Global relationships among traditional reflectance vegetation indices (NDVI and NDII), evapotranspiration (ET), and soil moisture variability on weekly timescales

Continued decline of global soil moisture content, with obvious soil stratification and regional difference

Soil is an important component connecting atmosphere and vegetation, and is an important 'regulator' of slope hydrological process. Global warming accelerates the global water cycle, and Soil Moisture Content (SMC) will change, but this change is not yet clear. Here, we study the global trend of SMC at different depths over the past 70 years and the next 70 years, based on the GLDAS-NOAH025 dataset and precipitation and temperature data from 15 CMIP6 models. We found that compared with the long-term average of 70 years, the global 0-200 cm SMC is decreasing at a rate of 1.284 kg/m² per year from 2000 to 2020, and the area showing a significant decreasing trend accounts for 31.67 % of the global. Over the past decade, 0-200 cm SMC reduction rate (2.251 kg/m²) doubled. Global warming and precipitation reduction are the main reasons for the attenuation of SMC at different depths in the global from 2000 to 2020. Under the SSP126, SSP245, SSP370 and SSP585 scenarios, the global 0-200 cm SMC will continue to decay in the future, and the area showing a significant reduction trend accounts for 22.73-49.71 % of the global, but the stratified soil and regional differences are obvious. The attenuation of SMC will further aggravate the global water cycle and enhance the variability of extreme meteorological disasters. We will face more severe soil drought problems.

The Potential of Optical UAS Data for Predicting Surface Soil Moisture in a Peatland across Time and Sites

Advances in unmanned aerial systems (UASs) have increased the potential of remote sensing to overcome scale issues for soil moisture (SM) quantification. Regardless, optical imagery is acquired using various sensors and platforms, resulting in simpler operations for management purposes. In this respect, we predicted SM at 10 cm depth using partial least squares regression (PLSR) models based on optical UAS data and assessed the potential of this framework to provide accurate predictions across dates and sites. For this, we evaluated models’ performance using several datasets and the contribution of spectral and photogrammetric predictors on the explanation of SM. The results indicated that our models predicted SM at comparable accuracies as other methods relying on more expensive and complex sensors; the best R2 was 0.73, and the root-mean-squared error (RMSE) was 13.1%. Environmental conditions affected the predictive importance of different metrics; photogrammetric-based metrics were relevant over exposed surfaces, while spectral predictors were proxies of water stress status over homogeneous vegetation. However, the models demonstrated limited applicability across times and locations, particularly in highly heterogeneous conditions. Overall, our findings indicated that integrating UAS imagery and PLSR modelling is suitable for retrieving SM measures, offering an improved method for short-term monitoring tasks.

Analysis on ecological status and spatial-temporal variation of Tamarix chinensis forest based on spectral characteristics and remote sensing vegetation indices

The reserve of Tamarix forest, located in Changyi, China, is the only national marine special reserve taking Tamarix as the main object of protection. Compared with conventional monitoring technology, remote sensing technology can more comprehensively reflect the ecological environment status and spatial-temporal variation of monitoring objects. Based on spectral characteristics and remote sensing vegetation indices, the ecological status and spatial-temporal variation of Tamarix chinensis forest in the reserve deserve further exploration. Therefore, spectral characteristic, typical vegetation indices, comprehensive health index, VFC, and REP were analyzed based on Sentinel-2A images. Spatial-temporal variation analysis during 2014 to 2018 was analyzed based on GF-1 images. The research result indicated that ecological quality of protection zone showed an overall growth trend with the help of artificial ecological restoration, and it is possible to continuously implement ecological recovery towards the protection zone.

Response of Ecohydrological Variables to Meteorological Drought under Climate Change

Drought is the most widespread climatic extreme that has negative impacts on ecohydrology. Studies have shown that drought can cause certain degrees of disturbances to different ecohydrological variables, but the duration and severity thresholds of drought that are sufficient to cause changes in ecohydrological variables remain largely unknown. At the same time, we should not ignore the dynamic variation of drought’s effect on ecohydrological variables under the condition of climate change. Here, we derived the thresholds of several ecohydrological variables in response to drought in a historical period (1982–2015), including evapotranspiration (ET), soil moisture (SM), the vapor pressure deficit (VPD) and the normalized difference vegetation index (NDVI), and we projected the occurrence probability’s change trend of drought events that cause changes in ecohydrological variables under future climate change. The results show that the impact of drought on ecohydrological variables is not dependent on drought indicators. ET and NDVI were expected to decrease in most parts of the world due to increases in radiation (RAD) and temperature (TEMP) and decreases in precipitation (PRE) during drought periods. SM decreased in most regions of the world (93.47%) during the drought period, while VPD increased in 85.41% of the globe. The response thresholds for different ecohydrological variables to drought in the same area did not differ significantly (especially for ET, SM and VPD). When a drought lasted for 8 to 15 months and the corresponding drought severity reached 10 to 15 (the inverse of the cumulative values of the drought index when the drought occurs), the drought caused changes in the ecohydrological variables in most regions of the world. Compared with arid and semiarid regions, ecohydrological variables are more sensitive to drought in humid and semihumid regions (p < 0.05), and high-intensity human activities in different climatic conditions increased significantly the severity of drought processes. Between 2071 and 2100, more than half of the world’s ecohydrological variables are expected to be more susceptible to drought disturbances (regions with shorter return periods of drought events that cause significant changes in ET, SM, VPD and NDVI account for 60.1%, 64.4%, 59.6% and 54.5% of the global land area, respectively).

Quantitative assessment of the effects of climate, vegetation, soil and groundwater on soil moisture spatiotemporal variability in the Mongolian Plateau

Soil moisture (SM) is a key parameter regulating the hydrothermal balance of global terrestrial ecosystems and plays an important role in local ecological environment, particularly in arid and semiarid areas. However, current studies have so far obtained insufficient knowledge of SM spatiotemporal variability and its primary control factors, which limits our understanding of the feedback effects of SM on surface vegetation and hydrothermal activity. Here, we chose the ecologically fragile Mongolian Plateau (MP) as the study area to quantitatively reveal the soil moisture spatiotemporal variability (SMSTV) and the influence of control factors (climate, vegetation, soil and groundwater) with the help of empirical orthogonal functions (EOFs) and geographical detector models. The results indicated that a significant trend of decreasing SM and one dominant spatial structure (EOF1) of SM was found in the MP from 1982 to 2019, which explained over 54% of the spatial variability in SM, and as the soil depth increased, the EOF1 interpretation capacity increased. In addition, EOF1 is high in the north and east and low in the south and west of the MP and that vegetation cover is also relatively greater in the high-value areas. Overall, groundwater has the greatest influence on SMSTV in the MP (q = 0.89); however, precipitation and potential evapotranspiration remain the main control factors for SMSTV for different ecological zones, while the influence of vegetation elements (NDVI and GPP) cannot be ignored, and soil textures (clay, sand, silt) have the least influence. Meanwhile, SMSTV is explained to a greater extent by the interaction of the factors rather than by a single factor. However, there are differences in the influence mechanisms of each factor on SMSTV. This study provides strong evidence that meteorological forcing is not the only factor that dominates SMSTV and that the dominant factors may vary considerably between ecological zones.

Assessing remote sensing as a tool to monitor hydrological stress in Irish catchments with Freshwater Pearl Mussel populations

The West Coast of Ireland hosts many of the few populations of Freshwater Peal Mussels (FPM) left in Europe. The decline of this keystone species is strongly related to deteriorating hydrological conditions, specifically to the threat of low flows during dry summers. Populations still capable of reproducing require a minimum discharge and flow velocity to support juvenile mussels, or else stress builds up and an entire generation may be lost. Monitoring environmental and hydrological conditions in small and remote FPM catchments is difficult due to the lack of infrastructure. Indices derived from remote sensing imagery can be used to assess hydrological variables at the catchment scale. Here, five indices are tested as possible surrogates for soil moisture and evapotranspiration, based on two relevant land-cover types: open peat habitats (OPH) and forestry. Selected indices are then assessed in their ability to reproduce seasonal patterns and in their response to a severe drought event. The moisture stress index (MSI) and normalized difference vegetation index (NDVI) were found to be the best surrogates for soil moisture and evapotranspiration respectively. Both indices showed seasonality patterns in the two land-cover types, although the variability of MSI was significantly higher. During the 2018 drought, MSI visibly increased only in OPH, while NDVI rose only for forestry. The results suggest that OPH enhances the long-term hydrological resilience of a catchment by conserving water in the peat substrate, while industrial forestry plantations exacerbate the pressure on water during drier periods. This has consequences for river discharge, freshwater biodiversity and specifically for FPM. Implementing these surrogates have the potential to identify land-use management strategies that reduce and even avert the effects of drought on FPM. Such strategies are increasingly necessary in a climate change context, as recurring summer droughts are expected in most of Europe.

A Deep Learning Method for Fully Automatic Stomatal Morphometry and Maximal Conductance Estimation

Stomata are integral to plant performance, enabling the exchange of gases between the atmosphere and the plant. The anatomy of stomata influences conductance properties with the maximal conductance rate, g _smax, calculated from density and size. However, current calculations of stomatal dimensions are performed manually, which are time-consuming and error prone. Here, we show how automated morphometry from leaf impressions can predict a functional property: the anatomical g_smax. A deep learning network was derived to preserve stomatal morphometry via semantic segmentation. This forms part of an automated pipeline to measure stomata traits for the estimation of anatomical g_smax. The proposed pipeline achieves accuracy of 100% for the distinction (wheat vs. poplar) and detection of stomata in both datasets. The automated deep learning-based method gave estimates for g_smax within 3.8 and 1.9% of those values manually calculated from an expert for a wheat and poplar dataset, respectively. Semantic segmentation provides a rapid and repeatable method for the estimation of anatomical g_smax from microscopic images of leaf impressions. This advanced method provides a step toward reducing the bottleneck associated with plant phenotyping approaches and will provide a rapid method to assess gas fluxes in plants based on stomata morphometry.

Quantitative Retrieval of Soil Salinity Using Landsat 8 OLI Imagery

Soil salinization is the main reason for declining soil quality and a reduction in agricultural productivity. We derive the spatial distribution of soil moisture from the temperature vegetation dryness index (TVDI) of Landsat TM-8 OLI images to analyze the effect of spatial heterogeneity of soil moisture on the retrieval accuracy of soil salinity. We establish five soil salinity inversion models for different soil moisture levels (drought levels) based on the canopy response salinity index (CRSI), normalized difference vegetation index (NDVI), and automatic water extraction index (AWEI) derived from Landsat TM-8 OLI images. The inversion accuracy of soil salinity is assessed using 42 field samples. The results show that the average accuracies of the five inversion models are higher than that of the traditional soil salinity inversion model of the entire study area. The proposed model underestimates soil salinity in high-moisture areas and overestimates it in drought areas. Therefore, inversion models of soil salinization should consider spatial differences in soil moisture to improve the inversion accuracy.

Mapping 30 m Fractional Forest Cover over China’s Three-North Region from Landsat-8 Data Using Ensemble Machine Learning Methods

The accurate monitoring of forest cover and its changes are essential for environmental change research, but current satellite products for forest coverage carry many uncertainties. This study used 30-m Landsat-8 data, and aggregated 1-m GaoFen-2 (GF-2) satellite images to construct the training samples and used multiple machine learning algorithms (MLAs) to estimate the fractional forest cover (FFC) in China’s Three North Region (TNR). In this study, multiple MLAs were merged to construct stacked generalization (SG) models based on the idea of SG, and the performances of the MLAs in the FFC estimation were evaluated. The results of the 10-fold cross-validation showed that all non-linear algorithms had a good performance, with an R2 value of greater than 0.8 and a root-mean square error (RMSE) of less than 0.05. In the bagging ensemble, the random forest (RF) (R2 = 0.993, RMSE = 0.020) model performed the best and in the boosting ensemble, the light gradient boosted machine (LGBM) (R2 = 0.992, RMSE = 0.022) performed the best. Although the evaluation index of the RF is slightly better than that of the LGBM, the independent validation results show that the two models have similar performances. The model evaluation results of the independent datasets showed that, in the SG model, the performance of the SG(LGBM) (R2 = 0.991, RMSE = 0.034) was better than that of the single or non-ensemble model. Comparing the FFC estimates of our model with those of existing datasets showed that our model exhibited more forest spatial distribution details and higher accuracy in complex landscapes. Overall, in this study, the method of using high-resolution remote sensing (RS) images to extract samples for FFC estimation is feasible. Our results demonstrate the potential of the ensemble MLAs to map the FFC. The research results also show that among many MALs, the RF algorithm is the most suitable algorithm for estimating FFC, which provides a reference for future research.

Effect of Ecological Construction Engineering on Vegetation Restoration: A Case Study of the Loess Plateau

Since the 1980s, with rapid economic development and increased attention given to ecological protection, China has launched a series of ecological-restoration programs to restore the local environment through afforestation and natural forest protection. The evaluation of vegetation restoration is an important part of evaluating the effectiveness of ecological restoration. The Loess Plateau is an area where ecological problems are concentrated, and it is a key area of ecological construction in China. This paper takes the Loess Plateau as the research area, using remote sensing and geographic information technology combined with ecosystem structural changes and an improved residual model to study vegetation restoration. The following main conclusions were drawn: (1) From 1990 to 2000, the farmland area increased by 3084.81 km2, resulting in the encroachment of a large area of grassland and shrubland. (2) With the implementation of ecological engineering, the area of returning farmland to forest and grassland reached 18,001.88 km2; in this period, the NDVI of vegetation increased rapidly, and the area that increased comprised 91.90% of the total area, of which the area of significant increase reached 65.78%. The quality of vegetation was restored to a great extent, and ecological engineering played a major role in this stage. (3) Under the background of large-scale implementation of ecological restoration, the urban area of the Loess Plateau continues to expand.

Water Conservation Estimation Based on Time Series NDVI in the Yellow River Basin

Accurate estimation of the water conservation is of great significance for ecological red line planning. The water conservation of the Yellow River Basin has a vital influence on the development of the environment and the supply of ecological services in China. However, the existing methods used to estimate water conservation have many disadvantages, such as requiring numerous parameters, a complex calculation model, and using data that is often difficult acquire. It is often hard to provide sufficiently precise parameters and data, resulting in a large amount of calculation time and the difficulties in the study of large scale and long time series. In this study, a time series of the Normalized Difference Vegetation Index (NDVI) was applied to estimate water conservation in two aspects using the idea of wholeness and stratification, respectively. The overall fitting results can explain nearly 30% of the water conservation by partial least squares regression and nearly 50% of it by a support vector machine. However, the results of a stratified simulation showed that water conservation and the NDVI have a certain stratified heterogeneity among different ecosystem types. The optimal fitting result was achieved in a water/wetland ecosystem with the highest coefficient of determination (R2P) of 0.768 by the stratified support vector machine (SVM) model, followed by the forest and grassland ecosystem (both R2P of 0.698). The spatial mapping results showed that this method was most suitable for grassland ecosystem, followed by forest ecosystem. According to the results generated using the NDVI time series data, it is feasible to complete a spatial simulation of water conservation. This research can provide a reference for calculating regional or large-scale water conservation and in ecological red line planning.

Normalized Difference Vegetation Index Temporal Responses to Temperature and Precipitation in Arid Rangelands

Rangeland degradation caused by increasing misuses remains a global concern. Rangelands have a remarkable spatiotemporal heterogeneity, making them suitable to be monitored with remote sensing. Among the remotely sensed vegetation indices, Normalized Difference Vegetation Index (NDVI) is most used in ecology and agriculture. In this paper, we research the relationship of NDVI with temperature, precipitation, and Aridity Index (AI) in four different arid rangeland areas in Spain’s southeast. We focus on the interphase variability, studying time series from 2002 to 2019 with regression analysis and lagged correlation at two different spatial resolutions (500 × 500 and 250 × 250 m2) to understand NDVI response to meteorological variables. Intraseasonal phases were defined based on NDVI patterns. Strong correlation with temperature was reported in phases with high precipitations. The correlation between NDVI and meteorological series showed a time lag effect depending on the area, phase, and variable observed. Differences were found between the two resolutions, showing a stronger relationship with the finer one. Land uses and management affected the NDVI dynamics heavily strongly linked to temperature and water availability. The relationship between AI and NDVI clustered the areas in two groups. The intraphases variability is a crucial aspect of NDVI dynamics, particularly in arid regions.

A Forecast Model Applied to Monitor Crops Dynamics Using Vegetation Indices (NDVI)

Vegetation dynamics is very sensitive to environmental changes, particularly in arid zones where climate change is more prominent. Therefore, it is very important to investigate the response of this dynamics to those changes and understand its evolution according to different climatic factors. Remote sensing techniques provide an effective system to monitor vegetation dynamics on multiple scales using vegetation indices (VI), calculated from remote sensing reflectance measurements in the visible and infrared regions of the electromagnetic spectrum. In this study, we use the normalized difference vegetation index (NDVI), provided from the MOD13Q1 V006 at 250 m spatial resolution product derived from the MODIS sensor. NDVI is frequent in studies related to vegetation mapping, crop state indicator, biomass estimator, drought monitoring and evapotranspiration. In this paper, we use a combination of forecasts to perform time series models and predict NDVI time series derived from optical remote sensing data. The proposed ensemble is constructed using forecasting models based on time series analysis, such as Double Exponential Smoothing and autoregressive integrated moving average with explanatory variables for a better prediction performance. The method is validated using different maize plots and one olive plot. The results after combining different models show the positive influence of several weather measures, namely, temperature, precipitation, humidity and radiation.

Multiscaling NDVI Series Analysis of Rainfed Cereal in Central Spain

Vegetation indices time series analysis is increasingly improved for characterizing agricultural land processes. However, this is challenging because of the multeity of factors affecting vegetation growth. In semiarid regions the rainfall, the soil properties and climate are strongly correlated with crop growth. These relationships are commonly analyzed using the normalized difference vegetation index (NDVI). NDVI series from two sites, belonging to different agroclimatic zones, were examined, decomposing them into the overall average pattern, residuals, and anomalies series. All of them were studied by applying the concept of the generalized Hurst exponent. This is derived from the generalized structure function, which characterizes the series’ scaling properties. The cycle pattern of NDVI series from both zones presented differences that could be explained by the differences in the climatic precipitation pattern and soil characteristics. The significant differences found in the soil reflectance bands confirm the differences in both sites. The scaling properties of NDVI original series were confirmed with Hurst exponents higher than 0.5 showing a persistent structure. The opposite was found when analyzing the residual and the anomaly series with a stronger anti-persistent character. These findings reveal the influences of soil–climate interactions in the dynamic of NDVI series of rainfed cereals in the semiarid.

A Practical Satellite-Derived Vegetation Drought Index for Arid and Semi-Arid Grassland Drought Monitoring

In semi-arid pasture areas, drought may directly influence livestock production, cause economic losses, and accelerate the processes of desertification along with destructive human activities (i.e., overgrazing). The aim of this article is to analyze the disadvantages of several drought indices derived from remote sensing data and develop a new vegetation drought index (VDI) for monitoring of grassland drought with high temporal frequency (dekad) and fine spatial resolution (1 km). The site-based soil moisture data from the field campaign in 2014 and the fenced biomass values at nine sites from 2000 to 2015 were adopted for validation. The results indicate that the proposed VDI would better reflect the extent, severity, and changes of drought compared with single drought indices or the vegetation health index (VHI); specifically, the VDI is more closely related to site-based soil moisture, with R human increasing to approximately 0.07 compared with the VHI; and with normalized fenced biomass (NFB) values, with average R human increasing to approximately 0.11 compared with the VHI. However, the correlations between VHI and VDI with NFB values are relatively lower in desert steppe regions. Furthermore, regional drought-affected data (RDA) are used to ensure spatial consistency of the evaluation; the VDI map is in good agreement with the RDA map based on field measurements. The presented VDI shows reliable and stable drought monitoring ability, which will play an important role in the future drought monitoring of inland grassland.

Analysis of Drought Impact on Croplands from Global to Regional Scale: A Remote Sensing Approach

Drought is one of the extreme climatic events that has a severe impact on crop production and food supply. Our main goal is to test the suitability of remote sensing-based indices to detect drought impacts on crop production from a global to regional scale. Moderate resolution imaging spectroradiometer (MODIS) based imagery, spanning from 2001 to 2017 was used for this task. This includes the normalized difference vegetation index (NDVI), land surface temperature (LST), and the evaporative stress index (ESI), which is based on the ratio of actual to potential evapotranspiration. These indices were used as indicators of drought-induced vegetation conditions for three main crops: maize, wheat, and soybean. The start and end of the growing season, as observed at 500 m resolution, were used to exclude the time steps that are outside of the growing season. Based on the three indicators, monthly standardized anomalies were estimated, which were used for both analyses of spatiotemporal patterns of drought and the relationship with yield anomalies. Anomalies in the ESI had higher correlations with maize and wheat yield anomalies than other indices, indicating that prolonged periods of low ESI during the growing season are highly correlated with reduced crop yields. All indices could identify past drought events, such as the drought in the USA in 2012, Eastern Africa in 2016–2017, and South Africa in 2015–2016. The results of this study highlight the potential of the use of moderate resolution remote sensing-based indicators combined with phenometrics for drought-induced crop impact monitoring. For several regions, droughts identified using the ESI and LST were more intense than the NDVI-based results. We showed that these indices are relevant for agricultural drought monitoring at both global and regional scales. They can be integrated into drought early warning systems, process-based crop models, as well as can be used for risk assessment and included in advanced decision-support frameworks.

Diverse response of vegetation growth to multi-time-scale drought under different soil textures in China's pastoral areas

The pastoral areas of China are mainly located in ecologically fragile regions, where its ecosystems are highly sensitive to drought trends. Although numerous studies have been carried out on the response of vegetation to droughts, it is not entirely clear whether soil properties can influence this relationship. Using the Normalized Difference Vegetation Index (NDVI) and the Standardized Precipitation Evapotranspiration Index (SPEI), covering the period 1982 to 2015, we carefully analyzed drought impacts on vegetation in China's pastoral areas, to determine the effects of vegetation communities and soil types on vegetation response to multi-time-scale drought. Significantly positive correlations between NDVI and SPEI were observed in most regions, properly indicating that vegetation was largely influenced by drought, particularly the pastures in Inner Mongolia. Generally, forest was sensitive to longer time-scales of droughts, while grassland and cropland showed a close relationship with shorter or median drought time-scales. However, noticeable differences were found on the Tibetan Plateau, mainly because drought was not the main factor affecting vegetation growth in the region. The NDVI-SPEI correlations and the corresponding SPEI time-scales of each soil texture differed considerably, even in areas of the same land cover type, revealing that soil properties, here mainly refer to soil texture (classified by fractions of each separate soil, i.e., sand, silt, and clay), can assuredly affect the resistance and resilience of vegetation to drought stress. The underlying mechanism is the difference in particle size and permeability which can alter the storage and position of available soil water, thus affecting water absorption by the root system. Our results highlight the considerable importance of properly integrating edaphic factors when exploring the impact of likely climate change on ecosystems.

Leaf water potential of coffee estimated by landsat-8 images

Traditionally, water conditions of coffee areas are monitored by measuring the leaf water potential (ΨW) throughout a pressure pump. However, there is a demand for the development of technologies that can estimate large areas or regions. In this context, the objective of this study was to estimate the ΨW by surface reflectance values and vegetation indices obtained from the Landsat-8/OLI sensor in Minas Gerais-Brazil Several algorithms using OLI bands and vegetation indexes were evaluated and from the correlation analysis, a quadratic algorithm that uses the Normalized Difference Vegetation Index (NDVI) performed better, with a correlation coefficient (R2) of 0.82. Leave-One-Out Cross-Validation (LOOCV) was performed to validate the models and the best results were for NDVI quadratic algorithm, presenting a Mean Absolute Percentage Error (MAPE) of 27.09% and an R2 of 0.85. Subsequently, the NDVI quadratic algorithm was applied to Landsat-8 images, aiming to spatialize the ΨW estimated in a representative area of regional coffee planting between September 2014 to July 2015. From the proposed algorithm, it was possible to estimate ΨW from Landsat-8/OLI imagery, contributing to drought monitoring in the coffee area leading to cost reduction to the producers.

Data Assimilation of High-Resolution Thermal and Radar Remote Sensing Retrievals for Soil Moisture Monitoring in a Drip-Irrigated Vineyard

Efficient water use assessment and irrigation management is critical for the sustainability of irrigated agriculture, especially under changing climate conditions. Due to the impracticality of maintaining ground instrumentation over wide geographic areas, remote sensing and numerical model-based fine-scale mapping of soil water conditions have been applied for water resource applications at a range of spatial scales. Here, we present a prototype framework for integrating high-resolution thermal infrared (TIR) and synthetic aperture radar (SAR) remote sensing data into a soil-vegetation-atmosphere-transfer (SVAT) model with the aim of providing improved estimates of surface- and root-zone soil moisture that can support optimized irrigation management strategies. Specifically, remotely-sensed estimates of water stress (from TIR) and surface soil moisture retrievals (from SAR) are assimilated into a 30-m resolution SVAT model over a vineyard site in the Central Valley of California, U.S. The efficacy of our data assimilation algorithm is investigated via both the synthetic and real data experiments. Results demonstrate that a particle filtering approach is superior to an ensemble Kalman filter for handling the nonlinear relationship between model states and observations. In addition, biophysical conditions such as leaf area index are shown to impact the relationship between observations and states and must therefore be represented accurately in the assimilation model. Overall, both surface and root-zone soil moisture predicted via the SVAT model are enhanced through the assimilation of thermal and radar-based retrievals, suggesting the potential for improving irrigation management at the agricultural sub-field scale using a data assimilation strategy.

Effect of traditional check dams (jessour) on soil and olive trees water status in Tunisia

In the arid regions of south eastern Tunisia, the land use is predominated by olive trees cropping, where two main cultivation strategies can be found: using of water harvesting techniques to overcome the scarcity and variability of rainfall (in the Matmata mountains) and dryland farming (in the Jeffara plain). In these arid areas, soil moisture is the main limiting factor for crop growth and it should be monitored to benchmark different management options. Different conventional methods are available for point soil moisture monitoring, but the increased availability of remotely sensed data offers major opportunities for spatial analyses. The aim of this paper is to perform a comparative study on the soil water status for rainfed olive tree growing in three major landscape areas: in the mountains with traditional water harvesting check dams (called jessour), in the piedmont on floodwater harvesting (called tabias), and in the plain with full dryland farming conditions. Time series of Normalized Difference Infrared Index (NDII), derived from Landsat 7 satellite, were retrieved from the novel Google Earth Engine platform. NDII values were related to measured soil water content, which was taken at non-regular time intervals between 2009 and 2017. The analysis of NDII data, indicating the water content of the vegetation, shows that jessour can adequately ensure water supply for olive trees. Increased soil moisture conditions in the jessour areas are visible both in the dry and the humid seasons, indicating the effectiveness of this traditional water harvesting system. Moreover, our results show that Landsat 7 NDII values are correlated with the root-zone soil moisture in the monitoring sites (r² ranging from 0.62 to 0.67), allowing the use of NDII to estimate soil water contents in our study area.

Copula-Based Abrupt Variations Detection in the Relationship of Seasonal Vegetation-Climate in the Jing River Basin, China

Understanding the changing relationships between vegetation coverage and precipitation/temperature (P/T) and then exploring their potential drivers are highly necessary for ecosystem management under the backdrop of a changing environment. The Jing River Basin (JRB), a typical eco-environmentally vulnerable region of the Loess Plateau, was chosen to identify abrupt variations of the relationships between seasonal Normalized Difference Vegetation Index (NDVI) and P/T through a copula-based method. By considering the climatic/large-scale atmospheric circulation patterns and human activities, the potential causes of the non-stationarity of the relationship between NDVI and P/T were revealed. Results indicated that (1) the copula-based framework introduced in this study is more reasonable and reliable than the traditional double-mass curves method in detecting change points of vegetation and climate relationships; (2) generally, no significant change points were identified during 1982–2010 at the 95% confidence level, implying the overall stationary relationship still exists, while the relationships between spring NDVI and P/T, autumn NDVI and P have slightly changed; (3) teleconnection factors (including Arctic Oscillation (AO), Pacific Decadal Oscillation (PDO), Niño 3.4, and sunspots) have a more significant influence on the relationship between seasonal NDVI and P/T than local climatic factors (including potential evapotranspiration and soil moisture); (4) negative human activities (expansion of farmland and urban areas) and positive human activities (“Grain For Green” program) were also potential factors affecting the relationship between NDVI and P/T. This study provides a new and reliable insight into detecting the non-stationarity of the relationship between NDVI and P/T, which will be beneficial for further revealing the connection between the atmosphere and ecosystems.

Do High-Voltage Power Transmission Lines Affect Forest Landscape and Vegetation Growth: Evidence from a Case for Southeastern of China

The rapid growth of the network of high-voltage power transmission lines (HVPTLs) is inevitably covering more forest domains. However, no direct quantitative measurements have been reported of the effects of HVPTLs on vegetation growth. Thus, the impacts of HVPTLs on vegetation growth are uncertain. Taking one of the areas with the highest forest coverage in China as an example, the upper reaches of the Minjiang River in Fujian Province, we quantitatively analyzed the effect of HVPTLs on forest landscape fragmentation and vegetation growth using Landsat imageries and forest inventory datasets. The results revealed that 0.9% of the forests became edge habitats assuming a 150 m depth-of-edge-influence by HVPTLs, and the forest plantations were the most exposed to HVPTLs among all the forest landscape types. Habitat fragmentation was the main consequence of HVPTL installation, which can be reduced by an increase in the patch density and a decrease in the mean patch area (MA), largest patch index (LPI), and effective mesh size (MESH). In all the landscape types, the forest plantation and the non-forest land were most affected by HVPTLs, with the LPI values decreasing by 44.1 and 20.8%, respectively. The values of MESH decreased by 44.2 and 32.2%, respectively. We found an obvious increasing trend in the values of the normalized difference vegetation index (NDVI) in 2016 and NDVI growth during the period of 2007 to 2016 with an increase in the distance from HVPTL. The turning points of stability were 60 to 90 meters for HVPTL corridors and 90 to 150 meters for HVPTL pylons, which indicates that the pylons have a much greater impact on NDVI and its growth than the lines. Our research provides valuable suggestions for vegetation protection, restoration, and wildfire management after the construction of HVPTLs.

Spatiotemporal Changes in Evapotranspiration from an Overexploited Water Resources Basin in Arid Northern China and Their Implications for Ecosystem Management

Evapotranspiration (ET), including evaporation from soil and water surfaces and transpiration from vegetation, influences water distribution in the soil-plant-atmosphere continuum, especially in arid areas where water is a key limiting factor. Therefore, understanding the spatiotemporal dynamics of ET, including its two components of soil evaporation (Es) and vegetation transpiration (Ec), can be useful for water resource management and ecological restoration in arid regions. Based on ET data from 2002 to 2012, the spatiotemporal variations in ET were evaluated in the Shiyang River Basin in arid Northwest China. The results showed the following: (1) spatially, ET decreased from upstream of the Qilian Mountains to the middle and downstream, with a mean annual value of 316 mm; (2) temporally, ET showed a single peak curve throughout the year, with the highest value occurring in summer; (3) ET showed a downward trend (from 350 to 265 mm) before 2009 and thereafter increased (from 265 to 345 mm); and (4) water use efficiency, indicated by the ratio of Ec to ET, was low in the cropland, with a mean value of 50.9%. Further analysis indicates that decreases in ET are mainly caused by vegetation decreases; in contrast, ecological restriction measures and strict water resource management policies in the middle reaches of the basin led to ET increases. It is concluded that understanding ET and its two components can elucidate the connections between water and human society.

Time Series Analysis of MODIS-Derived NDVI for the Hluhluwe-Imfolozi Park, South Africa: Impact of Recent Intense Drought

The variability of temperature and precipitation influenced by El Niño-Southern Oscillation (ENSO) is potentially one of key factors contributing to vegetation product in southern Africa. Thus, understanding large-scale ocean–atmospheric phenomena like the ENSO and Indian Ocean Dipole/Dipole Mode Index (DMI) is important. In this study, 16 years (2002–2017) of Moderate Resolution Imaging Spectroradiometer (MODIS) Terra/Aqua 16-day normalized difference vegetation index (NDVI), extracted and processed using JavaScript code editor in the Google Earth Engine (GEE) platform was used to analyze the vegetation response pattern of the oldest proclaimed nature reserve in Africa, the Hluhluwe-iMfolozi Park (HiP) to climatic variability. The MODIS enhanced vegetation index (EVI), burned area index (BAI), and normalized difference infrared index (NDII) were also analyzed. The study used the Modern Retrospective Analysis for the Research Application (MERRA) model monthly mean soil temperature and precipitations. The Global Land Data Assimilation System (GLDAS) evapotranspiration (ET) data were used to investigate the HiP vegetation water stress. The region in the southern part of the HiP which has land cover dominated by savanna experienced the most impact of the strong El Niño. Both the HiP NDVI inter-annual Mann–Kendal trend test and sequential Mann–Kendall (SQ-MK) test indicated a significant downward trend during the El Niño years of 2003 and 2014–2015. The SQ-MK significant trend turning point which was thought to be associated with the 2014–2015 El Niño periods begun in November 2012. The wavelet coherence and coherence phase indicated a positive teleconnection/correlation between soil temperatures, precipitation, soil moisture (NDII), and ET. This was explained by a dominant in-phase relationship between the NDVI and climatic parameters especially at a period band of 8–16 months.