The Brazilian Soil Spectral Service (BraSpecS): A User-Friendly System for Global Soil Spectra Communication

Although many Soil Spectral Libraries (SSLs) have been created globally, these libraries still have not been operationalized for end-users. To address this limitation, this study created an online Brazilian Soil Spectral Service (BraSpecS). The system was based on the Brazilian Soil Spectral Library (BSSL) with samples collected in the Visible–Near–Short-wave infrared (vis–NIR–SWIR) and Midinfrared (MIR) ranges. The interactive platform allows users to find spectra, act as custodians of the data, and estimate several soil properties and classification. The system was tested by 500 Brazilian and 65 international users. Users accessed the platform (besbbr.com.br), uploaded their spectra, and received soil organic carbon (SOC) and clay content prediction results via email. The BraSpecS prediction provided good results for Brazilian data, but performed variably for other countries. Prediction for countries outside of Brazil using local spectra (External Country Soil Spectral Libraries, ExCSSL) mostly showed greater performance than BraSpecS. Clay R2 ranged from 0.5 (BraSpecS) to 0.8 (ExCSSL) in vis–NIR–SWIR, but BraSpecS MIR models were more accurate in most situations. The development of external models based on the fusion of local samples with BSSL formed the Global Soil Spectral Library (GSSL). The GSSL models improved soil properties prediction for different countries. Nevertheless, the proposed system needs to be continually updated with new spectra so they can be applied broadly. Accordingly, the online system is dynamic, users can contribute their data and the models will adapt to local information. Our community-driven web platform allows users to predict soil attributes without learning soil spectral modeling, which will invite end-users to utilize this powerful technique

Sensoriamento remoto e próximo para caracterização da mineralogia do solo

The mineralogy is the gear of soil processes, playing a fundamental role in relevant issues for humanity. However, access to mineralogical analyses is difficult due the difficulty of acquisition through traditional methods and alternative forms to reach it must be explored. This thesis was divided in two chapters that aimed: 1) To understand the fundamental interactions of the energy on pXRF information with emphasis on iron forms, moisture and SOM for use on soil science and 2) To map the abundances of major soil mineralogical components for the whole Brazilian territory at the surface and subsurface. In order to reach the first objective, three selective dissolution treatments were applied to remove: (i) soil organic matter (SOM), ii) SOM and poorly crystalline iron forms (o), iii) SOM and poorly crystalline plus well crystalline iron forms (d). One additional treatment iv) including water addition (+W) was also carried out. The pXRF was able to detect changes caused by the selective dissolution treatments and soil particle size distribution. The kaolinite, gibbsite,Fe2O3, Al2O3, SiO2, TiO2 and MnO contents were quantified with satisfactory accuracy (0.61< R2 < 0.97). Sources of uncertainty, mainly soil moisture, must be considered. The understanding of the fundamentals of energy interaction with the sample matrix in the X-ray range is the starting point for characterizing the soil through pXRF. In order to reach the second objective, The Brazilian Spectral Library (BSSL) with Vis-NIR-SWIR spectral data, was used to assess the relative amounts of hematite (Hem), goethite (Gt), kaolinite (Kt) and gibbsite (Gbs) in soil samples from Brazil. Terrain attributes (TA) and a synthetic soil image (SySI) with bare soil pixel from multitemporal Landsat images (1984 to 2020) were used as predictors. A novel approach was performed in order to obtain a bare soil image for the whole Brazilian territory. The model Random Forest (RF) was used for spatial prediction to obtain the mineral maps and their uncertainty by bootstrapping procedure. The Hem presented the more accurate results in RF models with R2 ranging from 0.48 to 0.56, followed by Gbs (0.42 to 0.44), Kt (0.20 to 0.31) and Gt (0.16 to 0.26). The proposed approach was able to reveal the spatial distribution of the relative abundance of minerals for the Brazilian territory. The mineral maps were in accordance with geology and soil legacy maps and also with the climate and terrain conditions. The approach proposed is an efficient method to obtain mineralogy information for large areas.A mineralogia é a engrenagem dos processos do solo, desempenhando um papel fundamental em questões relevantes para a humanidade. Porém, o acesso às análises mineralógicas é difícil devido à dificuldade de aquisição pelos métodos tradicionais e formas alternativas de alcançá-las devem ser exploradas. Esta tese foi dividida em dois capítulos que visaram: 1) Compreender os fundamentos das interações da energia na informação do pXRF com ênfase nas formas de ferro, umidade e matéria orgânica do solo para uso na ciência do solo e 2) Mapear as abundâncias dos minerais predominantes para todo o território Brasileiro, em superfície e subsuperfície. Para atingir o primeiro objetivo, três tratamentos de dissolução seletiva foram aplicados para remover: (i) matéria orgânica do solo (MOS), ii) MOS e formas de ferro pouco cristalinas (o), iii) MOS e as formas de ferro pouco cristalinas e também as formas bem cristalinas de ferro (d). Um tratamento adicional, iv) incluindo a adição de água (+ W) também foi realizado. O pXRF foi capaz de detectar alterações pelos tratamentos de dissolução seletiva e distribuição granulométrica do solo. Os teores de caulinita, gibbsita, Fe2O3, Al2O3, SiO2, TiO2 e MnO foram quantificados com acurácia satisfatória (0,61 < R2 <0,97). Fontes de incerteza, principalmente a umidade do solo, devem ser consideradas nas análises. A compreensão dos fundamentos da interação da energia com a matriz da amostra na faixa de raios X é o ponto de partida para a caracterização do solo por meio de pXRF. Para atingir o segundo objetivo, a Biblioteca de Espectral Solos do Brasil (BESB) com dados espectrais no Vis-NIR-SWIR foi utilizada para acessar a abundância de hematita (Hem), goethita (Gt), caulinita (Kt) e gibbsita (Gbs) em amostras de solo do Brasil. Os atributos do terreno (TA) e uma imagem sintética do solo (SySI) com pixel de solo exposto de imagens multitemporais do Landsat (1984 a 2020) foram usados como preditores. Uma nova abordagem foi realizada a fim de obter uma imagem de solo exposto para todo o território brasileiro. O modelo Random Forest foi utilizado na predição espacial para obtenção dos mapas minerais e sua incerteza por procedimento de bootstrapping. O Hem apresentou os modelos mais acurados com R2 variando de 0,48 a 0,56, seguido por Gbs (0,42 a 0,44), Kt (0,20 a 0,31) e Gt (0,16 a 0,26). A abordagem proposta foi capaz de revelar a distribuição espacial da abundância relativa de minerais para o território brasileiro. Os mapas minerais estavam de acordo com mapas legados de geologia e pedologia e também com as condições de clima e terreno. A abordagem proposta é um método eficiente para obter informações de mineralogia para grandes áreas

A soil productivity system reveals most Brazilian agricultural lands are below their maximum potential

Abstract Food production is extremely dependent on the soil. Brazil plays an important role in the global food production chain. Although only 30% of the total Brazilian agricultural areas are used for crop and livestock, the full soil production potential needs to be evaluated due to the environmental and legal impossibility to expand agriculture to new areas. A novel approach to assess the productive potential of soils, called “SoilPP” and based on soil analysis (0–100 cm) - which express its pedological information - and machine learning is presented. Historical yields of sugarcane and soybeans were analyzed, allowing to identify where it is still possible to improve crop yields. The soybean yields were below the estimated SoilPP in 46% of Brazilian counties and could be improved by proper management practices. For sugarcane, 38% of areas can be improved. This technique allowed us to understand and map the food yield situation over large areas, which can support farmers, consultants, industries, policymakers, and world food security planning

Mapping Brazilian soil mineralogy using proximal and remote sensing data

International audienceMinerals control many soil functions and play a crucial role in addressing global existential issues. Measuring the abundance of soil minerals is a laborious, costly, and time-consuming task; however, soil spectroscopy can be a useful tool to overcome this issue. This work aimed to map the abundance of major mineralogical components of soils in Brazil from surface to 1 m deep and at a spatial resolution of 30 m. Spectral data of the Brazilian Soil Spectral Library with Vis-NIR-SWIR was used to estimate the abundance of haematite, goethite, kaolinite, and gibbsite. These minerals were spatialized using digital soil mapping techniques. We also developed a novel framework to obtain bare soil reflectance for areas without natural or anthropic soil exposure (continuous image) and used it as covariate. Soil minerals and their abundances were successfully estimated by Vis-NIR-SWIR reflectance. Haematite predictions presented the most accurate results with Random Forest models, followed by gibbsite, kaolinite, and goethite. The spatial validation with reference mineralogical data found R2 of 0.64 (haematite), 0.40 (goethite), 0.20 (kaolinite/Kt), 0.29 (gibbsite/Gbs), and 0.40 (Kt/Kt + Gbs). The resulting maps of soil minerals were in accordance with the geology, pedology, climate, and relief of Brazil and revealed the spatial distribution of mineral abundances at a finer resolution than existing geological and pedological maps, reaching a farm level detail

Remote sensing imagery detects hydromorphic soils hidden under agriculture system

Abstract The pressure for food production has expanded agriculture frontiers worldwide, posing a threat to water resources. For instance, placing crop systems over hydromorphic soils (HS), have a direct impact on groundwater and influence the recharge of riverine ecosystems. Environmental regulations improved over the past decades, but it is difficult to detect and protect these soils. To overcome this issue, we applied a temporal remote sensing strategy to generate a synthetic soil image (SYSI) associated with random forest (RF) to map HS in an 735,953.8 km2 area in Brazil. HS presented different spectral patterns from other soils, allowing the detection by satellite sensors. Slope and SYSI contributed the most for the prediction model using RF with cross validation (accuracy of 0.92). The assessments showed that 14.5% of the study area represented HS, mostly located inside agricultural areas. Soybean and pasture areas had up to 14.9% while sugar cane had just 3%. Here we present an advanced remote sensing technique that may improve the identification of HS under agriculture and assist public policies for their conservation

Proximal sensing approach for soil characterization and discrimination: a case of study in Brazil

The potential of using spectroscopy for the quantification of soil attributes through its spectral signature is widely documented in the literature. However, a protocol to support formal soil classification systems combining spectral data has not been established. This research proposed a protocol for soil profile classification by combining spectral data from the near visible, shortwave infrared (Vis-NIR-SWIR) and mid infrared (MIR). For this purpose, we used 15 soil profiles located in the Pernambuco State, Brazil. A quantitative analysis between soil attributes and spectral curves was performed for the selection of bands with the best correlations (method 1). In addition, the recursive feature elimination (RFE) function was used for the selection of discriminant bands between soil profiles (method 2). The results of this research indicated that the combined use of spectra is efficient to successfully grouping Ferralsol, Gleysol, and Acrisol. The integrated use of sensors, pedometric techniques, and the expertise of soil scientists can lead to an advanced understanding of soil science

The Brazilian Soil Spectral Service (BraSpecS): A User-Friendly System for Global Soil Spectra Communication

International audienceAlthough many Soil Spectral Libraries (SSLs) have been created globally, these libraries still have not been operationalized for end-users. To address this limitation, this study created an online Brazilian Soil Spectral Service (BraSpecS). The system was based on the Brazilian Soil Spectral Library (BSSL) with samples collected in the Visible–Near–Short-wave infrared (vis–NIR–SWIR) and Mid-infrared (MIR) ranges. The interactive platform allows users to find spectra, act as custodians of the data, and estimate several soil properties and classification. The system was tested by 500 Brazilian and 65 international users. Users accessed the platform (besbbr.com.br), uploaded their spectra, and received soil organic carbon (SOC) and clay content prediction results via email. The BraSpecS prediction provided good results for Brazilian data, but performed variably for other countries. Prediction for countries outside of Brazil using local spectra (External Country Soil Spectral Libraries, ExCSSL) mostly showed greater performance than BraSpecS. Clay R2 ranged from 0.5 (BraSpecS) to 0.8 (ExCSSL) in vis–NIR–SWIR, but BraSpecS MIR models were more accurate in most situations. The development of external models based on the fusion of local samples with BSSL formed the Global Soil Spectral Library (GSSL). The GSSL models improved soil properties prediction for different countries. Nevertheless, the proposed system needs to be continually updated with new spectra so they can be applied broadly. Accordingly, the online system is dynamic, users can contribute their data and the models will adapt to local information. Our community-driven web platform allows users to predict soil attributes without learning soil spectral modeling, which will invite end-users to utilize this powerful technique

Correction: Demattê et al. The Brazilian Soil Spectral Service (BraSpecS): A User-Friendly System for Global Soil Spectra Communication. <i>Remote Sens.</i> 2022, <i>14</i>, 740

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