@article{Zhao2025, 
author = {Long Zhao and Xincheng Lei and Yuehua Ding and Ningbo Cui and Dan Meng and Yi Shi and Minglei Zhang and Xinbo Zhao and Xiaoxian Zhang},
title = {Estimating soil moisture content in apple orchards using UAV remote sensing data: Application of LST/LAI two-stage feature space theory},
year = {2025},
journal = {International Journal of Agricultural and Biological Engineering},
volume = {18},
number = {4},
pages = {239-247},
keywords = {soil moisture, remote sensing, UAV, LST/LAI, two-stage feature space},
url = {https://www.sciopen.com/article/10.25165/j.ijabe.20251804.9730},
doi = {10.25165/j.ijabe.20251804.9730},
abstract = {Soil moisture is a critical component of the soil-plant-atmosphere continuum (SPAC) in fruit trees. However, high-precision monitoring of orchard soil moisture at the regional scale still remains a challenge. This study presents a two-stage feature space model to estimate root zone soil moisture using UAV remote sensing data. The results indicate that the temperature-leaf area index (TLDI) is negatively correlated with soil water content. The upper triangular space performs highly effectively for deep soil moisture inversion, with R2 values ranging from 0.56 to 0.66, RMSE between 0.20 and 0.27, and RPD from 1.25 to 1.50. Conversely, the lower triangular space yields superior results for shallow soil moisture inversion, with R2 values between 0.67 and 0.82, RMSE from 0.15 to 0.19, and RPD between 1.67 and 2.09. The results suggest that the lower triangular space is optimal for shallow soil moisture inversion, while the upper triangular space is more suited for deep soil moisture inversion. This study presents a novel approach for estimating deep soil moisture in orchards, providing a theoretical basis for improving soil moisture management.}
}