Cited by Gross primary productivity estimation through remote sensing and machine learning techniques in the high Andean Region of Ecuador

Development of an indicator system for solar-induced chlorophyll fluorescence monitoring to enhance early warning of flash drought DOI

Zixuan Qi, Yuchen Ye, Sun Lian

et al.

Agricultural Water Management, Journal Year: 2025, Volume and Issue: 312, P. 109397 - 109397

Published: March 9, 2025

Language: Английский

Citations

Revolutionizing the Future of Hydrological Science: Impact of Machine Learning and Deep Learning amidst Emerging Explainable AI and Transfer Learning DOI

Rajib Maity, Aman Srivastava,

Subharthi Sarkar

et al.

Applied Computing and Geosciences, Journal Year: 2024, Volume and Issue: 24, P. 100206 - 100206

Published: Nov. 9, 2024

Language: Английский

Citations

Simulation Method for Considering the Lag of Turbulent Evapotranspiration DOI

Yizhan Shu,

Keke Zhang, Honglang Duan

et al.

Published: Jan. 1, 2025

Language: Английский

Citations

Assessing salinity-induced impacts on plant transpiration through machine learning: from model development to deployment DOI

Niguss Solomon Hailegnaw,

Girma Worku Awoke,

Aline de Camargo Santos

et al.

Modeling Earth Systems and Environment, Journal Year: 2025, Volume and Issue: 11(3)

Published: March 13, 2025

Language: Английский

Citations

Can Typical Land Surface Model Parameterizations Support the Expected Soil Moisture Assimilation Efficiency? DOI

Jianhong Zhou, Jianzhi Dong, Huihui Feng

et al.

Water Resources Research, Journal Year: 2025, Volume and Issue: 61(4)

Published: March 29, 2025

Abstract Remote sensing (RS) soil moisture retrievals are frequently assimilated into land surface models (LSMs) to enhance model estimates. However, data assimilation (DA) efficiency is highly model‐dependent, making it imperative investigate whether current LSMs can achieve expected DA efficiencies and identify potential limitations for DA. Here, we examine based on a typical LSM by benchmarking against reference merging scheme (i.e., assigning weights combine multiple products single one). Both the merged estimates comparable since they identical error estimation theory same RS sets. In theory, should be superior results—since characterize temporal variation of propagate benefits subsequent forecast steps. ground‐based validation results indicate that performs worse than simply in regions where less precise retrievals. Further combing synthetic experiment, confirm unexpected primarily attributable parameterization uncertainty, which leads an unrealistic representation events (e.g., dry‐downs) significantly hampers application. Given this, likely remain suboptimal achieving its desired goals. Therefore, this study emphasizes urgency necessity reducing uncertainty systems.

Language: Английский

Citations

Regional simulation of water-carbon fluxes and winter wheat yield with assimilation of multi-source remote sensing data in the Haihe River Basin DOI

Gangqiang Zhang, Tongren Xu, Shaomin Liu

et al.

International Journal of Applied Earth Observation and Geoinformation, Journal Year: 2025, Volume and Issue: 140, P. 104578 - 104578

Published: May 10, 2025

Language: Английский

Citations

Gross primary production-coupled evapotranspiration in the global arid and semi-arid regions based on the NIRv index DOI

Yanxin Su,

Guojing Gan, Jingyi Bu

et al.

Journal of Hydrology, Journal Year: 2024, Volume and Issue: 643, P. 132012 - 132012

Published: Sept. 16, 2024

Language: Английский

Citations

Gross primary productivity estimation through remote sensing and machine learning techniques in the high Andean Region of Ecuador DOI

Cindy Urgilés, Johanna Orellana‐Alvear, Patricio Crespo

et al.

International Journal of Biometeorology, Journal Year: 2024, Volume and Issue: unknown

Published: Nov. 27, 2024

Language: Английский

Citations