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This article is based upon work from COST Action CA17134 Optical synergies for spatiotemporal SENsing of Scalable ECOphysiological traits (SENSECO), supported by COST. It was supported by European Plant Phenotyping Network (EPPN2020: Grant Agreement 731013), from EMPHASIS-PREP (Grant Agreement: 739514), from EOSC-Life (Grant Agreement: 824087). KB was funded within the EnMAP scientific preparation program (DLR Space and BMWi, Grant Number 50EE1923).

Spatial information on crop status and development is required by agricultural managers for a site specific and adapted management. Here, a prototype of a web application is presented for the visualization of biomass production of maize (Zea mays). The web application displays past biomass development and future predictions for user-defined regions of interest along with summary statistics. Biomass is modelled using the crop growth model (CGM) APSIM (Agricultural Production Systems Simulator) using meteorological data from 2001 to 2014. Information on current crop status and subfield heterogeneity is assimilated into APSIM through high-resolution optical satellite imagery. The use of recent satellite data and regional, historical meteorological data increases the reliability of the biomass information provided. Through its unique combination of high-resolution satellite imagery together with mechanistic crop growth modelling, this web application can overcome the often sparse temporal or sparse spatial resolution of biomass information, which is based on remote sensing images or on crop growth modelling alone. The prototype presented, with its high resolution biomass maps, can be the basis for variable rate application as farmers can react site-specifically to plant development.

Vegetation plays an important role in the exchange of water between the land surface and the atmosphere through evaporation and redistribution of water. Hence, changes in vegetation cover alter the terrestrial hydrological cycle. Large-scale forest restoration is an effective climate change mitigation strategy through carbon sequestration and is expected to impact the water availability. A better understanding of the impact of reforestation is needed, given the numerous different reforestation missions.Our study aims to provide an estimation of the hydrological effects of 900 million hectares of reforestation, called the ‘global tree restoration potential’ (Bastin et al., 2019). We include the effects of forest planting on evaporation and moisture recycling, where evaporation effects local water availability, and moisture recycling effects both local and remote water availability. We used the conventional Budyko’s moisture index framework to calculate the effects of reforestation on evaporation, and afterwards we used the UTrack dataset to calculate the changes in precipitation. The UTrack dataset presents the monthly climatological mean atmospheric moisture flows from evaporation to precipitation and is created using the Lagrangian moisture tracking model UTrack (Tuinenburg et al., 2020).The results show that reforesting the ‘global tree restoration potential’ would effect water availability for most of the Earth’s surface. The global mean increase in terrestrial evaporation is 8 mm yr-1. The increase in evaporation is highest around the equator (on average 20 mm yr-1), with local maximum changes of up to 200 mm yr-1. This is related to a relatively high restoration potential in low latitude areas, and a generally large evaporation response in high precipitation regions. Enhanced moisture recycling has the potential to partly compensate for this decreased water availability by increasing the downwind precipitation. Bastin, J.-F., Finegold, Y., Garcia, C., Mollicone, D., Rezende, M., Routh, D., Zohner, C.M., Crowther, T.W. The global tree restoration potential. Science, 365, 76-79, http://doi.org/10.1126/science.aax0848, 2019.Tuinenburg, O. A., Theeuwen, J. J. E., and Staal, A.: High-resolution global atmospheric moisture connections from evaporation to precipitation, Earth Syst. Sci. Data, 12, 3177–3188, https://doi.org/10.5194/essd-12-3177-2020, 2020.

In order to consider the effects of land use, and the land cover changes it causes, on ecosystem services in life cycle assessment (LCA), a new methodology is proposed and applied to calculate midpoint and endpoint characterization factors. To do this, a cause-effect chain was established in line with conceptual models of ecosystem services to describe the impacts of land use and related land cover changes. A high-resolution, spatially explicit and temporally dynamic modeling framework that integrates land use and ecosystem services models was developed and used as an impact characterization model to simulate that cause-effect chain. Characterization factors (CFs) were calculated and regionalized at the scales of Luxembourg and its municipalities, taken as a case to show the advantages of the modeling approach. More specifically, the calculated CFs enable the impact assessment of six land cover types on six ecosystem functions and two final ecosystem services. A mapping and comparison exercise of these CFs allowed us to identify spatial trade-offs and synergies between ecosystem services due to possible land cover changes. Ultimately, the proposed methodology can offer a solution to overcome a number of methodological limitations that still exist in the characterization of impacts on ecosystem services in LCA, implying a rethinking of the modeling of land use in life cycle inventory.

There is a need for a better understanding of the link between vegetation characteristics and tree transpiration to facilitate satellite derived transpiration estimation. Many studies use the normalized difference vegetation index (NDVI), a proxy for tree biophysical characteristics, to estimate evapotranspiration. In this study we investigated the link between sap velocity and 30 m resolution Landsat derived NDVI for twenty days during two contrasting precipitation years in a temperate deciduous forest catchment. Sap velocity was measured in the Attert catchment in Luxembourg in 25 plots of 20 × 20 m covering three geologies with sensors installed in 2–4 trees per plot. The results show that sap velocity and NDVI were significantly positively correlated in April, i.e., NDVI successfully captured the pattern of sap velocity during the phase of green-up. After green-up, a significant negative correlation was found during half of the studied days. During a dry period, sap velocity was uncorrelated to NDVI, but influenced by geology and aspect. In summary, in our study area, the correlation between sap velocity and NDVI was not constant, but varied with phenology and water availability. The same behaviour was found for the Enhanced Vegetation Index (EVI). This suggests that methods using NDVI or EVI to predict small-scale variability in (evapo)transpiration should be carefully applied and that NDVI and EVI cannot be used to scale sap velocity to stand level transpiration in temperate forest ecosystems.

Land cover information plays an elementary role for regional water and land management, and is an essential variable for the assessment of ecosystem services and regional climate impact. This paper describes the generation of a regionally optimized land cover dataset for West Africa with a spatial resolution of 250 m, which is based on earth observation data from three optical and radar instruments. The choice of sensors is based on their individual strengths and weaknesses in assessing specific land surface types. Annual profiles of the optical Moderate Resolution Imaging Spectroradiometer (MODIS) are analyzed for the classification of vegetated classes including agriculture. The classification approach builds on random forest classification with learning data extracted from higher resolution land cover maps. Envisat Advanced Synthetic Aperture Radar (ASAR) Wide Swath (WS) time series are used, in combination with MODIS data, to delineate permanent and seasonal water bodies. Here, an approach integrating threshold classification and morphological operations is applied. Built-up areas of different densities are identified based on a seamless coverage of radar imagery collected by the satellites TanDEM-X and TerraSAR-X. The detection of settlements is based on an unsupervised classification scheme which exploits texture metrics and backscattering amplitudes of the fine resolution radar sensors. The accuracy assessment of the multi-sensor land cover map yields an overall accuracy of 80% at legend level 1 (9 classes) and 73% at the more detailed legend level 2 (14 classes). Comparisons with available wall-to-wall datasets of the region demonstrate the valuable information content of the presented West African land cover map.

Complex crop growth models (CGM) require a large number of input parameters, which can cause large errors if they are uncertain. Furthermore, they often lack spatial information. The coupling of a CGM with a radiative transfer model offers the possibility to assimilate remote sensing data while taking into account uncertainties in input parameters. A particle filter was used to assimilate satellite data into a CGM coupled with a leaf-canopy radiative transfer model to update biomass simulations of maize. The synthetic experiment set up to test the reliability of the procedure, highlighted the importance of the acquisition time. The real case study with RapidEye observations confirmed these findings. Data assimilation increased the accuracy of biomass predictions in the majority of the six maize fields where biomass validation data was available, with improvements of up to 15%. The smallest and largest errors in biomass prediction after assimilation were 82?kg/ha and 2116?kg/ha, respectively. Furthermore, data assimilation enabled the production of biomass maps showing detailed spatial variability. Data assimilation using a particle filter for biomass estimation was conducted.Proof of concept with synthetic case studies.Multispectral satellite data (visible and near infrared) was found to be suitable for data assimilation.Assimilation of satellite data allowed biomass prediction on a pixel basis.

The fraction of photosynthetically active radiation FPAR absorbed by a vegetation canopy is an important variable for global vegetation modelling and is operationally available from data of the Terra Moderate Resolution Imaging Spectroradiometer MODIS satellite sensor starting from the year 2000. Product validation is ongoing and important for constant product improvement, but few studies have investigated the specific accuracy of MODIS FPAR using in situ measurements and none have focused on agricultural areas. This study therefore presents a validation of the collection 5 MODIS FPAR product in a heterogeneous agricultural landscape in western Uzbekistan. High-resolution FPAR maps were compiled via linear regression between in situ FPAR measurements and the RapidEye normalized difference vegetation index NDVI for the 2009 season. The data were aggregated to the MODIS scale for comparison. Data on the percentage cover of agricultural crops per MODIS pixel allowed investigation of the impact of spatial heterogeneity on MODIS FPAR accuracy. Overall, the collection 5 MODIS FPAR overestimated RapidEye FPAR between approximately 6% and 15%. MODIS quality flags, the underlying biome classification and spatial heterogeneity were investigated as potential sources of error. MODIS data quality was very good in all cases. A comparison of the MODIS land-cover product with high-resolution land-use classification revealed a significant misclassification by MODIS. Yet, we found that the overestimation of MODIS FPAR is independent of classification accuracy. The results indicate that the amount of background information, present even in the most homogeneous pixels ∼70% crop cover, is most likely the reason for the overestimation. The behaviour of pure pixels could not be investigated due to a lack of appropriate pixels.

This study focuses on the generation of reliable data for improving land and water use in Central Asia. An object-based remote sensing classification is applied and combined with the CropWat model developed by the Food and Agriculture Organization (FAO) to determine crop distribution and water requirements for irrigation of cotton and winter-wheat in Fergana Valley, Uzbekistan. The crop classification is conducted on RapidEye and Landsat data acquired before the onset of the main summer irrigation phases in July using a random forest algorithm. The ClimWat database of FAO is utilized for calculating crop water requirements (CWR) and crop irrigation requirements (CIR). Classification reveals an overall accuracy of 86.2% and exceeds a producer's (user's) accuracy of 95% (89%) for both, cotton and wheat. In 2010, cotton and winter-wheat are planted on 66.7% of the agricultural area under investigation, whereas orchard areas amount to 15.5%. The CWR modelled for winter-wheat and cotton cultivation revealed 5443 m 3 ha − 1 and 9278 m 3 ha − 1 , respectively. Subtracting effective precipitation leads to CIR of 4133 m 3 ha − 1 and 8813 m 3 ha − 1 . Comparisons of CWR and CIR for the area dominating crops with the total of water officially allocated for irrigation underline the pressure on the water resources in the entire Syr Darya catchment and suggest modifications of the cropping system towards more winter crops. The early season crop maps can be used for water saving as they enable modifications of water allocation plans within the different irrigation subsystems of the valley. The method for mapping spatially distributed CWR and CIR can be transferred to other irrigated areas in Central Asia and beyond.

Biogas production from energy crops by anaerobic digestion is becoming increasingly important. The amount of biogas that can be produced per unit of biomass is referred to as the biomethane potential (BMP). For energy crops, the BMP varies among varieties and with crop state during the vegetation period. Traditional ways of analytical BMP determination are based on fermentation trials and require a minimum of 30 days. Here, we present a faster method for BMP retrievals using near infrared spectroscopy and partial least square regression (PLSR). PLSR prediction models were developed based on two different sets of spectral reflectance data: (i) laboratory spectra of silage samples and (ii) airborne imaging spectra (HyMap) of maize canopies under field (in situ) conditions. Biomass was sampled from 35 plots covering different maize varieties and the BMP was determined as BMP per mass (BMPFM, Nm3 biogas/t fresh matter (Nm3/t FM)) and BMP per area (BMParea, Nm3 biogas/ha (Nm3/ha)). We found that BMPFM significantly differs among maize varieties; it could be well retrieved from silage samples in the laboratory approach (Rcv2 = 0.82, n = 35), especially at levels >190 Nm3/t. In the in situ approach PLSR prediction quality declined (Rcv2 = 0.50, n = 20). BMParea, on the other hand, was found to be strongly correlated with total biomass, but could not be satisfactorily predicted using airborne HyMap imaging data and PLSR.

Evaluations of existing land cover maps have revealed that high landscape heterogeneity and small patch sizes are a major reason for misclassification. These problems globally occur in biomes of mixed vegetation structure and are particularly relevant for African savannas. This paper presents a multi-resolution approach to derive fractional cover of vegetation growth forms at sub-pixel level, aiming at an improved mapping of land cover in the African grassland, savanna and shrubland biome. Fractional cover is delineated for woody growth forms (trees and shrubs), herbaceous growth forms, and bare surface. The approach incorporates very high resolution (QuickBird/IKONOS, 0.6–1 m), high resolution (Landsat TM/ETM+, 30 m), and medium resolution data (MODIS, 250 m). While QuickBird/IKONOS data are classified into discrete classes, at Landsat and MODIS resolutions, sub-pixel cover is delineated using non-parametric ensemble regression trees from the random forest family. The propagation of errors in the hierarchical multi-resolution approach is assessed with Monte Carlos simulations. The multi-resolution approach allows the adequate description of the heterogeneous vegetation structure in selected study regions of Southern Africa. The RMSE of the delineated fractional cover values range between 3.1% and 8.2% when compared with higher resolution data and between 4.4% and 9.9% when compared with field surveys. Errors at the Landsat resolution show minor influence on the accuracy of the MODIS results. Regarding the inter-resolution error propagation, for 90% of the Monte Carlo simulations, errors at the Landsat resolution resulted in RMSEs for MODIS increased by less than 4% (woody vegetation), 3.5% (herbaceous vegetation) and 2% (bare surface).

Advancing land degradation in the irrigated areas of Central Asia hinders sustainable development of this predominantly agricultural region. To support decisions on mitigating cropland degradation, this study combines linear trend analysis and spatial logistic regression modeling to expose a land degradation trend in the Khorezm region, Uzbekistan, and to analyze the causes. Time series of the 250-m MODIS NDVI, summed over the growing seasons of 2000–2010, were used to derive areas with an apparent negative vegetation trend; this was interpreted as an indicator of land degradation. About one third (161,000 ha) of the region’s area experienced negative trends of different magnitude. The vegetation decline was particularly evident on the low-fertility lands bordering on the natural sandy desert, suggesting that these areas should be prioritized in mitigation planning. The results of logistic modeling indicate that the spatial pattern of the observed trend is mainly associated with the level of the groundwater table (odds = 330 %), land-use intensity (odds = 103 %), low soil quality (odds = 49 %), slope (odds = 29 %), and salinity of the groundwater (odds = 26 %). Areas, threatened by land degradation, were mapped by fitting the estimated model parameters to available data. The elaborated approach, combining remote-sensing and GIS, can form the basis for developing a common tool for monitoring land degradation trends in irrigated croplands of Central Asia.

Remote sensing offers the opportunity to produce land cover classifications for large and remote areas on a yearly basis and is an important tool in regions that lack these information. However often training and validation data to generate annual land cover maps are not available in necessary quantity - being from one year only or covering only a small extent of the region of interest. This study was focused on land use classifications at regional scale with a special emphasize on annual updates under the constraint of limited sampling data. Often, sampling is reduced to one year or to an unrepresentative area extend within the region of interest. The investigations for the period between 2004 and 2009 were conducted in the irrigation systems of the Amu Darya Delta in Central Asia, where reliable information on crop rotations is required for sustainable land and water management. Annual training and validation data were extracted from high resolution land use classifications. For classification, statistical features based on MODIS time series of vegetation indices, reflectance and land surface temperature (LST) were calculated and a random forest algorithm was applied. By a combination of training data from different years, the accuracy could be enhanced from an overall accuracy of 70% to more than 90% for a focused subregion and also good consistency with high resolution images for the other parts of the delta, which has to be confirmed using quantitative validation. A combination of a different number of years was tested. Already two years can be sufficient to generate a robust and transferable random forest to produce yearly land use maps. The study shows the possibility to combine training data from different years for the annual classification of irrigated croplands on a regional scale.

Quantifying the regional effects of land cover change is imperative to improve future hydrological budget estimates within large river basins. In this study we aim to utilize binary logistic regressions models within a cellular automation (CA) modeling environment to find causalities for satellite remote sensing measured land cover change (LCC). We used 30-meter Landsat and 250-meter MODIS time-series observations to map LCC for different time trajectories in two large study areas in Burkina Faso and Ghana. We used the FAO land cover classification system (LCCS) legend to map LCC processes from the satellite trajectories. Socio-economic data on population density, distances to roads, and biophysical data sets were processed in the CA model. The neighborhood effect of the change predictors were accounted for by using an enrichment factor. The relationship between the satellite derived LCC and the major biophysical and socio-economic drivers showed that population density, and the increase of cropland areas are responsible for the conversion of forests and woodlands. This was observed for both study areas.