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| DOI：10.13522/j.cnki.ggps.2022594 |
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| Spectral Characteristics and Inversion Model of Water, Nitrogen and Salt in Saline Soil in Southern Xinjiang |
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ZHAO Zeyi, LI Zhaoyang, WANG Hongbo, ZHANG Nan, LI Guohui, TANG Maosong, WANG Xingpeng, GAO Yang
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1. College of Water Resource and Architecture Engineering,Tarim University, Aral 843300, China;
2. Farmland lrrigation Research lnstitute, Chinese Academy of Agricultural Sciences, Xinxiang 453002, China
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| Abstract: |
| 【Objective】Soil nitrogen and water are crucial factors influencing crop growth. Understanding their spatiotemporal variation at large scales is essential for improving agricultural management but challenging. This paper aims to investigate the application of airborne technologies for inversely estimating the spatiotemporal change in nitrogen and water in saline soils.【Method】The research area is located in southern Xinjiang. Remote sensing images were used to analyze the spectral characteristics of saline soils with different water, nitrogen, and salt contents. Inversion models for estimating water, nitrogen and salt contents were developed, using partial least squares regression (PLSR), support vector regression (SVR), and BP neural network (BPNN), respectively. The accuracy of each model was evaluated against ground-truth data.【Result】The characteristic bands of soil water are around 1 900 nm, the characteristic bands of soil nitrogen are between 1 490~1 506, 1 540~2 006, 2 011~2 500 nm, and the characteristic bands of soil salt are between 1 880~1 883 and 1 890~1 942 nm. The PLSR model has the best inversion effect on water, nitrogen and salt, followed by BPNN model and SVR model. 【Conclusion】 The characteristic spectral bands around 1 900 nm were sensitive to changes in soil water, nitrogen, and salt content. The optimal inversion model for estimating soil water, nitrogen, and salt involved using the Savitzky-Golay method for smoothing, principal component analysis for dimensionality reduction, and partial least squares regression for developing the inverse model. |
| Key words: soil spectral characteristics; saline soils; inversion model; soil salinity; soil nitrogen content; soil moisture |
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