Spectral Analysis and Estimations of Soil Salt and Organic Matter Contents

Abstract

Levels of soil salt content (SC) and organic matter content (SOMC) are key factors for assessing soil fertility. Field soils with high salt content hinder crop growth severely. Our objectives are to establish models for SC and SOMC and evaluate the models for prediction accuracy. Spectral reflectance of soil samples was obtained under controlled laboratory conditions using a portable spectrometer. A total of 211 samples were divided into a training set and a validating set for modeling soil properties. Derived from the originally observed reflectance (λ) data, six quantitative λ-related indices were applied to establish models for SC and SOMC by stepwise linear regression analysis using the training data. Adjusted coefficient of determination for each model was used for evaluating model stability. The established models were evaluated for the accuracy of predicting SC and SOMC using the validation data. Results showed that among the established models, the model relating λ to SC (the root mean square error [RMSE] = 2.99, the correlation coefficient [r] = 0.73) had a small difference with the model relating continuum-removed reflectance (CR) to SC (RMSE = 2.94, r = 0.76). Considering the convenience of utilization for the model and the statistical analysis of model outputs, the model relating λ to SC was selected as the best model for prediction. With the smallest RMSE (0.84) and the largest r (0.96) values among the six models, the model relating reciprocal values of λ to SOMC was selected as the best model for prediction. This work supplies a feasible method to estimate SC and SOMC. It has potential for remote sensing applications to determine soil properties.

Modeling soil cation concentration and sodium adsorption ratio using observed diffuse reflectance spectra

“…The transformation techniques of R e affect model performance. Based on the originally observed Vis-NIR R e data, Liu et al (2013) applied five transformation techniques to R e including continuum-removed R e , reciprocal values of R e , logarithm of reciprocal values of R e , the first-order derivative of R e , and the second-order derivative of R e , and performed SMLR for soil salt content and SOM. Their results showed that the model relating the continuum-removed R e to soil salt content, had the largest r (0.76) and the smallest RMSE (2.94) compared with the other four transformation techniques, and was selected as the best model.…”

“…The most commonly used bands for [Na + ], [Mg 2+ ], and [Ca 2+ ] were mid-infrared region, NIR, and Vis-NIR, but the modeling performance was property specific for different bands (Soriano-Disla et al 2014). Models using the characteristic absorption band also performed well in the prediction of SOM and soil salt content (Liu et al 2013;Rial et al 2015). In this research, reflectance data in the effective spectral bands of Vis-NIR were used.…”

“…Lü et al (2013) concluded that [SO 4 2− ], [Ca 2+ ], [Mg 2+ ], and soil salt content are the main characteristic factors of soil salinization in the Manasi River Basin in Xinjiang, China. Liu et al (2013) analyzed the quantitative relationship between soil salt content and soil organic matter (SOM) content, and six reflectance-related transformation techniques in the Beiwucha town in Xinjiang, China, using the stepwise multiple linear regression (SMLR) method. Their results indicated that the selected spectral models successfully predicted soil salt content and SOM.…”

Weather index insurance for transition to sustainable cotton production under climate change in Xinjiang, China

“…Agricultural production is extremely dependent on climatic conditions, and this vulnerability makes agriculture one of the most sensitive and significant sectors to climate change impacts (Lobell et al, 2011). Climate change, through variations in temperature and precipitation, causes changes in environmental elements for crop growth such as sunlight, moisture, and soil, which subsequently affects crop phenological periods, growth potential, cropping systems, pest control, farm management, and ultimately crop yield (Liu et al, 2013). Meanwhile, related studies have shown that the warming trend in northwest China is higher than the national average during climate change (Diarra et al, 2017).…”