Unleashing the power of machine learning and remote sensing for robust seasonal drought monitoring: A stacking ensemble approach-Reference-Cited by-同舟云学术

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Unleashing the power of machine learning and remote sensing for robust seasonal drought monitoring: A stacking ensemble approach

Published:2024-05 Issue: Volume:634 Page:131102
ISSN:0022-1694
Container-title:Journal of Hydrology
language:en
Short-container-title:Journal of Hydrology

Author:

Xu Xinlei^ORCID,Chen Fangzheng,Wang Bin,Harrison Matthew Tom,Chen Yong^ORCID,Liu Ke,Zhang Chenxia,Zhang Meiqin,Zhang Xueliang,Feng Puyu^ORCID,Hu Kelin^ORCID

Publisher

Elsevier BV

Reference76 articles.

1. Drought index revisited to assess its response to vegetation in different agro-climatic zones;Abrar Faiz;J. Hydrol.,2022

2. Spatio-temporal analysis of drought severity using drought indices and deterministic and geostatistical methods;Afzali;Desert,2016

3. Assessment of areal interpolation methods for spatial analysis of SPI and EDI drought indices;Akhtari;Int. J. Climatol.,2009

4. Evaluation of drought using SPEI drought class transitions and log-linear models for different agro-ecological regions of India;Alam;Phys. Chem. Earth,2017

5. A fusion-based methodology for meteorological drought estimation using remote sensing data;Alizadeh;Remote Sens. Environ.,2018

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1. Improving UAV hyperspectral monitoring accuracy of summer maize soil moisture content with an ensemble learning model fusing crop physiological spectral responses;European Journal of Agronomy;2024-10

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