%0 Generic %1 dlr115666 %A Möller, Markus %A Gerstmann, Henning %A Dahms, Thorsten %B IGARSS %D 2017 %K conference oral talk Dahms LSFE %P 1--4 %T Phenological NDVI time series for the dynamic derivation of soil coverage information %U http://elib.dlr.de/115666/ %X "Monitoring of agricultural used soils at frequent intervals is needed to get a better understanding of processes like soil erosion or harvest forecast. This is crucial to support decision making and refining soil policies especially in the context of climate change. Parcel-specific soil coverage information can be derived by satellite imagery with high temporal and geometric resolution. However, their usable number is mostly, due to cloud cover, not representative for the phenological characteristics of vegetated classes. To overcome temporal constraints, spatial and temporal fusion models like STARFM or ESTARFM are increasingly applied to derive high resolution time series of remotely sensed biophysical parameters based on high-spatial/low-temporal resolution imagery like Landsat or Sentinel-2 and low-spatial/high-temporal resolution imagery like MODIS. We show how their combination with corresponding phenological information enables the definition of temporal windows in which models predicting fractional Vegetation coverage (FV C) or bare soils (BS) can be selectively applied

@presentation{dlr115666, abstract = {"Monitoring of agricultural used soils at frequent intervals is needed to get a better understanding of processes like soil erosion or harvest forecast. This is crucial to support decision making and refining soil policies especially in the context of climate change. Parcel-specific soil coverage information can be derived by satellite imagery with high temporal and geometric resolution. However, their usable number is mostly, due to cloud cover, not representative for the phenological characteristics of vegetated classes. To overcome temporal constraints, spatial and temporal fusion models like STARFM or ESTARFM are increasingly applied to derive high resolution time series of remotely sensed biophysical parameters based on high-spatial/low-temporal resolution imagery like Landsat or Sentinel-2 and low-spatial/high-temporal resolution imagery like MODIS. We show how their combination with corresponding phenological information enables the definition of temporal windows in which models predicting fractional Vegetation coverage (FV C) or bare soils (BS) can be selectively applied}, added-at = {2020-09-11T12:04:50.000+0200}, author = {M{\"o}ller, Markus and Gerstmann, Henning and Dahms, Thorsten}, biburl = {https://www.bibsonomy.org/bibtex/2db0a089d75b53f61e5ba4dcaeee500a4/earthobs_uniwue}, booktitle = {IGARSS}, interhash = {2913ebb78dbcb4e8a98fd002b88016f6}, intrahash = {db0a089d75b53f61e5ba4dcaeee500a4}, keywords = {conference oral talk Dahms LSFE}, pages = {1--4}, timestamp = {2020-11-18T22:08:33.000+0100}, title = {Phenological NDVI time series for the dynamic derivation of soil coverage information}, url = {http://elib.dlr.de/115666/}, year = 2017 }