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"Spatially explicit soil moisture information in a high temporal resolution plays an essential role in environmental modeling for improving risk assessment, for quantifying the effects of rainfall seasonality and climatic variability, and for addressing ecosystem services. In this context, remote sensing data, particularly from the Copernicus mission is highly acknowledged to serve as a fast and available supplier for the derivation of area wide and high-resolution spatial-temporal information. However, to reliably estimate near-surface soil moisture patterns, remote sensing based analyses require robust ground-truthing for calibration and validation procedures. Against this background, the present study will introduce into an in situ sampling framework that is optimized towards an eventual spatial-temporal SAR (Synthetic Aperture Radar)-based near-surface soil moisture mapping. The framework comprises a survey scheme, measurement sequence, and survey calendar in accordance to satellite overpasses. The spatial focus of the study lies on an experimental site (denoted by the acronym MFC2) close to Monteforte Cilento (Salerno province) in southern Italy. MFC2 is a pilot agricultural field belonging to the Alento terrestrial observatory within the TERENO (TERrestrial ENvironmental Observatories) network across the Mediterranean region. Ground-based sensors, namely a cosmic-ray probe and a wireless sensor network comprising 20 measurement nodes were installed in 2016 in MFC2 for real-time soil moisture monitoring. During three weeks in November 2018 covering in total eight satellite overpasses at four days of satellite revisits, near-surface soil moisture (0-5 cm) was sampled each time at 20 different plots in MFC2. In addition, soil physical and chemical properties (i.e. soil texture, bulk density, and soil organic carbon content) and vegetation parameters (e.g., LAI) were sampled at times of satellite overpasses, supplemented by few UAV flights for the photogrammetric analysis of the soil surface roughness. To capture the fluctuation of near-surface soil moisture during the satellite overpasses and its dynamics for a longer period, namely from the wet period to the wet-to-dry transition period (November 2018 – March 2019), two further stationary soil moisture sensors were installed for high-temporal monitoring (1 min recording time). By doing so, acquisition lags between field measurements and overpass times will be addressed, too. The sensor locations were chosen to reflect real field conditions (i.e. non-bare soil plot in MFC2) and to act as a reference plot for calibration for SAR-based soil moisture mapping under non-vegetated conditions. Latter is represented by a 20 x 20 m square in close proximity to MFC2. The grass cover is frequently cut for keeping the bare soil conditions during the entire monitoring period. Gathered ground-truth data and eventual validated SAR-based soil moisture mapping will support manifold further research activities such as the development of a local-specific transfer function to relate soil moisture patterns from remote sensing data to soil moisture ground-based sensors and vice versa. By assimilating those data into hydrological models, groundwater flow and surface flow under consideration of the climatic variability and in feedback with the vegetation can be simulated for different scenarios of water usage in a Mediterranean environment."

Accurate quantification of land use/cover change (LULCC) is important for efficient environmental management, especially in regions that are extremely affected by climate variability and continuous population growth such as West Africa. In this context, accurate LULC classification and statistically sound change area estimates are essential for a better understanding of LULCC processes. This study aimed at comparing mono-temporal and multi-temporal LULC classifications as well as their combination with ancillary data and to determine LULCC across the heterogeneous landscape of southwest Burkina Faso using accurate classification results. Landsat data (1999, 2006 and 2011) and ancillary data served as input features for the random forest classifier algorithm. Five LULC classes were identified: woodland, mixed vegetation, bare surface, water and agricultural area. A reference database was established using different sources including high-resolution images, aerial photo and field data. LULCC and LULC classification accuracies, area and area uncertainty were computed based on the method of adjusted error matrices. The results revealed that multi-temporal classification significantly outperformed those solely based on mono-temporal data in the study area. However, combining mono-temporal imagery and ancillary data for LULC classification had the same accuracy level as multi-temporal classification which is an indication that this combination is an efficient alternative to multi-temporal classification in the study region, where cloud free images are rare. The LULCC map obtained had an overall accuracy of 92%. Natural vegetation loss was estimated to be 17.9% ± 2.5% between 1999 and 2011. The study area experienced an increase in agricultural area and bare surface at the expense of woodland and mixed vegetation, which attests to the ongoing deforestation. These results can serve as means of regional and global land cover products validation, as they provide a new validated data set with uncertainty estimates in heterogeneous ecosystems prone to classification errors.

Soil salinization is among the major constraints of sustainable agricultural production in the irrigated areas of the Aral Sea Basin (ASB). Existing field investigations on cotton production areas underlined that irrespectively of the salinity level, the salt moves downwards through irrigation events and re-enters upper soil layers when soils dry up. A good impression of the salinity level within a soil profile can thus be gathered after the last irrigation application. It is hypothesized that these salinity levels can be explained through multi-temporal optical remote sensing (RS) data that cover the entire cropping season. Time series of soil and vegetation indices calculated from Landsat imagery were used for indirect assessments of salinity levels in a cotton production area of 75.5 ha during 2008-2011. Accuracy of the RS-based maps was assessed against salinity maps obtained by dense points of EM-38 electromagnetic induction measurements (returns salinity in mS/cm). Several environmental variables such as topography, groundwater depth and salinity, were also included as predictors beside the spectral information. The application of random forest regression showed that RS allows for moderate precision of salinity prediction with coefficients of determination (r2) ranging between 0.44 and 0.58, while the inclusion of environmental variables did not improve the prediction quality. The estimated root mean square error (RMSE) ranged between 8.45 and 21.81. The variations of accuracy most likely depended on the different image acquisition dates among the years. The unexplained variance may result from the fact the indirect mapping of vegetation also shows effects of other growth-limiting factors, such as inappropriate timing of irrigation or reduced water supply. However, the results suggest that multi-temporal optical RS data can support the monitoring of soil salinity levels in the irrigated lowlands of the ASB and can hence be of practical use, e.g. for monitoring the success of maintenance operations in the drainage system.

Water scarcity in the Aral Sea Basin together with the persistent dependency from agricultural production as well as with the effects of population growth and climate change is supposed to imply future conflicts in all water-use sectors. An efficient and sustainable irrigation agriculture will essentially contribute to sustainable regional development. Optical satellite remote sensing (RS) data such as MODIS provides necessary information for monitoring irrigated cropland use on the regional, respectively landscape scale. In this context, the WUEMoCA tool intends to provide agriculturally relevant, spatio-temporal geoinformation to support planning in the water resources management sector. Potential groups of end-users comprise policy makers and advisors from national governments but also regional and transboundary decision makers and specialists dealing with geoinformation technology and RS based analysis for sustainable land and water resources management

Agriculture is one of the economy priorities of Kazakhstan. After the collapse of the Soviet Union in 1991, the country had to cope with enormous transformation processes including land privatization and implementation of new organizational structures in agriculture. Together with the existing land degradation problems that negatively affected crop production, these processes led to land abandonment, e.g. in the downstream rice cropping systems along the Syr Darya River. However, potential solutions for a more sustainable production demand for more detailed information on the use of croplands over time. Therefore, a temporal transect of land use maps for the rice-dominated Kazalinsk region, Southwest Kazakhstan, was generated through a remote sensing and GIS analysis. We processed 84 images of different Landsat sensors recorded between 1984 and 2016. For the elaboration of the approach and for the verification of the maps ground data were collected in 2015 and 2016. Three temporal windows (early, mid, and end of season) and a simple but effective mapping approach permit the accurate identification of actively used rice fields. The results returned a remarkable decline of rice production area during the late Soviet Union era (annually, 17,000 ha out of entirely 38,444 ha were used for rice production) until 2004 (5,000 ha), which was followed by some re-use of cropland for rice production afterwards (~10,000 ha). Counting the number of years under rice production highlighted that compact blocks of rice production are more likely to be active than fragmented fields, which could be attributed to the costs required for irrigation under the risk of water scarcity. Next steps include the overlay of the land use indicators with GIS information such as the irrigation canal network, soil quality, soil salinity, and groundwater tables in order to model the mechanisms that led to variations in rice production, or land abandonment.

Ungleich verteiltes Wasser in Zentralasien impliziert Konflikte in allen Bereichen der Wassernutzung. Eine effiziente Bewässerungslandwirtschaft ist ein wichtiger Beitrag zur regionalen Entwicklung. Fernerkundungsdaten können notwendige Informationen im Sinne eines Monitorings liefern. Satellitenbasierte Erdbeobachtung, insbesondere seit der Verfügbarkeit von frei zugänglichen Fernerkundungsdaten, bietet enormes Potenzial für die räumliche und zeitliche Bewertung einer Vielzahl sozioökonomischer und ökologischer Aspekte. Das nachhaltige Management von Wasserressourcen steht dabei im Fokus zahlreicher wissenschaftlicher und gesellschaftlicher Fragestellungen. Speziell im Kontext zwischenstaatlicher Wasserverteilungs- und Nutzungskonflikte wie in Zentralasien wird der Fernerkundung ein hoher Stellenwert bei der Analyse und Bewertung wesentlicher Prozesse und Faktoren zur Erfassung des water footprint in der Bewässerungslandwirtschaft beigemessen. Im Aralseebecken in Zentralasien, einem der global größten Trockengebiete, ist Wasser geographisch ungleich allokiert und muss zwischen sechs Staaten, deren Ökonomien stark von der Wasserverfügbarkeit abhängen, verteilt werden. Physio-geographische Gegebenheiten als auch konkurrierende Interessen bei der Wassernutzung (wie Bewässerungslandwirtschaft und Energiegewinnung) erfordern eine effektive nationale und vor allem grenzüberschreitende Koordination dieser Interessen unter Einbindung von Nutzern und lokalen sowie regionalen Entscheidungsträgern mit dem Ziel der langfristig ressourcenschonenden Wassernutzung. Dieser Beitrag widmet sich der Frage wie frei zugängliche Fernerkundungsdaten und daraus in GIS-basierten Analysen abgeleitete Geoinformationen sowie der Transfer generierten Wissens im Sinne eines Monitorings zur Erhöhung der Wassernutzungseffizienz in der regionalen Bewässerungslandwirtschaft beitragen können. Besonderer Fokus liegt auf der Anwendung von optischen Satellitenbildern (MODIS) für (1) das Monitoring der bewässerten Landnutzung und der Ernteabschätzung zur Ermittlung der Produktivität der bewässerten Landwirtschaft und (2) dem zeitlichen und räumlichen Vergleich der Produktivität der Bewässerungssysteme. Die wissenschaftliche Arbeit verortet sich im CAWa-Projekt („Regional Research Network Central Asian Water“) als wissenschaftlich-technische Komponente der vom deutschen Bundesaußenministerium geförderten Wasserinitiative für Zentralasien (“Berliner Prozess”).

Irrigation agriculture is of a major socio-economic importance for the Central Asian countries Afghanistan, Kazakhstan, Kyrgyzstan, Tajikistan, Turkmenistan and Uzbekistan. Remote agricultural areas, the lack of harmonized agricultural statistics, the intensification of agriculture combined with water scarcity as well as unsustainable water use management make the integrated and combined use of Remote Sensing and Web GIS a very valuable technique for crop monitoring and to support the mitigation of those problems. Thus, within the frame of the Central Asian Water project (CAWa), a web-based mapping platform, the Water Use Efficiency Monitor in Central Asia (WUEMoCA), is being currently developed. It is comprised of a coherent and automated interdisciplinary processing chain starting from the automated download of Terra MODIS data and the computation of NDVI time series. A subsequent land use classification and crop yield estimation via machine learning algorithms for image processing provides a retrospective crop inventory for all years between 2000 and 2016 so far. Parallely, the near-real time early estimation of the current agricultural vegetation condition provides information about phenological anomalies and drought-affected regions. The systematic storage of these large amounts of processed multitemporal MODIS data requires the deployment of a spatial database with supportive raster analysis functionalities such as PostgreSQL/PostGIS in which all results are spatially aggregated on administrative levels such as water use associations, state districts and state provinces. Morover, the connectivity between a GeoServer and PostGIS is natively integrated, allowing for the automatic creation of Web Map Services (WMS) directly from the database. Interactive maps are visualized in a client application based on the mapping framework OpenLayers which is tailored for the specific needs of Central Asian water management. This makes a web-based presentation of all results possible. At last, the use of a PostGIS database also provides the possibility to establish a land use indicator system for all administrative levels with secondary information such as land use coefficient, crop diversity, land use intensity and irrigated crop acreage. The CAWa project is a part of the Berlin Process and funded by the German Federal Foreign Office.

This study analyzed the spatiotemporal pattern of settlement expansion in Abuja, Nigeria, one of West Africa’s fastest developing cities, using geoinformation and ancillary datasets. Three epochs of Land-use Land-cover (LULC) maps for 1986, 2001 and 2014 were derived from Landsat images using support vector machines (SVM). Accuracy assessment (AA) of the LULC maps based on the pixel count resulted in overall accuracy of 82%, 92% and 92%, while the AA derived from the error adjusted area (EAA) method stood at 69%, 91% and 91% for 1986, 2001 and 2014, respectively. Two major techniques for detecting changes in the LULC epochs involved the use of binary maps as well as a post-classification comparison approach. Quantitative spatiotemporal analysis was conducted to detect LULC changes with specific focus on the settlement development pattern of Abuja, the federal capital city (FCC) of Nigeria. Logical transitions to the urban category were modelled for predicting future scenarios for the year 2050 using the embedded land change modeler (LCM) in the IDRISI package. Based on the EAA, the result showed that urban areas increased by more than 11% between 1986 and 2001. In contrast, this value rose to 17% between 2001 and 2014. The LCM model projected LULC changes that showed a growing trend in settlement expansion, which might take over allotted spaces for green areas and agricultural land if stringent development policies and enforcement measures are not implemented. In conclusion, integrating geospatial technologies with ancillary datasets offered improved understanding of how urbanization processes such as increased imperviousness of such a magnitude could influence the urban microclimate through the alteration of natural land surface temperature. Urban expansion could also lead to increased surface runoff as well as changes in drainage geography leading to urban floods.

The study aims at an optimized prediction of biophysical parameters of winter wheat and the identification of the best explaining spectral bands and vegetation indices from the RapidEye sensor system.

The worldwide demand for food has been increasing due to the rapidly growing global population, and agricultural lands have increased in extent to produce more food crops. The pattern of cropland varies among different regions depending on the traditional knowledge of farmers and availability of uncultivated land. Satellite images can be used to map cropland in open areas but have limitations for detecting undergrowth inside forests. Classification results are often biased and need to be supplemented with field observations. Undercover cropland inside forests in the Bale Mountains of Ethiopia was assessed using field observed percentage cover of land use/land cover classes, and topographic and location parameters. The most influential factors were identified using Boosted Regression Trees and used to map undercover cropland area. Elevation, slope, easterly aspect, distance to settlements, and distance to national park were found to be the most influential factors determining undercover cropland area. When there is very high demand for growing food crops, constrained under restricted rights for clearing forest, cultivation could take place within forests as an undercover. Further research on the impact of undercover cropland on ecosystem services and challenges in sustainable management is thus essential.

Crop mapping in West Africa is challenging, due to the unavailability of adequate satellite images (as a result of excessive cloud cover), small agricultural fields and a heterogeneous landscape. To address this challenge, we integrated high spatial resolution multi-temporal optical (RapidEye) and dual polarized (VV/VH) SAR (TerraSAR-X) data to map crops and crop groups in northwestern Benin using the random forest classification algorithm. The overall goal was to ascertain the contribution of the SAR data to crop mapping in the region. A per-pixel classification result was overlaid with vector field boundaries derived from image segmentation, and a crop type was determined for each field based on the modal class within the field. A per-field accuracy assessment was conducted by comparing the final classification result with reference data derived from a field campaign. Results indicate that the integration of RapidEye and TerraSAR-X data improved classification accuracy by 10%–15% over the use of RapidEye only. The VV polarization was found to better discriminate crop types than the VH polarization. The research has shown that if optical and SAR data are available for the whole cropping season, classification accuracies of up to 75% are achievable.

Continuous land use mapping is essential for improving irrigation water distribution, identifying deficiencies in irrigation water supply, and estimating yields. Findings from the ZEF-UNESCO research project in Khorezm, Uzbekistan, demonstrate how remote sensing and Geographical Information Systems (GIS) can contribute to monitoring and improving land use in irrigation systems.

This study presents a multi-scale procedure to derive continuous proportional cover of woody vegetation in savanna ecosystems. QuickBird data was classified to define a continuous training and validation data set of woody cover proportions. Using a regression tree algorithm based on Landsat TM data, this woody cover information was extrapolated to an area of approximately 185 km x 185 km. The resulting 30 m map of the Namibian North-eastern Kalahari Woodland was aggregated to 250 m and 500 m resolutions. Comparisons of the global MODIS VCF product with the regionally adjusted multi-scale fractional cover map indicate that VCF tree cover is generally underestimated in the study area and confusions between tree and dense shrub cover occur.

Rice production in the lowlands of the Aral Sea Basin (ASB) contributes to the national food baskets of Kazakhstan and Uzbekistan. However, due to the exposure to water scarcity in the downstream location of the river systems and enormous land degradation problems, a high risk of crop failure exist. Early and area-wide information on irregularities in rice crop growth through remote sensing could help to reduce this risk. In this study, statistical relations between vegetation indices (VIs) from Landsat 8 surface reflectance and biophysical in situ measurements (plant height, crop density, green biomass, fraction of absorbed photosynthetically active radiation and leaf area index) of broadcast sown and transplanted rice were investigated. Special attention laid on the accurate derivation of the LAI. Field experiments conducted in three different irrigation subsystems of the ASB during the growing period of rice in 2015 revealed enormous spatial variations in observed rice biophysical parameters, both among the fields and sites owing to different land, water and crop management. Linear regression analysis techniques of paired variables comprising each one biophysical parameter and one out of six VIs showed diverse coefficients of determination. Tasseled Cap Greenness (TCG) was superior to all other indices in its explanatory power. Multivariate linear regression and particularly Classification and Regression Trees exhibited stronger statistical relations between four VIs (TCG, Normalized Difference Vegetation Index - NDVI, Transformed Vegetation Index - TVI and Green Chlorophyll Index - GCI) and LAI than the univariate assessments. The multi-location field experiments can be concluded to be useful for estimating certain effects of crop management on modeling biophysical parameters of rice. The results suggest furthering multivariate assessments of rice biophysical parameters and promoting remote sensing techniques to support local and regional policies and planning approaches in the irrigated lowland of the ASB.