Estimation of Soil Salt and Ion Contents Based on Hyperspectral Remote Sensing Data: A Case Study of Baidunzi Basin, China
| Autor: | Rongpeng Yao, Shengyin Zhang, Huaidong Wei, Libing Wang, Yue Yao, Qian Shen, Bo Zhang, Yaowen Zhang |
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| Jazyk: | angličtina |
| Rok vydání: | 2021 |
| Předmět: |
Soil salinity
lcsh:Hydraulic engineering arid regions 010504 meteorology & atmospheric sciences Mean squared error soil salinization Geography Planning and Development Aquatic Science 01 natural sciences Biochemistry remote sensing lcsh:Water supply for domestic and industrial purposes lcsh:TC1-978 Partial least squares regression Linear regression 0105 earth and related environmental sciences Water Science and Technology Remote sensing lcsh:TD201-500 Hyperspectral imaging numerical modelling 04 agricultural and veterinary sciences Digital soil mapping Principal component analysis digital soil mapping 040103 agronomy & agriculture 0401 agriculture forestry and fisheries Environmental science Principal component regression |
| Zdroj: | Water Volume 13 Issue 4 Water, Vol 13, Iss 559, p 559 (2021) |
| ISSN: | 2073-4441 |
| DOI: | 10.3390/w13040559 |
| Popis: | Soil salinity due to irrigation diversion affects regional agriculture, and the development of soil composition estimation models for the dynamic monitoring of regional salinity is important for salinity control. In this study, we evaluated the performance of hyperspectral data measured using an analytical spectral device (ASD) field spec standard-res hand-held spectrometer and satellite sensor visible shortwave infrared advanced hyperspectral imager (AHSI) in estimating the soil salt content (SSC). First derivative analysis (FDA) and principal component analysis (PCA) were applied to the data using the raw spectra (RS) to select the best model input data. We tested the ability of these three groups of data as input data for partial least squares regression (PLSR), principal component regression (PCR), and multiple linear regression (MLR). Finally, an estimation model of the SSC, Na+, Cl−, and SO42− contents was established using the best input data and modeling method, and a spatial distribution map of the soil composition content was drawn. The results show that the soil spectra obtained from the satellite hyperspectral data (AHSI) and laboratory spectral data (ASD) were consistent when the SSC was low, and as the SSC increased, the spectral curves of the ASD data showed little change in the curve characteristics, while the AHSI data showed more pronounced features, and this change was manifested in the AHSI images as darker pixels with a lower SSC and brighter pixels with a higher SSC. The AHSI data demonstrated a strong response to the change in SSC therefore, the AHSI data had a greater advantage compared with the ASD data in estimating the soil salt content. In the modeling process, RS performed the best in estimating the SSC and Na+ content, with the R2 reaching 0.79 and 0.58, respectively, and obtaining low root mean squared error (RMSE) values. FDA and PCA performed the best in estimating Cl− and SO42−, while MLR outperformed PLSR and PCR in estimating the content of the soil components in the region. In addition, the hyperspectral camera data used in this study were very cost-effective and can potentially be used for the evaluation of soil salinization with a wide range and high accuracy, thus reducing the errors associated with the collection of individual samples using hand-held hyperspectral instruments. |
| Databáze: | OpenAIRE |
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