Local-scale agricultural drought monitoring with satellite-based multi-sensor time-series |
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| Authors: | Gohar Ghazaryan Olena Dubovyk Valerie Graw Nataliia Kussul Jürgen Schellberg |
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| Institution: | 1. Center for Remote Sensing of Land Surfaces (ZFL), University of Bonn , Bonn, Germany;2. Remote Sensing Research Group (RSRG), Department of Geography, University of Bonn , Bonn, Germany gghazary@uni-bonn.de https://orcid.org/0000-0003-4606-0140;4. Remote Sensing Research Group (RSRG), Department of Geography, University of Bonn , Bonn, Germany https://orcid.org/0000-0002-7338-3167;5. Remote Sensing Research Group (RSRG), Department of Geography, University of Bonn , Bonn, Germany https://orcid.org/0000-0001-5145-9223;6. Department of Space Information Technologies and Systems, Space Research Institute of National Academy of Sciences of Ukraine and State Space Agency of Ukraine (SRI NASU-SSAU) , Kyiv, Ukraine https://orcid.org/0000-0002-9704-9702;7. Institute of Crop Science and Resource Conservation (INRES), University of Bonn , Bonn, Germany |
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| Abstract: | ABSTRACT Globally, drought constitutes a serious threat to food and water security. The complexity and multivariate nature of drought challenges its assessment, especially at local scales. The study aimed to assess spatiotemporal patterns of crop condition and drought impact at the spatial scale of field management units with a combined use of time-series from optical (Landsat, MODIS, Sentinel-2) and Synthetic Aperture Radar (SAR) (Sentinel 1) data. Several indicators were derived such as Normalized Difference Vegetation Index (NDVI), Normalized Difference Moisture Index (NDMI), Land Surface Temperature (LST), Tasseled cap indices and Sentinel-1 based backscattering intensity and relative surface moisture. We used logistic regression to evaluate the drought-induced variability of remotely sensed parameters estimated for different phases of crop growth. The parameters with the highest prediction rate were further used to estimate thresholds for drought/non-drought classification. The models were evaluated using the area under the receiver operating characteristic curve and validated with in-situ data. The results revealed that not all remotely sensed variables respond in the same manner to drought conditions. Growing season maximum NDVI and NDMI (70–75%) and SAR derived metrics (60%) reflect specifically the impact of agricultural drought. These metrics also depict stress affected areas with a larger spatial extent. LST was a useful indicator of crop condition especially for maize and sunflower with prediction rates of 86% and 71%, respectively. The developed approach can be further used to assess crop condition and to support decision-making in areas which are more susceptible and vulnerable to drought. |
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| Keywords: | Crop stress Sentinel-1 Landsat-8 Sentinel 2 logistic regression data fusion |
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https://orcid.org/0000-0003-4606-0140;4. Remote Sensing Research Group (RSRG), Department of Geography, University of Bonn , Bonn, Germany