Agricultural drought assessment using deep learning and multi-sensor remote sensing data integration-Reference-Cited by-同舟云学术

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Agricultural drought assessment using deep learning and multi-sensor remote sensing data integration

Published:2025-10 Issue: Volume:140 Page:104006
ISSN:1474-7065
Container-title:Physics and Chemistry of the Earth, Parts A/B/C
language:en
Short-container-title:Physics and Chemistry of the Earth, Parts A/B/C

Author:

Kumar Prashant^ORCID,Chandrakant Sonvane Chetan^ORCID,Ranjan Sudhanshu^ORCID,Tripathi Akshar^ORCID

Publisher

Elsevier BV

Reference92 articles.

1. A near real-time satellite-based global drought climate data record;AghaKouchak;Environ. Res. Lett.,2012

2. A climatological study of evapotranspiration and moisture stress across the continental United States based on thermal remote sensing: 1. Model formulation;Anderson;J. Geophys. Res. Atmos.,2007

3. Evaluation of drought indices based on thermal remote sensing of evapotranspiration over the Continental United States;Anderson;J. Clim.,2011

4. Delineating flood zones upon employing synthetic aperture data for the 2020 flood in Bangladesh;Aziz;Earth Systems and Environment,2022

5. Who launched what, when and why; trends in global land-cover observation capacity from civilian earth observation satellites;Belward;ISPRS J. Photogrammetry Remote Sens.,2015

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