Abstract：
Rapid acquisition of the data of soil moisture content (SMC) and soil organic matter (SOM) content is crucial for the improvement and utilization of saline alkali farmland soil. Based on field measurements of hyperspectral reflectance and soil properties of farmland soil in the Hetao Plain, we used a competitive adaptive reweighted sampling algorithm (CARS) to screen sensitive bands after transforming the original spectral reflectance (Ref) into a standard normal variable (SNV). Strategies Ⅰ, Ⅱ, and Ⅲ were used to model the input variables of Ref, Ref SNV, Ref-SNV+ soil covariate (SC), and digital elevation model (DEM). We constructed SMC and SOM estimation models based on random forest (RF) and light gradient boosting machine (LightGBM), and then verified and compared the accuracy of the models. The results showed that after CARS screening, the sensitive bands of SMC and SOM were compressed to below 3.3% of the entire band, which effectively optimized band selection and reduced redundant spectral information. Compared with the LightGBM model, the RF model had higher accuracy in SMC and SOM estimation, and the input variable strategy Ⅲ was better than Ⅱ and Ⅰ. The introduction of auxiliary variables effectively improved the estimation ability of the model. Based on comprehensive analysis, the coefficient of determination (Rp2), root mean square error (RMSE), and relative analysis error (RPD) of the SMC estimation model validation based on strategy Ⅲ-RF were 0.63, 3.16, and 2.01, respectively. The SOM estimation models based on strategy Ⅲ-RF had Rp2, RMSE, and RPD of 0.93, 1.15, and 3.52, respectively. The strategy Ⅲ-RF model was an effective method for estimating SMC and SOM. Our results could provide a new method for the rapid estimation of soil moisture and organic matter content in saline alkali farmland.
快速获取土壤含水率(SMC)和土壤有机质(SOM)含量对于盐碱农田土壤的改良利用至关重要。本研究基于河套平原农田土壤野外高光谱反射率和土壤属性实测数据,对原始光谱反射率(Ref)进行标准正态变量(SNV)转换后,采用竞争性自适应重加权采样算法(CARS)筛选敏感波段,然后分别以Ref、Ref-SNV和Ref-SNV+土壤协变量(SC)及数字高程模型(DEM)作为建模输入变量的策略Ⅰ、Ⅱ和Ⅲ,基于随机森林(RF)和轻梯度提升机(LightGBM)建立SMC和SOM估算模型,并对模型精度进行验证和对比。结果表明:经CARS筛选后,SMC和SOM敏感波段压缩至全波段的3.3%以下,有效优化波段选择,减少了冗余光谱信息。与LightGBM 模型相比,RF模型在SMC和SOM估算中精度更高,输入变量策略Ⅲ优于Ⅱ和Ⅰ,辅助变量的引入有效提升了模型的估算能力。综合分析,基于策略Ⅲ-RF的SMC估算模型验证决定系数(Rp2)、均方根误差(RMSE)和相对分析误差(RPD)分别为0.63、3.16和2.01,基于策略Ⅲ-RF的SOM估算模型Rp2、RMSE和RPD分别为0.93、1.15和3.52,策略Ⅲ-RF模型是估算土壤水分和土壤有机质的有效方法。研究结论可为盐碱农田土壤水分和有机质含量快速估算提供新方法。.
快速获取土壤含水率(SMC)和土壤有机质(SOM)含量对于盐碱农田土壤的改良利用至关重要。本研究基于河套平原农田土壤野外高光谱反射率和土壤属性实测数据,对原始光谱反射率(Ref)进行标准正态变量(SNV)转换后,采用竞争性自适应重加权采样算法(CARS)筛选敏感波段,然后分别以Ref、Ref-SNV和Ref-SNV+土壤协变量(SC)及数字高程模型(DEM)作为建模输入变量的策略Ⅰ、Ⅱ和Ⅲ,基于随机森林(RF)和轻梯度提升机(LightGBM)建立SMC和SOM估算模型,并对模型精度进行验证和对比。结果表明:经CARS筛选后,SMC和SOM敏感波段压缩至全波段的3.3%以下,有效优化波段选择,减少了冗余光谱信息。与LightGBM 模型相比,RF模型在SMC和SOM估算中精度更高,输入变量策略Ⅲ优于Ⅱ和Ⅰ,辅助变量的引入有效提升了模型的估算能力。综合分析,基于策略Ⅲ-RF的SMC估算模型验证决定系数(Rp2)、均方根误差(RMSE)和相对分析误差(RPD)分别为0.63、3.16和2.01,基于策略Ⅲ-RF的SOM估算模型Rp2、RMSE和RPD分别为0.93、1.15和3.52,策略Ⅲ-RF模型是估算土壤水分和土壤有机质的有效方法。研究结论可为盐碱农田土壤水分和有机质含量快速估算提供新方法。.
摘要：
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