Quantitative retrieving of soil organic matter using field spectrometer and hyperspectral remote sensing

Abstract

As the important component of soil, soil organic matter not only provides every nutrient element for crop, but also has determinant effect for forming of soil structure and melioration the soil physical character. Mapping and dating soil organic matter is of great importance in soil use and evaluation. In this study we examine the feasibility of soil organic matter content by using Hyperspectrally reflective remote sensing methodology. This technique was tested in Xiaochang County located in Hubei province. The soil reflectance properties of samples were measured in the laboratory by ASD field spectrometer. The correlation analysis related with organic matter content was processed from three factors: the spectral reflectance parameter ((lgρ)', ρ/ ρ 450-750 and (1/lgρ 623 )'/ (1/lgρ 564 )'). The results show that the correlation coefficients of r values were: organic matter identification index (ρ/ ρ 450-750 ) ＞logarithmic first derivative of reflectivity ((lgρ)') ＞organic matter mix identification index ((1/lgρ 623 )'/ (1/lgρ 564 )'). Knowing these correlations we were able to use the best prominence correlation of organic matter identification index of 1850nm wavelength as the variable regression to build up statistical regression analysis. We used five model types (Linear Function, Logarithmic Function, Quadratic Function, Power Function and Exponential Function) to forecast the soil organic matter content Hyperion model. The accuracy assessment (R 2 ＝0.8484) by relating forecasted organic matter values with Quadratic Function regression showed that the model is reliable and significantly correlative with known stabilization processes throughout the study area. The quantitative methodology developed in this study for refutations soil organic matter content can be adapted to other regions throughout the world.

Some Peculiarities of Arable Soil Organic Matter Detection Using Optical Remote Sensing Data

“…Optical remote sensing data have been widely used to map the organic matter content in agricultural soils. Numerous studies conducted in various regions and with different types of remote sensing data have demonstrated that SOM content can be detected with different degrees of accuracy [14][15][16][17][18][19][20][21][22][23]. Due to short wavelengths, the optical range only provides information about the very thin soil surface layer [24].…”

Estimating Soil Organic Matter Content Using Sentinel-2 Imagery by Machine Learning in Shanghai

“…Hyperspectral imageries with narrow spectrum within VNIR-SWIR are proved to be an important data source for mapping soil properties [16,17]. Zhuo et al [18] estimated SOM content by using airborne hyperspectral remote sensing, and proved that the result was reliable. However, airborne hyperspectral technology is costly, and it is difficult to achieve continuous images for large areas.…”

Water erosion risk mapping using derived parameters from digital elevation model and remotely sensed data

“…The topographic parameters as plan and profile curvature were used to map the potential areas of denudation, transit, and accumulation of sediments) (10,14), while the hydrologic parameters as the water power index (WPI) were used to measure potential overland flow indicating the possibility of water erosion (10,11,(15)(16)(17)(18)(19). Moreover, various methods for soil degradation characterization were adapted using soil spectral indices derived from remote sensing data (11,(20)(21)(22)(23)(24)(25). Other studies also showed that remote sensing gave very good information about eroded areas, such as soils types, litho-logical units, and vegetation cover (10,(26)(27)(28)(29)(30)(31)(32)(33).…”