Article: Inversion of soil water and salt information based on UAV hyperspectral remote sensing and machine lear-ning.
Ying yong sheng tai xue bao = The journal of applied ecology
2023 Volume 34, Issue 11, Page(s) 3045–3052
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 ... ...
| Title translation | 基于无人机高光谱遥感和机器学习的土壤水盐信息反演. |
|---|---|
| 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 ( |
| MeSH term(s) | Soil/chemistry ; Hyperspectral Imaging ; Water ; Sodium Chloride ; Remote Sensing Technology |
| Chemical Substances | Soil ; Water (059QF0KO0R) ; Sodium Chloride (451W47IQ8X) |
| Language | English |
| Publishing date | 2023-11-21 |
| Publishing country | China |
| Document type | Journal Article |
| ZDB-ID | 2881809-X |
| ISSN | 1001-9332 |
| ISSN | 1001-9332 |
| DOI | 10.13287/j.1001-9332.202311.012 |
| Database | MEDical Literature Analysis and Retrieval System OnLINE |
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