical variability: Results of a numerical experiment

Understanding how biophysical and biochemical variables contribute to the spectral characteristics of vegetation canopies is critical for their monitoring. Quantifying these contributions, however, remains difficult due to extraneous factors such as the spectral variability of canopy background materials, including soil/crop-residue moisture, soil-type, and non-photosynthetic vegetation (NPV). This study focused on exploring the spectral response of two important agronomic variables (1) leaf chlorophyll content (Cab ) and (2) leaf area index (LAI) under various canopy backgrounds through a global sensitivity analysis of wheat-like canopy spectra simulated using the physically-based PROSAIL radiative transfer model. Our results reveal the following general findings: (1) the contribution of each agronomic variable to the simulated canopy spectral signature varies considerably with respect to the background optical properties; (2) the influence of the soil-type and NPV on the spectral response of canopy to Cab and LAI is more significant than that caused by soil/crop-residue moisture; (3) spectral bands at 560 and 704 nm remain sensitive to Cab while being least affected by the impacts of variations in the NPV, soil-type and moisture; (4) the near-infrared (NIR) spectral bands exhibit higher sensitivity to LAI and lower background effects only in the cases of soil/crop-residue moisture but are relatively strongly affected by soil-type and NPV. Comparative analysis of the correlations of twelve widely used vegetation indices with agronomic variables indicates that LICI (LAI-insensitive chlorophyll index) and Macc01 (Maccioni index) are more effective in estimating Cab , while OSAVI (optimized soil adjusted vegetation index) and MCARI2 (modified chlorophyll absorption ratio index 2) are better LAI predictors under the simulated background variability. Overall, our results highlight that background reflectance variability introduces considerable differences in the agronomic variables' spectral response, leading to inconsistencies in the VI- Cab /-LAI relationship. Further studies should integrate these results of spectral responsivity to develop trait-specific hyperspectral inversion models.

Temporal Stability of Seagrass Extent, Leaf Area, and Carbon Storage in St. Joseph Bay, Florida: a Semi-automated Remote Sensing Analysis

Seagrasses are globally recognized for their contribution to blue carbon sequestration. However, accurate quantification of their carbon storage capacity remains uncertain due, in part, to an incomplete inventory of global seagrass extent and assessment of its temporal variability. Furthermore, seagrasses are undergoing significant decline globally, which highlights the urgent need to develop change detection techniques applicable to both the scale of loss and the spatial complexity of coastal environments. This study applied a deep learning algorithmto a 30-year time series of Landsat 5 through 8 imagery to quantify seagrass extent, leaf area index (LAI), and belowground organic carbon (BGC) in St. Joseph Bay, Florida, between 1990 and 2020. Consistent with previous field-based observations regarding stability of seagrass extent throughout St. Joseph Bay, there was no temporal trend in seagrass extent (23 ± 3 km2, τ = 0.09, p = 0.59, n = 31), LAI (1.6 ± 0.2, τ = -0.13, p = 0.42, n = 31), or BGC (165 ± 19 g C m-2, τ = - 0.01, p = 0.1, n = 31) over the 30-year study period. There were, however, six brief declines in seagrass extent between the years 2004 and 2019 following tropical cyclones, from which seagrasses recovered rapidly. Fine-scale interannual variability in seagrass extent, LAI, and BGC was unrelated to sea surface temperature or to climate variability associated with the El Niño-Southern Oscillation or the North Atlantic Oscillation. Although our temporal assessment showed that seagrass and its belowground carbon were stable in St. Joseph Bay from 1990 to 2020, forecasts suggest that environmental and climate pressures are ongoing, which highlights the importance of the method and time series presented here as a valuable tool to quantify decadal-scale variability in seagrass dynamics. Perhaps more importantly, our results can serve as a baseline against which we can monitor future change in seagrass communities and their blue carbon.

Forest cover resilience to climate change over India using the MC2 dynamic vegetation model

It is imperative to understand the climate change impact on the forest ecosystem to develop appropriate mitigation and management strategies. We have employed a process-based dynamic vegetation modeling (MAPSS-CENTURY: MC) approach to project change in vegetation life forms under projected climate conditions that attained 81% overall accuracy. The present and projected climate conditions suggested highly resilient/stable forest covers in wet climate regimes and moderately resilient in dry semi-arid regions. Several forested grids in the seasonally dry tropical forest in the Eastern Ghats and dry Deccan peninsula regions are estimated to be less resilient, which may experience a regime shift toward scrub and grassland. The future prediction demonstrated an upward temperature shift in the Western Himalayas and trans-Himalaya, which may facilitate forest spread at higher elevations. Although the forest cover resilience may increase in future climate conditions, the disturbances in several regions in the Deccan Peninsula and the Eastern Ghats may trigger forest to scrub and grassland transition. The inaccuracy in model simulation in the Western Himalayas could be attributed to coarse resolution grids (0.5°) failing to resolve the narrow climate niches. The spatially explicit model simulation provides opportunities to develop long-term climate change adaptation and conservation strategies.

Moderate resolution LAI prediction using Sentinel-2 satellite data and indirect field measurements in Sikkim Himalaya

The leaf area index (LAI) has been traditionally used as a photosynthetic variable. LAI plays an essential role in forest cover monitoring and has been identified as one of the important climate variables. However, due to challenges in field sampling, complex topography, and availability of cloud-free optical satellite data, LAI assessment on larger scale is still unexplored in the Sikkim Himalayan area. We used two optical instruments, digital hemispherical photography (DHP) and LAI-2200C, to assess the LAI across four different forests following 20 × 20 m² elementary sampling units (ESUs) in the Himalayan state of Sikkim, India. The use of Sentinel-2 derived vegetation indices (VIs) demonstrated a better correlation with the DHP based LAI estimates than using LAI-2200C. Further, the combination of both reflectance bands and VIs were integrated to predict the LAI maps using random forest model. The temperate evergreen forests demonstrated the highest LAI value, while the predicted maps exhibited LAI maxima of 3.4. The estimated vs predicted LAI for DHP and LAI-2200C based estimation demonstrated reasonably good (R² = 0.63 and R² = 0.68, respectively) agreement. Further, improvements on the LAI prediction can be attempted by minimizing errors from the inherent field protocols, optimizing the density of field measurements, and representing heterogeneity. The recent rise of frequent forest fires in Sikkim Himalaya prompts for better understanding of fuel load in terms of surface fuel or canopy fuel that can be linked to LAI. The high-resolution LAI map could serve as input to forest fuel bed characterization, especially in seasonal forests with significant variations in green leaves and litter, thereby offering inputs for forest management in changing climate.

Modeling leaf area index using time-series remote sensing and topographic data in pure Anatolian black pine stands

We aimed to map and analyze LAI by using Landsat 8 and Sentinel-2 time series and the corresponding ground measurements collected in pure Anatolian black pine [Pinus nigra J.F. Arnold ssp. pallasiana (Lamb.) Holmboe] stands within seven-month (from June to December) period. A total of 30 sample plots were selected and seven-month changes of LAI values were determined through hemispherical photography for each sample plot. Remote sensing (reflectance values and vegetation indices obtained from Landsat-8 and Sentinel-2) and topographic (elevation, aspect, and slope) data were used to model the LAI for each month using multiple linear regression (MLR) method. Additionally, the data for all months were combined and modeled. In this case, autoregressive modeling techniques were used to solve the temporal autocorrelation problem. Our study indicated that the models developed from Sentinel-2 give more successful results than Landsat 8 on monthly LAI models. The most successful models were obtained in June by using the reflectance values (R adj 2  = 0.39, RMSE = 0.3138 m2 m-2), reflectance values-topographic data (R adj 2  = 0.59, RMSE = 0.3174 m2 m-2), vegetation indices-topographic data (R adj 2  = 0.82, RMSE = 0.2126 m2 m-2), and reflectance values-vegetation indices-topographic data (R adj 2  = 0.93, RMSE = 0.1060 m2 m-2). Among the autoregressive modeling techniques, the highest success was obtained with the Landsat 8 OLI using the moving average (2) procedure (R2 = 0.56). This study is significant that it is the first to analyze the monthly effect on LAI modeling and mapping in pure Anatolian black pine stands using both reflectance values, vegetation indices, and topographic data.

Spatiotemporal changes and driving factors of vegetation in 14 different climatic regions in the global from 1981 to 2018

Climate change affects the change of vegetation, and the analysis of vegetation change and its drivers in different globe climate zones is important for ecological conservation, energy balances, and climate change in different global climate zones. Based on the vegetation leaf area index (LAI) and climate factor datasets, this paper uses an integrated empirical model decomposition, sensitivity rate, contribution rate, and geographic detector analysis method to study the vegetation drivers and their changes in 14 different climate zones around the globe from 1981 to 2018. The results showed that (1) Vegetation changes were sensitive to precipitation and evapotranspiration in arid climate zones and to temperature and soil temperature in cold climate zones. In the tundra climate zone, the sensitivity of vegetation change to temperature was higher than that to precipitation and evapotranspiration. (2) Soil moisture has the highest contribution to vegetation change, and the areas with absolute contribution rates over 60% account for 50.26% of the total area of global vegetation cover. The areas with high contributions of temperature and soil temperature to the LAI are mainly distributed in the Northern Hemisphere, which indicates that temperature has a high contribution to vegetation change in low-temperature environments. (3) The areas with significant increasing trends for the global vegetation LAIs accounted for approximately 15.32% of the total global vegetation cover (slope ≥ 0.01), which are mainly located in equatorial savannahs with dry winters, warm temperate climates with dry winters, and warm temperate climates with fully humid climatic zones. (4) The LAIs were dominated by medium-high fluctuations and sustainable increasing changes, which accounted for 61.27% and 69.34% of the total global vegetation cover area, respectively. (5) Globally, the driving factors influencing LAI changes are specific humidity, temperature, soil temperature, evapotranspiration, precipitation, and soil moisture in descending order, with the largest interaction effect of specific humidity and soil moisture on LAI changes. This research provides a scientific basis for vegetation change monitoring, driving mechanisms, and ecological protection in different climate regions around the globe.

Emissions of biogenic volatile organic compounds from urban green spaces in the six core districts of Beijing based on a new satellite dataset

Urban green spaces (UGSs) are often positively associated with the health of urban residents. However, UGSs may also have adverse health effects by releasing biogenic volatile organic compounds (BVOCs) and increasing the ambient concentrations of ozone (O₃) and secondary organic aerosols in urban areas. BVOC emissions from UGSs might be underestimated because of the lack of consideration of the UGS land-use type in urban areas. As such, in this study, we used a newly released satellite dataset, Sentinel-2, with a resolution of 10 m, to derive the classification distribution of UGSs and predict the UGS emissions of BVOCs in Beijing in 2019. The results showed that the annual emissions of BVOCs from UGSs were approximately 2.9 Gg C (95% confidence interval (CI): 2.4-3.3) in the six core districts, accounting for approximately 39% of the total UGS emissions in Beijing. Compared with the results based on Sentinel-2, the BVOC emissions might be underestimated by approximately 37% (95% CI: 11-63) using the commonly used satellite dataset. UGSs produced the highest BVOC emissions in summer (from June to August), accounting for 75.2% of the annual emissions. UGSs contributed the most to the O₃ formation potential in summer, accounting for 41.5% of the total. We could attribute a considerable amount of the O₃ concentration (27.0 μg m^-3, 95% CI: 21.4-32.6) to the UGS BVOCs produced in the core districts of Beijing in July. The new BVOC emissions dataset based on Sentinel-2 vegetation information facilitates modeling studies on the formation of surface O₃ in urban areas and assessments of the impact of UGSs on public health.

The sensitivities of ozone and PM2.5 concentrations to the satellite-derived leaf area index over East Asia and its neighboring seas in the WRF-CMAQ modeling system

Vegetation plays an important role as both a sink of air pollutants via dry deposition and a source of biogenic VOC (BVOC) emissions which often provide the precursors of air pollutants. To identify the vegetation-driven offset between the deposition and formation of air pollutants, this study examines the responses of ozone and PM_2.5 concentrations to changes in the leaf area index (LAI) over East Asia and its neighboring seas, using up-to-date satellite-derived LAI and green vegetation fraction (GVF) products. Two LAI scenarios that examine (1) table-prescribed LAI and GVF from 1992 to 1993 AVHRR and 2001 MODIS products and (2) reprocessed 2019 MODIS LAI and 2019 VIIRS GVF products were used in WRF-CMAQ modeling to simulate ozone and PM_2.5 concentrations for June 2019. The use of up-to-date LAI and GVF products resulted in monthly mean LAI differences ranging from -56.20% to 96.81% over the study domain. The increase in LAI resulted in the differences in hourly mean ozone and PM_2.5 concentrations over inland areas ranging from 0.27 ppbV to -7.17 ppbV and 0.89 μg/m³ to -2.65 μg/m³, and the differences of those over the adjacent sea surface ranging from 0.69 ppbV to -2.86 ppbV and 3.41 μg/m³ to -7.47 μg/m³. The decreases in inland ozone and PM_2.5 concentrations were mainly the results of dry deposition accelerated by increases in LAI, which outweighed the ozone and PM_2.5 formations via BVOC-driven chemistry. Some inland regions showed further decreases in PM_2.5 concentrations due to reduced reactions of PM_2.5 precursors with hydroxyl radicals depleted by BVOCs. The reductions in sea surface ozone and PM_2.5 concentrations were accompanied by the reductions in those in upwind inland regions, which led to less ozone and PM_2.5 inflows. The results suggest the importance of the selective use of vegetation parameters for air quality modeling.

Improved forest dynamics leads to better hydrological predictions in watershed modeling

This study explored how the characterization of forest processes in hydrologic models affects watershed hydrological responses. To that end, we applied the widely used Soil and Water Assessment Tool (SWAT) model to two forested watersheds in the southeastern United States. Although forests can cover a large portion of watersheds, tree attributes such as leaf area index (LAI), biomass accumulation, and processes such as evapotranspiration (ET) are rarely calibrated in hydrological modeling studies. The advent of freely and readily available remote-sensing data, combined with field observations from forestry studies and published literature, allowed us to develop an improved forest parameterization for SWAT. We tested our proposed parameterization at the watershed scale in Florida and Georgia and compared simulated LAI, biomass, and ET with the default model settings. Our results showed major improvements in predicted monthly LAI and ET based on MODIS reference data (NSE > 0.6). Simulated forest biomass also showed better agreement with the USDA forest biomass gridded data. Through a series of modeling experiments, we isolated the benefits of LAI, biomass, and ET in predicting streamflow and baseflow at the watershed level. The combined benefits of improved LAI, biomass, and ET predictions yielded the most optimal model configuration where terrestrial and in-stream processes were simulated reasonably well. We performed automated model calibration using two calibration strategies. In the first calibration scheme (M₀), SWAT was calibrated for daily streamflow without adjusting LAI, biomass, and ET. In the second calibration scheme (M_LAI+BM+ET), previously calibrated parameters constraining LAI, biomass, and ET were incorporated into the model and daily streamflow was recalibrated. The M_LAI+BM+ET model showed superior performance and reduced uncertainties in predicting daily streamflow, with NSE values ranging from 0.52 to 0.8. Our findings highlight the importance of accurately representing forest dynamics in hydrological models.

Improving the representation of forests in hydrological models

Forests play a critical role in the hydrologic cycle, impacting the surface and groundwater dynamics of watersheds through transpiration, interception, shading, and modification of the atmospheric boundary layer. It is therefore critical that forest dynamics are adequately represented in watershed models, such as the widely applied Soil and Water Assessment Tool (SWAT). SWAT's default parameterization generally produces unrealistic forest growth predictions, which we address here through an improved representation of forest dynamics using species-specific re-parameterizations. We applied this methodology to the two dominant pine species in the southeastern U.S., loblolly pine (Pinus taeda L.) and slash pine (Pinus elliotti). Specifically, we replaced unrealistic parameter values related to tree growth with physically meaningful parameters derived from publicly available remote-sensing products, field measurements, published literature, and expert knowledge. Outputs of the default and re-parameterized models were compared at four pine plantation sites across a range of management, soil, and climate conditions. Results were validated against MODIS-derived leaf area index (LAI) and evapotranspiration (ET), as well as field observations of total biomass. The re-parameterized model outperformed the default model in simulating LAI, biomass accumulation, and ET at all sites. The two parametrizations also resulted in substantially different mean annual water budgets for all sites, with reductions in water yield ranging from 13 to 45% under the new parameterization, highlighting the importance of properly parameterizing forest dynamics in watershed models. Importantly, our re-parameterization methodology does not require alteration to the SWAT code, allowing it to be readily adapted and applied in ongoing and future watershed modeling studies.

Rapid assessment of plant diversity using MODIS biophysical proxies

The spectral information derived from satellite data provides important inputs for assessing plant diversity. If a suitable satellite-derived biophysical proxy is applicable to assess and monitor plant diversity of different biogeographic regions will be of interest to policy makers and conservationists. We selected four biogeographic regions of India, i.e., semi-arid, Eastern Ghats, Western Ghats, and Northeast as the test sites on the basis of variations in moisture availability. The flora data collected for the study sites are the extract of the national biodiversity project 'Biodiversity Characterization at Landscape Level'. The available Moderate Resolution Imaging Spectroradiometer (MODIS)-derived biophysical proxies at high temporal frequencies was considered to compare the biophysical proxies: surface reflectance-red and near-infrared, normalized difference vegetation index-NDVI, enhanced vegetation index-EVI, leaf area index-LAI, and fraction of absorbed photosynthetically active radiation-FAPAR at different temporal scales (monthly, post-monsoon, seasonal, annual) in each selected biogeographic regions of India. Generalized linear model (GLM) and multivariate adaptive regression spline (MARS) were utilized to evaluate the relationship between plant diversity and MODIS-derived biophysical proxies. MARS summarized the suitable biophysical proxies at monthly scale in descending order for the total forest area in semi-arid was red, NDVI, and FAPAR; for Eastern Ghats was EVI, FAPAR, and LAI; for Western Ghats was EVI, LAI, and FAPAR; and for Northeast was NDVI, near-infrared, and red. Furthermore, monthly FAPAR commonly found to be the suitable proxy to large scale monitoring of plant diversity in the moisture-varied biogeographic regions of India, except Northeast. Using artificial neural network, the relationship of plant diversity and monthly FAPAR/NDVI were modeled. The correlation between the predicted and reference plant diversity was found to be r = 0.56 for semi-arid, r = 0.52 for Eastern Ghats, r = 0.52 for Western Ghats and r = 0.61 for Northeast at p-value < 0.001. The study affirms that FAPAR is potentially an essential biodiversity variable (EBV) for carrying out rapid/indicative assessment of plant diversity in different biogeographic regions, and thereby, meeting various international commitments dealing with conservation and management measures for biodiversity.

Pesticide dose adjustment in fruit and grapevine orchards by DOSA3D: Fundamentals of the system and on-farm validation

Agricultural productivity cannot be sustained without the application of plant protection measures. Within the framework of integrated pest management (IPM), the use of chemical pesticides should be limited to the last option among the available practices. Even though their use remains common, it carries associated environmental and human health risks. One of the most accepted practices within IPM is the reduction of spraying events and/or pesticide applied doses. DOSA3D is a decision support system that allows the dose to be adjusted to the specific treatment scenario. For this, DOSA3D calculates the optimal application volume rate by estimating the leaf area index and takes into account the overall spraying efficiency and the pest or disease to be controlled. The system adopts specific minimum volume rates for fruit trees and vineyards without compromising the crop health status. To establish the adjusted dose, the labeled or the adviser prescription concentration is kept. Resulting adjusted doses provided by DOSA3D achieved pesticide savings up to 53% in fruit trees and 60% in vineyards. DOSA3D has been validated against the main diseases and pests of fruit trees and vineyards: brown spot and psylla in pear orchards; alternaria blotch disease, apple scab, codling moth, oriental moth and red spider mite in apple orchards; powdery mildew, brown rot, aphids, thrips and mites in peach orchards; and, powdery mildew, yellow spider mite and leafhoppers in grapevine orchards. In addition, a methodology called Green Way is presented to provide consistent and crop safety pesticide doses when these are labeled as concentration or ground area doses.

Carbon isotope discrimination and the factors affecting it in a summer maize field under different tillage systems

Based on two years of field experiments, under different soil tillage methods and straw management practices, which included conventional tillage (CT), subsoiling (SS), rotary tillage (RT), and no-tillage (NT), combined with either straw return (S) or straw removal (0), we characterized the dynamic changes in Δ¹³C among three height layers [upper (U, 240 cm above the ground), middle (M, 120 cm above the ground), and lower (L, 30 cm above the ground)] of the summer maize canopy. The Δ¹³C, the factors affecting it, and the relationships between Δ¹³C and soil water content (SWC), the leaf area index (LAI), canopy microclimate, and the CO₂ concentration were elucidated. The results indicated that the Δ¹³C of summer maize at the pre-filling stage was greater than that at the post-filling stage. Δ¹³C also varied at different heights, with the order of the Δ¹³C values being L > U > M. Among the different tillage methods, the Δ¹³C values were ordered SS_S > CT_S > RT_S > NT_S. SS_S and NT_S significantly increased the LAI; air temperature and relative humidity tended to gradually decrease with the increase in height of summer maize. Correlation analyses of the various influencing factors and Δ¹³C showed that SWC, LAI, air temperature, and CO₂ concentration were all positively correlated with Δ¹³C, in which LAI and air temperature were significantly or extremely significantly positively correlated with Δ¹³C. In addition, we show that Δ¹³C can be used as a prediction index for summer maize yield, providing a theoretical basis for future yield research that may save precious time in summer maize breeding efforts.

Complex effects of moisture conditions and temperature enhanced vegetation growth in the Arid/humid transition zone in Northern China

Ecosystems in the arid/humid transition zone (AHTZ) of northern China are highly sensitive to climate change and human activities. Accurately assessing the impact of climate change on these ecosystems is important for effectively reducing the risks faced by them under future climate change. In this study, the leaf area index during the selected growing season (LAI_GS) was used as an indicator for vegetation activity. After comparison different potential indicators, the growing season temperature (T_GS) was used to indicate temperature, and the growing season aridity index (AI_GS), which considers the regional water budget, was used to indicate moisture rather than precipitation, which is used more commonly. Correlation analysis and residual trends were used to study the influence of climatic and non-climatic factors on vegetation activity in the AHTZ from 1982 to 2016. The results for regions where LAI_GS increased significantly (0.037/10 yr, 53.58% of the study area), the regions where LAI_GS dominated by non-climatic factors (18.40%) was larger than areas dominated by climatic factors (9.61%). However, most (25.57%) of the regions in the selected study area were mainly driven by both climatic and non-climatic factors. In about half (49.73%) of the climate-affected regions, significant changes in LAI_GS were driven jointly by T_GS and AI_GS. These regions were mainly in the northern and western Loess Plateau. The regions where changes were driven mainly by AI_GS, and those where changes were driven mainly by T_GS, each accounted for nearly a quarter of climate-affected regions (24.87% and 25.40%, respectively). The former regions were on the western Songliao Plain, the northern North China Plain, and the northern Loess Plateau, and the latter regions were in the northern Greater Khingan Mountains, on the southern North China Plain, in the western mountains of North China, and on the southern Loess Plateau.

Spatiotemporal dynamics of ecosystem water use efficiency over the Chinese Loess Plateau base on long-time satellite data

Ecosystem water use efficiency (eWUE), defined as the ratio between carbon gains and water loss from the system, has been recognized as an important characteristic of carbon and water balances. The long-lasting "Grain for Green" Program (GFGP) initiated in 1999 in China's Loess Plateau (CLP) is a large-scale ecological program in the world, which aims to improve the CLP's ecosystem resilience by enhancing vegetation cover and productivity. Understanding how the GFGP can affect eWUE is imperative to ensuring sustainable water resources and to promoting sustainable management strategies. In this study, we evaluated the spatiotemporal variability of growing-season eWUE and examined its response to both climate change and vegetation coverage from 1982 to 2017. Our results indicate that growing-season eWUE, gross primary productivity (GPP), and evapotranspiration (ET) in CLP area increased significantly from 1982 to 2017. Specifically, eWUE, GPP, and ET increased more rapidly after China established the program. The most significant growth area of eWUE was found in main areas conducting GFGP project, including the Loess hilly and gully area (LHGA). Spatially, eWUE, GPP, and ET in the growing season increased from northwest to southeast, and higher eWUE was found in areas with high vegetation cover. The spatial and temporal variability of eWUE was related to vegetation cover (expressed as leaf area index, LAI) and climatic variability. Significant positive correlations were observed between growing-season LAI, temperature, and eWUE, because the LAI and temperature have a greater effect on photosynthesis than ET. Our results suggested that the GFGP was the main driving force that causes the spatial-temporal variability of eWUE in CLP.

Temporal-Spatial changes of monthly vegetation growth and their driving forces in the ancient Yellow river irrigation system, China

Irrigation systems play vital roles not only in food production but also in supporting ecosystems. Understanding how the ecosystem has evolved in response to human activities is crucial for sustainable food production, especially for arid and semi-arid regions. In this study, we examined the trends of vegetation growth on a monthly basis in the ancient Yellow River irrigation system in Ningxia, China. We used the leaf area index (LAI) to characterize the vegetation growth from 2007 to 2019. The LAI trends were associated with a series of driving forces, explaining the spatial and temporal change of vegetation growth. With the provision of the Wilks feature importance method, 2-month averaged air temperature and irrigation were identified as the two most important variables for monthly LAI simulation. Future climate projections based on the Regional Climate Model system (RegCM) suggested dryer and longer summers under the RCP 8.5 scenario. These changes will increase the crop water demand during the growing months. In the future, water conflict might be further intensified in May, in which the present irrigation water has already led to a decreased crop growth. Our findings demonstrated that the Mann Kendall monthly trend analysis could provide more helpful information for monitoring the vegetation growth than the trend analysis on a yearly and seasonal basis.

Estimating the phenological dynamics of irrigated rice leaf area index using the combination of PROSAIL and Gaussian Process Regression

The growth of rice is a sequence of three different phenological phases. This sequence of change in rice phenology implies that the condition of the plant during the vegetative phase relates directly to the health of leaves functioning during the reproductive and ripening phases. As such, accurate monitoring is important towards understanding rice growth dynamics. Leaf Area Index (LAI) is an important indicator of rice yields and the availability of this information during key phenological phases can support more informed farming decisions. Satellite remote sensing has been adopted as a proxy to field measurements of LAI and with the launch of freely available high resolution Satellite images such as Sentinel-2, it is imperative that accurate retrieval methods are adopted towards monitoring LAI at irrigated rice fields. Here, we evaluate the potential of a hybrid radiative transfer model (i.e., PROSAIL - Gaussian Process Regression (GPR), for estimating the phenological dynamics of irrigated rice LAI using imager derived from the Sentinel-2 multispectral instrument. LAI field measurements were obtained from an experimental rice field in Nasarawa state, Nigeria during the dry season. We used the PROSAIL radiative transfer model to create a look up table (LUT) that was subsequently used to train a GPR model. Afterwards, we evaluated the potential of the hybrid modelling approach by assessing the overall model accuracy and the extent to which LAI was able to accurately predict LAI during key rice phenological phases. We compared the predicted hybrid GPR LAI values with LAI values generated from the SNAP toolbox, based on a hybrid Artificial Neural Network (ANN) modelling approach. Our results show that the overall predictive accuracy of the hybrid GPR model (R2 = 0.82, RMSE = 1.65) was more accurate than that of the hybrid ANN model (R2 = 0.66, RMSE = 3.89) for retrieving LAI values from Sentinel-2 imagery. Both models underestimated LAI values during the reproductive and ripening phases . However, the accuracy during the phenological phases were more significant when using the hybrid GPR model (P < 0.05). During the different phenological phases, the hybrid GPR model predicted LAI more accurately during the reproductive (R² = 0.7) and ripening (R² = 0.59) phases compared to the hybrid ANN reproductive and ripening phases. When monitoring LAI phenological profiles of both hybrid models, the hybrid GPR and ANN models underestimated LAI during the reproductive and ripening phases. However, the ANN model underestimations were statistically significantly greater than those for the hybrid GPR model (P < 0.05). Our results highlight the potential of hybrid GPR models for estimating the phenological dynamics of irrigated rice LAI from Sentinel-2 data. They provided more accurate estimation of LAI patterns from varying nitrogen and water applications than hybrid ANN models.

Remote estimation of leaf area index (LAI) with unmanned aerial vehicle (UAV) imaging for different rice cultivars throughout the entire growing season

BACKGROUND

Rice is one of the most important grain crops worldwide. The accurate and dynamic monitoring of Leaf Area Index (LAI) provides important information to evaluate rice growth and production.

METHODS

This study explores a simple method to remotely estimate LAI with Unmanned Aerial Vehicle (UAV) imaging for a variety of rice cultivars throughout the entire growing season. Forty eight different rice cultivars were planted in the study site and field campaigns were conducted once a week. For each campaign, several widely used vegetation indices (VI) were calculated from canopy reflectance obtained by 12-band UAV images, canopy height was derived from UAV RGB images and LAI was destructively measured by plant sampling.

RESULTS

The results showed the correlation of VI and LAI in rice throughout the entire growing season was weak, and for all tested indices there existed significant hysteresis of VI vs. LAI relationship between rice pre-heading and post-heading stages. The model based on the product of VI and canopy height could reduce such hysteresis and estimate rice LAI of the whole season with estimation errors under 24%, not requiring algorithm re-parameterization for different phenology stages.

CONCLUSIONS

The progressing phenology can affect VI vs. LAI relationship in crops, especially for rice having quite different canopy spectra and structure after its panicle exsertion. Thus the models solely using VI to estimate rice LAI are phenology-specific and have high uncertainties for post-heading stages. The model developed in this study combines both remotely sensed canopy height and VI information, considerably improving rice LAI estimation at both pre- and post-heading stages. This method can be easily and efficiently implemented in UAV platforms for various rice cultivars during the entire growing season with no rice phenology and cultivar pre-knowledge, which has great potential for assisting rice breeding and field management studies at a large scale.

Greater increases in China's dryland ecosystem vulnerability in drier conditions than in wetter conditions

Dryland ecosystems are experiencing dramatic climate change, either drier or wetter. However, the differences in response amplitudes of dryland ecosystems to drier and wetter climates have not been frequently discussed, especially when using composite indicators at large scales. This study explores the changing patterns of ecosystem vulnerability in China's drylands by comprehensively considering exposure, sensitivity and resilience indicators using leaf area index (LAI) datasets and meteorological data within two periods from 1982 to 1999 (P1) and from 2000 to 2016 (P2). The results show that nearly 57% of China's drylands have experienced drier conditions in 2000-2016 based on the average aridity index (AI) values compared with the conditions in 1982-1999. Compared with the conditions in 1982-1999, ecosystem vulnerability has increased in 78% of dryland, and ecosystem resilience has decreased in 46% of the area in 2000-2016. The amplitudes of vulnerability increase are higher in drier conditions than in wetter conditions. Ecosystem resilience has obviously increased in wetter conditions but has decreased in drier conditions, especially in farming-pastoral ecotones with an obvious land use change. Consequently, vegetation-climate composite indicators provide a holistic pattern of China's dryland ecosystem response to climate change, and the decreased ecosystem resilience in drier conditions in northeast China should be a warning signal under the national vegetation greening background. This research highlights that the impact of drying on ecosystem resilience leads the response of ecosystems to drier environment.

Exploring the long-term vegetation dynamics of different ecological zones in the farming-pastoral ecotone in northern China

The vegetation in the farming-pastoral ecotone in northern China is influenced by both natural and anthropogenic factors and has undergone drastic changes in the past decades. The farming-pastoral ecotone is the transition zone from agriculture to animal husbandry. The ecological environment of this ecotone is complex and fragile. Most researches have primarily focused on the entire farming-pastoral ecotone, seldomly considering the differences between different ecological zones characterized by soil, climate, and biome conditions. Based on the long time series of leaf area index (LAI) data, meteorological data, and land-use dataset, this study analyzed LAI variation trends, the correlations between LAI and climate factors, and the impact of land-use type change on vegetation in the farming-pastoral ecotone in northern China. Moreover, this paper makes a full study of the changes of the whole study area from the perspective of the differences between different ecological zones. The results showed that over 36 years, areas with vegetation improvements were considerably larger than those with degradations. However, there were still 49.56% of the total area showing no significant vegetation change. There are differences in vegetation change and response to climate between the forest ecological zones and the grassland ecological zones. The vegetation improvement trends of the forest ecological zones were larger and more sensitive to temperature, while the vegetation improvements of the grassland ecological zones were relatively small, and were more sensitive to precipitation. Human activities promote LAI changes in areas close to the forest ecological zones. The change of land use indicates that the decrease of the overall natural vegetation area has not resulted in decreasing LAI. And there is a growing trend of woodland area in the grassland ecological zones. The study provides a theoretical basis for the management of the environment and vegetation in the farming-pastoral ecotone in northern China.

Leaf area index estimation model for UAV image hyperspectral data based on wavelength variable selection and machine learning methods

BACKGROUND

To accurately estimate winter wheat leaf area index (LAI) using unmanned aerial vehicle (UAV) hyperspectral imagery is crucial for crop growth monitoring, fertilization management, and development of precision agriculture.

METHODS

The UAV hyperspectral imaging data, Analytical Spectral Devices (ASD) data, and LAI were simultaneously obtained at main growth stages (jointing stage, booting stage, and filling stage) of various winter wheat varieties under various nitrogen fertilizer treatments. The characteristic bands related to LAI were extracted from UAV hyperspectral data with different algorithms including first derivative (FD), successive projections algorithm (SPA), competitive adaptive reweighed sampling (CARS), and competitive adaptive reweighed sampling combined with successive projections algorithm (CARS_SPA). Furthermore, three modeling machine learning methods including partial least squares regression (PLSR), support vector machine regression (SVR), and extreme gradient boosting (Xgboost) were used to build LAI estimation models.

RESULTS

The results show that the correlation coefficient between UAV and ASD hyperspectral data is greater than 0.99, indicating the UAV data can be used for estimation of wheat growth information. The LAI bands selected by using different algorithms were slightly different among the 15 models built in this study. The Xgboost model using nine consecutive characteristic bands selected by CARS_SPA algorithm as input was proved to have the best performance. This model yielded identical results of coefficient of determination (0.89) for both calibration set and validation set, indicating a high accuracy of this model.

CONCLUSIONS

The Xgboost modeling method in combine with CARS_SPA algorithm can reduce input variables and improve the efficiency of model operation. The results provide reference and technical support for nondestructive and rapid estimation of winter wheat LAI by using UAV.

Examining land surface phenology in the tropical moist forest eco-zone of South America

Using leaf area index (LAI) data from 1981 to 2014 in the tropical moist forest eco-zone of South America, we extracted start (SOS) and end (EOS) dates of the active growing season in forest and savanna at each pixel. Then, we detected spatiotemporal characteristics of SOS and EOS in the two vegetation types. Moreover, we analyzed relationships between interannual variations of SOS/EOS and climatic factors, and simulated SOS/EOS time series based on preceding mean air temperature and accumulated rainfall. Results show that mean SOS and EOS ranged from 260 to 330 day of year (DOY) and from 150 to 260 DOY across the study region, respectively. From 1981 to 2014, SOS advancement is more extensive than SOS delay, while EOS advancement and delay are similarly extensive. For most pixels of forest and savanna in tropical moist forest eco-zone, preceding rainfall correlates predominantly negatively with SOS but positively with EOS, while the relationship between preceding temperature and phenophases is location-specific. In addition, preceding rainfall is more extensive than preceding temperature in simulating SOS, while both preceding rainfall and temperature play an important role for simulating EOS. This study highlights the reliability of using LAI data for long-term phenological analysis in the tropical moist forest eco-zone.

The impact of tree canopy structure on understory variation in a boreal forest

Information on understory composition and its relationships with the overstory tree canopy, especially leaf area index (LAI), is crucially needed in, e.g., modeling land-atmosphere interactions and productivity of forests. There are also several global LAI products produced from satellite data which need to be validated with ground reference data. However, to date, only scarce field data on simultaneous structural properties of under- and overstory vegetation, and tree canopy LAI, have been available in boreal forests. This paper shows how understory composition and fractional cover of different species types varies in a boreal forest site, and how it is linked to structural properties of the tree layer. The study is based on 301 understory plots collected in an area of ∼16 km² around Hyytiälä forestry field station, Finland (61°50'N, 24°17'E) in a southern boreal forest site. Forest understory plot data was accompanied with measurements of both standard forest inventory variables and optically-based canopy light transmittance data. Clear differences in average species composition between different site fertility types were observed, but also large variation within each site fertility type was noted. Forest understory composition was better correlated with structural forest canopy measures (e.g., tree canopy LAI, canopy cover, canopy openness) than with traditional forest inventory variables such as tree height or diameter. Forest canopy LAI and the fractional cover of understory were strongly related, especially in more fertile sites. Our results highlight the role of tree canopy structural metrics as modifiers of the understory light climate and growing conditions, also, in boreal forests.

Dataset of tree canopy structure and variation in understory composition in a boreal forest site

A field data set from 301 forest plots was collected during peak-growing season (June 24 - July 17, 2013) around Hyytiälä forestry field station in Southern Finland (61° 50' N, 24° 17' E). For all plots, forest variables were collected following local forest inventory practice, and understory cover fractions were estimated using a traditional sampling quadrat. The understory layer in each plot was classified into four site fertility types: herb-rich, mesic, sub-xeric, and xeric. The upper understory layer fractional covers were estimated for: (1) dwarf shrubs, (2) pteridophytes and herbaceous species, and (3) graminoids, and the lower ground layer fractional covers for: (1) mosses, (2) lichens, and (3) litter (including all non-photosynthetic material). Canopy transmittance data were collected using two LAI-2000 device. The transmittance data were used to calculate effective leaf area index, true leaf area index, canopy openness and canopy cover for all plots. The data can be used to parameterize tree canopy and understory compositions in e.g., physically-based reflectance models, land surface models, and regional carbon cycle models. Interpretations of the results are provided in the related article [1].

Growth response of greenhouse-produced muskmelon and tomato to sub-surface drip irrigation and soil aeration management factors

BACKGROUND

Hypoxia causes injury and yield loss. Soil aeration has been reported to accelerate the growth of plants and increase crop yield. The aim of this study was to examine growth response of greenhouse-produced muskmelon to 3 levels of sub-surface drip irrigation (I), 3 different installation depths of drip laterals in the soil (D), and 4 levels of supplemental soil aeration frequency (A). A fractional factorial experiment was designed to examine these treatment effects on marketable fresh fruit yield, leaf area index during 3 growth stages, and dry matter partitioning at harvest. In addition, we studied the response of fruit yield and dry matter of tomato to 2 levels of burial depths of subsurface tubing in combination with 3 frequency levels of soil aeration.

RESULTS

Results showed that soil aeration can positively influence the yield, leaf area index, dry matter and irrigation use efficiency of the muskmelon (p < 0.05). The fruit yield of muskmelon and tomato were increased by 21.5 and 30.8% respectively with 1-d and 2-d aeration intervals compared with the no aeration treatment.

CONCLUSIONS

The results suggest that soil aeration can positively impact the plant root zone environment and more benefits can be obtained with aeration for both muskmelon and tomato plants.

Estimation of leaf area index using PROSAIL based LUT inversion, MLRA-GPR and empirical models: Case study of tropical deciduous forest plantation, North India

Forests play a vital role in biological cycles and environmental regulation. To understand the key processes of forest canopies (e.g., photosynthesis, respiration and transpiration), reliable and accurate information on spatial variability of Leaf Area Index (LAI), and its seasonal dynamics is essential. In the present study, we assessed the performance of biophysical parameter (LAI) retrieval methods viz. Look-Up Table (LUT)-inversion, MLRA-GPR (Machine Learning Regression Algorithm-Gaussian Processes Regression) and empirical models, for estimating the LAI of tropical deciduous plantation using ARTMO (Automated Radiative Transfer Models Operator) tool and Sentinel-2 satellite images. The study was conducted in Central Tarai Forest Division, Haldwani, located in the Uttarakhand state, India. A total of 49 ESUs (Elementary Sampling Unit) of 30m×30m size were established based on variability in composition and age of plantation stands. In-situ LAI was recorded using plant canopy imager during the leaf growing, peak and senescence seasons. The PROSAIL model was calibrated with site-specific biophysical and biochemical parameters before used to the predicted LAI. The plantation LAI was also predicted by an empirical approach using optimally chosen Sentinel-2 vegetation indices. In addition, Sentinel-2 and MODIS LAI products were evaluated with respect to LAI measurements. MLRA-GPR offered best results for predicting LAI of leaf growing (R2 = 0.9, RMSE = 0.14), peak (R2 = 0.87, RMSE = 0.21) and senescence (R2 = 0.86, RMSE = 0.31) seasons while LUT inverted model outperformed VI's based parametric regression model. Vegetation indices (VIs) derived from 740 nm, 783 nm and 2190 nm band combinations of Sentinel-2 offered the best prediction of LAI.

Carrying capacity for vegetation across northern China drylands

Revegetation and afforestation across drylands for establishing sustainable ecosystems requires a comprehensive understanding of the carrying capacity for vegetation (CCV) at the regional scale. To determine the CCV across drylands in northern China, we developed a technical framework based on two measures of leaf area index (LAI): maximum LAI (Max-LAI) and safe LAI (Safe-LAI), and their thresholds, CCV_max and CCV_safe, for six drylands (Horqin, Hulun Buir, Otindag, Mu Us, Tengger, and Junggar) using remote sensing datasets from 2000 to 2014. We also predicted dynamics of CCV of the drylands over the next decade (2015-2024) by establishing optimal prediction models based on environmental factors (temperature, precipitation, potential evapotranspiration, and elevation). According to these models, the Max-LAI threshold (range: 0.36-1.03 m²/m²) and Safe-LAI threshold (0.29-0.70 m²/m²) declined from east to west with decreases in aridity index. Under current climatic variability and anthropogenic disturbances, the CCV in most drylands would have positive increments (approximately 15%), except in the Horqin (approximately -15%) and Tengger (slight changes), during the following decade. This indicates that there is scope for improving vegetation coverage in most drylands, except in the Horqin and Tengger. Our results suggest that revegetation and ecosystem management to prevent ongoing desertification should be carried out at the regional scale. Although it does not account for biocrusts, artificially introduced vegetation, underground water, and other vegetation attributes (e.g., density and biomass), our technical framework and results might nonetheless be valuable in evaluating regional ecological security and guiding vegetation restoration of drylands across northern China.

Evaluating sensitivity of hyperspectral indices for estimating mangrove chlorophyll in Middle Andaman Island, India

Mangroves are the highly productive and extensive ecosystem in the tropical coasts. Chlorophyll is the key foliar determinant of mangrove productivity. Optical characteristics of mangrove markedly differ from land vegetation; hence, defining narrowband spectral indices most sensitive to mangrove chlorophyll is crucial, in view of their importance to the coastal environment and mounting biotic pressures. We assessed the sensitivity of a set of satellite hyperspectral remote sensing indices to mangrove canopy chlorophyll in Middle Andaman Island, India, and propose most robust spectral indices for mangrove chlorophyll estimation. We generated simple, modified simple, normalized difference vegetation, and non-linear indices from all possible two band combinations of EO-1 Hyperion bands in the 500-900 nm spectral range. The strength of correlation between each pair of spectral indices to mangrove chlorophyll was analyzed in 2D correlograms and validated using k-fold cross-validation technique. Results show that 549 nm, 559 nm (green) and 702 nm, 722 nm, 742 nm, and 763 nm (red-edge) wavelengths are the most sensitive to mangrove chlorophyll. We report performance of traditional chlorophyll indices and new indices with higher predictive capability for mangrove chlorophyll prediction. Simple ratio (559 nm/885 nm) offered the strongest correlation with mangrove chlorophyll (R²-0.75, RMSE-0.60, p < 0.05). Study findings will help researchers in deciding suitable chlorophyll indices for mangrove productivity and stress assessment. The best calibrated index was used to prepare mangrove chlorophyll spatial variability map of the study area.

Changes in global terrestrial ecosystem water use efficiency are closely related to soil moisture

Ecosystem water use efficiency (WUE), defined as the ratio between gross primary productivity (GPP) and evapotranspiration (ET), is an indicator of the tradeoff between carbon assimilation and water loss that is controlled by climate and ecosystem structure. However, how GPP and ET impact WUE remains poorly understood. In this study, we provide a global analysis of WUE trends from 1982 to 2011 using multi-model ensemble mean WUE values derived from seven process-based carbon cycle models and investigate the relative effects of leaf area index (LAI), soil moisture (SM), and vapor pressure deficit (VPD) on GPP and ET. Increasing WUE trend was derived for all models, with an average rate of 0.0057 ± 0.0018 g C·kg^-1 H₂O·yr^-1 (p = 0.00), with a spatially increasing WUE across ~84% of the global land area, and increasing trends which are statistically significant over ~72% (p < 0.05). Spatially, GPP primarily dominated WUE variability in humid regions, i.e., boreal Eurasia, eastern America, and the tropics, whereas ET dominated WUE variability in dryland regions, i.e., northeast China, the Middle East, southern South America, and South Australia. Soil moisture is likely the most influential factor on GPP and ET variations, with ~63% and ~61% of the global land area dominated by SM, and therefore WUE, for GPP and ET respectively from 1982 to 2011. Our findings enrich the understanding of WUE trends and provide direct evidence for SM-induced variability in WUE.

Tree distribution, morphology and modelled air pollution in urban parks of Hong Kong

Trees offer a range of ecosystem services and remain important in providing human benefits. However, emerging literature questions the long-accepted view of trees being able to improve air quality in urban parks. The aerodynamic effect of trees was identified as a major reason for the change of pollutant distribution in near-road parks, where trees can act as porous barriers and cause localised concentration increase. Although not yet fully developed, planting strategies aiming to mitigate the negative effect of vegetation on air quality should be encouraged in future park design. In this study, we explored the effect of tree planting design on pollutant diffusion by integrating field surveys in urban parks in Hong Kong with computational fluid dynamic (CFD) modelling. A series of indicators associated with tree morphology and landscape were derived from the surveys and their influence on air pollutant distribution in parks was examined using ENVI-MET. Dense trees with low crown base were found effective in improving air quality within parks when planted as barriers with a width of ~15 m at borders. However, more extensive planting led to a decrease in wind velocity and an increase in pollutant concentrations, which should be avoided. Tall trees tended to have little influence on airflow at the pedestrian level, which means they seem appropriate for small urban parks where wide barriers are not applicable and rapid ventilation should be encouraged. The tree distribution also altered the airflow and pollutant dispersion in parks. Our study provides clues for thoughtful planting strategies which can optimise air quality in urban parks.

Growth, development and nitrogen uptake efficiency of some sali rice genotypes under delayed dates of sowing

Sali rice which is the major rice crop of Assam faces recurrent floods co-inciding with different phonological stages, especially the seedling stage. Owing to the damage caused to the seedlings, the transplanting also gets delayed. Delayed transplanting results in poor grain yield due to poor biomass accumulation as influenced by prevailing photoperiodic and thermal regimes during that period of the year. From this angle, selection of suitable genotypes appears to be the viable option that can have better early vegetative growth by utilizing available resources and should possess considerable degree of thermo- and photo- insensitivity. Keeping in view the above points, a study was conducted during the sali seasons at the experimental plots of Instructional cum Research (ICR) farm, Assam Agricultural University, Jorhat with already shortlisted seven sali rice genotypes namely, Satya, Luit, Monoharsali, Jaya, Bordhan, Basundhara and Srimanta under delayed dates of sowing using thirty days old seedlings for transplanting laid out in split plot design. Results revealed that as compared to timely sowing, delayed sowing resulted in progressively lower values of various physiological parameters. While comparison was made between timely sowing and the deferred dates of sowing lowest reduction in the values of grain yield were recorded in genotypes of Manoharsali and Srimanta (35.66% and 35.03% with a delay of transplanting by 35 days compared to the recommended date of sowing i.e., 15^th June). These two genotypes recorded better performance in terms of parameters like leaf area index, nitrogen accumulation in biomass and plant biomass etc. The better performing genotypes namely Srimanta and Monoharsali recorded higher values of nitrogen uptake efficiency.