Enhanced Spring Wheat Soil Plant Analysis Development (SPAD) Estimation in Hetao Irrigation District: Integrating Leaf Area Index (LAI) Under Variable Irrigation Conditions

Qiang Wu, Dingyi Hou, Min Xie, Qi Gao, Mengyuan Li, Shuiyuan Hao, Chao Cui, Keke Fan, Yu Zhang () and Yongping Zhang ()
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Qiang Wu: College of Agronomy, Inner Mongolia Agricultural University, Huhhot 010019, China
Dingyi Hou: Hohhot Agricultural and Animal Husbandry Technology Extension Center, Huhhot 010019, China
Min Xie: College of Agronomy, Inner Mongolia Agricultural University, Huhhot 010019, China
Qi Gao: College of Agronomy, Inner Mongolia Agricultural University, Huhhot 010019, China
Mengyuan Li: College of Agronomy, Inner Mongolia Agricultural University, Huhhot 010019, China
Shuiyuan Hao: Department of Agriculture, Hetao College, Bayannur 015000, China
Chao Cui: Department of Agriculture, Hetao College, Bayannur 015000, China
Keke Fan: College of Agronomy, Henan Agricultural University, Zhengzhou 450046, China
Yu Zhang: College of Agronomy, Henan Agricultural University, Zhengzhou 450046, China
Yongping Zhang: College of Agronomy, Inner Mongolia Agricultural University, Huhhot 010019, China

Agriculture, 2025, vol. 15, issue 13, 1-19

Abstract: Non-destructive monitoring of chlorophyll content through Soil Plant Analysis Development (SPAD) values is essential for precision agriculture in water-limited regions. However, current estimation methods using spectral information alone face significant limitations in sensitivity and transferability under variable irrigation conditions. While integrating canopy structural parameters with spectral data represents a promising solution, systematic investigation of this approach throughout the entire growth cycle of spring wheat under different irrigation regimes remains limited. This study evaluated three machine learning algorithms (Random Forest, Support Vector Regression, and Multi-Layer Perceptron) for SPAD estimation in spring wheat cultivated in the Hetao Irrigation District. Using a split-plot experimental design with two irrigation treatments (conventional: four irrigations; limited: two irrigations) and five nitrogen levels (0–300 kg·ha −1 ), we analyzed ten vegetation indices derived from Unmanned Aerial Vehicle (UAV) multispectral imagery, with and without Leaf Area Index (LAI) integration, across six growth stages. Results demonstrated that incorporating LAI significantly improved SPAD estimation accuracy across all algorithms, with Random Forest exhibiting the most substantial enhancement (R 2 increasing from 0.698 to 0.842, +20.6%; RMSE decreasing from 5.025 to 3.640, −27.6%). Notably, LAI contributed more significantly to SPAD estimation under limited irrigation conditions (R 2 improvement: +17.6%) compared to conventional irrigation (+11.0%), indicating its particular value for chlorophyll monitoring in water-stressed environments. The Green Normalized Difference Vegetation Index (GNDVI) emerged as the most important predictor (importance score: 0.347), followed by LAI (0.213), confirming the complementary nature of spectral and structural information. These findings provide a robust framework for non-destructive SPAD estimation in spring wheat and highlight the importance of integrating canopy structural information with spectral data, particularly in water-limited agricultural systems.

Keywords: SPAD; LAI; Random Forest; spring wheat; water stress; UAV remote sensing (search for similar items in EconPapers)
JEL-codes: Q1 Q10 Q11 Q12 Q13 Q14 Q15 Q16 Q17 Q18 (search for similar items in EconPapers)
Date: 2025
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