基于CARS算法的湖滨绿洲土壤表层有机碳估算

Estimation of Surface Soil Organic Carbon in Lakeside Oasis Based on CARS Algorithm

文章以博斯腾湖西岸湖滨绿洲为研究区,将实测的土壤有机碳含量数据与土壤高光谱数据结合,对原始光谱R进行数学变换及微分变换,应用竞争自适应重加权采样(CARS)筛选特征波段,并采用其筛选的特征波段构建BP神经网络模型估算土壤表层有机碳含量。结果表明:(1)研究区土壤表层有机碳含量范围在0.80~63.15 g/kg,平均值为17.57 g/kg,变异系数为71.48%,呈中等变异性。(2)CARS算法将建模输入波段压缩至全波段数目的2.76%以下,R、R′、1/R、(1/R)′、log(1/R)、log(1/R)′、1/log R、(1/log R)′光谱形式下筛选的特征波段,较多集中于近红外长波1500~2500 nm与可见光波段380~760 nm;R″、(1/R)″、log(1/R)″、(1/log R)″光谱形式下筛选的特征波段,较多集中于近红外波段760~2500 nm。(3)二阶微分变换构建的CARS-BP估算模型精度优于一阶微分,R″-CARS-BP估算效果最好,训练集和验证集R2分别为0.81、0.83,RPD分别为2.30、2.45,RMSE分别为5.75、4.89 g/kg。

Taking the lakeside oasis on the west bank of Bosten Lake as the study area, and using measured soil organic carbon content data and soil hyperspectral data, the original spectrum R was mathematically transformed and differentially transformed. The competitive adaptive reweighted sampling(CARS) was used to screen the characteristic variables under different spectral forms, and the selected characteristic band was used to build a BP neural network model to estimate the carbon content of soil organic. The results showed that the soil surface organic carbon content in the study area ranged from0.80 to 63.15 g/kg, with an average value of 17.57 g/kg, and the coefficient of variation was 71.48%, which was medium variability. The CARS algorithm compressed the modeling input bands to less than 2.76% of the full band number, and R, R′,1/R,(1/R)′, log(1/R), log(1/R)′, 1/log R, and(1/log R)′ spectral forms were more concentrated in the NIR long-wave 1 500~2 500 nm and visible band 380~760 nm;R″,(1/R)″, log(1/R)″, and(1/log R)″ spectral forms were more concentrated in the NIR band 760~2 500 nm. The accuracy of CARS-BP estimation model constructed by second-order differential transformation was better than that of first-order differential transformation, with the best estimation effect as R″-CARS-BP. The R2of the training set and validation set were 0.81, 0.83, RPD were 2.30, 2.45, and RMSE were 5.75 g/kg, 4.89 g/kg, respectively.