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Improving Predictions of Vegetation Condition by Optimally Merging Satellite Remote Sensing-based Soil Moisture Products (SOILMOISTURE)
Start date: 14 Sep 2014, End date: 13 Sep 2018 PROJECT  FINISHED 

"Agricultural drought impacts global food security as well as financial flexibility of countries. Considering the natural variabilityof climate, agricultural drought is common phenomenon within appropriate time-frame, while the expected change in climateand the expected decrease in available water resources will further increase the stress exerted on vegetation. Precipitation-based indices are commonly used to trace the current status of the potential drought, while such indicators may miss theonset and only give reliable information about long-term events. On the other hand agricultural drought and its onset can beaccurately traced via monitoring of soil moisture, which has been recently shown to be a skillful predictor of vegetationconditions. In this study, soil moisture datasets from multiple platforms will be merged in least squares framework to obtainan optimal soil moisture estimate, while the optimality will be assured through the use of product specific error estimatesobtained separately using triple collocation error estimation methodology. Validation will be performed via investigation oflagged-correlation between soil moisture and Normalized Difference Vegetation Index (NDVI) which is very sensitive to theconditions of vegetation and can be accurately obtained from space through remote sensing. In particular, this study willconsider merging The Atmosphere-Land Exchange Inverse (ALEXI)-, Noah-, Soil Moisture Ocean Salinity (SMOS)-, andAdvanced Scatterometer on METOP (ASCAT)-based soil moisture information. Resulting optimally merged soil moistureproduct is expected to have higher skill in predicting NDVI than individual products alone."
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