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.