A framework of crop water productivity estimation from UAV observations: A case study of summer maize

Minghan Cheng, Ni Song, Josep Penuelas, Matthew F. McCabe, Xiyun Jiao, Yuping Lv, Chengming Sun and Xiuliang Jin

Agricultural Water Management, 2025, vol. 317, issue C

Abstract: This investigation establishes Crop Water Productivity (CWP) - quantified as yield per unit water consumption (kg/m³) - as a pivotal metric for agricultural water resource optimization. However, current methodologies face limitations in estimation accuracy and operational efficiency due to the multidisciplinary complexity integrating agronomic and hydrological expertise. To address this challenge, our research develops an innovative UAV-based monitoring framework through systematic integration of long-term multispectral/thermal infrared observations with multi-model fusion: (1) Surface Energy Balance Algorithm for Land (SEBAL) and FAO-56 Penman-Monteith models for evapotranspiration (ET) estimation; (2) Random Forest algorithm incorporating four phenotypical growth indicators for yield estimation, ultimately enabling CWP quantification. Key scientific findings demonstrate: (1) SEBAL outperformed FAO-56 in daily ET estimation (R² = 0.76 vs. 0.71, RMSE = 1.15 vs. 1.31 mm/d). (2) The machine learning yield model exhibited robust predictive capability (R² = 0.77, RMSE = 0.98 t/ha), successfully capturing yield variability across treatments. (3) Error propagation analysis validated framework reliability (CWP RMSE = 0.67 kg/m³), effectively differentiating CWP performance among management practices. This breakthrough validates the operational efficacy of UAV remote sensing for precision agricultural water assessment, providing decision-support for field-scale irrigation scheduling optimization, drought-resilient cultivar selection through CWP benchmarking and sustainable intensification strategies. The methodology establishes novel methodological benchmarks for crop-water relationship studies through its innovative fusion of multi-source remote sensing data and multiple model combination.

Keywords: Crop water productivity; Maize; Surface energy balance; Machine learning; Error propagation theory (search for similar items in EconPapers)
Date: 2025
References: Add references at CitEc
Citations:

Downloads: (external link)
http://www.sciencedirect.com/science/article/pii/S037837742500335X
Full text for ScienceDirect subscribers only

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:eee:agiwat:v:317:y:2025:i:c:s037837742500335x

DOI: 10.1016/j.agwat.2025.109621

Access Statistics for this article

Agricultural Water Management is currently edited by B.E. Clothier, W. Dierickx, J. Oster and D. Wichelns

More articles in Agricultural Water Management from Elsevier
Bibliographic data for series maintained by Catherine Liu ().