Quantification of soil organic matter using mathematical models based on colorimetry in the Munsell color system

O presente estudo teve como objetivo desenvolver modelos matemáticos para a quantificação do teor de matéria orgânica, a partir da cor do solo, obtida por aparelho colorímetro no sistema Munsell de cores. Para esse fim, 912 amostras de solo foram coletadas na região de Porto Grande (Amapá) e enviadas para análises química, granulométrica e determinação da cor em amostras secas e úmidas. Os componentes valor e croma da cor do solo no sistema Munsell, obtidos por colorímetro, foram utilizados para quantificar através de regressão múltipla passo a passo (stepwise) o teor de matéria orgânica do solo. O modelo de predição com base em todas as amostras apresentou R² de 0,66 para amostras úmidas e 0,56 para amostras secas, ao serem validados utilizando amostras independentes. Foi possível ainda melhorar os modelos quando as amostras foram separadas por classe de solo ou textura, e os modelos gerados com base em cores de amostras úmidas foram sistematicamente superiores àqueles utilizando amostras secas. Em relação às classes de solo, os melhores resultados foram obtidos para Argissolos e Latossolos, ambos gerando um R² de validação independente de 0,73 (amostra úmida). Para textura, os melhores resultados foram obtidos para solos de textura muito argilosa, com R² de validação de 0,81 (amostra úmida). Os modelos de predição de matéria orgânica em função da cor do solo possuem simplicidade e potencial para serem utilizados no laboratório e no campo, especialmente para Argissolos e Latossolos de textura argilosa, de maneira automática e sem necessidade de uso de produtos ou reagentes.This study aimed to derive mathematical models to predict the soil organic matter content based on soil color obtained by a colorimeter in the Munsell color system. A total of 907 soil samples were collected in the region of Porto Grande (Amapá, Brazil) and analyzed in the laboratory for chemical properties, particle size distribution and color of dry and wet samples. The Munsell color components value and croma obtained using a colorimeter were used to predict soil organic matter content based on stepwise multiple linear regression. Models derived using all samples had R² of 0.66 for wet samples and 0.56 for dry samples, respectively, when validated using independent samples. It was possible to improve the models by separating the samples by soil class or texture. The models derived using colors obtained from wet samples were systematically better than those based on dry samples. Among soil classes, best results were obtained for Argissolos (Ultisols) and Latossolos (Oxisols), both having an R² of independent validation of 0.73 (wet sample). For texture, best results were obtained for very clayey soils, with an R² of validation of 0.81 (wet sample). The soil organic matter prediction models based on soil color have simplicity and potential to be used in the laboratory and in the field with quick and unnecessary chemical products, especially for Ultisols and Oxisols of clayey texture.CNPq/PQ e CNPq/PIBI

Photopedology and orbital spectral pedology on the evaluation of soils developed from basalt

O Estado do Mato Grosso do Sul (MS) é um dos principais produtores do setor agropecuário brasileiro. Entretanto, para manter essa condição, terá que ter à disposição metodologias que auxiliem no planejamento do uso racional de suas terras. Desta forma, tornam-se necessárias pesquisas que visem obter métodos de investigação dos solos que atuem de forma rápida, sejam eficazes e, principalmente, de baixo custo. Sabendo-se que o relevo é importante fator de formação dos solos e que fotos aéreas detectam a variação de superfície, espera-se que, juntamente com informações espectrais quantitativas da superfície, possam caracterizar e discriminar solos ou grupamento de solos na paisagem. Assim, este estudo teve por objetivos: (a) identificar diferentes classes de solos e verificar suas relações com os aspectos qualitativos e quantitativos da paisagem; (b) utilizar dados radiométricos de imagens de satélite para discriminar classes ou grupamento de solos. Para tal, foi avaliada a relação entre a classificação dos solos e os aspectos da rede de drenagem, obtidos através da interpretação de fotografias aéreas, e dados espectrais, conseguidos através da análise de imagens de satélite, de 14 amostras circulares (ACs) da região de Maracaju (MS), onde os solos são desenvolvidos a partir de basaltos. A densidade de drenagem (Dd) apresentou correlação com o índice de intemperismo (Ki) e com a saturação de bases do solo (V%), permitindo discriminar classes de solos dentro da área de estudo com 85,7 % de certeza, enquanto os dados espectrais somente discriminaram solos quanto às classes texturais da camada superficial. Além disso, observou-se que nos solos com teores de Fe2O3 acima de 180 g kg-1 a diferenciação por classes texturais foi prejudicada mediante o do uso das imagens de satélite.The state of Mato Grosso do Sul (MS) is one of the main Brazilian producers in the agricultural sector. To maintain this status, however, it will be necessary both to know and use rationally its soil resources. This way, it will be necessary methods of soil research that are efficient and fast in obtain information, as well as of low-cost to support land use planning. Since relief is an important factor of soil formation, aerial photos and satellite images analysis can be used to detect landscape features that help to characterize and discriminate soils. Therefore, the goals of this study were: (a) to identify different soil classes and verify their relationships with landscape aspects by the interpretation of aerial photos; and (b) to use radiometric data, obtained from satellite images analysis, to discriminate soils or groups of soils on landscape. To do so, it was evaluated the relationship between soil classes and relief and drainage aspects, obtained by the interpretation of aerial photos in conjunction with spectral data obtained by satellite image analysis of 14 circular samples (CS) of the Maracaju city in the MS state. The drainage density (Dd), determined in the CS, showed positive correlation with the soil Ki index and base saturation (V%) index, thus allowing discriminate soil classes of the studied area with 85.7 % of certainty, whereas the spectral data only discriminated soils by the textural classes of their surface layer. On the other hand, it was also observed that soils with Fe2O3 content higher than 180 g kg-1 had their textural classes poorly differentiated by using the spectral data

Geomorphometric segmentation of complex slope elements for detailed digital soil mapping in southeast Brazil

Hillslope elements have considerable potential in predicting soil properties and types in the landscape, making them likely to be a useful basis for detailed soil mapping. The goal of this research was to apply a previously developed digital hillslope position (DHP) model, calibrate it as needed to a Brazilian landscape, and test its utility as a basis for identification of detailed soil map units. The study area covers 2500 ha and is located on the border between the municipalities of Piracicaba and Santa Bárbara d\u27Oeste, São Paulo state, Brazil. A digital elevation model, with spatial resolution of 5 m, was used to obtain slope gradient, profile curvature and relative elevation with different analysis scales. Hierarchical rules for these digital terrain derivatives were used to segment the landscape into hillslope positions. The user-calibrated hillslope position model was verified against local experience by identifying the hillslope position in the field and comparing it with the model classification using the Kappa statistic and a confusion matrix. Soil samples were collected across multiple hillslopes with different lithologies. The samples were analyzed for chemical composition and soil particle size separates. The measured soil properties were assessed for statistical significance by variance analysis among hillslope position, parent material, and the interaction between the two. Student\u27s t-tests were performed iteratively across each hillslope position within a given parent material to identify specifically which soil properties were significantly different among the hillslope position map units. Variance analysis of soil samples located within the respective parent material map units identified significant differences for all soil properties measured, but only for some soil properties when categorized by DHP. Focusing on the parent material with a sufficient quantity of samples, there was always at least one hillslope position that was significantly different from the others for each soil property. Because each of these map units presented a significant difference in at least one soil property, they are useful for detailed soil mapping

Soil class map of the Rio Jardim watershed in Central Brazil at 30 meter spatial resolution based on proximal and remote sensed data and MESMA method

Geospatial soil information is critical for agricultural policy formulation and decision making, land-use suitability analysis, sustainable soil management, environmental assessment, and other research topics that are of vital importance to agriculture and economy. Proximal and Remote sensing technologies enables us to collect, process, and analyze spectral data and to retrieve, synthesize, visualize valuable geospatial information for multidisciplinary uses. We obtained the soil class map provided in this article by processing and analyzing proximal and remote sensed data from soil samples collected in toposequences based on pedomorphogeological relashionships. The soils were classified up to the second categorical level (suborder) of the Brazilian Soil Classification System (SiBCS), as well as in the World Reference Base (WRB) and United States Soil Taxonomy (ST) systems. The raster map has 30 m resolution and its accuracy is 73% (Kappa coefficient of 0.73). The soil legend represents a soil class followed by its topsoil color

A Spectral Transfer Function to Harmonize Existing Soil Spectral Libraries Generated by Different Protocols

Soil spectral libraries (SSLs) are important big-data archives (spectra associated with soil properties) that are analyzed via machine-learning algorithms to estimate soil attributes. Since different spectral measurement protocols are applied when constructing SSLs, it is necessary to examine harmonization techniques to merge the data. In recent years, several techniques for harmonization have been proposed, among which the internal soil standard (ISS) protocol is the most largely applied and has demonstrated its capacity to rectify systematic effects during spectral measurements. Here, we postulate that a spectral transfer function (TF) can be extracted between existing (old) SSLs if a subset of samples from two (or more) different SSLs are remeasured using the ISS protocol. A machine-learning TF strategy was developed, assembling random forest (RF) spectral-based models to predict the ISS spectral condition using soil samples from two existing SSLs. These SSLs had already been measured using different protocols without any ISS treatment the Brazilian (BSSL, generated in 2019) and the European (LUCAS, generated in 2009-2012) SSLs. To verify the TF's ability to improve the spectral assessment of soil attributes after harmonizing the different SSLs' protocols, RF spectral-based models for estimating organic carbon (OC) in soil were developed. The results showed high spectral similarities between the ISS and the ISS-TF spectral observations, indicating that post-ISS rectification is possible. Furthermore, after merging the SSLs with the TFs, the spectral-based assessment of OC was considerably improved, from R2 = 0.61, RMSE (g/kg) = 12.46 to R2 = 0.69, RMSE (g/kg) = 11.13. Given our results, this paper enhances the importance of soil spectroscopy by contributing to analyses in remote sensing, soil surveys, and digital soil mapping