Guo Jia-Xin, Zhu Qing, Zhao Xiao-Min, Guo Xi, Han Yi, Xu Zhe
College of Land Resources and Environment, Nanchang 330045, China.
Jiangxi Province Key Laboratory of Poyang Lake Basin Agricultural Resources and Ecology, Nanchang 330045, China.
Ying Yong Sheng Tai Xue Bao. 2020 Mar;31(3):863-871. doi: 10.13287/j.1001-9332.202003.014.
Soil spectral information differ across different land use types. Understanding the appropriate modeling methods for different land use types can efficiently and accurately invert soil organic carbon content. We collected 248 samples from forest, cultivated land and orchard in the north-central part of Fengxin County, Jiangxi Province. First, original spectral reflectance curves were reduced noises with Savitzky-Golay (SG) filter. Then 10 nm resampling method was used to reduce data redundancy. We used partial least squares regression (PLSR), support vector machine regression based on grid search method (GRID-SVR) and support vector machine regression based on particle swarm optimization (PSO-SVR) to construct the inversion models of soil organic carbon content. The results showed that when constructing a single land-use type inversion model, RPD of the PLSR method for forest, cultivated land and orchard was 1.536, 1.315 and 1.493 respectively. RPD of GRID-SVR method increased 0.150, 0.183 and 0.502 than that of PLSR method, respectively. The PSO-SVR method had higher accuracy, with RPD being 20.8%, 10.0% and 2.7% higher than GRID-SVR for forest, cultivated land and orchard, respectively. The RPD of forest and orchard were 2.036 and 2.049, which well predicts soil organic carbon. The RPD of cultivated land was 1.647, which can make a rough estimate of soil organic carbon. The PSO-SVR model had the best prediction effect on soil organic carbon of different land use types, with the prediction accuracy of soil organic carbon content in forest and orchard being close and higher than cultivated land. Soil nutrition diffed acorss different land use types, which affect the prediction of soil organic carbon content. Models for inversion of soil organic carbon should be constructed separately for different land use types.
不同土地利用类型的土壤光谱信息存在差异。了解不同土地利用类型适用的建模方法能够高效且准确地反演土壤有机碳含量。我们从江西省奉新县中北部的森林、耕地和果园采集了248个样本。首先，利用Savitzky-Golay（SG）滤波器对原始光谱反射率曲线进行降噪处理。然后采用10 nm重采样方法减少数据冗余。我们使用偏最小二乘回归（PLSR）、基于网格搜索法的支持向量机回归（GRID-SVR）和基于粒子群优化的支持向量机回归（PSO-SVR）构建土壤有机碳含量反演模型。结果表明，构建单一土地利用类型反演模型时，PLSR方法对森林、耕地和果园的RPD分别为1.536、1.315和1.493。GRID-SVR方法的RPD分别比PLSR方法提高了0.150、0.183和0.502。PSO-SVR方法具有更高的精度，其RPD分别比森林、耕地和果园的GRID-SVR高20.8%、10.0%和2.7%。森林和果园的RPD分别为2.036和2.049，能够很好地预测土壤有机碳。耕地的RPD为1.647，可对土壤有机碳进行粗略估计。PSO-SVR模型对不同土地利用类型的土壤有机碳具有最佳预测效果，森林和果园土壤有机碳含量的预测精度相近且高于耕地。不同土地利用类型的土壤养分不同，这影响了土壤有机碳含量的预测。应针对不同土地利用类型分别构建土壤有机碳反演模型。
