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Soybean is an important crop for food and animal feed. Production and area both continue to increase and expand into new areas and countries. Spatially explicit information on soybean cultivation is essential to crop monitoring, production estimation, and national accounting systems. However, its cultivation in diverse climate conditions, landscapes, and agricultural systems poses challenges to accurately map soybean across different regions and years. We propose an innovative soybean mapping method combining phenological alignment with machine learning (named here RF-DTW), which can be applied to diverse geographies and years by aligning phenological shifts and using distinctive features from Sentinel-2 time-series. The method first uses the dynamic time warping (DTW) algorithm to align the growing season between pixels across different sites. Then, based on the harmonized time-series, a set of distinctive features was identified and used to build random forest (RF) models to classify soybean across ten globally distributed sites and multiple years. Results show that the green chlorophyll vegetation index (GCVI), greenness and water content composite index (GWCCI), normalized difference senescent vegetation index (NDSVI), red edge position (REP), and short-wave infrared bands are important inputs for distinguishing soybean from other crops. Spectral-phenological features, particularly the curve slope metrics of GCVI and GWCCI during the peak to late growing season, rank as the most important features for mapping soybean. RF-DTW demonstrates good generalizability across ten study sites, achieving an overall accuracy (OA) of 0.92 and an F1-score of 0.84. F1-scores for eight out of ten sites ranged between 0.82 and 0.98, outperforming a benchmark method, although they were lower (F1-score < 0.60) for the two sites in Sub-Saharan Africa. Additionally, RF-DTW performs robustly when transferred to untrained regions and years, with most cases showing an F1-score higher than 0.70. Our proposed method, as a combination of phenological alignment and machine learning, can be used to map soybean accurately and efficiently across different regions and years, to provide crucial information for understanding the rapid dynamics of soybean cultivation and its global-scale impacts. [ABSTRACT FROM AUTHOR]

Soybean is an important crop for food and animal feed. Production and area both continue to increase and expand into new areas and countries. Spatially explicit information on soybean cultivation is essential to crop monitoring, production estimation, and national accounting systems. However, its cultivation in diverse climate conditions, landscapes, and agricultural systems poses challenges to accurately map soybean across different regions and years. We propose an innovative soybean mapping method combining phenological alignment with machine learning (named here RF-DTW), which can be applied to diverse geographies and years by aligning phenological shifts and using distinctive features from Sentinel-2 time-series. The method first uses the dynamic time warping (DTW) algorithm to align the growing season between pixels across different sites. Then, based on the harmonized time-series, a set of distinctive features was identified and used to build random forest (RF) models to classify soybean across ten globally distributed sites and multiple years. Results show that the green chlorophyll vegetation index (GCVI), greenness and water content composite index (GWCCI), normalized difference senescent vegetation index (NDSVI), red edge position (REP), and short-wave infrared bands are important inputs for distinguishing soybean from other crops. Spectral-phenological features, particularly the curve slope metrics of GCVI and GWCCI during the peak to late growing season, rank as the most important features for mapping soybean. RF-DTW demonstrates good generalizability across ten study sites, achieving an overall accuracy (OA) of 0.92 and an F1-score of 0.84. F1-scores for eight out of ten sites ranged between 0.82 and 0.98, outperforming a benchmark method, although they were lower (F1-score < 0.60) for the two sites in Sub-Saharan Africa. Additionally, RF-DTW performs robustly when transferred to untrained regions and years, with most cases showing an F1-score higher than 0.70. Our proposed method, as a combination of phenological alignment and machine learning, can be used to map soybean accurately and efficiently across different regions and years, to provide crucial information for understanding the rapid dynamics of soybean cultivation and its global-scale impacts. [ABSTRACT FROM AUTHOR]

Weather extremes affect crop production. Remote sensing can help to detect crop damage and estimate lost yield due to weather extremes over large spatial extents. We propose a novel scalable method to predict in-season yield losses at the sub-field level and attribute these to weather extremes. To assess our method's potential, we conducted a proof-of-concept case study on winter cereal paddocks in South Australia using data from 2017 to 2022. To detect crop growth anomalies throughout the growing season, we aligned a two-band Enhanced Vegetation Index (EVI2) time series from Sentinel-2 with thermal time. The deviation between the expected and observed EVI2 time series was defined as the Crop Damage Index (CDI). We assessed the performance of the CDI within specific phenological windows to predict yield loss. Finally, by comparing instances of substantial increase in CDI with different extreme weather indicators, we explored which (combinations of) extreme weather events were likely responsible for the experienced yield reduction. We found that the use of thermal time diminished the temporal deviation of EVI2 time series between years, resulting in the effective construction of typical stress-free crop growth curves. Thermal-time-based EVI2 time series resulted in better prediction of yield reduction than those based on calendar dates. Yield reduction could be predicted before grain-filling (approximately two months before harvest) with an R2 of 0.83 for wheat and 0.91 for barley. Finally, the combined analysis of CDI curves and extreme weather indices allowed for timely detection of weather-related causes of crop damage, which also captured the spatial variations of crop damage attribution at sub-field level. The proposed framework provides a basis for early warning of crop damage and attributing the damage to weather extremes in near real-time, which should help to adopt appropriate crop protection strategies. [ABSTRACT FROM AUTHOR]

Abstract Global food security is negatively affected by drought. Climate projections show that drought frequency and intensity may increase in different parts of the globe. These increases are particularly hazardous for developing countries. Early season forecasts on drought occurrence and severity could help to better mitigate the negative consequences of drought. The objective of this study was to assess if interannual variability in agricultural productivity in Chile can be accurately predicted from freely-available, near real-time data sources. As the response variable, we used the standard score of seasonal cumulative NDVI (zcNDVI), based on 2000–2017 data from Moderate Resolution Imaging Spectroradiometer (MODIS), as a proxy for anomalies of seasonal primary productivity. The predictions were performed with forecast lead times from one- to six-month before the end of the growing season, which varied between census units in Chile. Predictor variables included the zcNDVI obtained by cumulating NDVI from season start up to prediction time; standardised precipitation indices derived from satellite rainfall estimates, for time-scales of 1, 3, 6, 12 and 24 months; the Pacific Decadal Oscillation and the Multivariate ENSO oscillation indices; the length of the growing season, and latitude and longitude. For each of the 758 census units considered, the time series of the response and the predictor variables were averaged for agricultural areas resulting in a 17-season time series per unit for each variable. We used two prediction approaches: (i) optimal linear regression (OLR) whereby for each census unit the single predictor was selected that best explained the interannual zcNDVI variability, and (ii) a multi-layer feedforward neural network architecture, often called deep learning (DL), where all predictors for all units were combined in a single spatio-temporal model. Both approaches were evaluated with a leave-one-year-out cross-validation procedure. Both methods showed good prediction accuracies for small lead times and similar values for all lead times. The mean R cv 2 values for OLR were 0.95, 0.83, 0.68, 0.56, 0.46 and 0.37, against 0.96, 0.84, 0.65, 0.54, 0.46 and 0.38 for DL, for one, two, three, four, five, and six months lead time, respectively. Given the wide range of climates and vegetation types covered within the study area, we expect that the presented models can contribute to an improved early warning system for agricultural drought in different geographical settings around the globe. Highlights • Variability in seasonal biomass productivity was predicted for 758 units in Chile. • Anomalies of vegetation indices, rainfall, and climate indices were used as input. • We compared unit-specific linear regression with a global deep learning approach. • Overall, deep learning and linear regression have similar results for all lead times. • Our approach can be easily replicated across the globe. [ABSTRACT FROM AUTHOR]

Remote sensing studies of vegetation phenology increasingly benefit from freely available satellite imagery acquired with high temporal frequency at fine spatial resolution. Particularly for heterogeneous landscapes this is good news, given the drawback of medium-resolution sensors commonly used for phenology retrieval (e.g., MODIS) to properly represent the fine-scale spatial variability of vegetation types. The Sentinel-2 mission acquires spectral data globally at 10 to 60 m resolution every five days. To illustrate the mission's potential for studying vegetation phenology, we retrieved phenological parameters for the Dutch barrier island Schiermonnikoog for a full season of Sentinel-2A observations in 2016. Overlapping orbits resulted in two acquisitions per 10 days, similar to what is achieved globally since the launch of Sentinel-2B. For eight locations on the island's salt marsh we compared greenness chromatic coordinate (GCC) series derived from digital repeat RGB-cameras with vegetation index series derived from Sentinel-2 (NDVI and GCC). For each series, a double hyperbolic tangent model was fitted and thresholds were applied to the modelled data to estimate start-, peak-, and end-of-season (SOS/PS/EOS). Variability in Sentinel-2 derived SOS, when taken as the midpoint between minimum and peak NDVI, was well-explained by camera GCC-based SOS (R 2  = 0.74, MSD = 8.0 days, RMSD = 13.0 days). However, EOS estimates from camera GCC series were on average almost two months before NDVI-based estimates. This could partially be explained by the observed exponential relationship between GCC and NDVI, as well as by the combined effect of viewing angle differences and the presence of non-photosynthetic elements in the vegetation canopy. A two-layer canopy radiative transfer model incorporating reduced chlorophyll levels in the upper layer provided a physically-based explanation of the viewing angle effect. Finally, we applied the phenology retrieval approach to NDVI series for all pixels of the island in order to map spatial patterns of phenology at fine resolution. Our results demonstrate the potential of the Sentinel-2 mission for providing spatially-detailed retrievals of phenology. [ABSTRACT FROM AUTHOR]

Detailed spatial information of changes in surface water extent is needed for water management and biodiversity conservation, particularly in drier parts of the globe where small, temporally-variant wetlands prevail. Although global surface water histories are now generated from 30 m Landsat data, for many locations they contain large temporal gaps particularly for longer periods (>10 years) due to revisit intervals and cloud cover. Daily Moderate Resolution Imaging Spectrometer (MODIS) imagery has potential to fill such gaps, but its relatively coarse spatial resolution may not detect small water bodies, which can be of great ecological importance. To address this problem, this study proposes and tests options for estimating the surface water fraction from MODIS 16-day 500 m Bidirectional Reflectance Distribution Function (BRDF) corrected surface reflectance image composites. The spatial extent of two Landsat tiles over Spain were selected as test areas. We obtained a 500 m reference dataset on surface water fraction by spatially aggregating 30 m binary water masks obtained from the Landsat-derived C-version of Function of Mask (CFmask), which themselves were evaluated against high-resolution Google Earth imagery. Twelve regression tree models were developed with two approaches, Random Forest and Cubist, using spectral metrics derived from MODIS data and topographic parameters generated from a 30 m spatial resolution digital elevation model. Results showed that accuracies were higher when we included annual summary statistics of the spectral metrics as predictor variables. Models trained on a single Landsat tile were ineffective in mapping surface water in the other tile, but global models trained with environmental conditions from both tiles can provide accurate results for both study areas. We achieved the highest accuracy with Cubist global model ( R 2 = 0.91, RMSE = 11.05%, MAE = 7.67%). Our method was not only effective for mapping permanent water fraction, but also in accurately capturing temporal fluctuations of surface water. Based on this good performance, we produced surface water fraction maps at 16-day interval for the 2000–2015 MODIS archive. Our approach is promising for monitoring surface water fraction at high frequency time intervals over much larger regions provided that training data are collected across the spatial domain for which the model will be applied. [ABSTRACT FROM AUTHOR]

• Transferring tsetse habitat models between different regions is possible. • Models transferred better to environmentally similar regions. • F1-scores of >0.65 could be obtained for models transferred to similar regions. • Transferring models to highly dissimilar regions resulted in poor accuracy. Accurate and reliable information on the distribution of tsetse habitats is crucial for the effective management of African Trypanosomiasis in sub-Saharan Africa. However, conducting large-scale surveillance of tsetse flies to develop distribution maps is impractical due to vast areas infested and limited resources available. To address this challenge, we evaluated the applicability of tsetse habitat models developed in the intensively sampled Shimba Hills National Reserve in Kenya for both the wet and the dry season, to two other regions in Kenya (Ruma National Park and Nguruman Conservancy) and one region in Rwanda (Akagera National Park). The models utilized satellite-based estimates of vegetation greenness, land cover, and land surface temperature, combined with tsetse occurrence data, to predict habitat suitability. An independent dataset of tsetse occurrence was used to benchmark the performance of the transferred models. The performance of the transferred models was significantly influenced by the similarity in environmental conditions between the model's development area and the transfer area. In regions with high dissimilarity, such as Nguruman Conservancy during the dry season, model transfer was unsuccessful with an F1-score of zero. In all other regions and seasons, the transferred models showed satisfactory performance, with F1-score values exceeding 0.65. Nevertheless, site-specific models outperformed (>0.8 F1-score) the transferred models, indicating that models specifically developed with data for each location can provide more accurate information on tsetse distribution. In conclusion, our study demonstrates that tsetse habitat models can be transferred with relatively good accuracies to seasons and regions that exhibit environmental similarity with the model training area. Despite the higher accuracy of site-specific models, transferring models to similar sites remains a meaningful exercise in the absence of detailed surveillance data. [Display omitted] [ABSTRACT FROM AUTHOR]

Vegetation indices derived from satellite image time series have been extensively used to estimate the timing of phenological events like season onset. Medium spatial resolution (≥250 m) satellite sensors with daily revisit capability are typically employed for this purpose. In recent years, phenology is being retrieved at higher resolution (≤30 m) in response to increasing availability of high-resolution satellite data. To overcome the reduced acquisition frequency of such data, previous attempts involved fusion between high- and medium-resolution data, or combinations of multi-year acquisitions in a single phenological reconstruction. The objectives of this study are to demonstrate that phenological parameters can now be retrieved from single-season high-resolution time series, and to compare these retrievals against those derived from multi-year high-resolution and single-season medium-resolution satellite data. The study focuses on the island of Schiermonnikoog, the Netherlands, which comprises a highly-dynamic saltmarsh, dune vegetation, and agricultural land. Combining NDVI series derived from atmospherically-corrected images from RapidEye (5 m-resolution) and the SPOT5 Take5 experiment (10m-resolution) acquired between March and August 2015, phenological parameters were estimated using a function fitting approach. We then compared results with phenology retrieved from four years of 30 m Landsat 8 OLI data, and single-year 100 m Proba-V and 250 m MODIS temporal composites of the same period. Retrieved phenological parameters from combined RapidEye/SPOT5 displayed spatially consistent results and a large spatial variability, providing complementary information to existing vegetation community maps. Retrievals that combined four years of Landsat observations into a single synthetic year were affected by the inclusion of years with warmer spring temperatures, whereas adjustment of the average phenology to 2015 observations was only feasible for a few pixels due to cloud cover around phenological transition dates. The Proba-V and MODIS phenology retrievals scaled poorly relative to their high-resolution equivalents, indicating that medium-resolution phenology retrievals need to be interpreted with care, particularly in landscapes with fine-scale land cover variability. [ABSTRACT FROM AUTHOR]

Pastoralist households across East Africa face major livestock losses during drought periods that can cause persistent poverty. For Kenya and southern Ethiopia, an existing index insurance scheme aims to reduce the adverse effects of such losses. The scheme insures individual households through an area-aggregated seasonal forage scarcity index derived from remotely-sensed normalized difference vegetation index (NDVI) time series. Until recently, insurance contracts covered animal losses and indemnity payouts were consequently made late in the season, based on a forage scarcity index incorporating both wet and dry season NDVI data. Season timing and duration were fixed for the whole area (March–September for long rains, October–February for short rains). Due to demand for asset protection insurance (pre-loss intervention) our aim was to identify earlier payout options by shortening the temporal integration period of the index. We used 250 m-resolution 10-day NDVI composites for 2001–2014 from the Moderate Resolution Imaging Spectroradiometer (MODIS). To better describe the period during which forage develops, we first retrieved per-pixel average season start- and end-dates using a phenological model. These dates were averaged per insurance unit to obtain unit-specific growing period definitions. With these definitions a new forage scarcity index was calculated. We then examined if shortening the temporal period further could effectively predict most (> 90%) of the interannual variability of the new index, and assessed the effects of shortening the period on indemnity payouts. Our analysis shows that insurance payouts could be made one to three months earlier as compared to the current index definition, depending on the insurance unit. This would allow pastoralists to use indemnity payments to protect their livestock through purchase of forage, water, or medicines. [ABSTRACT FROM AUTHOR]

Global insurance markets are vast and diverse, and may offer many opportunities for remote sensing. To date, however, few operational applications of remote sensing for insurance exist. Papers claiming potential application of remote sensing typically stress the technical possibilities, without considering its contribution to customer value for the insured or to the profitability of the insurance industry. Based on a systematic search of available literature, this review investigates the potential and actual support of remote sensing to the insurance industry. The review reveals that research on remote sensing in classical claim-based insurance described in the literature revolve around crop damage and flood and fire risk assessment. Surprisingly, the use of remote sensing in claim-based insurance appears to be instigated by government rather than the insurance industry. In contrast, insurance companies are offering various index insurance products that are based on remote sensing. For example, remotely sensed index insurance for rangelands and livestock are operational, while various applications in crop index insurance are being considered or under development. The paper discusses these differences and concludes that there is particular scope for application of remote sensing by the insurance industry in index insurance because (1) indices can be constructed that correlate well with what is insured; (2) these indices can be delivered at low cost; and (3) it opens up new markets that are not served by claim-based insurance. The paper finally suggests that limited adoption of remote sensing in insurance results from a lack of mutual understanding and calls for greater cooperation between the insurance industry and the remote sensing community. [ABSTRACT FROM AUTHOR]

Droughts induce livestock losses that severely affect Kenyan pastoralists. Recent index insurance schemes have the potential of being a viable tool for insuring pastoralists against drought-related risk. Such schemes require as input a forage scarcity (or drought) index that can be reliably updated in near real-time, and that strongly relates to livestock mortality. Generally, a long record (>25 years) of the index is needed to correctly estimate mortality risk and calculate the related insurance premium. Data from current operational satellites used for large-scale vegetation monitoring span over a maximum of 15 years, a time period that is considered insufficient for accurate premium computation. This study examines how operational NDVI datasets compare to, and could be combined with the non-operational recently constructed 30-year GIMMS AVHRR record (1981–2011) to provide a near-real time drought index with a long term archive for the arid lands of Kenya. We compared six freely available, near-real time NDVI products: five from MODIS and one from SPOT-VEGETATION. Prior to comparison, all datasets were averaged in time for the two vegetative seasons in Kenya, and aggregated spatially at the administrative division level at which the insurance is offered. The feasibility of extending the resulting aggregated drought indices back in time was assessed using jackknifed R 2 statistics (leave-one-year-out) for the overlapping period 2002–2011. We found that division-specific models were more effective than a global model for linking the division-level temporal variability of the index between NDVI products. Based on our results, good scope exists for historically extending the aggregated drought index, thus providing a longer operational record for insurance purposes. We showed that this extension may have large effects on the calculated insurance premium. Finally, we discuss several possible improvements to the drought index. [ABSTRACT FROM AUTHOR]

Abstract: Soil erosion by water occurs on sloped terrain when erosive rainfall and consequent surface runoff act on soils that are not well-protected by vegetation or other soil protective measures. Both rainfall erosivity and vegetation cover are highly variable through space and time. Joint accounting for the variability of these factors is required to effectively map and monitor soil erosion. However, most studies merely use average annual erosivity values, partly due to data paucity. This study analyses the variability of rainfall erosivity across Africa through the use of 3-hourly TRMM Multi-satellite Precipitation Analysis (TMPA) precipitation data. We obtained average annual erosivity estimates from 15yr of TMPA data (1998–2012) using intensity–erosivity relationships. Our estimates showed a positive correlation (r =0.84) with long-term annual erosivity values of 37 stations obtained from literature. Our TMPA-analysis confirmed and mapped the large interannual variability, with maximum annual erosivity often exceeding two to three times the mean value, especially in semi-arid areas. Seasonal variability of erosivity was investigated from TMPA-based average monthly erosivity estimates, which resulted in similar seasonal patterns as those reported in literature. We conclude that (1) spatial and temporal variability of erosivity is important and needs to be accounted for in combination with vegetation cover when monitoring soil erosion; and (2) 3-hourly TMPA data allow for a good first estimate of the variability of erosivity in Africa, which could be improved by upcoming techniques that provide more accurate rainfall information at higher spatial and temporal resolutions. [Copyright &y& Elsevier]

The use of night-time livestock enclosures, often referred to as "bomas", "corrals", or "kraals", is a common practice across African rangelands. Bomas protect livestock from predation by wildlife and potential theft. Due to the concentration of animal faeces inside bomas, they not only become nutrient-rich patches that can add to biodiversity, but also hotspots for the emission of nitrous oxide (N 2 O), an important greenhouse gas, especially when animals are kept inside for long periods. To provide an accurate estimate of such emissions for wider landscapes, bomas need to be accounted for. Moreover, initial experiments indicated that more frequent shifts in the boma locations could help to reduce N 2 O emissions. This stresses the need for better understanding where bomas are located, their numbers, as well as when they are actively used. Given the recent advances in satellite technology, resulting in high-frequent spectral measurements at fine spatial resolution, solutions to address these needs are now within reach. This study is a first effort to map and monitor the appearance of bomas with the use of satellite image time series. Our main dataset was a dense times series of 3 m resolution PlanetScope multispectral imagery. In addition, a reference dataset of boma and non-boma locations was created using GPS-collar tracking data and 0.5 m resolution Pléiades imagery. The reduction of vegetation cover and increase of organic material following boma installation result in typical spectral changes when contrasted against its surroundings. Based on these spectral changes we devised an empirical approach to infer approximate boma installation dates from PlanetScope's near infrared (NIR) band and used our reference dataset for setting optimal parameter values. A NIR spatial difference index resulted in clear temporal patterns, which were more apparent during the wet season. At landscape scale our approach reveals clear spatio-temporal patterns of boma installation, which could not be revealed from less frequent sub-meter resolution imagery alone. While further improvements are possible, we show that small-sized (150–500 m2) temporary surface changes, such as those that occur when pastoralists use mobile bomas, can be detected with dense image time series like those offered by the PlanetScope constellation. In future, this could lead to better assessment of a) spatio-temporal livestock distribution, b) the contribution of bomas to N 2 O emissions and soil fertility at landscape scale, and c) the uptake of enclosure rotation practices at large spatial scales. • Livestock enclosures, or 'bomas', are hotspots of nutrients and N 2 O emissions. • We made a dataset on boma location/timing from GPS collars and Pléiades images. • We then used dense PlanetScope time series to assess the appearance of mobile bomas. • An automatic boma detection approach was developed using a spatial difference index. • We revealed clear spatio-temporal patterns of boma installation at landscape scale. [ABSTRACT FROM AUTHOR]

Highlights: [•] We evaluate two daily solar radiation products derived from Meteosat satellites. [•] Solar radiation from Meteosat First and Second Generation has a similar accuracy. [•] A time series for 1983-present can be obtained through combination of both products. [•] ERA-Interim is an effective backup solution for satellite-based solar radiation. [ABSTRACT FROM AUTHOR]

Highlights: [•] Three solar radiation models were calibrated using Meteosat Second Generation data. [•] Accuracy of daily solar radiation retrieval improved significantly for two models. [•] Spatial interpolation resulted in maps of model coefficients for Europe. [Copyright &y& Elsevier]

Frequent spatial reorganization of administrative units is common in many countries. It may comprise the merging or division of spatial units, or boundary changes between units. These reorganizations prevent the effective assessment of longer-term population dynamics at a detailed spatial level. To deal with this problem in the Netherlands, we developed a new temporal correction method for the populations of municipalities. Rather than estimating the affected population, we used existing data on the number of persons affected by each spatial change. We assumed that before any boundary changes took place, population development was spatially uniform within a municipality. Systematically transferring proportions of the population from original to newly defined target municipalities back in time provided a corrected time series for 1988–2011, based on the 2011 municipal boundaries. Overall, our results correspond well with a detailed reconstruction for 1999–2009 based on data for individual households. Our procedure may be applicable in other countries with effective population registration systems. [ABSTRACT FROM PUBLISHER]

The spatial distribution of crops and farming systems in Africa is determined by the duration of the period during which crop and livestock water requirements are met. The length of growing period (LGP) is normally assessed from weather station data-scarce in large parts of Africa-or coarse-resolution rainfall estimates derived from weather satellites. In this study, we analyzed LGP and its variability based on the 1981-2011 GIMMS NDVI3g dataset. We applied a variable threshold method in combination with a searching algorithm to determine start- and end-of-season. We obtained reliable LGP estimates for arid, semi-arid and sub-humid climates that are consistent in space and time. This approach effectively mapped bimodality for clearly separated wet seasons in the Horn of Africa. Due to cloud contamination, the identified bimodality along the Guinea coast was judged to be less certain. High LGP variability is dominant in arid and semi-arid areas, and is indicative of crop failure risk. Significant negative trends in LGP were found for the northern part of the Sahel, for parts of Tanzania and northern Mozambique, and for the short rains of eastern Kenya. Positive trends occurred across western Africa, in southern Africa, and in eastern Kenya for the long rains. Our LGP analysis provides useful information for the mapping of farming systems, and to study the effects of climate variability and other drivers of change on vegetation and crop suitability. [ABSTRACT FROM AUTHOR]

Food security exists when people have access to sufficient, safe and nutritious food at all times to meet their dietary needs. The natural resource base is one of the many factors affecting food security. Its variability and decline creates problems for local food production. In this study we characterize for sub-Saharan Africa vegetation phenology and assess variability and trends of phenological indicators based on NDVI time series from 1982 to 2006. We focus on cumulated NDVI over the season (cumNDVI) which is a proxy for net primary productivity. Results are aggregated at the level of major farming systems, while determining also spatial variability within farming systems. High temporal variability of cumNDVI occurs in semiarid and subhumid regions. The results show a large area of positive cumNDVI trends between Senegal and South Sudan. These correspond to positive CRU rainfall trends found and relate to recovery after the 1980's droughts. We find significant negative cumNDVI trends near the south-coast of West Africa (Guinea coast) and in Tanzania. For each farming system, causes of change and variability are discussed based on available literature (Appendix A). Although food security comprises more than the local natural resource base, our results can perform an input for food security analysis by identifying zones of high variability or downward trends. Farming systems are found to be a useful level of analysis. Diversity and trends found within farming system boundaries underline that farming systems are dynamic. [ABSTRACT FROM AUTHOR]

Summary: Rainfall erosivity is a measure for the erosive force of rainfall. Rainfall kinetic energy determines the erosivity and is in turn greatly dependent on rainfall intensity. Attempts for its large-scale mapping are rare. Most are based on interpolation of erosivity values derived from rain gauge data. For data-poor regions this is not an option. This study examines whether erosivity can be accurately mapped for Africa using 3-hourly TRMM Multi-satellite Precipitation Analysis (TMPA) precipitation data. Using intensity–erosivity relationships and 11years of TMPA data (1998–2008) we calculated average annual erosivity. We also calculated erosivity from the monthly TMPA data product using the often-applied Fournier and modified Fournier indices. From literature 39 locations with long-term erosivity values were retrieved. Comparison showed that the modified Fournier index attains a much stronger correlation (r =0.84) than the results based on 3-hourly data (r =0.71). We conclude that (1) the 3-hourly and 0.25-degree TMPA data provide insufficient detail to represent high-intensity erosive events and (2) monthly satellite-based precipitation provides good spatial estimates of average annual erosivity. [ABSTRACT FROM AUTHOR]

Abstract: Erosion reduces soil productivity and causes negative downstream impacts. Erosion processes occur on areas with erodible soils and sloping terrain when high-intensity rainfall coincides with limited vegetation cover. Timing of erosion events has implications on the selection of satellite imagery, used to describe spatial patterns of protective vegetation cover. This study proposes a method for erosion risk mapping with multi-temporal and multi-resolution satellite data. The specific objectives of the study are: (1) to determine when during the year erosion risk is highest using coarse-resolution data, and (2) to assess the optimal timing of available medium-resolution images to spatially represent vegetation cover during the high erosion risk period. Analyses were performed for a 100-km2 pasture area in the Brazilian Cerrados. The first objective was studied by qualitatively comparing three-hourly TRMM rainfall estimates with MODIS NDVI time series for one full year (August 2002–August 2003). November and December were identified as the months with highest erosion risk. The second objective was examined with a time series of six available ASTER images acquired in the same year. Persistent cloud cover limited image acquisition during high erosion risk periods. For each ASTER image the NDVI was calculated and classified into five equally sized classes. Low NDVI was related to high erosion risk and vice versa. A DEM was used to set approximately flat zones to very low erosion risk. The six resulting risk maps were compared with erosion features, visually interpreted from a fine-resolution QuickBird image. Results from the October ASTER image gave highest accuracy (84%), showing that erosion risk mapping in the Brazilian Cerrados can best be performed with images acquired shortly before the first erosion events. The presented approach that uses coarse-resolution temporal data for determining erosion periods and medium-resolution data for effective erosion risk mapping is fast and straightforward. It shows good potential for successful application in other areas with high spatial and temporal variability of vegetation cover. [Copyright &y& Elsevier]

Abstract: Water erosion creates negative impacts on agricultural production, infrastructure, and water quality across the world. Regional-scale water erosion assessment is important, but limited by data availability and quality. Satellite remote sensing can contribute through providing spatial data to such assessments. During the past 30 years many studies have been published that did this to a greater or lesser extent. The objective of this paper is to review methodologies applied for water erosion assessment using satellite remote sensing. First, studies on erosion detection are treated. This comprises the detection of erosion features and eroded areas, as well as the assessment of off-site impacts such as sediment deposition and water quality of inland lakes. Second, the assessment of erosion controlling factors is evaluated. Four types of factors are discussed: topography, soil properties, vegetation cover, and management practices. Then, erosion mapping techniques are described that integrate products derived from satellite remote sensing with additional data sources. These techniques include erosion models and qualitative methods. Finally, validation methods used to assess the accuracy of maps produced with satellite data are discussed. It is concluded that a general lack of validation data is a main concern. Validation is of utmost importance to achieve regional operational monitoring systems, and close collaboration between the remote sensing community and field-based erosion scientists is therefore required. [Copyright &y& Elsevier]

Controlling tsetse flies is critical for effective management of African trypanosomiasis in Sub-Saharan Africa. To enhance timely and targeted deployment of tsetse control strategies a better understanding of their temporal dynamics is paramount. A few empirical studies have explained and predicted tsetse numbers across space and time, but the resulting models may not easily scale to other areas. We used tsetse catches from 160 traps monitored between 2017 and 2019 around Shimba Hills National Reserve in Kenya, a known tsetse and trypanosomiasis hotspot. Traps were divided into two groups: proximal (<1.0 km)) to and distant (> 1.0 km) from the outer edge of the reserve boundary. We fitted zero-inflated Poisson and generalized linear regression models for each group using as temporal predictors rainfall, NDVI (Normalized Difference Vegetation Index), and LST (land surface temperature). For each predictor, we assessed their relationship with tsetse abundance using time lags from 10 days up to 60 days before the last tsetse collection date of each trap. Tsetse numbers decreased as distance from the outside of reserve increased. Proximity to croplands, grasslands, woodlands, and the reserve boundary were the key predictors for proximal traps. Tsetse numbers rose after a month of increased rainfall and the following increase in NDVI values but started to decline if the rains persisted beyond a month for distant traps. Specifically, tsetse flies were more abundant in areas with NDVI values greater than 0.7 for the distant group. The study suggests that tsetse control efforts beyond 1.0 km of the reserve boundary should be implemented after a month of increased rains in areas having NDVI values greater than 0.7. To manage tsetse flies effectively within a 1.0 km radius of the reserve boundary, continuous measures such as establishing an insecticide-treated trap or target barrier around the reserve boundary are needed. Author summary: To control African trypanosomiasis, tsetse populations must be reduced to levels that impede disease transmission. Limited reliable information on tsetse temporal dynamics hinders the implementation of effective control strategies. We used two empirical modelling strategies (zero-inflated Poisson and generalized linear Poisson) to link tsetse abundance with environmental and weather data, to understand and explain tsetse temporal dynamics in a heterogeneous environment. Tsetse numbers were consistently high within 1.0 km of the reserve outer boundary due to favorable breeding sites, resting sites, and host availability. Beyond 1.0 km of the reserve boundary, tsetse numbers increased after a month of increased rainfall, but started to decline if rainfall persisted beyond a month. For these localities, higher tsetse numbers were also found in areas with NDVI values above 0.7. These insights provide valuable guidance for the timing and location of tsetse control strategies around Shimba Hills National Reserve, particularly considering the limited resources available. [ABSTRACT FROM AUTHOR]

The short revisit times afforded by recently-deployed optical satellite sensors that acquire 3–30 m resolution imagery provide new opportunities to study seasonal vegetation dynamics. Previous studies demonstrated a successful retrieval of phenology with Sentinel-2 for relatively stable annual growing seasons. In semi-arid East Africa however, vegetation responds rapidly to a concentration of rainfall over short periods and consequently is subject to strong interannual variability. Obtaining a sufficient density of cloud-free acquisitions to accurately describe these short vegetation cycles is therefore challenging. The objective of this study is to evaluate if data from two satellite constellations, i.e., PlanetScope (3 m resolution) and Sentinel-2 (10 m resolution), each independently allow for accurate mapping of vegetation phenology under these challenging conditions. The study area is a rangeland with bimodal seasonality located at the 128-km2 Kapiti Farm in Machakos County, Kenya. Using all the available PlanetScope and Sentinel-2 imagery between March 2017 and February 2019, we derived temporal NDVI profiles and fitted double hyperbolic tangent models (equivalent to commonly-used logistic functions), separately for the two rainy seasons locally referred to as the short and long rains. We estimated start- and end-of-season for the series using a 50% threshold between minimum and maximum levels of the modelled time series (SOS 50 /EOS 50). We compared our estimates against those obtained from vegetation index series from two alternative sources, i.e. a) greenness chromatic coordinate (GCC) series obtained from digital repeat photography, and b) MODIS NDVI. We found that both PlanetScope and Sentinel-2 series resulted in acceptable retrievals of phenology (RMSD of ~8 days for SOS 50 and ~15 days for EOS 50 when compared against GCC series) suggesting that the sensors individually provide sufficient temporal detail. However, when applying the model to the entire study area, fewer spatial artefacts occurred in the PlanetScope results. This could be explained by the higher observation frequency of PlanetScope, which becomes critical during periods of persistent cloud cover. We further illustrated that PlanetScope series could differentiate the phenology of individual trees from grassland surroundings, whereby tree green-up was found to be both earlier and later than for grass, depending on location. The spatially-detailed phenology retrievals, as achieved in this study, are expected to help in better understanding climate and degradation impacts on rangeland vegetation, particularly for heterogeneous rangeland systems with large interannual variability in phenology and productivity. • Phenology was retrieved for multiple rainfall seasons from PlanetScope and Sentinel-2. • Retrievals were compared with greenness series from RGB-cameras and MODIS imagery. • PlanetScope resulted in phenology maps with fewer spatial artefacts than Sentinel-2. • Phenology patterns suggest that vegetation response to rainfall differs per season. [ABSTRACT FROM AUTHOR]

• We constructed 8-day water extent time series (2000–2017) for 340 wetlands with MODIS. • Validation was performed with a Landsat dataset and water level data. • Allows monitoring the dynamics of small wetlands (<1 km2) with high varibility. • Provides a useful tool for capturing abnormal changes and short-duration events. Wetlands are among the most biodiverse ecosystems in the world, due largely to their dynamic hydrology. Frequent observations by satellite sensors such as the Moderate Resolution Imaging Spectrometer (MODIS) allow for monitoring the seasonal, inter-annual and long-term dynamics of surface water extent. However, existing MODIS-based studies have only demonstrated this for large water bodies (>100 km2) despite the ecological importance of smaller-sized wetland systems. In this paper, we assessed if the temporal dynamics of surface water extent can be effectively captured with MODIS for a wide range of water body sizes, specifically 0.01 km2 and larger. We constructed the temporal dynamics of surface water extent for 340 individual water bodies in the Mediterranean region between 2000 and 2017, using a previously developed 8-day 500 m MODIS surface water fraction (SWF) dataset. We then evaluated how MODIS SWF time series corresponded to water extent derived from a Landsat-based dataset and to satellite-altimetry derived water level data. Results showed that although correlations between MODIS- and Landsat-derived SWF were poor for relative static water bodies (r = 0.17), they increased for more dynamic water bodies (r = 0.81). For dynamic water bodies>100 km2, time series of water extent derived from MODIS SWF showed good correlation with both Landsat (r ≥ 0.76) and water level data (ρ ≥ 0.63). Our MODIS SWF dataset can also effectively monitor the variability of very small water bodies (<1 km2) when comparing with Landsat data as long as the temporal variability in their surface water area was high. We conclude that MODIS SWF is a useful product to help understand hydrological dynamics for both small and larger-sized water bodies, and to monitor their seasonal, intermittent, inter-annual and long-term changes. [ABSTRACT FROM AUTHOR]

• We propose a new design that strengthens the ecological significance of an currently operational forage scarcity index for livestock insurance. • The proposed design has an improved relationship with livestock mortality data. • It provides a flexible structure for indemnity calculation, allowing also per pixel análisis of forage. • Improvements are relevant for the geographic expansion of the insurance scheme. • The proposed design can be adopted for other NDVI-based early warning monitoring systems. Recurrent drought represents a major threat in arid and semi-arid regions of East Africa where pastoralists depend on their livestock for subsistence. In Kenya and southern Ethiopia, an existing satellite-based index insurance scheme aims to protect pastoralists against the adverse effects of drought. Under that scheme, payouts are made based on an area-aggregated seasonal forage scarcity index derived from remotely-sensed Normalized Difference Vegetation Index (NDVI). NDVI values are directly averaged per unit areas of insurance (UAI), which are based on administrative borders but take into limited account the ecological variability within the unit. The choice of administrative boundaries at the onset of the analysis may negatively impact the performance of the product. Our study explores an alternative index design based on an ecological stratification of the study area. First, we performed an unsupervised classification of NDVI time series from the Moderate Resolution Imaging Spectroradiometer (MODIS) to group pixels with similar temporal NDVI trajectories. Next, we used average NDVI profiles and ancillary data to discard areas deemed insignificant for forage production. We then transformed NDVI values into z-scores to assess how each pixel relates to the multi-year distribution of NDVI values per class and season. In the final step, we calculated the alternative forage scarcity index, which is the percentage of pixels with anomalously low NDVI values per season and UAI (i.e. z-score ≤ −1.0). To evaluate its performance, we compared unit-level results for both the original and alternative designs against spatially-aggregated monthly household survey data on livestock mortality from 16 sample sites corresponding to eight administrative units within the study area. Besides performing better in predicting livestock mortality (i.e. increases of 53% and 39% in correlation coefficients as measured by Pearson's r and Spearman's ρ, respectively) and strengthening the ecological significance of the index, the proposed design has a number of other advantages: 1) the index is calculated using a much larger statistical basis, 2) it allows for analysis of forage conditions at sub-unit level, and 3) it offers a more flexible structure for payout calculation. These advantages could be of particular relevance for expanding the index-insurance scheme to agro-pastoral regions characterized by more heterogeneous landscapes. Finally, we propose to consider using this approach to better account for the ecological variability of rangelands in other NDVI-based early warning and monitoring systems. [ABSTRACT FROM AUTHOR]

Background: African trypanosomiasis, which is mainly transmitted by tsetse flies (Glossina spp.), is a threat to public health and a significant hindrance to animal production. Tools that can reduce tsetse densities and interrupt disease transmission exist, but their large-scale deployment is limited by high implementation costs. This is in part limited by the absence of knowledge of breeding sites and dispersal data, and tools that can predict these in the absence of ground-truthing. Methods: In Kenya, tsetse collections were carried out in 261 randomized points within Shimba Hills National Reserve (SHNR) and villages up to 5 km from the reserve boundary between 2017 and 2019. Considering their limited dispersal rate, we used in situ observations of newly emerged flies that had not had a blood meal (teneral) as a proxy for active breeding locations. We fitted commonly used species distribution models linking teneral and non-teneral tsetse presence with satellite-derived vegetation cover type fractions, greenness, temperature, and soil texture and moisture indices separately for the wet and dry season. Model performance was assessed with area under curve (AUC) statistics, while the maximum sum of sensitivity and specificity was used to classify suitable breeding or foraging sites. Results: Glossina pallidipes flies were caught in 47% of the 261 traps, with teneral flies accounting for 37% of these traps. Fitted models were more accurate for the teneral flies (AUC = 0.83) as compared to the non-teneral (AUC = 0.73). The probability of teneral fly occurrence increased with woodland fractions but decreased with cropland fractions. During the wet season, the likelihood of teneral flies occurring decreased as silt content increased. Adult tsetse flies were less likely to be trapped in areas with average land surface temperatures below 24 °C. The models predicted that 63% of the potential tsetse breeding area was within the SHNR, but also indicated potential breeding pockets outside the reserve. Conclusion: Modelling tsetse occurrence data disaggregated by life stages with time series of satellite-derived variables enabled the spatial characterization of potential breeding and foraging sites for G. pallidipes. Our models provide insight into tsetse bionomics and aid in characterising tsetse infestations and thus prioritizing control areas. [ABSTRACT FROM AUTHOR]

Reversing ecological degradation through restoration activities is a key societal challenge of the upcoming decade. However, lack of evidence on the effectiveness of restoration interventions leads to inconsistent, delayed, or poorly informed statements of success, hindering the wise allocation of resources, representing a missed opportunity to learn from previous experiences. This study contributes to a better understanding of spatial and temporal dynamics of ecosystem services at ecological restoration sites. We developed a method using Landsat satellite images combined with a Before-After-Control-Impact (BACI) design, and applied this to an arid rural landscape, the Baviaanskloof in South Africa. Since 1990, various restoration projects have been implemented to halt and reverse degradation. We applied the BACI approach at pixel-level comparing the conditions of each intervened pixel (impact) with 20 similar control pixels. By evaluating the conditions before and after the restoration intervention, we assessed the effectiveness of long-term restoration interventions distinguishing their impact from environmental temporal changes. The BACI approach was implemented with Landsat images that cover a 30-year period at a spatial resolution of 30 meter. We evaluated the impact of three interventions (revegetation, livestock exclusion, and the combination of both) on three ecosystem services; forage provision, erosion prevention, and presence of iconic vegetation. We also evaluated whether terrain characteristics could partially explain the variation in impact of interventions. The resulting maps showed spatial patterns of positive and negative effects of interventions on ecosystem services. Intervention effectiveness differed across vegetation conditions, terrain aspect, and soil parent material. Our method allows for spatially explicit quantification of the long-term restoration impact on ecosystem service supply, and for the detailed visualization of impact across an area. This pixel-level analysis is specifically suited for heterogeneous landscapes, where restoration impact not only varies between but also within restoration sites. [ABSTRACT FROM AUTHOR]

• ET EB and ET WB exhibit large disparities regardless of terrestrial water storage. • Varying degrees of consistency in ET EB -ET WB across time scales are evaluated. • Closure errors of water budgets closely follow excess precipitation in humid areas. • Budyko-simulated ET considering water and energy constraints agreed well with ET EB. Evapotranspiration (ET) calculated as the residual of catchment water balance (ET WB) has often been used as a benchmark to evaluate satellite-based ET retrievals that use the energy-balance approach (ET EB). However, errors from water balance components will accrue in ET WB , leading to considerable disparities with ET EB. In this study, we set out to investigate whether ET EB from multiple sources (MOD16, GLEAM, PT-JPL, and PT-hybrid) can capture the spatiotemporal variability of ET WB across 53 catchments in central-western Europe with a humid climate. Using ET retrievals from the Budyko framework that accounts for the control of energy demand on water supply and upscaled ET from FLUXCOM as references, we explored the causes of discrepancies between ET WB and ET EB at long-term, annual, and monthly scales. We found that (1) ET EB significantly diverged from ET WB at the mean annual scale (r = 0.35), particularly for energy-limited catchments, but Budyko-simulated ET considering energy limit correlated well with ET EB (r > 0.86); (2) neither ET EB nor upscaled ET can reproduce annual ET WB time series (r < 0.40), and the closure errors in water budgets closely follow excess precipitation beyond energy demand; (3) monthly ET WB exhibited better correspondences with ET EB (r = 0.73), presumably because of similarity in seasonal patterns. Our results demonstrate that errors from precipitation and terrestrial water storage anomalies introduce large uncertainties in ET WB , thereby complicating water balance validation in humid regions across multiple timesteps. To improve the application of ET WB for benchmarking ET EB in humid regions, high-quality input data should be used or – like the Budyko framework – energy constraints should be considered. [ABSTRACT FROM AUTHOR]

• Intra-seasonal grassland dynamics for community greenness retrieved from digital repeat photography. • Precipitation, temperature, and grazing influence grassland community greenness at different times. • Digital repeat photographs add value by providing temporal dynamics, spatial variability, and estimation of grazing regimes. • Grassland community greenness, precipitation, temperature, and grazing drivers demonstrate the complexity of interactions in these drylands. Understanding grassland dynamics and their relationship to weather and grazing is critical for pastoralists whose livelihoods depend on grassland productivity. Studies investigating the impacts of climate and human factors on inter-seasonal grassland dynamics have focused mostly on changes to vegetation structure. Yet, quantifying the impact of these on the inter-seasonal dynamics of specific grassland communities is not known. This study uses digital repeat photography to examine how intra-seasonal grassland dynamics of different grassland communities are affected by precipitation, temperature, and grazing in a heterogeneous semi-arid savannah in Kenya. A low-cost digital repeat camera network allowed for fine-scale temporal and spatial variability analysis of grassland dynamics and grazing intensity. Over all grass communities, our results show precipitation driving mainly early-season and in some cases mid-season flushing, temperature driving end-of-season senescence, and grazing influencing mid-season declines. Yet, our study quantifies how these three drivers do not uniformly impact grassland species communities. Specifically, Cynodon and Cynodon/Bothriochloa communities are rapidly and positively associated with precipitation, where mid-season declines in Cynodon communities are associated with grazing and late-season declines in Cynodon/Bothriochloa communities are associated with temperature increases. Setaria communities, on the other hand, have weaker associations with the drivers, with limited positive associations with precipitation and grazing. Kunthii/Digitaria diverse communities had no association with the three drivers. Highly diverse mixed communities were associated with increased precipitation and temperature, as well as lower intensity grazing. Our research sheds light on the complex interactions between plants, animals, and weather. Furthermore, this study also demonstrates the potential of digital repeated photography to inform about fine-scale spatial and temporal patterns of semi-arid grassland vegetation and grazing, with the goal of assisting in the formulations of management practises that better capture the intra-annual variability of highly heterogeneous dryland systems. [ABSTRACT FROM AUTHOR]

Extreme weather conditions can strongly affect agricultural production, with negative impacts that can at times be detected at regional scales. In France, crop yields were greatly influenced by drought and heat stress in 2003 and by extremely wet conditions in 2007. Reported regional maize and wheat yields where historically low in 2003; in 2007 wheat yields were lower and maize yields higher than long-term averages. An analysis with a spatial version (10 × 10 km) of the EPIC crop model was tested with regards to regional crop yield anomalies of wheat and maize resulting from extreme weather events in France in 2003 and 2007, by comparing simulated results against reported regional crops statistics, as well as using remotely sensed soil moisture data. Causal relations between soil moisture and crop yields were specifically analyzed. Remotely sensed (AMSR-E) JJA soil moisture correlated significantly with reported regional crop yield for 2002-2007. The spatial correlation between JJA soil moisture and wheat yield anomalies was positive in dry 2003 and negative in wet 2007. Biweekly soil moisture data correlated positively with wheat yield anomalies from the first half of June until the second half of July in 2003. In 2007, the relation was negative the first half of June until the second half of August. EPIC reproduced observed soil dynamics well, and it reproduced the negative wheat and maize yield anomalies of the 2003 heat wave and drought, as well as the positive maize yield anomalies in wet 2007. However, it did not reproduce the negative wheat yield anomalies due to excessive rains and wetness in 2007. Results indicated that EPIC, in line with other crop models widely used at regional level in climate change studies, is capable of capturing the negative impacts of droughts on crop yields, while it fails to reproduce negative impacts of heavy rain and excessively wet conditions on wheat yield, due to poor representations of critical factors affecting plant growth and management. Given that extreme weather events are expected to increase in frequency and perhaps severity in coming decades, improved model representation of crop damage due to extreme events is warranted in order to better quantify future climate change impacts and inform appropriate adaptation responses. [ABSTRACT FROM AUTHOR]

Abstract: Variations in agricultural production due to rainfall and temperature fluctuations are a primary cause of food insecurity on the African continent. Analysis of changes in phenology can provide quantitative information on the effect of climate variability on growing seasons in agricultural regions. Using a robust statistical methodology, we describe the relationship between phenology metrics derived from the 26year AVHRR NDVI record and the North Atlantic Oscillation index (NAO), the Indian Ocean Dipole (IOD), the Pacific Decadal Oscillation (PDO), and the Multivariate ENSO Index (MEI). We map the most significant positive and negative correlation for the four climate indices in Eastern, Western and Southern Africa between two phenological metrics and the climate indices. Our objective is to provide evidence of whether climate variability captured in the four indices has had a significant impact on the vegetative productivity of Africa during the past quarter century. We found that the start of season and cumulative NDVI were significantly affected by large scale variations in climate. The particular climate index and the timing showing highest correlation depended heavily on the region examined. In Western Africa the cumulative NDVI correlates with PDO in September–November. In Eastern Africa the start of the June–October season strongly correlates with PDO in March–May, while the PDO in December–February correlates with the start of the February–June season. The cumulative NDVI over this last season relates to the MEI of March–May. For Southern Africa, high correlations exist between SOS and NAO of September–November, and cumulative NDVI and MEI of March–May. The research shows that climate indices can be used to anticipate late start and variable vigor in the growing season of sensitive agricultural regions in Africa. [ABSTRACT FROM AUTHOR]

The frequent acquisitions of fine spatial resolution imagery (10 m) offered by recent multispectral satellite missions, including Sentinel-2, can resolve single agricultural fields and thus provide crop-specific phenology metrics, a crucial information for crop monitoring. However, effective phenology retrieval may still be hampered by significant cloud cover. Synthetic aperture radar (SAR) observations are not restricted by weather conditions, and Sentinel-1 thus ensures more frequent observations of the land surface. However, these data have not been systematically exploited for phenology retrieval so far. In this study, we extracted crop-specific land surface phenology (LSP) from Sentinel-1 and Sentinel-2 of major European crops (common and durum wheat, barley, maize, oats, rape and turnip rape, sugar beet, sunflower, and dry pulses) using ground-truth information from the "Copernicus module" of the Land Use/Cover Area frame statistical Survey (LUCAS) of 2018. We consistently used a single model-fit approach to retrieve LSP metrics on temporal profiles of CR (Cross Ratio, the ratio of the backscattering coefficient VH/VV from Sentinel-1) and NDVI (Normalized Difference Vegetation Index from Sentinel-2). Our analysis revealed that LSP retrievals from Sentinel-1 are comparable to those of Sentinel-2, particularly for winter crops. The start of season (SOS) timings, as derived from Sentinel-1 and -2, are significantly correlated (average r of 0.78 for winter and 0.46 for summer crops). The correlation is lower for end of season retrievals (EOS, r of 0.62 and 0.34). Agreement between LSP derived from Sentinel-1 and -2 varies among crop types, ranging from r = 0.89 and mean absolute error MAE = 10 days (SOS of dry pulses) to r = 0.15 and MAE = 53 days (EOS of sugar beet). Observed deviations revealed that Sentinel-1 and -2 LSP retrievals can be complementary; for example for winter crops we found that SAR detected the start of the spring growth while multispectral data is sensitive to the vegetative growth before and during winter. To test if our results correspond reasonably to in-situ data, we compared average crop-specific LSP for Germany to average phenology from ground phenological observations of 2018 gathered from the German Meteorological Service (DWD). Our study demonstrated that both Sentinel-1 and -2 can provide relevant and at times complementary LSP information at field- and crop-level. • Crop-specific phenology retrieved from Senetinel-1 and Sentinel-2 time series. • European Union wide survey LUCAS exploited to focus on major European crops. • Comparable phenology from cross polarization ratio VH/VV and from Sentinel-2 NDVI. • Retrieved phenology consistent with ground phenological observation from DWD. • Sentinel-1 and -2 provide relevant and at times complementary LSP information. [ABSTRACT FROM AUTHOR]

Landscape processes fluctuate over time, influencing the intra-annual dynamics of ecosystem services. However, current ecosystem service assessments generally do not account for such changes. This study argues that information on the dynamics of ecosystem services is essential for understanding and monitoring the impact of land management. We studied two regulating ecosystem services (i. erosion prevention, ii. regulation of water flows) and two provisioning services (iii. provision of forage, iv. biomass for essential oil production) in thicket vegetation and agricultural fields in the Baviaanskloof, South Africa. Using models based on Sentinel-2 data, calibrated with field measurements, we estimated the monthly supply of ecosystem services and assessed their intra-annual variability within vegetation cover types. We illustrated how the dynamic supply of ecosystem services related to temporal variations in their demand. We also found large spatial variability of the ecosystem service supply within a single vegetation cover type. In contrast to thicket vegetation, agricultural land showed larger temporal and spatial variability in the ecosystem service supply due to the effect of more intensive management. Knowledge of intra-annual dynamics is essential to jointly assess the temporal variation of supply and demand throughout the year to evaluate if the provision of ecosystem services occurs when most needed. [ABSTRACT FROM AUTHOR]

For those developing satellite-based insurance products, there is no consensus in the scientific community on which of the many available indices most accurately track agro-ecological shocks as experienced by farmers and pastoralists. Furthermore, metrics commonly used by the remote sensing community for assessing the accuracy of indices in retrieving biophysical variables are insufficient in the case of insurance, because they do not consider the value of insurance coverage in terms of household welfare. This study begins to fill this knowledge gap by bridging two index insurance literatures: the remote sensing science literature that focuses on the predictive power of indices, and the economic literature that focuses on welfare outcomes. The article uses a longitudinal panel of household survey data from Kenya to compare the quality of existing and potential insurance products. These products are developed from different processing chains applied to time series of the satellite-based Normalized Difference Vegetation Index (NDVI). Although the indices are highly correlated to each other (ρ > 0.95), a utility analysis provides insight into how small differences can lead to larger differences in product value. Our results highlight that index accuracy, cost, and timeliness of payments must be considered jointly when assessing insurance quality for clients. [ABSTRACT FROM AUTHOR]

• Leaf chlorophyll of spruce stands retrieved through inversion of INFORM utilizing Sentinel-2 and RapidEye data. • The retrieved and measured leaf chlorophyll content from the Sentinel-2 data had higher R2 and NRMSE values. • Using the mean of the ten best solutions the retrieval error for both Sentinel-2 and RapidEye data decreased. • The retrieval error of leaf chlorophyll decreased when Sentinel-2 red edge bands were used in the inversion process. Leaf chlorophyll plays an essential role in controlling photosynthesis, physiological activities and forest health. In this study, the performance of Sentinel-2 and RapidEye satellite data and the Invertible Forest Reflectance Model (INFORM) radiative transfer model (RTM) for retrieving and mapping of leaf chlorophyll content in the Norway spruce (Picea abies) stands of a temperate forest was evaluated. Biochemical properties of leaf samples as well as stand structural characteristics were collected in two subsequent field campaigns during July 2015 and 2016 in the Bavarian Forest National Park (BFNP), Germany, parallel with the timing of the RapidEye and Sentinel-2 images. Leaf chlorophyll was measured both destructively and nondestructively using wet chemical spectrophotometry analysis and a hand-held chlorophyll content meter. The INFORM was utilised in the forward mode to generate two lookup tables (LUTs) in the spectral band settings of RapidEye and Sentinel-2 data using information obtained from the field campaigns. Before generating the LUTs, the sensitivity of the model input parameters to the spectral data from RapidEye and Sentinel-2 were examined. The canopy reflectance of the studied plots were obtained from the satellite images and used as input for the inversion of LUTs. The coefficient of determination (R2), root mean square errors (RMSE), and the normalised root mean square errors (NRMSE), between the retrieved and measured leaf chlorophyll, were then used to examine the attained results from RapidEye and Sentinel-2 data, respectively. The use of multiple solutions and spectral subsets for the inversion process were further investigated to enhance the retrieval accuracy of foliar chlorophyll. The result of the sensitivity analysis demonstrated that the simulated canopy reflectance of Sentinel-2 is sensitive to the alternation of all INFORM input parameters, while the simulated canopy reflectance from RapidEye did not show sensitivity to leaf water content variations. In general, there was agreement between the simulated and measured reflectance spectra from RapidEye and Sentinel-2, particularly in the visible and red-edge regions. However, examining the average absolute error from the simulated and measured reflectance revealed a large discrepancy in spectral bands around the near-infrared shoulder. The relationship between retrieved and measured leaf chlorophyll content from the Sentinel-2 data had a higher coefficient of determination with a higher NRMSE (NRMSE = 0.36 μg/cm2, R2 = 0.45) compared to those obtained using the RapidEye data (NRMSE = 0.31 μg/cm2 and R2 = 0.39). Using the mean of the ten best solutions (retrieved chlorophyll) the retrieval error for both Sentinel-2 and RapidEye data decreased (NRMSE = 0.34, NRMSE = 0.26, respectively), as compared to only selecting the single best solution. When the Sentinel-2 red edge bands were used as the spectral subset, the retrieval error of leaf chlorophyll decreased indicating the importance of red edge, as well as properly located spectral bands, for leaf chlorophyll estimation. The chlorophyll maps produced by the inversion of the two LUTs effectively represented the variation of foliar chlorophyll in BFNP and confirmed our earlier findings on the observed stress pattern caused by insect infestation. Our findings emphasise the importance of multispectral satellites which benefits from red edge spectral bands such as Sentinel-2 as well as RapidEye for regional mapping of vegetation foliar properties, particularly, chlorophyll using RTMs such as INFORM. [ABSTRACT FROM AUTHOR]

Knowledge about changes in wildlife poaching risk at fine spatial scale can provide essential background intelligence for law enforcement and crime prevention. We assessed interannual trends and seasonal changes in elephant poaching risk for Kenya’s Greater Tsavo ecosystem for 2002 to 2012 using spatio-temporal Bayesian modeling. Poaching data were obtained from the Kenya Wildlife Service’s database on elephant mortality. The novelty of our paper is (1) combining space and time when defining poaching risk for elephant; (2) the inclusion of environmental risk factors to improve the accuracy of the spatio-temporal Bayesian model; and (3) the separate analysis of dry and wet seasons to understand season-dependent poaching patterns. Although Tsavo’s overall poaching level increased over time, the risk of poaching differed significantly across space. Three of the 34 spatial units had a consistently high poaching risk regardless of whether models included environmental risk factors. Adding risk factors enhanced the model’s predictive power. We found that highest poaching risk areas differed between the wet and dry season. The findings improve our understanding of elephant poaching and highlight high risk areas within Tsavo where action to reduce elephant poaching is required. [ABSTRACT FROM PUBLISHER]

To mitigate impacts of climate-related reduced productivity of French grasslands, a new insurance scheme bases indemnity payouts to farmers on a Moderate Resolution Imaging Spectroradiometer (MODIS)-derived forage production index (FPI). The objective of this study is to compare several approaches for deriving FPI from satellite data to assess whether better relationships with forage productivity can be attained. The approaches assess pasture productivity using as five input factors estimated from remote sensing and ancillary data, i.e.: (1) fraction of absorbed photosynthetically active radiation (fAPAR); (2) radiation use efficiency estimates; (3) PAR estimates; (4) leaf senescence modelling; and (5) growing season modelling. All the possible combinations from these five factors, including different modalities to estimate some of them, lead to 768 models. Model outputs are compared to reference grassland production estimates provided by a mechanistic model (Information et Suivi Objectif des Prairies - ISOP - system) for a sample of 25 forage regions across France for the years 2003, 2007, 2009, 2011, and 2012 (containing one humid, two normal, and two dry years). Results revealed that: (1) the baseline model based on the fraction of green vegetation cover (fCover) seasonal integral has a reasonable linear relationship to production estimates (standardized root mean square error - SRMSE = 0.57 and coefficient of determination - R2 = 0.68); (2) performance of the baseline model improved with a quadratic function (SRMSE = 0.54 and R2 = 0.71); (3) 34 models outperform the baseline model. We, therefore, suggest to replace the baseline model with the best-performing model (SRMSE = 0.42 and R2 = 0.83) in the insurance product. This model integrates daily fCover with a water stress index and sums these over a variable monitoring period in space and time characterized by the phenological indicators start of season and end of season derived from the fCover annual profile. [ABSTRACT FROM AUTHOR]

We analysed the performance and timeliness of the Standardized Precipitation Index (SPI) in anticipating deviations from mean seasonal vegetation productivity in the Sahel. Gridded rainfall estimates are used to compute the SPI for 1–6-month timescales, whereas theZ-score of the cumulative value of the Fraction of Absorbed Photosynthetically Active Radiation over the growing season (zCFAPAR) is used as a proxy of seasonal productivity. Results show that the strength of the link varies in space as a function of both the SPI timescale and the timing of the SPI calculation with respect to the vegetative season’s progress. For productivity forecasting, we propose an operational strategy to select per grid cell the SPI timescale and computation time with the highest correlation with zCFAPAR at different moments of the season. The linear relationship between SPI and zCFAPAR is significant for 32–66% of the study area, depending on the timing at which SPI is considered (at 0% and 75% of the seasonal progress, respectively). For these areas, the selected SPI explains on average about 40% of the variance of zCFAPAR and may thus assist in the earlier identification of agricultural drought. [ABSTRACT FROM AUTHOR]

Bayesian statistical methods are being used increasingly in crime research because they overcome data quality problems that arise due to the covert nature of crime, but the use of such methods is still in its infancy in the field of wildlife poaching—a specific form of crime. We analyzed poaching risk for African elephant ( Loxodonta africana ) by comparing spatial and non-spatial Bayesian models. Reports on elephant poaching in the Tsavo ecosystem were obtained for 2002–2012 from the Kenya Wildlife Service. The ecosystem was divided into 34 spatial units for which poaching data were aggregated and served as the base units for analysis. Spatial and non-spatial Bayesian models were fed with expert knowledge obtained through survey responses from 30 experts. The predictive accuracy of both models was assessed using the Deviance Information Criterion (DIC). Our results indicated that spatial Bayesian modeling improved the model fit for mapping elephant poaching risk compared to using non-spatial Bayesian models (DIC value of 193.05 vs 199.03). The results further showed that the seasonal timing of elephant poaching (i.e., in dry and wet seasons), density of waterholes, livestock density and elephant population density were factors significantly influencing the spatial patterns of elephant poaching risk in the Tsavo ecosystem for both models. Although there were similarities in the high risk areas for elephant poaching recognized in both models, risk probability values per spatial unit could differ. Furthermore, spatial Bayesian modeling also identified areas of high poaching risk that were not predicted by the non-spatial model. These findings provide vital information for identifying priority areas for combating elephant poaching and for informing conservation management decisions. The model we present here can be applied to poaching data for other threatened species. [ABSTRACT FROM AUTHOR]

Background: To-date, Rift Valley fever (RVF) outbreaks have occurred in 38 of the 69 administrative districts in Kenya. Using surveillance records collected between 1951 and 2007, we determined the risk of exposure and outcome of an RVF outbreak, examined the ecological and climatic factors associated with the outbreaks, and used these data to develop an RVF risk map for Kenya. Methods: Exposure to RVF was evaluated as the proportion of the total outbreak years that each district was involved in prior epizootics, whereas risk of outcome was assessed as severity of observed disease in humans and animals for each district. A probability-impact weighted score (1 to 9) of the combined exposure and outcome risks was used to classify a district as high (score ≥ 5) or medium (score ≥2 - <5) risk, a classification that was subsequently subjected to expert group analysis for final risk level determination at the division levels (total = 391 divisions). Divisions that never reported RVF disease (score < 2) were classified as low risk. Using data from the 2006/07 RVF outbreak, the predictive risk factors for an RVF outbreak were identified. The predictive probabilities from the model were further used to develop an RVF risk map for Kenya. Results: The final output was a RVF risk map that classified 101 of 391 divisions (26%) located in 21 districts as high risk, and 100 of 391 divisions (26%) located in 35 districts as medium risk and 190 divisions (48%) as low risk, including all 97 divisions in Nyanza and Western provinces. The risk of RVF was positively associated with Normalized Difference Vegetation Index (NDVI), low altitude below 1000m and high precipitation in areas with solonertz, luvisols and vertisols soil types (p <0.05). Conclusion: RVF risk map serves as an important tool for developing and deploying prevention and control measures against the disease. [ABSTRACT FROM AUTHOR]

According to the green wave hypothesis, herbivores follow the flush of spring growth of forage plants during their spring migration to northern breeding grounds. In this study we compared two green wave indices for predicting the timing of the spring migration of avian herbivores: the satellite-derived green wave index (GWI), and an index of the rate of acceleration in temperature (GDDjerk). The GWI was calculated from MODIS normalized difference vegetation index (NDVI) satellite imagery and GDDjerk from gridded temperature data using products from the global land data assimilation system (GLDAS). To predict the timing of arrival at stopover and breeding sites, we used four years (2008–2011) of tracking data from 12 GPS-tagged barnacle geese, a long-distance herbivorous migrant, wintering in the Netherlands, breeding in the Russian Arctic. The stopover and breeding sites for these birds were identified and the relations between date of arrival with the date of 50% GWI and date of peak GDDjerk at each site were analyzed using mixed effect linear regression. A cross-validation method was used to compare the predictive accuracy of the GWI and GDDjerk indices. Significant relationships were found between the arrival dates at the stopover and breeding sites for the dates of 50% GWI as well as the peak GDDjerk ( p < 0.01). The goose arrival dates at both stopover and breeding sites were predicted more accurately using GWI ( R 2 cv = 0.68, RMSD cv = 5.9 and R 2 cv = 0.71, RMSD cv = 3.9 for stopover and breeding sites, respectively) than GDDjerk. The GDDjerk returned a lower accuracy for prediction of goose arrival dates at stopover ( R 2 cv = 0.45, RMSD cv = 7.79) and breeding sites ( R 2 cv = 0.55, RMSD cv = 4.93). The positive correlation between the absolute residual values of the GDDjerk model and distance to the breeding sites showed that this index is highly sensitive to latitude. This study demonstrates that the satellite-derived green wave index (GWI) can accurately predict the timing of goose migration, irrespective of latitude and therefore is suggested as a reliable green wave index for predicting the timing of avian herbivores spring migration. [ABSTRACT FROM AUTHOR]

Spatial and spatiotemporal cluster methods are used for a wide range of applications including the study of criminal activities, but have never been compared for studying a specific form of crime, i.e. wildlife poaching. We aimed to identify elephant poaching hotspots by analyzing the differences in clusters of poached elephants in the Tsavo ecosystem (Kenya) that emerged from different cluster detection methods. Reports of elephant poaching in the Tsavo ecosystem were obtained for 2002–2012 from the Kenya Wildlife Service. The study area was divided into 34 blocks for analysis. Two spatial- and two spatiotemporal clustering methods were applied to the data. The predictive accuracy of the spatial methods in defining hotspots was assessed using the prediction accuracy index (PAI), which was then modified (MPAI) for measuring the predictive accuracy of the spatiotemporal methods. The results from the spatial methods indicated eight consistent poaching blocks, with Kulldorff's spatial scan statistic having a slightly higher PAI value than the flexible scan statistic (2.39 vs 2.12). The spatiotemporal clustering methods revealed four consistent poaching blocks. The MPAI value was higher for the spatiotemporal scan statistic than the spatiotemporal permutation scan statistic (1.46 vs 0.97). The results demonstrated that although the hotspot predictions varied for the different methods, three blocks were consistently identified as poaching hotspots. Our findings may assist wildlife departments such as the Kenyan Wildlife Service to allocate their financial and human resources as effectively as possible in combating poaching. Further research is needed to examine the environmental and human factors contributing to the patterns that have been observed in elephant poaching. [ABSTRACT FROM AUTHOR]

Abstract: Solar radiation data are an important input for estimating evapotranspiration and modelling crop growth. Direct measurement of solar radiation is now carried out in most European countries, but the network of measuring stations is too sparse for reliable interpolation of measured values. Instead of direct measurements, solar radiation may be estimated from empirical solar radiation models that employ more commonly measured variables or direct outputs of general and regional circulation models (such as air temperature). Coefficients for these models are site-dependent. This usually implies that they are estimated for stations with direct radiation measurements, but need to be interpolated for other locations. In this paper, we introduce a procedure to auto-calibrate empirical solar radiation models that are based on daily air temperature range, i.e. Bristow and Campbell (1984), and Hargreaves et al. (1985). Meteosat Second Generation data were used to create two static look-up tables of mean cloud cover and clear-sky transmissivity as input for the auto-calibration procedure. We demonstrate that daily solar radiation can be accurately estimated from daily air temperature range measurements without site-specific empirical coefficients that require stations that measure solar radiation. The average relative root mean square error for our auto-calibrated models was comparable to ground-measurement-based calibration; only 1% higher for the Bristow and Campbell model (p < 0.05, n = 126), and 2% higher for the Hargreaves model (p < 0.05, n = 126). The mean bias error, relative mean bias error and the slope of linear regression were not statistically different in comparison to ground-measurement-based calibration for the Bristow and Campbell model. When our new solar radiation retrieval algorithm is used to estimate evapotranspiration, we found similar accuracies when using solar radiation input from ground- and auto-calibration. We conclude that our auto-calibration procedure results in accurate solar radiation retrievals, and requires only daily air temperature time series as input. The same procedure could easily be applied to other empirical solar radiation models. [Copyright &y& Elsevier]

• The soil moisture representation of PT-JPL can be improved using satellite retrievals. • The SWIR-based SMDI improved the accuracy of PT-JPL for irrigated cropland. • The microwave-based PT-SMI provided the best performance for grassland. • Soil moisture estimates had limited ability to improve ET accuracy for cropland. Evapotranspiration (ET) accounts for water movements from land to air and plays a vital role in the terrestrial water, energy, and carbon cycles. Reliable estimates of ET for agricultural landscapes can facilitate water resources management and food security analysis. The widely used Priestley-Taylor Jet Propulsion Laboratory (PT-JPL) model has the most potential to operationally simulate ET over large areas, but its inability to fully track soil evaporation dynamics using atmospheric humidity limits its application in agroecosystems. In this study, we isolated the uncertainties resulting from soil evaporation and assessed three Earth observation-based alternatives - apparent thermal inertia (ATI), microwave soil moisture (SM), and optical spectral indices based on shortwave infrared (SWIR) to formulate soil evaporation. Our results illustrate that the incorporation of the SWIR-based soil moisture divergence index (SMDI) and microwave-based SM into monthly soil evaporation led to 6% and 5% increase in explained ET variances and reduced RMSE by 23.2% and 13.1% for cropland and grassland, respectively, as compared to PT-JPL using atmospheric reanalysis data only. Further analyses demonstrated that PT-SMDI explained more observed ET variances than PT-JPL using in-situ measurements of atmospheric humidity during the crop growing season, particularly for irrigated cropland (R2=0.65 for PT-SMDI; R2=0.62 for PT-JPL). On the other hand, the use of microwave SM outperformed other indices for ET assessment in grasslands but had lower performance in croplands. Our results suggest that a combination of optical SWIR and microwave SM has good potential to improve the PT-JPL model accuracy for agricultural landscapes. [ABSTRACT FROM AUTHOR]

Delineation of shellfish beds through field surveys is time consuming. Remote sensing can help in detecting the location and boundaries of shellfish beds. This can be achieved through the use of aerial photographs and optical satellite sensors during cloud-free and low-tide conditions. Cloud penetrating radar is an alternative, but still requires careful selection of low tide imagery. Manual selection becomes cumbersome for large areas, such as the entire Dutch Wadden Sea. We therefore developed a method that automatically combines dense time series of Sentinel-1 radar imagery into a useful mosaic that allows for effective delineation of shellfish beds. The method consists of temporal compositing many images in the cloud-based Google Earth Engine. We evaluated different combinations of 1) compositing options (average backscatter and various percentiles), 2) temporal periods for compositing, 3) different polarizations, and 4) imagery from ascending versus descending satellite paths. The resulting composite images were visually compared with in-situ records to identify which composite visually best allowed for shellfish bed delineation. Although the average VH-polarized backscatter for one full year provided effective demarcation, an RGB color composite containing three average VH images for four months each provided improved visibility of the shellfish beds. We then quantitatively compared which compositing option was most effective in separating pixels with shellfish beds from those without. For this purpose, the unsupervised classification approach of K-means clustering was applied to the different composite images, and outcomes (i.e. selected classes) were compared with field data derived from the annual in-situ survey. The multi-season image composites (i.e. Jan–Apr, May–Aug, and Sep–Dec) that were made by combining VH image acquisitions of only descending and of combined ascending and descending paths resulted in higher accuracies than other compositing options tested. The producer and user accuracy of the multi-season descending composite were respectively 40% and 70% for the full Dutch Wadden Sea, against 59% and 73% for a smaller subset. These accuracies are similar to what is previously achieved for small study areas with carefully selected radar images acquired around low tide. Moreover, our approach could identify various shellfish beds that were initially missed by the in-situ surveys, but for which later the presence was confirmed (so the actual producer accuracy would increase to about 82%). The composite images can be created on the fly with Google Earth Engine shortly before a field campaign and used as a base for sampling design. • We performed rapid temporal compositing of Sentinel-1 images in Google Earth Engine. • We evaluated different compositing options for the entire Dutch Wadden Sea. • A multi-season VH composite performed best for shellfish bed classification. • Producer and user accuracies reached 82% and 73% respectively, for a smaller subset. • Our approach allows for effective monitoring and already guides field inventories. [ABSTRACT FROM AUTHOR]

Satellites offer a privileged view on terrestrial ecosystems and a unique possibility to evaluate their status, their resilience and the reliability of the services they provide. In this study, we introduce two indicators for estimating the resilience of terrestrial ecosystems from the local to the global levels. We use the Normalized Differential Vegetation Index (NDVI) time series to estimate annual vegetation primary production resilience. We use annual precipitation time series to estimate annual green water resource resilience. Resilience estimation is achieved through the annual production resilience indicator, originally developed in agricultural science, which is formally derived from the original ecological definition of resilience i.e., the largest stress that the system can absorb without losing its function. Interestingly, we find coherent relationships between annual green water resource resilience and vegetation primary production resilience over a wide range of world biomes, suggesting that green water resource resilience contributes to determining vegetation primary production resilience. Finally, we estimate the changes of green water resource resilience due to climate change using results from the sixth phase of the Coupled Model Inter-comparison Project (CMIP6) and discuss the potential consequences of global warming for ecosystem service reliability. [ABSTRACT FROM AUTHOR]

The Sentinel satellite fleet of the Copernicus Programme offers new potential to map and monitor plant traits at fine spatial and temporal resolutions. Among these traits, leaf area index (LAI) is a crucial indicator of vegetation growth and an essential variable in biodiversity studies. Numerous studies have shown that the radiative transfer approach has been a successful method to retrieve LAI from remote-sensing data. However, the suitability and adaptability of this approach largely depend on the type of remote-sensing data, vegetation cover and the ecosystem studied. Saltmarshes are important wetland ecosystems threatened by sea level rise among other human- and animal-induced changes. Therefore, monitoring their vegetation status is crucial for their conservation, yet few LAI assessments exist for these ecosystems. In this study, the retrieval of LAI in a saltmarsh ecosystem is examined using Sentinel-2 and RapidEye data through inversion of the PROSAIL radiative transfer model. Field measurements of LAI and some other plant traits were obtained during two succeeding field campaigns in July 2015 and 2016 on the saltmarsh of Schiermonnikoog, a barrier island of the Netherlands. RapidEye (2015) and Sentinel-2 (2016) data were acquired concurrent to the time of the field campaigns. The broadly employed PROSAIL model was inverted using two look-up tables (LUTs) generated in the spectral band's settings of the two sensors and in which each contained 500,000 records. Different solutions from the LUTs, as well as, different Sentinel-2 spectral subsets were considered to examine the LAI retrieval. Our results showed that generally the LAI retrieved from Sentinel-2 had higher accuracy compared to RapidEye-retrieved LAI. Utilising the mean of the first 10 best solutions from the LUTs resulted in higher R2 (0.51 and 0.59) and lower normalised root means square error (NRMSE) (0.24 and 0.16) for both RapidEye and Sentinel-2 data respectively. Among different Sentinel-2 spectral subsets, the one comprised of the four near-infrared (NIR) and shortwave infrared (SWIR) spectral bands resulted in higher estimation accuracy (R2 = 0.44, NRMSE = 0.21) in comparison to using other studied spectral subsets. The results demonstrated the feasibility of broadband multispectral sensors, particularly Sentinel-2 for retrieval of LAI in the saltmarsh ecosystem via inversion of PROSAIL. Our results highlight the importance of proper parameterisation of radiative transfer models and capacity of Sentinel-2 spectral range and resolution, with impending high-quality global observation aptitude, for retrieval of plant traits at a global scale. [ABSTRACT FROM AUTHOR]