Is It Possible to Classify Topsoil Texture Using a Sensor Located 800 km Away from the Surface?

Abstract

ABSTRACT:It is often difficult for pedologists to "see" topsoils indicating differences in properties such as soil particle size. Satellite images are important for obtaining quick information for large areas. However, mapping extensive areas of bare soil using a single image is difficult since most areas are usually covered by vegetation. Thus, the aim of this study was to develop a strategy to determine bare soil areas by fusing multi-temporal satellite images and classifying them according to soil textures. Three different areas located in two states in Brazil, with a total of 65,000 ha, were evaluated. Landsat images of a specific dry month (September) over five consecutive years were collected, processed, and subjected to atmospheric correction (values in surface reflectance). Non-vegetated areas were discriminated from vegetated ones using the Linear Spectral Mixture Model (LSMM) and Normalized Difference Vegetation Index (NDVI). Thus, we were able to fuse images with only bare soil. Field samples were taken from bare soil pixel areas. Pixels of soils with different textures (soil texture classifications) were used for supervised classification in which all areas with exposed soil were classified. Single images reached an average of 36 % bare soil, where the mapper could only "see" these points. After using the proposed methodology, we reached a maximum of 85 % in bare areas; therefore, a pedologist would have proper conditions for generating a continuous map of spatial variations in soil properties. In addition, we mapped soil textural classes with accuracy up to 86.7 % for clayey soils. Overall accuracy was 63.8 %. The method was tested in an unknown area to validate the accuracy of our classification method. Our strategy allowed us to discriminate and categorize different soil textures in the field with 90 % accuracy using images. This method can assist several professionals in soil science, from pedologists to mappers of soil properties, in soil management activities.

Use of Sentinel-2 Time-Series Images for Classification and Uncertainty Analysis of Inherent Biophysical Property: Case of Soil Texture Mapping

“…The classification models provided low to moderate performances for soil texture classification depending on the date of acquisition of the Sentinel-2 image used (accuracy ranging from 0.43 to 0.5). These performances were lower than those obtained by [18], who reported an accuracy of approximately 0.58 to 0.64 for soil texture classification using Landsat-5 TM images and lower than those obtained by [19], who reported an overall accuracy of 0.64 for soil texture classification and using Landsat-5 TM images.…”

“…Low confusion was obtained between the C and SL classes corresponding to remote textural classes (Table 1), and high confusion was obtained between neighboring textural classes, i.e., between C and SC and between SL and SCL, independent of the acquisition time of the Sentinel-2 image (Table 1). Dematte et al [19] also succeeded in discriminating remote textural classes (clayey from sandy soils) using Landsat-5 TM images. As neighboring textural classes (e.g., C and SC) require almost similar types of management and behave similarly in crop production, whereas remote textural classes require different types of management, soil resource management policies may strongly benefit from the capacity of Sentinel-2 data to accurately identify these soil texture properties over regional scales.…”

“…This could be due to (i) insufficient spectral information limiting prediction accuracy for soil properties [16] and (ii) the insufficient spatial resolution of VNIR-SWIR multispectral satellite sensors (e.g., 15 and 30 m for ASTER data depending on spectral bands, 30 m for LANDSAT data), which does not match with the patterns of interest required for the definition of soil structure [17]. Nevertheless, VNIR-SWIR multispectral satellite imagery has been successfully used for qualitative soil surface property mapping [18,19].…”

Linking Remote Sensing and Geodiversity and Their Traits Relevant to Biodiversity—Part I: Soil Characteristics

“…Blasch et al [147] applied multi-temporal RapidEye composites to predict spatial variations in soil organic matter (SOM). Multi-temporal Landsat image composites have also been used by Dematte et al [148] in order to perform soil texture classification. Since soil-related indicators based on multiand hyper-temporal time series or composites are less affected by atmospheric conditions and soil moisture variations, they can be considered as a key for the automatic and operational derivation of standardized soil mapping products.…”

Barest Pixel Composite for Agricultural Areas Using Landsat Time Series

“…These spectral ranges are known to contain information on soil properties [15]. Cloud-free pixels were selected based on the cfmask property (classifies every pixel according to five classes: clear, water, cloud shadow, clouds, and snow), equal to clear; snow pixels were excluded based on the normalised difference snow index (NDSI) [27], threshold of 0.7.…”