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

Abstract

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.