Spectral Mixture Modeling of an ASTER Bare Soil Synthetic Image Using a Representative Spectral Library to Map Soils in Central-Brazil

Abstract

Pedological maps in suitable scales are scarce in most countries due to the high costs involved in soil surveying. Therefore, methods for surveying and mapping must be developed to overpass the cartographic material obtention. In this sense, this work aims at assessing a digital soil map (DSM) built by multispectral data extrapolation from a source area to a target area using the ASTER time series modeling technique. For that process, eight representative toposequences were established in two contiguous micro-watersheds, with a total of 42 soil profiles for analyses and classification. We found Ferralsols, Plinthosols, Regosols, and a few Cambisols, Arenosols, Gleisols, and Histosols, typical of tropical regions. In the laboratory, surface soil samples were submitted to spectral readings from 0.40 µm to 2.50 µm. The soil spectra were morphologically interpreted, identifying shapes and main features typical of tropical soils. Soil texture grouped the curves by cluster analysis, forming a spectral library (SL). In parallel, an ASTER time series (2001, 2004, and 2006) was processed, generating a bare soil synthetic soil image (SySI) covering 39.7% of the target area. Multiple Endmember Spectral Mixture Analysis modeled the SL on the SySI generating DSM with 73% of Kappa index, in which identified about 77% is covered by rhodic Ferralsols. Besides the overestimation, the DSM represented the study area’s pedodiversity. Given the discussion raised, we consider including subsoil data and other features using other sensors in operations modeled by machine learning algorithms to improve results.

VNIR-SWIR Spectroscopy, XRD and Traditional Analyses for Pedomorphogeological Assessment in a Tropical Toposequence

“…The results of the chemical, physical, mineralogical, and spectral analyses of 10 diagnostic horizons for each pedological class of the studied region were used to perform Pearson's correlation, spectral median, and hierarchical clustering analyses (HCA) of the local database of soil attributes (oxide content) [6,7]. This information was extracted from the Brazilian Spectral Soil Library-https://bibliotecaespectral.wixsite.com/english (accessed in 13th June 2023) database of the GeoCiS Research Group from Esalq/USP-https://esalqgeocis.wixsite.…”

“…Terra et al, 2018 [3] stated that soils formed in a reductiomorphic environment have a greyish, greenish, or bluish appearance due to the absence of oxidized iron, which undergoes a gleization process. Novais et al, 2023 [7], who studied soils in DF, attributed the causes of soil colors to the predominance of hydromorphic conditions in the area. In addition, the transitional plinthic diagnostic horizon (R) showed variegated colors due to an oxidized and reduced Fe mixture.…”

“…The high SOM content causes strong darkish colors, with a hue of 10YR, and a value and chroma varying between 2 and 1. Higher SOM content causes large and prismatic structures in this soil's O and A horizons [6,7]. Regarding land use, the area was under pasture cultivation, with the remaining riparian forest preserved along the creek.…”

“…Ds showed values ranging from 0.71 to 0.85 g cm −3 in subsurface horizons and from 0.72 to 0.91 g cm −3 in diagnostic horizons. The Central Plateau soils are very porous, usually exceeding 70% of TP for Ferralsols [7].…”

“…Soil formation factors provide distinctive characteristics that enable surveying soil properties, classification, and pedological mapping [1]. Soil attributes, such as color, texture, and mineralogy, for instance, are essential for studies of pedogenic processes [2][3][4], soil classification [5][6][7][8], and soil attribute mapping [9][10][11]. All this information is essential for agricultural engineering because it drives actions of management and localization for better productivity.…”