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- Title
基于无人机高光谱遥感和机器学习的 土壤水盐信息反演.
- Authors
王怡婧; 丁启东; 张俊华; 陈睿华; 贾科利; 李小林
- Abstract
Accurate diagnosis of water and salt information in saline agricultural lands is crucial for long-term soil quality improvement and arable land conservation. In this study, we extracted field-scale vegetation canopy spectral information by UAV hyperspectral information, transforming the reflectance (R) to standard normal variate transformation (SNV), multiplicative scatter correction (MSC), first derivative of reflectance (FDR) and second derivative of reflectance (SDR). We determined the optimal spectral transformation forms of soil water content (SWC), soil pH, and soil salt content (SSC) by the maximum absolute correlation coefficient (MACC), and extracted the feature bands by competitive adaptive reweighted sampling (CARS). We constructed an inversion model of soil water and salt information by partial least squares regression (PLSR), random forest (RF), and extreme gradient boosting (XGBoost). The results showed that R, FDR and MSC were the best spectral transformation types for soil water content, soil pH, and soil salt content, and the corresponding MACC were 0. 730, 0. 472 and 0. 654, respectively. The CARS algorithm effectively eliminated the irrelevant variables, optimally selecting 16--17 feature bands from 150 spectral bands. Both soil water content and soil pH performed best with XGBoost model, achieving determination coefficient of validation (Rp2) 0. 927 and 0. 743, and the relative percentage difference (RPD) amounted to 3.93 and 2.45. For soil salt content, the RF model emerged as the best inversion method with Rp2 and RPD of 0.427 and 1.64, respectively. The study could provide a reference solution for the integrated remote sensing monitoring of soil water and salt information in space and sky, serving as a scientific guide for the amelioration and sustainable management of saline lands.
- Subjects
RANDOM forest algorithms; REMOTE sensing
- Publication
Yingyong Shengtai Xuebao, 2023, Vol 34, Issue 11, p3045
- ISSN
1001-9332
- Publication type
Article
- DOI
10.13287/j.1001--9332.202311.012