Download This Paper Open PDF in Browser Add Paper to My Library Share: Permalink Using these links will ensure access to this page indefinitely Copy URL Drivers of Soil Carbon Accumulation and Agave Expansion Potential in Brazilian Drylands 21 Pages Posted: 27 Feb 2024 See all articles by Carlos Roberto Pinheiro JuniorCarlos Roberto Pinheiro Junioraffiliation not provided to SSRNTiago Osório FerreiraUniversity of São Paulo (USP) - Department of Soil ScienceJosé de Souza Oliveira Filhoaffiliation not provided to SSRNHermano QueirozUniversity of Sao PauloLucas Pecci CanisaresUniversity of KentuckyLucas Tadeu Greschukaffiliation not provided to SSRNCarlos Eduardo Pellegrino CerriUniversity of Sao PauloMarcos Gervasio Pereiraaffiliation not provided to SSRNGonçalo Pereiraaffiliation not provided to SSRNMaurício R. Cherubinaffiliation not provided to SSRN Abstract Shallow soils (i.e., Lithic Entisols) cover about 20% of Brazilian drylands. Inherent soil characteristics (i.e., shallow depths) and water scarcity restrict plant growth and carbon (C) inputs in these soils. In such conditions, the cultivation of species with higher water use efficiency and adapted to shallow soils, such as Agave spp are an alternative. Here, we use a dataset of 50 Lithic Entisol profiles distributed within the Brazilian drylands (northeastern region) to: (i) investigate the effects of land-use (cropland, grassland, and native forest), climate (semi-arid and dry sub-humid), and slope classes (0-3%, 3-8%, 8-20% and 20-45%) on soil C accumulation; and (ii) identify areas with favorable conditions for expansion of agave cultivation for biofuel production. The results suggested that land use does not affect C stock and C/N ratio, nevertheless, they were affected by climate. Under dry sub-humid climate conditions, the carbon stocks were 44 % higher than under semi-arid climate, a result that reflects the effect of higher biomass production in wetter environments. Under slopes of 20-45%, the C stocks were172% higher than under slopes of 0-3%, as a result of the higher altitudes and wetter conditions under steep slopes. We identified that 54% of the shallow soils are under cropland and grassland in sites under 0-3% and 3-8% slope classes, which occur at lower altitudes and have lower aridity indices (drier conditions) and lower C stocks (especially under slopes of 0-3%). In these areas, the expansion of agave cultivation represents an opportunity to produce biofuels and other co-products, as well as promoting the socio-economic development of the Brazilian drylands. In addition, the possibility of reintroducing organic residues from ethanol production can promote an increase in C stocks and improve soil health, contributing to climate change mitigation. Although our study reveals the potential areas for agave expansion over shallow dryland soils in Brazil, several aspects of production, mechanization, industry development, private sector interest, public policy implementation, as well as cultural aspects and technical assistance are needed to establish a resilient biofuel ecosystem. The results of our study provide insights from a holistic view of the drivers of SOC accumulation, support in the use of the marginal land and explaining the potential for expanding agave cultivation in tropical drylands around the world. Keywords: Land-use change, soil carbon storage, semi-arid, renewable energy Suggested Citation: Suggested Citation Pinheiro Junior, Carlos Roberto and Ferreira, Tiago Osório and Oliveira Filho, José de Souza and Queiroz, Hermano and Canisares, Lucas Pecci and Greschuk, Lucas Tadeu and Cerri, Carlos Eduardo Pellegrino and Pereira, Marcos Gervasio and Pereira, Gonçalo and Cherubin, Maurício R., Drivers of Soil Carbon Accumulation and Agave Expansion Potential in Brazilian Drylands. Available at SSRN: https://ssrn.com/abstract=4740996 Carlos Roberto Pinheiro Junior (Contact Author) affiliation not provided to SSRN ( email ) No Address Available Tiago Osório Ferreira University of São Paulo (USP) - Department of Soil Science ( email ) Brazil José de Souza Oliveira Filho affiliation not provided to SSRN ( email ) No Address Available Hermano Queiroz University of Sao Paulo ( email ) Lucas Pecci Canisares University of Kentucky ( email ) Lexington, KY 40506United States Lucas Tadeu Greschuk affiliation not provided to SSRN ( email ) No Address Available Carlos Eduardo Pellegrino Cerri University of Sao Paulo ( email ) Marcos Gervasio Pereira affiliation not provided to SSRN ( email ) No Address Available Gonçalo Pereira affiliation not provided to SSRN Maurício R. Cherubin affiliation not provided to SSRN ( email ) No Address Available Download This Paper Open PDF in Browser Do you have negative results from your research you’d like to share? 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Climate and land use are recognized as two of the main drivers of changes in soil organic carbon (SOC) on a global scale. Both factors play an important role in understanding SOC sequestration and mitigation of climate change. Particularly important, black soils are mineral soils with high SOC contents and high natural fertility and play an important role in national and global food and climate security. Here, we used a database of 90 black soils in Brazil - under different climate and land use conditions across the country - to test the hypothesis that C stock is richer in wetter climate conditions and that agricultural land use reduces C stock and the percentage of carbon saturation (PCS%). Climate data were obtained from the National Oceanic Atmospheric Administration (NOAA) and used to classify Thornthwaite's climate. The land use information was obtained in the MapBiomas platform and was grouped into three major types: cropland, pasture, and native vegetation. The nonparametric Kruskal-Wallis test showed no differences for C stock, C/N ratio, and PCS% for both land use and climate. The low C/N ratio and the strong correlation between Ca2+, CEC, clay, and SOC suggest that organo-mineral interactions - which are stronger in soils with high-activity clays (e.g., Chernozems, Kastanozems, and Phaeozems) - promotes greater stabilization of the SOC and its long-term persistence and, thus being less sensitive to variations in climate and land use. Considering the total area of approximately 3.7 x 106 ha and the average value of C stock of 93.2 Mg/ha, the total SOC stored in Brazil's black soils is in the order of 0.35 Gt, and the carbon stock stabilization potential is 0.25 Gt. Our results highlight the potential of Brazil's black soils to promote carbon sequestration and climate change mitigation.

Shallow soils (i.e., Lithic Entisols) cover about 20% of Brazilian drylands. Inherent soil characteristics (i.e., shallow depths) and water scarcity restrict plant growth and carbon (C) inputs in these soils. In such a sensitive ecosystem, sustainable land management options are key to promoting socio-economic development and ensuring food security. Here, we use a dataset of 50 Lithic Entisol profiles distributed within the Brazilian drylands (northeastern region) to: (i) investigate the effects of land-use (cropland, grassland, and native forest), climate (semi-arid and dry sub-humid), and slope classes (0-3%, 3-8%, 8-20% and 20-45%) on soil C accumulation; and (ii) evaluate how understanding multiple drivers C accumulation can support the identification of sustainable land management options. The results suggested that land use does not affect C stock and C/N ratio, nevertheless, they were affected by climate. Under dry sub-humid climate conditions, C stocks were 41.7 Mg ha(-1), 44% higher than under semi-arid climate (28.9 Mg ha(-1)), a result that reflects the effect of higher biomass production in wetter environments. Under slopes of 20-45%, C stocks were 54.3 Mg ha(-1), 172% higher than under slopes of 0-3% (19.9 Mg ha(-1)), because of the higher altitudes and wetter conditions under steeper slopes. Our results showed that areas under lower slopes have lower C stocks and lower aridity index. These drier conditions reduce the productive potential of annual crops and grasslands but enable the cultivation of high-yielding Crassulacean Acid Metabolism (CAM) crops, such as Agave spp, which have a high potential for biofuel production. Furthermore, the possibility of reintroducing organic residues from ethanol production can promote an increase in C stocks, contributing to climate change mitigation. Ultimately, our study provides insights from a holistic view of SOC accumulation drivers, supporting land use planning of highly sensitive environments in tropical drylands around the world.

Summary Bibliometric research illuminates the scientific development of soil health (SH) studies in the Brazilian Semiarid, which are crucial for sustainable agriculture and mitigating climate change impacts. However, research trends on SH in the Brazilian Semiarid are still not well understood. This study aimed to illustrate how SH has been addressed in research concerning the Brazilian Semiarid. Terms such as ‘soil health,’ ‘soil quality,’ ‘biological quality,’ ‘chemical quality,’ ‘physical quality,’ as well as ‘Caatinga,’ ‘Brazilian Semiarid,’ and ‘Brazilian Northeast’ were searched in the Scopus® database. Bibliometric parameters were catalogued by the number of publications per year, most cited articles, primary institutions, main journals, and keyword frequency. The articles were evaluated based on the examination of chemical, physical, and biological indicators, and a similarity test was conducted to group articles according to these indicators. The bibliometric analysis reveals a significant increase in scientific output since 2020. Embrapa research centres contribute significantly to this expanding body of knowledge, with the leading journal ‘Revista Caatinga’ reflecting a specialized focus on the region’s unique challenges. The most evaluated indicators were pH, soil organic carbon, P, Ca2+, Mg2+, K+, and bulk density, but the prevalence of biological indicators, such as soil organic carbon (SOC), activity of enzymes such as alkaline and acid phosphatase, and ß-glucosidase, microbial activity, and soil fauna, underscores key research themes. These findings highlight the practical implications of SH research, but while increased research is commendable and increasingly necessary, studies are still scarce. Increased research is vital for the development of strategies that contribute to the long-term sustainability of the Brazilian Semiarid region.

Soil depth is one of the most critical factors which impact on culture productivity and makes difficult appropriate management decisions. However, assessing this parameter is also the most challenging tasks in the agronomic field. The objective of this work was to predict the spatial distribution of soil depth from space techniques (remote sensing, RS) and machine learning. A total of 292 sites were allocated (based on the toposequence approach) and drilled (0-2 m depth) at three different locations in Brazil. Based on these, in-situ traditional depth maps (denominated field-map) were elaborated for validation. Afterwards, we created a strategy to ach-ieve these different depths by RS (RS) approach. Landsat 8 OLI bands, Land Surface Temperature (LST), Normalized Difference Vegetation Index (NDVI) and emissivity in dry and rainy seasons as well as terrain at-tributes were applied to predict soil depth. For this purpose, the most important covariates were selected using Recursive Feature Selection (RFE) based on Random Forest (RF) and Support Vector Machine (SVM). Afterwards, the application of RF and SVM by selected covariates were compared based on tenfold cross validation for each location. The best model was selected based on R2, RMSE and MAE, accuracy and bootstrapping approach and uncertainties. Terrain attributes were important to discriminate soil depth. Although, LST and NDVI also pre-sented important contribution to this task. Different seasons implies on water and plant dynamics in deep and shallow soils. This impacted on NDVI and LST as detected by RS. Thus, the method brings more variables to infer soil depth. The RF model performed better than SVM to predict soil depth with an average of 0.77 R2. The accuracy between a digital soil mapping and a field-map reached 0.58 to 0.81 indicating an important result considering the difficulty of the objective This may help pedologists and farmers as well as water and plants environmental monitoring.

Soil color is a key indicator of soil properties and conditions, exerting influence on both agronomic and environmental variables. Conventional methods for soil color determination have come under scrutiny due to their limited accuracy and reliability. In response to these concerns, we developed an innovative system that leverages 35 years of satellite imagery in conjunction with in-situ soil spectral measurements. This approach enables the creation of a global soil color map with a fine spatial resolution of 30 m x 30 m. The system initially identifies bare earth areas worldwide using reflectance bands acquired from Landsat 4 through Landsat 8 between 1985 and 2020. Soil color was quantified using the CIE-XYZ coordinates, utilizing 8005 soil spectral measurements within the visible range (380-780 nm) as ground truth data. We established transfer functions to convert Landsat reflectance bands to standardized XYZ color coordinates. These transfer functions were subsequently applied to images of bare surfaces, covering approximately 38.5% of the Earth's surface. We validated the resulting global soil color map using statistical indices derived from an independent set of ground-truth spectral data, demonstrating a high degree of agreement. By creating the world's first global soil color map, we have set a baseline for future spatial and temporal monitoring of soil conditions, thus enhancing our understanding and management of our planet's vital soil resources.

Minerals 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.

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.

Soil depth is one of the most critical factors which impact on culture productivity and makes difficult appropriate management decisions. However, assessing this parameter is also the most challenging tasks in the agronomic field. The objective of this work was to predict the spatial distribution of soil depth using remote sensing data and machine learning techniques. 292 sites were allocated (based on the toposequence approach) and perforated (from 0 to 2 m) at three different locations in Brazil. Based on these, in-situ traditional depth maps (denominated empirical) were elaborated as for future validation. Afterwards, we elaborated a strategy to achieve these different depths by remote sensing (RS) approach. Landsat 8 OLI bands, Land Surface Temperature (LST), Normalized Difference Vegetation Index (NDVI) and emissivity in dry and rainy seasons as well as terrain attributes were applied to predict soil depth. For this purpose, the most important covariates were selected using Recursive Feature Selection (RFE) based on Random Forest (RF) and Support Vector Machine (SVM). Afterwards, the application of RF and SVM by selected covariates were compared based on ten-fold cross validation for each location. The best model was selected based on R 2 (coefficient of determination), RMSE (Root Mean Square Error) and MAE (Mean Absolute Error) were used to assess the accuracy of developed models and bootstrapping approach was applied to make uncertainties in each location. Finally, the predicted map was compared with empirical one. The results in all locations indicated that the terrain attributes were the most important factors and RF had the highest performance in predicting soil depth.

Spectroscopy has been extensively used in soil analysis. However, users, such as commercial laboratories, still do not know the application, potential, and limitations of this technique. This paper aims to present a given course going from theory to practice for end-users regarding spectroscopy technique. The paper combines the use of soil spectroscopy and wet laboratories to perform a scientific-teaching dynamic using their own data, allowing for a better understanding of the technique. The course is denominated ProBASE (The Brazilian Program of Soil Analysis via Spectroscopy). Soil samples from 35 laboratories were sent (34 from Brazil and 1 from Paraguay) to a central spectroscopy laboratory. Samples were measured for visible-near-short-wave-infrared, Vis-NIR (400-2500 nm), and mid-infrared, MIR (3000-25000 nm) ranges and by portable X-ray Fluorescence (pXRF) sensor. We also used the Brazilian Soil Spectral Library (BSSL) dataset (Vis-NIR). We performed three different population models with Vis-NIR as follows: a) using the dataset of each laboratory (Local), b) using the entire ProBASE-dataset (Regional), and c) using the BSSL (Country). Afterward, we developed spectral models using the other spectral ranges for comparison. We also used a qualitative approach to detect errors from the wet laboratory analyses using Vis-NIR data and evaluated their impact on spectral modeling. The models that used Local samples had the best performance, with R2 in validation reaching up to 0.93 for clay, 0.92 for sand, 0.86 for P, 0.82 for pH, 0.81 for organic matter (OM), 0.75 for Ca2+, 0.72 for cation exchange capacity (CEC), 0.71 for aluminum saturation, 0.71 for Al3+, 0.70 for Mg2+, 0.64 for base saturation (V%), and 0.56 for K+. However, the base saturation presented greater variation from good to poor results. For the comparison dataset, the results can be summarized as follows: a) pXRF was better for P, Ca2+ B, V% and Mn; b) MIR was better for clay, sand, OM, pH, Mg2+, CEC and Mn; c) Vis-NIR was better for H + Al; d) the three spectral ranges had good performance for OM, sand, silt and clay. In addition, our findings indicate that all spectral ranges are useful for a wet laboratory, where each model has advantages and limitations, but they can be used complementary to each other. Spectroscopy can detect inconsistencies of the wet laboratory analyses, affecting thus the quality of the results. The commercial laboratory community viewed the techniques positively. The results indicate the viability to create a hybrid laboratory, combining both wet and dry (soil spectroscopy) chemistry.

Proximal sensing is a tool of relevance to pedology, as it provides suitable and quick information about soil properties. The majority of the studies focus on the stand-alone use of proximal sensors in different wavelengths of the spectrum to get information on soil properties. Although sensors work on a specific spectral range, the combined use of sensors from different spectral ranges makes it easier to explore several kinds of information about the soil. This study aimed at carrying out a discriminant analysis of soil profiles, through the evaluation of data from different spectral ranges along the electromagnetic spectrum. The spectral ranges used included the X-ray (Portable X-Ray fluorescence (pXRF)), visible, near and short-wave infrared (Vis-NIR-SWIR, 350-2500 nm), and mid-infrared (Mid-IR, 2500-25,000 nm) ranges. The study had five main steps: 1) collecting soil samples; 2) acquiring spectral data; 3) identifying the most important spectral ranges for soil discrimination; 4) grouping analysis of soil profiles by color and spectral behavior by cluster analysis; and 5) characterizing and discriminating each group by their spectral behavior. The clustering using the combined spectral ranges (Vis-NIR-SWIR and Mid-IR) joined the soil profiles due to their spectral similarity. The qualitative analysis of the spectral curves allowed us to understand which were the soil properties that influenced the grouping by spectrum. The methodology used in this work was effective for soil discrimination, in terms of soil color, particle size distribution, mineralogy and drainage conditions. The combined use of Vis-NIR-SWIR and Mid-IR showed high efficiency in surveying and detailing information about soil profiles, contributing to its characterization and discrimination.

Soil is one of the most important factors for agricultural production. In tropical regions, soil variability is considerable, with the most diverse combinations of physical and chemical characteristics, an influence factor in crop growth and productivity. In this research, the main objective was to identify how soil characteristics and parent material can influence sugarcane development over time using remote sensing. An area located in Sao Paulo, Brazil, of 182 ha (one point per ha with soil analysis), with high variability in the parent material and soil types, was selected. Images from the Sentinel2-MSI satellite were used to describe the spectral behavior of sugarcane over a period of one year. The NDRE (normalized difference red-edge index) was calculated for each image and then the leaf area index (LAI) was obtained from it. Maps of soil classes, soil properties at two depths (0–0.20 and 0.80–1.0 m), and parent material classes were related to sugarcane LAI variability over time. Production environment zones, which is a classification based on soil characteristics to support sugarcane development, were also obtained and related to LAI variability. Spectral signatures of the crop presented different behaviors through the season, soil types and soil attributes provided useful responses for this variability. At the beginning of the season, the surface and subsurface soil properties (texture and fertility) impacted differently on crop development. On the other hand, soil classes and parent material influenced LAI in all production environments studied. The results indicated that the soil types and their properties at different depths have a significant impact on sugarcane development. Furthermore, RS was able to monitor the plant evolution and be related to soil types which may assist in plant management. The results can bring light on how better sugarcane management can be conducted using remote sensing data and soils variability.

Bare soil triggers several undesirable processes for its quality and remote sensing can be a powerful tool to monitoring its occurrence. This work aims to apply multi-temporal satellite image techniques to detect bare soil areas under sugarcane cultivation and relate with soil security. The study was carried out in an area of 2,574 km² located in Brazil. The MapBiomas land use and cover collection was used to know the sugarcane area changes from 1985 to 2019. A collection of Landsat images over 35 years (1985 to 2019) were used to create Synthetic Soil Images (SYSIs) and the Bare Soil Frequency Images (BSF) of the area. SYSIs were generated annually, in the rainy and dry season. BSFs was generated in the total period and every five years by dry and rainy season. Thus, the land use changes and bare soil occurrence were compared to categorical maps of soil types, surface clay classes and slope, and also with economic, social and political changes in the period. In general, the bare soil increased from 1985 to 2006, and began to decline thereafter because of "Agro-environmental Protocol' that anticipated the end of pre-harvest burning in sugarcane crop. BSF in the rainy season decreased over the period motivated by knowledge of farmers and changes in management. Despite this, many prone to erosion soils classes (Arenosols, Lixisols/Acrisols) remain under conventional tillage in the rainy season. We concluded that the use of multi-temporal satellite images is an important approach to monitoring soil management contributing to soil security.

Soil apparent electrical conductivity (ECa) is related to various soil attributes and processes. This research aimed to understand the relationship between ECa and paedogenesis, lithology and attributes. The study area is located in São Paulo State, Brazil. Soil samples were collected for physical-chemical analysis from 79 locations (0–20 cm layer). A geophysical field-portable equipment (Geonics EM38-MK2 conductivity meter) was used to measure soil ECa. For that, four toposequences were selected on landscape. Sixteen soil profiles were allocated in the landscape for soil characterization and classification. The ECa values were measured across toposequences, soil types and lithology variations, at the same 79 locations used for physico-chemical characterization. The statistical analysis and Kruskal–Wallis rank sum test and Cluster analysis by k-means and Wilcoxon–Mann–Whitney test was performed to verify differences in ECa values over lithology, relief and soil types. The values of ECa measurements were related to soil attributes variation. The results showed that lithology strongly affects ECa values, mainly in diabase-derived soils, followed by metamorphosed siltstone. In fluvial sediments, the ECa exhibits different behaviours. In less evolved soils, the lithology contributed more to ECa than paedogenesis, as the opposite occurs in more evolved ones. The ECa decreases downslope and correlates with lithology alteration and soil types. This study demonstrates that soil ECa can be a tool to differentiate between various soil types and lithological transitions across the landscape, supporting the soil survey and mapping at finer scales.

Portable X-ray fluorescence (pXRF) has great potential for numerous applications in soil science. However, the basic knowledge about the effects of soil properties on pXRF spectra are still poorly studied, which may lead users to biased interpretations of mathematical models. The present study aimed to evaluate the outcomes of moisture, soil organic matter content (SOM), and iron forms on pXRF data. The work was conducted with seventeen soil samples from the central region of Sa & SIM;o Paulo state (Brazil). 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 effects of treatments were evaluated for sandy and clayey samples. Soil particle size distribution and elemental content affected the bremsstrahlung and characteristic peaks counts. In +W, there was a generalized decrease in counts mainly for the light elements (magnesium, aluminum and silicon). Regarding the selective dissolution procedures, alterations were verified, reflecting mainly the removal power of reagents. Generally, the most pronounced alterations occurred for-d and moderate alterations for-SOM and-o. The pXRF data showed high correlation with particle size distribution and mineralogy attributes. The kaolinite, gibbsite, Fe(2)O3, Al(2)O3, SiO2, TiO2 and MnO contents were quantified with satisfactory accuracy (0.61 < R-2 < 0.97). The pXRF was able to detect changes caused by the selective dissolution treatments and soil particle size distribution. 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.

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.

Soil degradation is a major challenge in the 21st Century. Tropical regions are having the strongest expansion in agricultural lands. Therefore, novel researches on the soil degradation process are imperative to prevent damage to social and environmental dynamics. The main goal of this research was to generate a Soil Degradation Index in a tropical region that includes the entire agricultural areas of the Sa similar to o Paulo State, Brazil, making use of the factors that can be directly related to it as different environmental indicators. The determination of the areas with exposed soil, based on Landsat time series data (1985-2019), was processed with the Geospatial Soil Sensing System methodology. Additionally, thematic maps of clay, cation exchange capacity and organic matter were generated from the calibration of pixels of Landsat images, taking into account surface soil samples (0-20 cm). The spatialization was performed using a random forest algorithm. The average precipitation was obtained for the period of analysis, using the CHIRPS dataset to generate the historical mean information about the years of study. The surface temperature was determined based on the Landsat 5 and 8 thermal bands. Using the elevation model, other terrain data were obtained, such as LS factor and Slope. The land use information was acquired from the Mapbiomas platform and reclassified into five categories of use. The k-means clustering al-gorithm was used to generate the Soil Degradation Index (SDI), which classified the values of the variables into five degradation categories: from 1, very low, to 5, very high. The model was validated using the OM infor-mation. There was an important relationship between the SDI and the spectral surface reflectance obtained by Landsat. Locations with less OM presented a higher degradation level. Integrating multitemporal remote sensing data and environmental variables proved to be effective to assist the SDI, which can monitor and improve the land use decision-making and public policies, in order to prevent economic and environmental issues.

The Amazon rainforest is one of the most important ecosystems on the planet; however, the environmental pressure created by anthropic activities require monitoring of this critical biome. In this study, we assessed the evolution of deforestation in an open mine pit and its impact on surface environment (i.e., temperature and carbon stocks) using remote sensing techniques. The study was carried out on an area of 11,283.3 ha in the municipality of Maraba, Para State, Brazil, where the "Salobo " copper mine is located. A temporal analysis was conducted, using Landsat satellite images (2005-2020). Subsequently, the Land Surface Temperature (LST) and the Normalized Difference Vegetation Index (NDVI) of the deforested area were determined. Mining-induced deforestation has expanded from 0.9 ha in 2005 to 2214 ha in 2020 with an increase in surface temperature of 10 ? in the period. The temperature difference between the pit and the adjacent forest ranged from 30 to 40 ? over the 15 years, while the temperature at the forest edges rose by 4 ?. The correlation coefficient between exposed soil temperatures and mining deforestation was 0.66. CO2 emissions, increasing from 0.005 Tg CO2 in 2005 to 1.82 Tg CO2 in 2020 due to mining deforestation. The findings demonstrate the significant environmental impact of deforestation, which may also have an influence on local climate. The results can provide scientific support for public policies aimed at mitigating the issue.