Abstract

Numerous studies have investigated the direct retrieval of soil properties, including soil texture, using remotely sensed images. However, few have considered how soil properties influence dynamic changes in remote images or how soil processes affect the characteristics of the spectrum. This study investigated a new method for mapping regional soil texture based on the hypothesis that the rate of change of land surface temperature is related to soil texture, given the assumption of similar starting soil moisture conditions. The study area was a typical flat area in the Yangtze-Huai River Plain, East China. We used the widely available land surface temperature product of MODIS as the main data source. We analyzed the relationships between the content of different particle soil size fractions at the soil surface and land surface day temperature, night temperature and diurnal temperature range (DTR) during three selected time periods. These periods occurred after rainfalls and between the previous harvest and the subsequent autumn sowing in 2004, 2007 and 2008. Then, linear regression models were developed between the land surface DTR and sand (> 0.05 mm), clay (< 0.001 mm) and physical clay (< 0.01 mm) contents. The models for each day were used to estimate soil texture. The spatial distribution of soil texture from the studied area was mapped based on the model with the minimum RMSE. A validation dataset produced error estimates for the predicted maps of sand, clay and physical clay, expressed as RMSE of 10.69%, 4.57%, and 12.99%, respectively. The absolute error of the predictions is largely influenced by variations in land cover. Additionally, the maps produced by the models illustrate the natural spatial continuity of soil texture. This study demonstrates the potential for digitally mapping regional soil texture variations in flat areas using readily available MODIS data.

Highlights

  • Detailed soil resource information is essential for fully satisfying the requirements of agricultural development and environmental management

  • The results show that the sand, clay and physical clay contents exhibit close linear regression relationships with land surface diurnal temperature range (DTR) (Tables 4 and 5)

  • This study proposed a simple linear regression model based on the DTR available from MODIS archives to obtain soil texture with adequate accuracy

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Summary

Introduction

Detailed soil resource information is essential for fully satisfying the requirements of agricultural development and environmental management. As a relatively stable natural property of soil, soil texture is an important factor that influences a series of physical and chemical properties, such as soil structure, soil porosity, hydraulic properties, and nutrient retention ability. Conventional soil texture measurement methods depend on physical analyses in a laboratory, are expensive, require a large number of samples and involve a lengthy analysis to obtain the spatial distribution of soil texture over large areas. To overcome this problem, we propose the use of soil mapping and prediction based on quantitative soillandscape models [1] and geo-statistics [2]. The use of topography and vegetation to estimate soil properties may not be suitable for plains and gently undulating topographic areas due to the high variability of soil properties that occur in similar topographic and vegetation conditions

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