Regionalization of Uncovered Agricultural Soils Based on Organic Carbon and Soil Texture Estimations

Abstract

Abstract:The determination of soil texture and organic carbon across agricultural areas provides important information to derive soil condition. Precise digital soil maps can help to till agricultural fields with more accuracy, greater cost-efficiency and better environmental protection. In the present study, the laboratory analysis of sand, silt, clay and soil organic carbon (SOC) content was combined with hyperspectral image data to estimate the distribution of soil texture and SOC across an agricultural area. The aim was to identify regions with similar soil properties and derive uniform soil regions based on this information. Soil parameter data and corresponding laboratory spectra were used to calibrate cross-validated (leave-one-out) partial least squares regression (PLSR) models, resulting in robust models for sand (R 2 = 0.77, root-mean-square error (RMSE) = 5.37) and SOC (R 2 = 0.89, RMSE = 0.27), as well as moderate models for silt (R 2 = 0.62, RMSE = 5.46) and clay (R 2 = 0.53, RMSE = 2.39). The regression models were applied to Airborne Imaging Spectrometer for Applications DUAL (aisaDUAL) hyperspectral image data to spatially estimate the concentration of these parameters. Afterwards, a decision tree, based on the Food and Agriculture Organization (FAO) soil texture classification scheme, was developed to determine the soil texture for each pixel of the hyperspectral airborne data. These soil texture regions were further refined with the spatial SOC estimations. The developed method is useful to identify spatial regions with similar soil properties, which can provide a vital information source for an adapted treatment of agricultural fields in terms of the necessary amount of fertilizers or water. The approach can also be adapted to wider regions with a larger sample size to create detailed digital soil maps (DSMs). Further, the presented method should be applied to future hyperspectral satellite missions like Environmental Mapping and Analysis Program (EnMap) and Hyperspectral Infrared Imager (HyspIRI) to cover larger areas in shorter time intervals. Updated DSMs on a regular basis could particularly support precision farming aspects.

Soil Organic Carbon Estimation in Croplands by Hyperspectral Remote APEX Data Using the LUCAS Topsoil Database

“…Hbirkou et al [13] obtained a RPD of 2.32 and a RMSE of 1.1 g·kg −1 in northern Germany with higher and more variable SOC concentrations. The aisaDUAL hyperspectral scanner (367 bands between 400 nm and 2500 nm) provided good cross-validation results in Germany [63]; in this case, the RMSE was 2.7 g·kg −1 , but the standard deviation was quite high (8.2 g·kg −1 ). The full VNIR-SWIR data (400-2500 nm) from the airborne hyperspectral scanner (AHS) was employed by Stevens et al [11] in Belgium, obtaining a RPD of 1.47; while Stevens et al [46] in Luxembourg discarded the SWIR region because of the low SNR, thus using only 20 bands from 442 nm and 1019 nm, and they obtained a RPD of 1.9.…”

“…Many authors report SOC estimation from airborne data, especially exploiting the VNIR-SWIR region [10][11][12][13]16,62,63], but also the long-wave infrared region (LWIR; 8-14 µm) [15]. However, all of them build a calibration dataset for the airborne spectra on samples analysed in the laboratory (i.e., the traditional approach).…”

“…The full VNIR-SWIR data (400-2500 nm) from the airborne hyperspectral scanner (AHS) was employed by Stevens et al [11] in Belgium, obtaining a RPD of 1.47; while Stevens et al [46] in Luxembourg discarded the SWIR region because of the low SNR, thus using only 20 bands from 442 nm and 1019 nm, and they obtained a RPD of 1.9. Most of the aforementioned papers provided cross-validation statistics [10][11][12]63], and only Hbirkou et al [13] and Stevens et al [46] tested their models on a validation dataset. In both cases, the entire dataset was split into calibration and validation ensuring a representative subset of the data both in the calibration and validation datasets.…”

Improving Spectral Estimation of Soil Organic Carbon Content through Semi-Supervised Regression

“…However, testing all unlabeled samples exhaustively from a large dataset is inefficient. It is especially true for hyperspectral imaging applications, in which a large population of soil spectra can be collected [48][49][50]. In such a scenario, testing all available unlabeled samples is impractical.…”

Multiscale Superpixel-Based Sparse Representation for Hyperspectral Image Classification

“…Therefore, hyperspectral images consist of rich spectral-spatial information, which have attracted great attention in different application domains, such as national defense [1], urban planning [2], precision agriculture [3,4] and environment monitoring [5][6][7].…”