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SOILMOISTURE

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Improving Predictions of Vegetation Condition by Optimally Merging Satellite Rem...

Improving Predictions of Vegetation Condition by Optimally Merging Satellite Remote Sensing based Soil Moisture Products "Agricultural drought impacts global food security as well as financial flexibility of countries. Considering the natural variability of climate, agricultural drought is common phenomenon within appropriate time-frame, while the e... ver más

13/09/2018

METU

100K€

Presupuesto del proyecto: 100K€

Líder del proyecto

MIDDLE EAST TECHNICAL UNIVERSITY No se ha especificado una descripción o un objeto social para esta compañía.

TRL 4-5

Fecha límite participación Sin fecha límite de participación.

Financiación concedida El organismo FP7 notifico la concesión del proyecto el día 2018-09-13 No tenemos la información de la convocatoria

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1 Participantes

METU

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Líder del proyecto

MIDDLE EAST TECHNICAL UNIVERSITY No se ha especificado una descripción o un objeto social para esta compañía.

TRL 4-5

Presupuesto del proyecto 100K€

Fecha límite de participación Sin fecha límite de participación.

Descripción del proyecto "Agricultural drought impacts global food security as well as financial flexibility of countries. Considering the natural variability of climate, agricultural drought is common phenomenon within appropriate time-frame, while the expected change in climate and 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 the onset and only give reliable information about long-term events. On the other hand agricultural drought and its onset can be accurately traced via monitoring of soil moisture, which has been recently shown to be a skillful predictor of vegetation conditions. In this study, soil moisture datasets from multiple platforms will be merged in least squares framework to obtain an optimal soil moisture estimate, while the optimality will be assured through the use of product specific error estimates obtained separately using triple collocation error estimation methodology. Validation will be performed via investigation of lagged-correlation between soil moisture and Normalized Difference Vegetation Index (NDVI) which is very sensitive to the conditions of vegetation and can be accurately obtained from space through remote sensing. In particular, this study will consider merging The Atmosphere-Land Exchange Inverse (ALEXI)-, Noah-, Soil Moisture Ocean Salinity (SMOS)-, and Advanced Scatterometer on METOP (ASCAT)-based soil moisture information. Resulting optimally merged soil moisture product is expected to have higher skill in predicting NDVI than individual products alone."

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