Abstract
The soil spectral dynamic feedback captured from high temporal resolution remote sensing data, such as MODIS, during the soil drying process after a rainfall could assist with digital soil mapping. However, this method is ineffective in utilizing the images with a relatively high spatial resolution. There are an insufficient number of images in the soil drying process since those high spatial resolution images tend to have a low temporal resolution. This study is aimed at generating soil spectral dynamic feedback by integrating the feedback captured from the images with a high spatial resolution during the process of multiple drying after different rainfall events. The Landsat 8 data with a temporal resolution of 16 day was exemplified. Each single spectral feedback obtained from Landsat 8 was first adjusted to eliminate the impact of different rainfall magnitudes. Then, the soil spectral dynamic feedback was reorganized and generated based on the adjusted feedback. Finally, the soil spectral dynamic feedback generated based on Landsat 8 was used for mapping topsoil texture and compared with the mapping results based on the MODIS data and the fusion data of MODIS and Landsat 8. As revealed by the results, not only could the generated soil spectral dynamic feedback based on Landsat 8 data improve the details of the spatial distribution of soil texture, but it also enhances the accuracy of mapping. The mapping accuracy based on Landsat 8 data is higher than that based on the MODIS data and fusion data. The improvements of accuracy are more obvious in the areas with more complex surface conditions. This study widens the scope of application for soil spectral dynamic feedback and provides support for large-scale and high-precision digital soil mapping.
Highlights
The spatial distribution of soil property provides the basic information to guide crop planting, as well as the preservation and utilization of soil resources
The characteristics extracted from the soil spectral dynamic feedback based on the Landsat 8 data, fusion data and only Moderate-Resolution Imaging Spectroradiometer (MODIS) data are presented in Figures 6–8, respectively
CAmean and cAstd are approximation characteristics or trend characteristics of the soil spectral dynamic feedback. cHmean and cHstd, cVmean and cVstd, cDmean and cDstd are the detailed characteristics at horizontal direction, vertical direction, diagonal direction of the soil spectral dynamic feedback, respectively
Summary
The spatial distribution of soil property provides the basic information to guide crop planting, as well as the preservation and utilization of soil resources. Digital soil mapping (DSM) is an effective way to obtain soil spatial distribution information. It is defined as “computer-assisted production of digital representations of soil type or soil properties, which involve the creation and population of spatially-explicit information by the use of field and laboratory methods, coupled with spatial and non-spatial soil inference systems” [1,2,3]. It is difficult to apply this approach to the low-relief areas since the commonly-used soil environmental covariates such as topography and vegetation do not co-vary with soil conditions simultaneously [6,7,8,9]. The land use of low-relief areas is usually farmland and long-term human activities will weaken the relationships between soil and natural environmental covariates
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