Abstract
The transferability of a visible and near-infrared (VNIR) model for soil organic matter (SOM) estimation in riparian landscapes is explored. The results indicate that for the soil samples with air-drying, grinding and 2-mm sieving pretreatment, the model calibrated from the soil sample set with mixed land-use types can be applied in the SOM prediction of cropland soil samples (r2Pre = 0.66, RMSE = 2.78 g∙kg−1, residual prediction deviation (RPD) = 1.45). The models calibrated from cropland soil samples, however, cannot be transferred to the SOM prediction of soil samples with diverse land-use types and different SOM ranges. Wavelengths in the region of 350–800 nm and around 1900 nm are important for SOM estimation. The correlation analysis reveals that the spectral wavelengths from the soil samples with and without the air-drying, grinding and 2-mm sieving pretreatment are not linearly correlated at each wavelength in the region of 350–1000 nm, which is an important spectral region for SOM estimation in riparian landscapes. This result explains why the models calibrated from samples without pretreatment fail in the SOM estimation. The Kennard–Stone algorithm performed well in the selection of a representative subset for SOM estimation using the spectra of soil samples with pretreatment, but failed in soil samples without the pretreatment. Our study also demonstrates that a widely applicable SOM prediction model for riparian landscapes should be based on a wide range of SOM content.
Highlights
IntroductionSoil organic matter (SOM) content is a key soil property in soil surveying, because of its important role in the global carbon cycle, precise agricultural management and soil erosion evaluation [1,2,3].In riparian landscapes, SOM is considered as an important indicator for soil quality, because of its relationship with critical soil functions, such as productivity, erodibility and purification ability [4].In spite of their important roles in geomorphology and hydrology, many of the riparian zones in the population-dense areas of China are suffering from the adverse impacts of agricultural practices [5,6].it is necessary to monitor the spatial and temporal dynamics of SOM for a better management of the land resources in riparian landscapes.Conventional measurements of organic matter in soil still require time-consuming field sampling and intensive laboratory work, which could be costly [7,8]
The aim of this paper is to explore the transferability of several Visible and near-infrared (VNIR) models for Soil organic matter (SOM) estimation in riparian landscapes, where quick and easy access to SOM data is becoming an increasingly important concern for the sustainable use of land resources
The SOM content of the 1381 samples collected in the paddy fields of the Jianghan plain ranged from 9.1 g∙kg−1 to 56.5 g∙kg−1, with an average of 26.9 g∙kg−1 [44]
Summary
Soil organic matter (SOM) content is a key soil property in soil surveying, because of its important role in the global carbon cycle, precise agricultural management and soil erosion evaluation [1,2,3].In riparian landscapes, SOM is considered as an important indicator for soil quality, because of its relationship with critical soil functions, such as productivity, erodibility and purification ability [4].In spite of their important roles in geomorphology and hydrology, many of the riparian zones in the population-dense areas of China are suffering from the adverse impacts of agricultural practices [5,6].it is necessary to monitor the spatial and temporal dynamics of SOM for a better management of the land resources in riparian landscapes.Conventional measurements of organic matter in soil still require time-consuming field sampling and intensive laboratory work, which could be costly [7,8]. Soil organic matter (SOM) content is a key soil property in soil surveying, because of its important role in the global carbon cycle, precise agricultural management and soil erosion evaluation [1,2,3]. SOM is considered as an important indicator for soil quality, because of its relationship with critical soil functions, such as productivity, erodibility and purification ability [4]. In spite of their important roles in geomorphology and hydrology, many of the riparian zones in the population-dense areas of China are suffering from the adverse impacts of agricultural practices [5,6].
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