| This article has been：Browse 32Times Download 10Times |
scan it! |
|
|
| DOI：10.13522/j.cnki.ggps.2025171 |
|
| Estimating red soil moisture using optimized spectral indices and machine learning |
|
XING Mingjie, XU Xingqian, XU Weiheng, WANG Er, ZHU Xiang, ZHAO Lin
|
|
1. College of Water Conservancy, Yunnan Agricultural University, Kunming 650201, China;
2. International College, Yunnan Agricultural University, Kunming 650201, China;
3. College of Big Data and Intelligent Engineering, Southwest Forestry University, Kunming 650224, China
|
| Abstract: |
| 【Objective】Efficient monitoring of soil moisture at large scales is required for optimizing water resource management and smart agriculture, particularly in red soils where water retention is low and irrigation efficiency is limited. This paper investigates the feasibility of using multispectral remote sensing to indirectly measure the moisture content in red soils. 【Method】The study area was in Yunnan province. Using unmanned aerial vehicle (UAV) multispectral images (green, red, red-edge, and near-infrared bands) and moisture data measured in the field, we selected 21 classical and improved spectral indices to construct an inversion model. Sensitive indices were screened using three algorithms: the Pearson correlation coefficient (PCC), variable importance in projection (VIP) and grey relational analysis (GRA). Four machine learning models: random forest (RF), back propagation neural network (BPNN), support vector regression (SVR), and light gradient boosting machine (Light-GBM) were used to estimate soil moisture content using the optimized indices.【Result】The VIP algorithm screened out six optimized spectral variables, which significantly improved computational efficiency. Among the four machine learning models we compared, the BPNN was the most robust and general. The combination of VIP and BPNN was the most accurate, and the statistical metrics of its comparison with measured field data were R2 = 0.72, RMSE = 0.03 and RPD = 1.90. The R2 of the RF model was 0.94 in the training set, but was reduced to 0.56 in the test set, indicating overfitting.【Conclusion】The multispectral inversion model using VIP and BPNN effectively captured the spatiotemporal distribution of red soil moisture in the study area. When combined with additional spectral bands and environmental parameters, this model can be applied in smart agriculture and ecological management. |
| Key words: multispectral; red soil moisture; variable screening; inversion model |
|
|
