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Article

Estimation of Cotton LAI and Yield Through Assimilation of the DSSAT Model and Unmanned Aerial System Images

by
Hui Peng
1,
Esirige
1,
Haibin Gu
1,2,3,4,*,
Ruhan Gao
1,
Yueyang Zhou
1,
Xinna Men
1,2,3,4 and
Ze Wang
1,2,3,4
1
College of Resources and Environment, Xinjiang Agricultural University, Urumqi 830052, China
2
Xinjiang Engineering Technology Research Center of Soil Big Data, Urumqi 830052, China
3
The Green Production Engineering Technology Research Center of Xinjiang Planting Industry, Urumqi 830052, China
4
Xinjiang Key Laboratory of Soil and Plant Ecological Processes, Urumqi 830052, China
*
Author to whom correspondence should be addressed.
Drones 2026, 10(1), 27; https://doi.org/10.3390/drones10010027
Submission received: 16 November 2025 / Revised: 30 December 2025 / Accepted: 31 December 2025 / Published: 3 January 2026
(This article belongs to the Special Issue Advances in the Use of UAVs for Monitoring and Analysis of Forage and Agricultural Crops: Technologies, Methods and Applications)
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Abstract

Cotton (Gossypium hirsutum L.) is a primary global commercial crop, and accurate monitoring of its growth and yield prediction are essential for optimizing water management. This study integrates leaf area index (LAI) data derived from unmanned aerial system (UAS) imagery into the Decision Support System for Agrotechnology Transfer (DSSAT) model to improve cotton growth simulation and yield estimation. The results show that the normalized difference vegetation index (NDVI) exhibited higher estimation accuracy for the cotton LAI during the squaring stage (R2 = 0.56, p < 0.05), whereas the modified triangle vegetation index (MTVI) and enhanced vegetation index (EVI) demonstrated higher and more stable accuracy in the flowering and boll-setting stages (R2 = 0.64 and R2 = 0.76, p < 0.05). After assimilating LAI data, the optimized DSSAT model accurately represented canopy development and yield variation under different irrigation levels. Compared with the DSSAT, the assimilated model reduced yield prediction error from 40–52% to 3.6–6.3% under 30%, 60%, and 90% irrigation. These findings demonstrate that integrating UAS-derived LAI data with the DSSAT substantially enhances model accuracy and robustness, providing an effective approach for precision irrigation and sustainable cotton management.
Keywords: water stress; yield prediction; vegetation index; Particle Swarm Optimization algorithm water stress; yield prediction; vegetation index; Particle Swarm Optimization algorithm

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MDPI and ACS Style

Peng, H.; Esirige; Gu, H.; Gao, R.; Zhou, Y.; Men, X.; Wang, Z. Estimation of Cotton LAI and Yield Through Assimilation of the DSSAT Model and Unmanned Aerial System Images. Drones 2026, 10, 27. https://doi.org/10.3390/drones10010027

AMA Style

Peng H, Esirige, Gu H, Gao R, Zhou Y, Men X, Wang Z. Estimation of Cotton LAI and Yield Through Assimilation of the DSSAT Model and Unmanned Aerial System Images. Drones. 2026; 10(1):27. https://doi.org/10.3390/drones10010027

Chicago/Turabian Style

Peng, Hui, Esirige, Haibin Gu, Ruhan Gao, Yueyang Zhou, Xinna Men, and Ze Wang. 2026. "Estimation of Cotton LAI and Yield Through Assimilation of the DSSAT Model and Unmanned Aerial System Images" Drones 10, no. 1: 27. https://doi.org/10.3390/drones10010027

APA Style

Peng, H., Esirige, Gu, H., Gao, R., Zhou, Y., Men, X., & Wang, Z. (2026). Estimation of Cotton LAI and Yield Through Assimilation of the DSSAT Model and Unmanned Aerial System Images. Drones, 10(1), 27. https://doi.org/10.3390/drones10010027

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