| 引用本文: | 周世勋,尹 娟,王军涛,等.灌区土壤含盐量的高光谱估测与空间分布研究[J].灌溉排水学报,2025,44(2):72-82. |
| ZHOU Shixun,YIN Juan,WANG Juntao,et al.灌区土壤含盐量的高光谱估测与空间分布研究[J].灌溉排水学报,2025,44(2):72-82. |
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| 摘要: |
| 【目的】通过高光谱反演灌区土壤含盐量，探究灌区盐渍化土壤空间分布规律。【方法】以河套灌区的沈乌灌域为研究区，野外采集253个土壤样品光谱反射率与盐分数据，对原始光谱数据进行15种光谱变换并优选相关性高的特征波段，以变换后的光谱数据为自变量，土壤含盐量为因变量，构建了多元线性逐步回归（MLSR）、偏最小二乘回归（PLSR）、支持向量机回归（SVR）和BP神经网络（BPNN）模型，旨在寻找该灌区土壤含盐量高光谱反演精度最高的建模方法并分析盐分的空间分布特征。【结果】①土壤光谱反射率随着土壤盐渍化程度的加重而增加；光谱变换能显著提高光谱数据同土壤含盐量的相关性。②综合对比分析多元线性逐步回归、偏最小二乘回归、支持向量机回归和BP神经网络4种建模方法，BPNN模型稳定性和预测精度更好，最优光谱变换形式为倒数对数的一阶微分[lg(1/R)]'，决定系数为0.825，均方根误差为2.254 g/kg。③结合高光谱数据与GIS技术，可对土壤含盐量进行估测和空间反演，结合实地调研，得出了东南、西侧和北侧含盐量高、湖泊周围盐渍化严重的空间分布特征。【结论】通过实测高光谱建立的[lg(1/R)]'-BPNN模型可以更好地估测灌区土壤含盐量，探究灌区土壤盐渍化空间分布规律，为河套灌区的土壤盐渍化监测提供参考。 |
| 关键词: 土壤盐渍化；高光谱；光谱变换；反演模型；空间分布 |
| DOI：10.13522/j.cnki.ggps.2024066 |
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| Mapping soil salinity in irrigated areas using hyperspectral UAV imagery |
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ZHOU Shixun, YIN Juan, WANG Juntao, CHANG Buhui, YANG Zhen
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1. School of Civil and Hydraulic Enigineering, Ningxia University, Yinchuan 750021, China;
2. Yellow River Institute of Hydraulic Research, Zhengzhou 450045, China
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| Abstract: |
| 【Objective】Soil salinization induced by poor irrigation management poses a significant challenge to irrigated agriculture, reducing soil productivity and crop yields. Estimating soil salinity and its spatial distribution in irrigated areas can help improve soil and irrigation management. The objective of this paper is to use hyperspectral inversion techniques and a develop model to accurately estimate soil salinity and its distribution in the Hetao Irrigation District. 【Method】The experiment was conducted in the Shenwu Irrigation Area, where spectral reflectance and salinity data were measured and collected from 253 soil samples. Fifteen spectral transformations were applied to improve the correlation between hyperspectral data and soil salinity. Four models, including multiple linear stepwise regression (MLSR), partial least squares regression (PLSR), support vector machine regression (SVR), and backpropagation neural network (BPNN), were evaluated for their accuracy to estimate soil salinity. The most accurate model was then integrated with GIS to map soil salinity across the region.【Result】① Soil spectral reflectance increased with soil salinity, and spectral transformations significantly improved the correlation between hyperspectral data and soil salinity. ② Among the four models we compared, the BPNN model proved to be most accurate and stable. The optimal spectral transformation was the first derivative of the reciprocal logarithm of transformed reflectance data (represented by R), that is, lg(1/R)]'. This model achieved a determination coefficient of 0.825 and a root mean square error of 2.254 g/kg. ③ Integrating the BPNN model with GIS enabled estimation of spatial variation of soil salinity. Validation against ground-truth data revealed spatial pattern in soil salinity distribution, with high soil salinity found in the southeast, west and north, and severe soil salinization found in areas adjacent to the lake.【Conclusion】The BPNN model using [lg(1/R)]' we developed is accurate and reliable for estimating soil salinity using hyperspectral data. Combined with GIS, it facilitates accurate mapping of soil salinization in irrigated areas, offering valuable insights for salinity monitoring and sustainable management in the Hetao Irrigation District and similar regions. |
| Key words: soil salinization; hyperspectral; spectral transformation; inversion model; spatial distribution |
