[Hyper-spectral inversion of soil organic carbon content under different land use types].

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.不同土地利用类型下土壤光谱信息存在差异,了解不同土地利用类型下合适的建模方法可以高效准确地进行土壤有机碳含量反演。本研究以江西省奉新县中北部林地、耕地和园地3种土地利用类型共248个土壤样本为对象,首先对土壤原始光谱反射率曲线使用Savitzky-Golay(SG)滤波去噪并进行10 nm重采样减少数据冗余,之后采用偏最小二乘回归(PLSR)、基于网格搜索法的支持向量机回归(GRID-SVR)和基于粒子群算法的支持向量机回归(PSO-SVR)3种方法分别构建土壤有机碳含量的反演模型。结果表明: 构建单一土地利用类型反演模型时,PLSR方法在林地、耕地和园地的相对分析误差(RPD)分别为1.536、1.315和1.493,采用GRID-SVR方法时,其RPD分别提升0.150、0.183和0.502。采用PSO-SVR方法时精度最高,相较GRID-SVR方法,其林地、耕地和园地的RPD分别提高20.8%、10.0%和2.7%,林地和园地的RPD分别为2.036和2.049,可以极好地预测土壤有机碳含量,耕地的RPD为1.647,可以对土壤有机碳含量进行粗略估测。PSO-SVR方法对不同土地利用类型土壤有机碳反演效果最优,林地和园地土壤有机碳含量的反演精度相近且高于耕地。研究区不同土地利用类型对土壤有机碳含量的反演结果存在一定的影响,今后可以考虑在反演土壤有机碳时分不同土地利用类型进行建模。.