Actual evapotranspiration (ETact) is an essential component of the water balance and its determination for large areas is difficult on regional scale and can be explored within an innovation laboratory. The use of remote sensing data to determine ETact is particularly suitable to provide area based indicators for the evaluation of the efficiency and productivity of irrigation systems. Seasonal analysis of ETact is hampered by either spatial (MODIS) or temporal (Landsat) resolution. In order to provide a high-resolution (temporal and spatial) and dense remote sensing dataset Landsat and MODIS data will be fussed using the ESTARFM algorithm.
Remote sensing based crop mapping is still challenging when just relying on optical information as the only data source. Due to the unavailability of adequate optical satellite images the integration of SAR is promising and can be explored within an innovation laboratory. The combination of SAR (TERRA-SAR-X and Sentinel-1) and optical images (RapidEye and Sentinel-2) for classification will improve the reliability and accuracy of crop maps. In addition, a sequential masking classification technique will be used to classify individual crop classes. These results will be compared with results of a one-step classification, in which all crop classes were classified at the same time. It has to be determined if the sequential masking approach will improve overall classification accuracies, compared to the one-step classification. As a study site the TERENO test site DEMMIN in Mecklenburg-Western Pomerania is suggested.
A canopy height model (CHM) overlaid on a hillshade product based on LiDAR point cloud
Nowadays there are plenty of 3D data sources which offer the possibility to model and monitor structural attributes of the landscape via the derivation of terrain, surface and canopy models. The core idea of this research project within an innovation laboratory is to leverage different sources of space- and airborne remote sensing data sources to derive digital surface models (DSMs) from forest stands. This can further provide the necessary database to compare their horizontal/vertical error as well as develop an error budget model by which the propagated error can be characterized. The remote sensing data sources which will be used here represent both landscape- and local scales, with airborne LiDAR and stereo SPOT data representing the landscape and unmanned aerial vehicle (UAV) data representing the local level. The methods include the state of the art as implemented in the available open-source software (FUSION, SAGA) as well as commercial photogrammetric suites (ERDAS Imagine).