Enhancing field soil moisture content monitoring using laboratory-based soil spectral measurements and radiative transfer models

作者：Jibo Yue, Ting Li, Haikuan Feng, Yuanyuan Fu, Yang Liu, Jia Tian, Hao Yang, Guijun Yang · 发表于：Agriculture Communications · 年份：2024 · DOI：10.1016/j.agrcom.2024.100060 · 被引用次数：16 · 研究领域：Soil Moisture and Remote Sensing、Soil Geostatistics and Mapping、Remote Sensing in Agriculture

Accurate information on the soil moisture content in croplands is essential for monitoring crop growth conditions. This study aims to enhance soil moisture monitoring by employing laboratory-based soil spectral measurements and radiative transfer models. This study comprises three main components: (i) Utilizing laboratory-measured soil spectra to investigate the influence of soil moisture content on soil spectral properties (n=178), and describing the impact of canopy coverage on the mixed spectra of wheat and soil in croplands using a radiative transfer model (RTM) (n=144, 180); (ii) Employing a deep learning model trained on extensive simulated datasets to estimate soil moisture ben eath the canopy from wheat‒soil mixed spectra (n=200); and (iii) Comparing the performance of deep learning model with statistical regression techniques based on soil moisture spectral index for estimating wheat fractional vegetation cover (FVC) and relative soil moisture content (RMC) under medium to low canopy coverage. The conclusions of this study are as follows: (1) Compared with the conventional statistical regression approaches, the deep learning model exhibits superior accuracy in estimating RMC across all levels of normalized difference vegetation index (NDVI). (2) By combining laboratory soil spectral measurements with an RTM, a pretrained dataset can be created. When combined with transfer learning techniques (FVC: R 2 =0.782, RMSE=0.107, and RMC: R 2 =0.825, RMSE=0.130), this approac...