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Retaining a residue cover (RC) over the soil is the key to ensure its health. Since tropical agriculture systems, mostly in sugarcane, are characterized by intensive management, the discrimination ...

The present study was conducted to evaluate the effectiveness of combining proximal, and remote sensing with environmental variables for predicting USDA (United States Department of Agriculture) so...

Soil classification has traditionally been developed by combining the interpretation of taxonomic rules that are related to soil information with the pedologist's tacit knowledge. Hence, a more quantitative approach is necessary to characterize soils with less subjectivity. The objective of this study was to develop a soil grouping system based on spectral, climate, and terrain variables with the aim of establishing a quantitative way of classifying soils. Spectral data were utilized to obtain information about the soil, and this information was complemented by climate and terrain variables in order to simulate the pedologist knowledge of soil–environment interactions. We used a data set of 2287 soil profiles from five Brazilian regions. The soil classes of World Reference Base (WRB) system were predicted using the three above-mentioned variables, and the results showed that they were able to correctly classify the soils with an overall accuracy of 88 %. To derive the new system, we applied the spectral, climatic, and terrain variables, which – using cluster analysis – defined eight groups; thus, these groups were not generated by the traditional taxonomic method but instead by grouping areas with similar characteristics expressed by the variables indicated. They were denominated as “soil environment groupings” (SEGs). The SEG system facilitated the identification of groups with equivalent characteristics using not only soil but also environmental variables for their distinction. Finally, the conceptual characteristics of the eight SEGs were described. The new system has been designed to incorporate applicable soil data for agricultural management, to require less interference from personal/subjective/empirical knowledge (which is an issue in traditional taxonomic systems), and to provide more reliable automated measurements using sensors.

It is impossible to make pedological maps without understanding subsurface attributes. Several strategies can be used for soil mapping, from a tacit knowledge to mathematical modeling. However, there are still gaps in knowledge regarding how to optimize subsurface mapping. This work aimed to quantify subsurface soil attributes using satellite spectral reflectance and geographically weighted regression (GWR) techniques. The study was carried out in Sao Paulo, Brazil, in an area spanning 47,882 ha. Multitemporal satellite images (Landsat-5) were initially processed in order to retrieve spectral reflectance from the bare soil surface. Based on a toposequence method, 328 points were then distributed across the area (at depths between 0 and 20 cm and 80 and 100 cm) and analyzed for their soil chemical and physical attributes (including the reflectance spectra (400 to 2500 nm)) in the laboratory. We achieved 67.72% of bare soil for the whole study area, with the remaining 32.28% of the unmapped surface being filled by kriging interpolation. All 328 samples were modeled using surface (Landsat-5 TM spectral reflectance) and subsurface (acquired in the laboratory) data, reaching up to 0.72 R2adj. The correlation between the spectra of both depths was significant and the soil attributes prediction reached an R2adj of validation above 0.6 for clay, hue, value, and chroma at 0–20 and 80–100 cm depths. The satellite soil surface reflectance allowed the estimation of soil subsurface attributes. These results demonstrate that diagnostic soil attributes can be quantified based on spectral pedotransfer (SPEDO) functions to assist digital soil mapping and soil monitoring. Despite our efforts to determine soil subsurface properties using digital soil mapping approach, this task still need considerable refinement. Thus, research must continue to aggregate outcomes from other techniques.

Considering the hypothesis that the predictive capacity of models is tied to soil characteristics, the stratification of a spectral library into groups is a strategy to improve the accuracy of the predictions. Thus, the objective of this study was to i) characterize and identify differences among spectra obtained for subtropical soils samples, ii) evaluate different pre-processing techniques and multivariate methods to propose SOC prediction models from the spectral data and iii) evaluate the performance of SOC prediction models calibrated from the stratification of a local library. A local spectral library of soils (n = 841 samples) was used in the Planalto region of the State of Rio Grande do Sul, Brazil. Soil classes that occur in the area are: Rhodic Ferralsol (FR) and Dystric Gleysol (GL). Land uses are: native forest (NFo), native field (NFi) and crops in no-tillage system (CTS). SOC was determined via wet combustion with sulphochromic solution. Spectral reflectance measurements were performed in the laboratory with a spectroradiometer in the range of 350–2500 nm. Six pre-processing techniques were applied to the spectra (including derivatives, normalization and non-linear transformations) and four multivariate calibration methods, namely, partial least squares regression (PLSR), multiple linear regression (MLR), support vector machines (SVM) and random forest (RF), were used with the objective of identifying the best combination to predict SOC. After determining the best combination, the spectral library was stratified into groups based on soil class, land use, sample layer and spectral characteristics. The models were built with 70% of the samples for calibration and 30% for independent validation. The coefficient of determination (R2v), root mean square error (RMSEv) and ratio of performance to interquartile range (RPIQv) of the independent validation were used to evaluate the performance of the models. The spectral curves presented different absorption characteristics in relation to soil classes and land uses. SGD pre-processing technique had the highest R2v and RMSEv values for all models. Among the multivariate methods, PLSR had the best performance for SOC prediction for the total set of samples (R2v = 0.74, RMSEv = 0.52% and RPIQv = 2.23), followed by models SVM, MLR, and RF. The FR-CTS (n = 445) group showed the best model performance after stratification, with R2v = 0.82, RMSEv = 0.29% and RPIQv = 2.60. For some stratified groups, the use of a smaller number of samples to build the model reduced the performance of the models, suggesting that one must be careful when working with small datasets. This study highlights the potential for the application of VIS-NIR-SWIR spectroscopy as a reliable and economical tool to quantify SOC concentrations for subtropical soils with high levels of iron oxides and clay on a local scale. Predictive models can be improved when the variation in soil characteristics is considered, underscoring the need for a preliminary study examining the grouping of the sample set to validate the use of local spectral libraries for the prediction of soil properties.

Soil organic carbon (C) is an important indicator of agricultural and environmental quality. It improves soil fertility and helps to mitigate greenhouse gas emissions. Soil spectroscopy with either vis–NIR (350–2500 nm) or mid-IR (4000–400 cm−1) spectra have been used successfully to predict organic C concentrations in soil. However, research to improve predictions of soil organic C by simply combining vis–NIR and mid-IR spectra to model them together has been unsuccessful. Here we use the Outer Product Analysis (OPA) to fuse vis–NIR and mid-IR spectra by bringing them into a common spectral domain. Using the fused data, we derived models to predict soil organic C and compared its predictions to those derived with vis–NIR and mid-IR models separately. We analyzed 1259 tropical soil samples from surface and subsurface layers across agricultural areas in Central Brazil. Soil organic C contents were determined by a modified Walkley-Black method, and vis–NIR and mid-IR reflectance spectra were obtained with a FieldSpec Pro and a Nicolet 6700 Fourier Transformed Infrared (FT-IR), respectively. Reflectances were log-transformed into absorbances. The mean content of soil organic C was 9.14 g kg−1 (SD = 5.64 g kg−1). The OPA algorithm was used to emphasize co-evolutions of each spectral domain into the same one by multiplying the absorbances from both sets of spectra to produce a matrix with all possible products between them. Support Vector Machine with linear kernel function was used for the spectroscopic modeling. Predictions of soil organic C using vis–NIR, mid-IR, and fused spectra were statistically compared by the Tukey's test using the coefficient of determination (R2), root mean squared error (RMSE), and ratio of performance to interquartile distance (RPIQ). Absorbances in vis–NIR and mid-IR were emphasized in the common spectral domain presenting stronger correlations with soil organic C than individual ranges. Soil organic C predictions with the OPA fused spectra were significantly better (R2 = 0.81, RMSE = 2.42 g kg−1, and RPIQ = 2.87) than those with vis–NIR (R2 = 0.69, RMSE = 3.38 g kg−1, and RPIQ = 2.08) or mid-IR spectra (R2 = 0.77, RMSE = 2.90 g kg−1, and RPIQ = 2.43). Fusing vis–NIR and mid-IR spectra by OPA improves predictions of soil organic C.

Visible (V), Near Infrared (NIR) and Short Waves Infrared (SWIR) spectroscopy has been indicated as a promising tool in soil studies, especially in the last decade. However, in order to apply this method, it is necessary to develop prediction models with the capacity to capture the intrinsic differences between agricultural areas and incorporate them in the modeling process. High quality estimates are generally obtained when these models are applied to soil samples displaying characteristics similar to the samples used in their construction. However, low quality predictions are noted when applied to samples from new areas presenting different characteristics. One way to solve this problem is by recalibrating the models using selected samples from the area of interest. Based on this premise, the aim of this study was to use the spiking technique and spiking associated with hybridization to expand prediction models and estimate organic matter content in a target area undergoing different uses and management. A total of 425 soil samples were used for the generation of the state model, as well as 200 samples from a target area to select the subsets (10 samples) used for model recalibration. The spectral readings of the samples were obtained in the laboratory using the ASD FieldSpec 3 Jr. Sensor from 350 to 2500 nm. The spectral curves of the samples were then associated to the soil attributes by means of a partial least squares regression (PLSR). The state model obtained better results when recalibrated with samples selected through a cluster analysis. The use of hybrid spectral curves did not generate significant improvements, presenting estimates, in most cases, lower than the state model applied without recalibration. The use of the isolated spiking technique was more effective in comparison with the spiked and hybridized state models, reaching r2, square root of mean prediction error (RMSEP) and ratio of performance to deviation (RPD) values of 0.43, 4.4 g dm−3, and 1.36, respectively.

Soil contamination by potentially toxic elements (PTEs) is intensifying under increasing industrialization. Thus, the ability to efficiently delineate contaminated sites is crucial. Visible–near infrared (vis–NIR: 350–2500 nm) and X-ray fluorescence (XRF: 0.02–41.08 keV) spectroscopic techniques have attracted tremendous attention for the assessment of PTEs. Recently, the application of fused vis–NIR and XRF spectroscopy, which is based on the complementary effect of data fusion, is also increasing. Moreover, different data manipulation methods, including feature selection approaches, affect the prediction performance. This study investigated the feasibility of using single and fused vis–NIR and XRF spectra while exploring feature selection algorithms for the assessment of key soil PTEs. The soil samples were collected from one of the most heavily polluted areas of the Czech Republic and scanned using laboratory vis–NIR and XRF spectrometers. Univariate filter (UF) and genetic algorithm (GA) were used to select the bands of greater importance for the PTE prediction. Support vector machine (SVM) was then used to train the models using the full-range and feature-selected spectra of single sensors and their fusion. It was found that XRF spectra alone (primarily GA-selected) performed better than single vis–NIR and fused spectral data for predictions of PTEs. Moreover, the prediction models that were derived from the fused data set (particularly the GA-selected) enhanced the models’ accuracies as compared with the single vis–NIR spectra. In general, the results suggest that the GA-selected spectra obtained from the single XRF spectrometer (for As and Pb) and from the fusion of vis–NIR and XRF (for Pb) are promising for accurate quantitative estimation detection of the mentioned PTEs.

Despite the natural spatial variability, cropland soils are subject to many interventions that can lead to alterations of soil functioning. As the cropland expansion took place in Brazil the last decades, leading to significant land-use change and environmental impacts, detailed information about soils is fundamental for sustainable development. Thus, considering the lack of spatially explicit information about cropland soils in Brazil, we aimed at performing high-resolution mapping of key topsoil attributes using spectral and terrain features extracted from Earth observation data (EOD). With the resulting information, we also aimed at performing a general examination of the main agricultural regions and estimate the total organic carbon stocks on croplands soils. For this, we obtained environmental predictors from the historical collection of Landsat data and the digital elevation model from Shuttle Radar Topographic Mission at the cloud-based platform of Google Earth Engine. The environmental predictors (30 m spatial resolution) with georeferenced soil samples (n = 5097) were used for predicting the topsoil content (0–20 cm) of clay, sand, silt, cation exchange capacity, pH, soil organic carbon (SOC) and SOC stock. Prediction models of clay, sand, SOC content, and SOC stocks had the best performance metrics, achieving a R2 ranging from 0.44 to 0.74 and ratio of performance to the interquartile range higher than 1.5. The predicted maps revealed the variability of topsoil among the cropped areas, indicating that the agricultural expansion took place on sandy soils. The SOC stock map provided consistent estimates compared to previous datasets but revealed additional information at the local and regional scales. Thus, this study supports the proposition that EOD is a valuable source for extracting environmental features for mapping and monitoring cropland soils at finer resolutions, assisting the evaluation of soil spatial distribution and the historical agriculture expansion over large geographical areas.

Geophysical methods, such as gamma-ray spectrometry, have great potential to enhance knowledge of the pedosphere (pedogenesis, pedogeochemistry and pedogeomorphology), helping to predict tropical soil attributes. We applied proximal gamma-ray spectrometry to evaluate tropical landscape dynamics, pedogenesis and spatial distribution of radionuclides and selected soil attributes. This study was carried out in southeast Brazil, where 79 soil samples (0–20 cm) along transects were collected to perform physical-chemical analysis coupled with collection of surface gamma-ray spectrometric data, allowing the detection of the radionuclides uranium (U238), thorium (Th232), and potassium (K40). Additionally, we analyzed soils in four toposequences, with varying lithology, relief, and hydrology. The radionuclide concentrations in soils showed a direct relationship with the parent material composition, either rocks or sediments. Weathering degree and the geochemical behavior of each radionuclide determines its permanence or removal in soils. Denudation processes along the toposequences also influence the distribution of radionuclides. On one hand, the radionuclide contents of mature, weathered soils are closely associated with advanced pedogenesis, with a higher clay contents and argic horizon formation, whereas in younger, less weathered soils the parent material exerts a greater influence than pedogenesis. Uranium decreased with altitude, and showed greater mobility compared with thorium. Thorium presented a higher correlation with clay content, and the opposite with sand content. Potassium detection increased with soil cation exchange capacity (CEC) and clay content. Gamma-spectrometry detected significant variations in some segments along the toposequences, undetectable by conventional soil survey techniques. This may indicate changes from one soil class to another or the continuity of a particular soil class, demonstrating the potential of this tool in digital soil mapping, pedogeochemical, pedogeomorphological and pedogenesis processes studies.

Simple Summary The whitefly Bemisia tabaci has become a primary pest in soybean fields in Brazil over the last decades, causing losses in the yield. Its reduced size and fast population growth make monitoring a challenge for growers. The use of hyperspectral proximal sensing (PS) is a tool that allows the identification of arthropod infested areas without contact with the plants. This optimizes the time spent on crop monitoring, which is important for large cultivation areas, such as soybean fields in Brazilian Cerrado. In this study, we investigated differences in the responses obtained from leaves of soybean plants, non-infested and infested with Bemisia tabaci in different levels, with the aim of its differentiation by using hyperspectral PS, which is based on the information from many contiguous wavelengths. Leaves were collected from soybean plants to obtain hyperspectral signatures in the laboratory. Hyperspectral curves of infested and non-infested leaves were differentiated with good accuracy by the responses of the bands related to photosynthesis and water content. These results can be helpful in improving the monitoring of Bemisia tabaci in the field, which is important in the decision-making of integrated pest management programs for this key pest. Abstract Although monitoring insect pest populations in the fields is essential in crop management, it is still a laborious and sometimes ineffective process. Imprecise decision-making in an integrated pest management program may lead to ineffective control in infested areas or the excessive use of insecticides. In addition, high infestation levels may diminish the photosynthetic activity of soybean, reducing their development and yield. Therefore, we proposed that levels of infested soybean areas could be identified and classified in a field using hyperspectral proximal sensing. Thus, the goals of this study were to investigate and discriminate the reflectance characteristics of soybean non-infested and infested with Bemisia tabaci using hyperspectral sensing data. Therefore, cages were placed over soybean plants in a commercial field and artificial whitefly infestations were created. Later, samples of infested and non-infested soybean leaves were collected and transported to the laboratory to obtain the hyperspectral curves. The results allowed us to discriminate the different levels of infestation and to separate healthy from whitefly infested soybean leaves based on their reflectance. In conclusion, these results show that hyperspectral sensing can potentially be used to monitor whitefly populations in soybean fields.

Geotechnologies allow natural resources to be surveyed more quickly and cheaply than traditional methods. This paper aimed to produce a digital soil map (DSM) based on Landsat time series data. The study area, located in the eastern part of the Brazilian Federal District (Rio Preto hydrographic basin), comprises a representative basin of the Central Brazil plateau in terms of pedodiversity. A spectral library was produced based on the soil spectroscopy (from the visible to shortwave infrared spectral range) of 42 soil samples from 0–15 cm depth using the Fieldspec Pro equipment in a laboratory. Pearson’s correlation and principal component analysis of the soil attributes revealed that the dataset could be grouped based on the texture content. Hierarchical clustering analysis allowed for the extraction of 13 reference spectra. We interpreted the spectra morphologically and resampled them to the Landsat 5 Thematic Mapper satellite bands. Afterward, we elaborated a synthetic soil/rock image (SySI) and a soil frequency image (number of times the bare soil was captured) from the Landsat time series (1984–2020) in the Google Earth Engine platform. Multiple Endmember Spectral Mixture Analysis (MESMA) was used to model the SySI, using the endmembers as the input and generating a DSM, which was validated by the Kappa index and the confusion matrix. MESMA successfully modeled 9 of the 13 endmembers: Dystric Rhodic Ferralsol (clayic); Dystric Rhodic Ferralsol (very clayic); Dystric Haplic Ferralsol (loam-clayic); Dystric Haplic Ferralsol (clayic); Dystric Petric Plinthosol (clayic); Dystric Petric Plinthosol (very clayic); Dystric Regosol (clayic); Dystric Regosol (very clayic); and Dystric, Haplic Cambisol (clayic). The root mean squared error (RMSE) varied from 0 to 1.3%. The accuracy of DSM achieved a Kappa index of 0.74, describing the methodology’s effectiveness to differentiate the studied soils.

Made available in DSpace on 2020-12-12T02:17:47Z (GMT). No. of bitstreams: 0 Previous issue date: 2020-12-01 Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq) The use of diffuse reflectance spectroscopy (DRS) has gained prominence in the quantification of soil attributes due to its ease and practicality of obtaining data. This study aimed to evaluate the potential of different methodologies applied to spectral curves given by DRS to estimate kaolinite (Kt) and gibbsite (Gb), and their spatial variability characterization for the Western Plateau of São Paulo. The Western Plateau of São Paulo has 13 million hectares, 2 million of them covered by basalt and 11 million by sandstones. A total of 600 samples were collected at a depth of 0.0–0.20 m. Calibration curves were constructed with pure minerals for x-ray diffraction (XRD) and DRS techniques. The Kt/(Kt + Gb) ratio and the percentages of Kt and Gb were determined by XRD and using the following three methodologies applied to spectral curves: continuum removal technique (CR), direct ratio of the valley (DRV), and multivariate analysis by partial least squares regression (PLSR). The CR procedure had means similar to those observed by XRD, i.e., 0.90 and 0.92, respectively, while DRV overestimated the ratio, with a mean of 1.32. DRS allowed the estimation of the Kt/(Kt + Gb) ratio for the different geological and landscape compartments of the Western Plateau of São Paulo for the CR and DRV procedures. CR procedure allowed constructing models to be more efficient compared to those obtained by DRV and PLSR. The use of geostatistics to interpolate the data of the ratio Kt/(Kt + Gb) by DRS provided important information to define specific management zones accurately and more economically. Department of Soils and Fertilizers Research Group CSME - Soil Characterization for Specific Management Faculty of Agrarian and Veterinary Sciences São Paulo State University (FCAV/UNESP) Department of Animal Science Faculty of Agrarian and Veterinary Sciences São Paulo State University (FCAV/UNESP) Departament of Soil Science Luiz de Queiroz College of Agriculture (ESALQ) University of São Paulo (USP) Departamento os Soil State University of Goiás (UEG) Department of Soils and Fertilizers Research Group CSME - Soil Characterization for Specific Management Faculty of Agrarian and Veterinary Sciences São Paulo State University (FCAV/UNESP) Department of Animal Science Faculty of Agrarian and Veterinary Sciences São Paulo State University (FCAV/UNESP)

International audience; The soil mineralogical constitution directly influences its chemical, physical and hydraulic characteristics. Although very important, it is still rarely used for decision-making in agriculture, mainly due to the complexity and cost of standard analyzes. In this sense, the middle infrared spectroscopy (mid-IR, 4000 to 400 cm(-1)) has great potential to obtain soil mineralogical information quickly and accurately. Nevertheless, some soil constituents can severely influence the spectra and produce misinterpretations. In this research, we aim to detect changes in the mid-IR spectra caused by water, iron forms and organic matter (OM), and to relate soil attributes to laboratory spectra and remote sensing simulated spectral bands. The research area is located in Sao Paulo State, Brazil, where seventeen soil samples were collected. The reflectance intensities, shapes and absorption features of the mid-IR spectra before and after the removal of OM and iron forms and the addition of water were described. Soil attributes, such as kaolinite, gibbsite, 2:1 minerals among others were correlated with the mid-IR spectra and simulated ASTER spectral bands by Pearson's analysis, to verify its potential on mineralogical evaluation. The description of mid-IR revealed that the removal of the OM from the soil samples decreased the reflectance intensities between 4000 and 2000 cm(-1). Iron forms mainly influence the 3250 - 1200 cm(-1) spectral range and mask the spectral features of other minerals as well. The addition of water masked several absorption features and decreased the reflectance intensities from 3700 to 2700 cm(-1). High correlation coefficients were obtained between soil attributes and ASTER simulated spectral bands, which allowed the selection of potential spectral regions for future satellite sensors: 2760 - 2500 cm(-1) (3600 - 4000 nm), 2150 - 1875 cm(-1) (4600 - 5300 nm), and 840 - 740 cm(-1) (11900 - 3500 nm).

Terrain analysis is an important tool for modeling environmental systems. Aiming to use the cloud-based computing capabilities of Google Earth Engine (GEE), we customized an algorithm for calculating terrain attributes, such as slope, aspect, and curvatures, for different resolution and geographical extents. The calculation method is based on geometry and elevation values estimated within a 3 × 3 spheroidal window, and it does not rely on projected elevation data. Thus, partial derivatives of terrain are calculated considering the great circle distances of reference nodes of the topographic surface. The algorithm was developed using the JavaScript programming interface of the online code editor of GEE and can be loaded as a custom package. The algorithm also provides an additional feature for making the visualization of terrain maps with a dynamic legend scale, which is useful for mapping different extents: from local to global. We compared the consistency of the proposed method with an available but limited terrain analysis tool of GEE, which resulted in a correlation of 0.89 and 0.96 for aspect and slope over a near-global scale, respectively. In addition to this, we compared the slope, aspect, horizontal, and vertical curvature of a reference site (Mount Ararat) to their equivalent attributes estimated on the System for Automated Geospatial Analysis (SAGA), which achieved a correlation between 0.96 and 0.98. The visual correspondence of TAGEE and SAGA confirms its potential for terrain analysis. The proposed algorithm can be useful for making terrain analysis scalable and adapted to customized needs, benefiting from the high-performance interface of GEE.

The Earth’s surface dynamics provide essential information for guiding environmental and agricultural policies. Uncovered and unprotected surfaces experience several undesirable effects, which can affect soil ecosystem functions. We developed a technique to identify global bare surface areas and their dynamics based on multitemporal remote sensing images to aid the spatiotemporal evaluation of anthropic and natural phenomena. The bare Earth’s surface and its changes were recognized by Landsat image processing over a time range of 30 years using the Google Earth Engine platform. Two additional products were obtained with a similar technique: a) Earth’s bare surface frequency, which represents where and how many times a single pixel was detected as bare surface, based on Landsat series, and b) Earth’s bare soil tendency, which represents the tendency of bare surface to increase or decrease. This technique enabled the retrieval of bare surfaces on 32% of Earth’s total land area and on 95% of land when considering only agricultural areas. From a multitemporal perspective, the technique found a 2.8% increase in bare surfaces during the period on a global scale. However, the rate of soil exposure decreased by ~4.8% in the same period. The increase in bare surfaces shows that agricultural areas are increasing worldwide. The decreasing rate of soil exposure indicates that, unlike popular opinion, more soils have been covered due to the adoption of conservation agriculture practices, which may reduce soil degradation.

Studies on soil degradation are essential for environmental preservation. Since almost 30% of the global soils are degraded, it is important to study and map them for improving their management and use. We aimed to obtain a Soil Degradation Index (SDI) based on multi-temporal satellite images associated with climate variables, land use, terrain and soil attributes. The study was conducted in a 2598 km2 area in Sao Paulo State, Brazil, where 1562 soil samples (0–20 cm) were collected and analyzed by conventional methods. Spatial predictions of soil attributes such as clay, cation exchange capacity (CEC) and soil organic matter (OM) were performed using machine learning algorithms. A collection of 35-year Landsat images was used to obtain a multi-temporal bare soil image, whose spectral bands were used as soil attributes predictors. The maps of clay, CEC, climate variables, terrain attributes and land use were overlaid and the K-means clustering algorithm was applied to obtain five groups, which represented levels of soil degradation (classes from 1 to 5 representing very low to very high soil degradation). The SDI was validated using the predicted map of OM. The highest degradation level obtained in 15% of the area had the lowest OM content. Levels 1 and 4 of SDI were the most representative covering 24% and 23% of the area, respectively. Therefore, satellite images combined with environmental information significantly contributed to the SDI development, which supports decision-making on land use planning and management.

The demand for quality and low-cost soil information is growing due to the demands of land use planning and precision agriculture. Soil texture is one of the key soil properties, as it determines other vital soil characteristics such as soil structure, water and thermal regime, diversity of living organisms, plant growth, as well as the soil quality in general. It is usually not constant over an area, varying in space and with soil depth. Routine soil texture analysis is, however, time consuming and expensive. Because of this, the success of proximal soil sensing techniques in estimate soil properties using the VIS-NIR-SWIR and MIR regions is increasing. Advantages of soil spectroscopy include time efficiency, economic convenience, non-destructive application and freeing of chemical agents involved. Therefore, the objectives of this study were: (a) to explore the potential of clay, sand and silt prediction using reflectance spectroscopy; (b) assess the performance of predictive models in different spectral regions, i.e. VIS-NIR-SWIR and MIR; (c) assess the effect of different soil depths on predictive models; and finally (d) explain the differences in prediction accuracy in the means of the input data structure. Soil samples were collected at three depths (0–20, 20–40 and 40–60 cm) at 70 sampling sites over a study area located in the State of Rio Grande do Sul (Brazil). The content of soil texture was determined by Pipette method, and soil spectra were obtained with FieldSpec Pro (VIS-NIR-SWIR) and by Alpha Sample Compartment RT (MIR). Cubist regression algorithm was applied to train predictive models in three separate modeling modes differing in spectral region: (i) VIS-NIR-SWIR, (ii) MIR and (iii) VIS-NIR-SWIR plus MIR. The results showed that the combination of all three soil depths led to a more accurate prediction of soil texture compared to subdivided soil depths. This was explained by variability of the data, which was larger for the total dataset than for the depth-specific data. Consequently, we suggested that no precise comparison between different studies can be made without a proper description of the input data. For all-depths models, the MIR calibration obtained the best accuracy, which was explained due to more information comprised in the MIR region against the VIS-NIR-SWIR. The bands that were more important in predicting soil texture in MIR are related to mineralogy, specifically to kaolinite. This study demonstrated that the MIR spectroscopy technique is capable to complement the standard soil particle size analysis, specially where a large number of soil samples need to be treated in a short period of time.

The number of samples used in the calibration data set affects the quality of the generated predictive models using visible, near and shortwave infrared (VIS–NIR–SWIR) spectroscopy for soil attributes. Recently, the convolutional neural network (CNN) has been regarded as a highly accurate model for predicting soil properties on a large database. However, it has not yet been ascertained how large the sample size should be for CNN model to be effective. This paper investigates the effect of the training sample size on the accuracy of deep learning and machine learning models. It aims at providing an estimate of how many calibration samples are needed to improve the model performance of soil properties predictions with CNN as compared to conventional machine learning models. In addition, this paper also looks at a way to interpret the CNN models, which are commonly labelled as a black box. It is hypothesised that the performance of machine learning models will increase with an increasing number of training samples, but it will plateau when it reaches a certain number, while the performance of CNN will keep improving. The performances of two machine learning models (partial least squares regression – PLSR; Cubist) are compared against the CNN model. A VIS–NIR–SWIR spectra library from Brazil, containing 4251 unique sites with averages of two to three samples per depth (a total of 12 044 samples), was divided into calibration (3188 sites) and validation (1063 sites) sets. A subset of the calibration data set was then created to represent a smaller calibration data set ranging from 125, 300, 500, 1000, 1500, 2000, 2500 and 2700 unique sites, which is equivalent to a sample size of approximately 350, 840, 1400, 2800, 4200, 5600, 7000 and 7650. All three models (PLSR, Cubist and CNN) were generated for each sample size of the unique sites for the prediction of five different soil properties, i.e. cation exchange capacity, organic carbon, sand, silt and clay content. These calibration subset sampling processes and modelling were repeated 10 times to provide a better representation of the model performances. Learning curves showed that the accuracy increased with an increasing number of training samples. At a lower number of samples (< 1000), PLSR and Cubist performed better than CNN. The performance of CNN outweighed the PLSR and Cubist model at a sample size of 1500 and 1800, respectively. It can be recommended that deep learning is most efficient for spectra modelling for sample sizes above 2000. The accuracy of the PLSR and Cubist model seems to reach a plateau above sample sizes of 4200 and 5000, respectively, while the accuracy of CNN has not plateaued. A sensitivity analysis of the CNN model demonstrated its ability to determine important wavelengths region that affected the predictions of various soil attributes.

Reflectance of light across the visible, near-infrared and shortwave infrared (VIS-NIR-SWIR, 0.4–2.5 µm) spectral region is very useful for investigating mineralogical, physical and chemical properties of soils, which can reduce the need for traditional wet chemistry analyses. As many collections of multispectral satellite data are available for environmental studies, a large extent with medium resolution mapping could be benefited from the spectral measurements made from remote sensors. In this paper, we explored the use of bare soil composites generated from the large historical collections of Landsat images for mapping cropland topsoil attributes across the European extent. For this task, we used the Geospatial Soil Sensing System (GEOS3) for generating two bare soil composites of 30 m resolution (named synthetic soil images, SYSI), which were employed to represent the median topsoil reflectance of bare fields. The first (framed SYSI) was made with multitemporal images (2006–2012) framed to the survey time of the Land-Use/Land-Cover Area Frame Survey (LUCAS) soil dataset (2009), seeking to be more compatible to the soil condition upon the sampling campaign. The second (full SYSI) was generated from the full collection of Landsat images (1982–2018), which although displaced to the field survey, yields a higher proportion of bare areas for soil mapping. For evaluating the two SYSIs, we used the laboratory spectral data as a reference of topsoil reflectance to calculate the Spearman correlation coefficient. Furthermore, both SYSIs employed machine learning for calibrating prediction models of clay, sand, soil organic carbon (SOC), calcium carbonates (CaCO3), cation exchange capacity (CEC), and pH determined in water, using the gradient boosting regression algorithm. The original LUCAS laboratory spectra and a version of the data resampled to the Landsat multispectral bands were also used as reference of prediction performance using VIS-NIR-SWIR multispectral data. Our results suggest that generating a bare soil composite displaced to the survey time of soil observations did not improve the quality of topsoil reflectance, and consequently, the prediction performance of soil attributes. Despite the lower spectral resolution and the variability of soils in Europe, a SYSI calculated from the full collection of Landsat images can be employed for topsoil prediction of clay and CaCO3 contents with a moderate performance (testing R2, root mean square error (RMSE) and ratio of performance to interquartile range (RPIQ) of 0.44, 9.59, 1.77, and 0.36, 13.99, 1.54, respectively). Thus, this study shows that although there exist some constraints due to the spatial and temporal variation of soil exposures and among the Landsat sensors, it is possible to use bare soil composites for mapping key soil attributes of croplands across the European extent.

Soil texture and organic carbon (OC) content influence the spectral response. These attributes are relevant for the preservation and proper management of land use in the pursuit of a sustainable agriculture. Laboratory and satellite sensors have been applied as a powerful tool for studying so is, but their analysis using these sensors has mainly focused on the visible (Vis), near infrared (NIR) and shortwave infrared (SWIR) regions of the electromagnetic spectrum, with few studies in the Medium Infrared (MIR). The aim of this study was to identify the spectral pattern of soils with different granulometry (sand and clay) and OC content using laboratory and satellite sensors in the MIR region, specifically in the Thermal Infrared (TIR) range (ASTER, Landsat satellites). The study performed qualitative and quantitative analyses of clay, OC and sand fractions (fine and coarse). The study area is located in the region of Piracicaba, Sao Paulo, Brazil, where collected 150 soil samples (0–20 cm depth). Soil texture was determined by the pipette method and OC via dry combustion. Reflectance and emissivity (Ɛ) spectral data were obtained with the Fourier Transform Infrared (FT-IR) Alpha sensor (Bruker Optics Corporation). An image “ASTER_05” from July 15, 2017 was acquired with values of Ɛ. Samples were separated by textural classes and the spectral behavior in the TIR region was described. The data obtained by the laboratory sensor were resampled to the satellite sensor bands. The behavior between spectra of both sensors was similar and had significant correlation with the studied attributes, mainly sand. For the partial least squares regression (PLSR) models, six strategies were used (MIR, MIR_ASTER, ASTER, TIR, TIR Correlation Index (TIR_CID), and MIR Correlation Index (MIR_CID)), which consisted in the use of all sensors bands, or by the selection of bands that presented the most significant correlations with each one of the attributes. Models presented a good performance in the prediction of all attributes using the whole MIR. In the TIR region, the models for total sand content and for fine and coarse fractions were good. Models created with ASTER sensor data were not as promising as those with laboratory ones. The use of specific bands was useful in estimating some attributes in the MIR and TIR, improving the predictive performance and validation of models. Therefore, the discrimination of soil attributes with satellite sensors can be improved with the identification of specific bands, as observed in the results with laboratory sensors.

The soil carbon pool which is the sum of soil organic carbon (SOC) and soil inorganic carbon (SIC) is the second largest active store of carbon after the oceans and it is an important component of the global carbon cycle. Hence, accurate estimation of SOC and SIC as important carbon reservoirs in terrestrial ecosystems using fast, inexpensive and non-destructive methods is crucial for planning different climate change policies. The aim of the current research was to examine the effectiveness of Vis-NIR (visible and near-infrared spectroscopy: 350–2500 nm) and MIR (mid-infrared spectroscopy: 4000–400 cm−1) to characterize and estimate soil organic matter (SOM) and carbonates as main components of soil carbon stocks in Juneqan, Charmahal va Bakhtiari, Iran. To do so, a total of 548 soil samples from this area were collected (October 2015) and analyzed in laboratory (August 2017). In order to develop models capable of predicting SOM and carbonates content, seven spectral preprocessing methods comprising Absorbance (Abs), De-trending (Det), Continuum removal (CR), Savitzky-Golay derivatives (SGD), standard normal variate transformation (SNV), multiplicative scatter correction (MSC) and Normalization by range (NBR) were conducted along with five multivariate methods including Random Forest (RF), Partial Least-Squares Regression (PLSR), Artificial Neural Network (ANN), Support Vector Machine (SVM) and Gaussian Process Regression (GPR). The content of carbonates caused spectral reflectance intensity to augment on several ranges of spectrum and strong absorption feature at 2338 nm in the Vis-NIR and 714, 850, 870, 1796, 2150 and 2510 cm−1 in the MIR spectra range. SOM absorbed energy in several ranges, but also showed specific peaks in MIR. Both facts are associated with the structure of carbonates and SOM and its interaction with energy. The best combination of preprocessing and calibration models for carbonates quantification in Vis-NIR spectra was Det/PLSR (R2= 0.74, RPD= 2.19, RMSE= 6.45). For SOM, it was Det/PLSR (R2= 0.82, RPD= 2.41, RMSE= 0.75). The Det/RF (R2= 0.87, RPD= 2.44, RMSE= 0.66) for the quantification of SOM and MSC/RF (R2= 0.84, RPD= 2.84, RMSE= 5.50) for carbonates in MIR spectra range showed the greatest results. The stronger occurrence of spectral bands in MIR as well as the specificity of the absorption features indicated that this range produced better predictions. The obtained results highlighted the significant role of soil spectroscopy technique in predicting SOC and soil carbonates as key components of soil carbon stocks in the study area. Therefore, this technique can be used as a more cost-effective, time saving and nondestructive alternative to traditional methods of soil analysis.

The objective of the present study was to evaluate the efficiency of soil magnetic parameters for assessment of soil drainage classes in Juneqan district, Charmahal and Bakhtiari province, western Iran. Four soil drainage classes including well drained (WD), moderately well drained (MWD), intermittent poor drained (IPD) and poorly drainage (PD) were selected. A total of 89 soil pedons were described and soil samples were collected within the moisture control section. Magnetic susceptibility (MS) at high (χhf) and low (χlf) frequencies and frequency-dependent MS (χfd) were evaluated in the laboratory. Poorly crystalline iron (Feo) and pedogenic iron (Fed) values of all soil samples were also measured. The results revealed that among the four drainage classes, PD class showed the lowest χlf and χhf values, greatest of Feo and Feo/Fed, lowest contents of Fed, as well as the highest average increase of χlf on heating (at 500 °C). However, all mentioned features showed an inverse trend in the WD class as compared to PD. The results of discriminant analysis demonstrated that magnetic measures could prosperously discriminate between the selected drainage classes in this study area (average accuracy = 83.1%). Therefore, it can be concluded that MS technique could be used as a powerful, nondestructive and fast technique for separation of soil drainage classes in the present case.

Reflectance spectroscopy is an alternative to describe soil properties, with potential to reduce costs and environmental impacts of conventional practices related to this activity. Acquisition of soil spectra on-the-go has several advantages over 'in-situ' static approaches, like deriving information with high spatial density. However, issues concerning on-the-go spectral measurements exist, mainly due to sensor movement and heterogeneous soil condition in the field. Procedures to mitigate these drawbacks, like external parameter orthogonalization (EPO) and direct standardization (DS), have mainly been applied so far to static spectral readings. In this study, EPO, DS and orthogonal signal correction (OSC) are tested in the context of on-the-go spectra acquisition for prediction of soil properties related to liming (i.e., pH in CaCl 2 , pH in SMP buffer, concentration of organic matter, calcium and magnesium, potential acidity, sum of basis, cation exchange capacity and its saturation by basis, lime requirement and moisture content). A detailed dataset (300 soil samples coupled with laboratory and field spectral measurements) was acquired in two sites in Brazil with contrasting soil attributes (site 1 with ‘clayey texture’ – Ferralsol; site 2 with ‘sandy texture’ – Alisol), and variability of soil properties was increased in these sites through application of different limestone rates in experimental plots. Spectral correction procedures slightly improved the accuracy of lime requirement predictions, with reduction of root mean squared error (RMSE) from 1.43 to 1.17 t ha −1 , for study site 1 after applying OSC, and from 0.59 to 0.44 t ha −1 , for study site 2 after DS was implemented. However, models based on laboratory data still performed considerably better with RMSE of 0.99 and 0.43 t ha −1 for site 1 and 2, respectively. ‘Global’ (i.e., one general correction model for a given field) or ‘specific’ models (i.e., several correction models, derived according to clusters obtained through fuzzy k-means applied to OSC components) performed considerably worse in comparison with other studies. Probably occurrence of external factors affecting the spectral information was not constant in the mapped fields. Also, different external factors may have affected the spectra at the same time and efficiency of the correction procedures decreased. Considering the high sensitivity of predictions based on field data to the approach used to interpolate the spectra and the poor performance of the correction methods applied in this context, more investigation is needed to improve predictions based on spectral data acquired on-the-go. Homogeneous spatial distribution of factors not related to the properties of interest, or at least in a degree allowing correction by the methods tested here, may not happen when current measurement systems are used. Despite that, spatial patterns described by wet-chemical analysis could be represented, at certain extent, through predicted values of lime requirement (LR), derived from field spectra. For instance, predictions after spectral correction resulted in autocorrelation patterns and map of LR comparable with those observed using conventional methods, for the site 1. These results, coupled with a semiquantitative potential of predictions based on field data after spectral correction, indicate that on-the-go measurements have potential for soil properties characterization, although full quantitative potential will require further advances in sensing solutions and chemometric methods applied in this context.

Soil Visible–Near-Infrared (Vis-NIR) spectroscopy has become an applicable and interesting technique to evaluate a number of soil properties because it is a fast, cost-effective, and non-invasive measurement technique. The main objective of the study to predict soil erodibility (K-factor), soil organic matter (SOM), and calcium carbonate equivalent (CaCO3) in calcareous soils of semi-arid regions located in south of Iran using spectral reflectance information in the Vis-NIR range. The K-factor was measured in 40 erosion plots under natural rainfall and the spectral reflectance of soil samples were analyzed in the laboratory. Various soil properties including the CaCO3, soil particle size distribution, SOM, permeability, and wet-aggregate stability were measured. Partial least-squares regression (PLSR) and stepwise multiple linear regression (SMLR) were used to obtain effective bands and develop Spectrotransfer Function (STF) using spectral reflectance information and Pedotransfer Function (PTF) to predict the K-factor, respectively. The derived STF was compared with developed PTF using measurable soil properties by Ostovari et al. (2016) and the Universal Soil Loss Equation (USLE) predictions of the K-factor. The results revealed that the USLE over-predicts (0.030 t h MJ− 1 mm− 1) the K-factor when compared to the ground-truth measurements (0.015 t h MJ− 1) in the semi-arid region of Iran. Results showed that developed PTF had the highest performance (R2 = 0.74, RMSE = 0.004 and ME = − 0.003 t h MJ− 1 mm− 1) to predict K-factor. The results also showed that the PLSR method predicted SOM with R2 values of 0.67 and 0.65 and CaCO3 with R2 values of 0.51 and 0.71 for calibration and validation datasets, respectively. We found good predictions for K-factor with R2 = 0.56 and ratio of predicted deviation (RPD) = 1.5 using the PLSR model. The derived STF (R2 = 0.64, RMSE = 0.002 and ME = 0.001 thMJ− 1 mm− 1) performed better than the USLE (R2 = 0.06, RMSE = 0.0171 and ME = 0.0151 thMJ− 1 mm− 1) for estimating the K-factor.

Soil loss is a major cause of land degradation worldwide, especially in large areas of arid and semi-arid regions. With advent of new software and technologies such as remote sensing (RS) and GIS, there is a necessity to integrate them to achieve important information in a faster manner. The aims of present study were to evaluate soil erodibility (K-factor) using standard plots under natural rainfall and prediction of soil loss by integrating RUSLE, GIS and RS in Fars Iran. The RUSLE factors were evaluated as following: the R-factor was calculated using modified Fournier index; K-factor was measured in the field using erosion plots and estimated by the USLE equation; the C-factor map was created using the NDVI; the LS-factor map was generated from digital elevation model with 10 m resolution, and the P-factor map was assumed as 1. Spatial distribution of annual soil loss in the Simakan watershed was obtained by multiplying these factors in GIS. The average of the measured K was 0.014 th MJ− 1 mm− 1 and 2.08 times less than the average of the estimated K (0.030 th MJ− 1 mm− 1). The performance of RUSLE was highly influenced by the K, because the annual soil loss predicted using estimated K (11.0 th− 1 ya− 1) was about twice as much as the measured K (5.7 th− 1 ya− 1). The spatial distribution of soil loss classes predicted was: 73.64% very low, 14.79% low, 10.19% moderate and 1.25% severe. Areas of severe soil loss are situated in the northern portion of the study area, which needs suitable conservation practices.

This study combined proximal sensors and soil analyses for the assessment of a 4.5-m deep Oxisol representative of southeastern Brazil. The soil was derived from gneiss. Soil samples were collected in a 20 × 20 cm grid design across the profile wall, analyzed for texture and chemical properties, and scanned by visible near infrared diffuse reflectance spectroscopy (Vis-NIR DRS), portable X-ray fluorescence spectrometry (pXRF), and X-ray diffraction (XRD). Total elemental contents by pXRF analyses were mapped to provide 2D elemental content maps. The elemental contents revealed alternating patterns in the C horizon attributed to the mafic and felsic bands of the gneiss. The extremely low exchangeable/available nutrient contents in the C horizon are remarkable. Profile maps showed Si accumulation downwards and relative Fe and Al accumulation in the upper horizons indicating desilication and intense weathering. Vis-NIR DRS spectra differentiated horizons and detected mineralogy traits. Vis-NIR DRS data correlated well with XRD data, strengthening its potential for assessing soil mineralogy. Proximal sensors can detect variations of soil properties in apparently homogeneous soil profiles, and improve studies of soil genesis.

Most research applying spectral sensors has focused on soil attributes quantification, with strong statistics and hundreds of data. Nevertheless, the explanation of the fundamental relationship between several spectral ranges and specific soil processes remains unclear. Soil sensing can be performed in many spectral ranges, however, the focus is usually on a specific one, which limits the knowledge of the phenomenon. Thus, this work investigated the synergistic performance of several spectral ranges (Gamma-ray; X-ray fluorescence, XRF; visible, Vis; near infrared, NIR; shortwave infrared, SWIR; and mid-infrared, MIR) on the weathering process of soils developed from magmatic material. Two soil profiles from Southeast Brazil (Sao Paulo state) were analyzed, assessing their chemical, physical and mineralogical data. The same profiles were analyzed by the indicated spectral ranges, which were related to mineralogy, weathering indices and mass balance (MB). The elements provided by Gamma-ray were related to the soil mineralogy and weathering degree of horizons. The XRF data enabled the calculation of MB and weathering indices, which were similar to those calculated by the traditional analysis. The spectra of the Vis-NIR-SWIR-MIR region presented alterations in their behavior related to the weathering degree of horizons. This synergic approach separated soil horizons by weathering degree more efficiently than a single spectral region. Single spectral measurements identified several elements and indices, which explained the weathering process. These results support further understanding of large datasets and their validation in statistical modeling.

Proximal and remote sensors are emerging as powerful sources of soil spectral information at an array of temporal and spatial resolutions. This study investigated clay content prediction at three spectral acquisition levels: laboratory, airborne, and spaceborne. Two approaches were tested, the use of prediction models developed with local and regional spectral libraries (52 samples for local scale and 950, 200 e 224 samples for regional scale), termed internal and external models respectively. Local soil samples (52), were collected in a smallholder area, 83 ha, located in southeastern Brazil. Spectral data in the visible (Vis), near-infrared (NIR), and shortwave infrared (SWIR) regions were acquired in the laboratory using FieldSpec 3 sensor, and the clay content was determined by sedimentation technique. Afterward, bare soil images from AISA-FENIX, Planetscope, Sentinel-2 MultiSpectral Instrument (MSI) and Landsat-8 Operational Land Imager (OLI) were obtained. The clay content determined in the laboratory was related to the soil spectra acquired by each of the sensors and was predicted using the Cubist regression tree algorithm. The results obtained from local spectral libraries showed good predictions using FieldSpec 3 and AISA-FENIX sensors. Landsat-8 OLI and Sentinel-2 MSI provided satisfactory results, while PlanetScope gave poor results. For the prediction using regional spectral libraries, only lab-based FieldSpec 3 sensor provided a fair prediction, while other sensors gave poor results. This study demonstrated that soil sensing is possible at any level taking into account its advantages and limitations. This approach paves the way for acquiring soil spectra for smallholder farms.

The physical and chemical attributes of soils are strongly influenced by the nature of the minerals they contain and their concentration. Thus, soil texture is directly dependent on the content in clayey minerals, which influences a number of characteristics such as water dynamics. Although the mineralogical composition of soil is usually determined by X-ray diffraction spectroscopy, this technique is expensive and time-consuming, and uses toxic materials, all of which makes it impractical for obtaining large data sets. Also, available methods for acquiring, interpreting and examining visible–near infrared–shortwave infrared (VIS–NIR–SWIR) spectra are largely ineffective with tropical soils. The aim of this work was to ascertain whether VIS–NIR–SWIR reflectance spectroscopy (350–2500 nm) is useful for identifying minerals in subtropical soils as classified by textural class. For this purpose, soil samples were collected at 66 points at three different soil depths (0–20, 20–40 and 40–60 cm) over a study area located in the State of Rio Grande do Sul (Brazil). The soil texture were determined with the pipette method, and soil spectra were recorded on a FieldSpec Pro VIS-NIR-SWIR laboratory spectrophotometer. Soil minerals were identified, and their proportions determined, from the second-derivative of the Kubelka–Munk (KM) function for the spectra. Five main minerals were thus identified from their spectral signatures, namely: hematite, goethite, kaolinite, chlorite and illite. Identification of the minerals was facilitated by classifying the samples according to texture. The higher the clay content was, the higher was the spectral amplitude of the minerals identified. Those textural classes with the highest clay contents exhibited the greatest proportions of iron oxides and of clay minerals such as kaolinite. These relationships allowed more comprehensive analysis of the soils and expeditious characterization of the study area in terms of texture and mineralogy with a view to facilitating decision-making agricultural support policies.

Interdisciplinary concepts have been used to manage the environmental crisis of the 21st century. Ecological problems and solutions require an increasingly comprehensive soil study and this has been carried out in order to consider the entire regolith profile. Regoliths are the entire column of materials above the fresh rock, whether this is saprolite (literally “rotten rock”) or soil, material with intense biological and chemical process. Groundwater contamination is particularly enhanced in shallow, porous regolith profiles, due to the easiness of surface water flow into the groundwaters. This is particularly critical in the Guarani aquifer, which waters supplies 15 million people in 4 countries. Several morphological, chemical, physical and mineralogical analyses were applied to samples collected in three regolith profiles to infer, based on weathering and pedogenesis characteristics, their susceptibility to allow groundwater contamination. A particular challenge was that most of the weathering indices were proposed for igneous rocks, and the studied profiles had sedimentary rocks as parent materials. The most weathered profile (P2) had the least potential for groundwater contamination due to its higher cation exchange capacity (CEC) and clay activity, differentiated structure and consistency in the Bi horizon that can favor the lateral flow and the greatest thickness. Profile P1 had sandy texture, small CEC and thickness that facilitate the passage of soluble contaminants towards the groundwater. Some anox features in the Cr layer suggest the existence of a physical limitation that may delay infiltration or diverge water flow to surface outlets. The profile P3 had the greatest potential for groundwater contamination because it had low CEC, weathering degree and thickness. The only feature to prevent the polluted infiltration water to reach groundwaters in this profile was its sub-horizontal structure and transition from soft/very friable consistency in CA layer to very hard/unbreakable consistency in Cr layer, which also may diverge or delay the vertical infiltration of water. However, no anox features were found in it. Results indicate the importance of understanding the soil weathering and relate with their potential on underground water contamination, and thus, to bring light to public environmental monitoring policies.

Soil organic carbon (SOC) stocks are a remarkable property for soil and environmental monitoring. The understanding of their dynamics in crop soils must go forward. The objective of this study was to determine the impact of temporal environmental controlling factors obtained by satellite images over the SOC stocks along soil depth, using machine learning algorithms. The work was carried out in São Paulo state (Brazil) in an area of 2577 km2. We obtained a dataset of boreholes with soil analyses from topsoil to subsoil (0–100 cm). Additionally, remote sensing covariates (30 years of land use history, vegetation indexes), soil properties (i.e., clay, sand, mineralogy), soil types (classification), geology, climate and relief information were used. All covariates were confronted with SOC stocks contents, to identify their impact. Afterwards, the abilities of the predictive models were tested by splitting soil samples into two random groups (70 for training and 30% for model testing). We observed that the mean values of SOC stocks decreased by increasing the depth in all land use and land cover (LULC) historical classes. The results indicated that the random forest with recursive features elimination (RFE) was an accurate technique for predicting SOC stocks and finding controlling factors. We also found that the soil properties (especially clay and CEC), terrain attributes, geology, bioclimatic parameters and land use history were the most critical factors in controlling the SOC stocks in all LULC history and soil depths. We concluded that random forest coupled with RFE could be a functional approach to detect, map and monitor SOC stocks using environmental and remote sensing data.

Wetlands are important ecosystems characterized by redoximorphic environments producing typical soil forming processes and organic carbon accumulation. Assessments and management of these areas are dependent on knowledge about soil characteristics and variability. By reflectance spectroscopy, information about soils can be obtained since their spectral behaviors are directly related to their chemical, physical, and mineralogical properties reflecting the pedogenetic processes and environment conditions. Our aims were: (a) to characterize the main soil classes of wetlands regarding their spectral behaviors in VIS-NIR-SWIR (350-2500 nm) and relate them to pedogenesis and environmental conditions, (b) to determine spectral ranges (bands) with greater expression of the main soil properties, (c) to identify spectral variations and similarities between hydromorphic soils from wetlands and other soils under different moisture conditions, and (d) to propose spectral models to quantify some chemical and physical soil properties used as environmental quality indicators. Nine soil profiles from the Pantanal region (Mato Grosso State, Brazil) and one from the Serra do Espinhaco Meridional (Minas Gerais State, Brazil) were investigated. Spectral morphology interpretation allowed identifying horizon differences regarding shape, absorption features and reflectance intensity. Some pedogenetic processes of wetland soils related to organic carbon accumulation and oxide iron variation were identified by spectra. Principal Component Analysis allowed discriminating soils from wetland and outside this area (oxidic environment). Quantification of organic carbon was possible with R2 of 0.90 and low error. Quantification of clay content was masked by soils with organic carbon content over 2% where it was not possible to quantify with high R2 and low error both properties when dataset has soil samples with high organic carbon content. By reflectance spectroscopy, important characteristics of wetland soils can be identified and used to distinguish from soils of different environments at low costs, reduced time, and with environmental quality.

Crop residue decomposition is an essential process that affects soil properties and ecosystem services related to nutrient recycling, soil organic matter, and aggregation. Therefore, understanding crop residue decomposition is recommended to evaluate soil changes and define a more sustainable sugarcane straw removal management for bioenergy production. The straw chemical composition may be an efficient indicator to investigate this complex process. Thus, we conducted a field study to assess the sugarcane straw composition changes over 1 year using DRIFT spectroscopy technique. The experimental design consisted of a split-plot and randomized block design, and straw removal rates provided the main plot factor with time of sampling as the split-plot factor. Three sugarcane straw removal rates were evaluated: no removal (~ 14.0 Mg ha−1 of dry mass), 50 (~ 7.0 Mg ha−1) and 75% (~ 3.5 Mg ha−1). Decreases in the absorbance associated with labile C (1200 to 1100 cm−1), and increases in more recalcitrant C (1800 to 1600 cm−1) were showed by the DRIFT spectra. In regional scale, the straw chemical composition was affected by environmental conditions, whereas at a local scale, the main driver was the decomposition time. Specific DRIFT peaks were correlated with the referential wet chemical method, commonly named Van Soest method. From the correlations between the Van Soest and DRIFT methods, we suggest using the absorbance of 896, 987, 1173, and 1447 cm−1 bands to assess cellulose and hemicellulose changes and 1500 cm−1 band for lignin changes in sugarcane straw. The application of DRIFT analysis to follow the decomposition process is a feasible and clean methodology to detect straw chemical changes induced by the environmental conditions and decomposition time.

The effects of climate changes on biotic expansion or divergence is a widely debated topic. This discussion is particularly relevant for northern Amazonia where patches of open vegetation environments that harbor high endemic and specialized species are present in a matrix of tall closed canopy forest. This paper presents the depositional chronology and evolution of an 8.7-m thick stabilized fluvial and eolian sediment profile in a sandy plain substrate that underpins the largest open vegetation enclave in northern Amazonia. Three depositional units were identified using optically stimulated luminescence and radiocarbon ages coupled with grain size, magnetic susceptibility, and reflectance analyses. A lower unit of coarse fluvial silt deposited between 53 and 28 ka is overlain unconformably by a 5-m thick middle unit of fine eolian sand deposited at high accumulation rates between the Last Glacial Maximum (23–19 ka) and Heinrich Stadial 1 (HS1; 18.1–14.7 ka) when persistent and long-lasting shifts of the Intertropical Convergence Zone (ITCZ) to the Southern Hemisphere promoted dry and windy conditions in northern South America. An upper ~2-m thick unit was deposited when the climate became wetter after HS1, promoting the formation of soils that support open vegetation habitats. This study indicates that abrupt millennial-scale climate events can induce significant changes in the Amazonian landscape, which in turn play an essential role in the distribution and diversification of specialized biota.

Soil analysis is an important information in agriculture and environmental monitoring. It is usually performed by wet chemical analysis with high cost and chemical products consumption. In the world, it is estimated that 1.5 billion ha is used as agricultural area. If every 5 ha 2 samples (2 depths) were collected, we would have 600 million soil samples for chemical and granulometric analysis. Considering just the analysis of organic matter (OM) by wet combustion method in the laboratory as an example, we would be utilizing about 840 thousand kg of dichromate and ammonium ferrous sulfate and 3 million L of sulfuric acid. The use of these reagents can have a huge ecological consequence if they do not have an adequate final disposal. An alternative methodology such as proximal sensing can be utilized with low environmental impact. Therefore, the objective of this study was to: i) evaluate the analytical quality of soil attributes via different traditional laboratories and sensors, ii) evaluate the prediction of the models using sensors, iii) assess the uncertainties of lime recommendation analyzed by the laboratories. We applied 96 soil samples at two depths collected in Sao Paulo State, Brazil. The determination of 15 soil attributes was performed by four different routine laboratories, and they were predicted by 4 sensors (400–2500 nm). Results indicate that the determination of attributes via chemical analysis with low quality led to high error in spectral models. The great predictive performances of clay, OM, cation exchange capacity (CEC), and pH enable the use of sensors in the evaluation of these attributes. Overall, the criteria for classification of analytical results showed that sand, silt, clay, pH, OM, CEC, and base saturation were the attributes that can be determined by the spectroscopy technique with high-quality outcome. The lime recommendation derived from proximal sensor analysis can be used as an efficient method, since it presented a high correlation with the laboratory result. In this sense, a hybrid laboratory analysis can be developed to optimize analysis with better quality control, which is indicated as a great opportunity in the near future.

Made available in DSpace on 2019-10-06T16:42:11Z (GMT). No. of bitstreams: 0 Previous issue date: 2019-11-15 Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq) Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP) The present study was developed in a joint partnership with the Brazilian pedometrics community to standardize and evaluate spectra within the 350–2500 nm range of Brazilian soils. The Brazilian Soil Spectral Library (BSSL) began in 1995, creating a protocol to gather soil samples from different locations in Brazil. The BSSL reached 39,284 soil samples from 65 contributors representing 41 institutions from all 26 states. Through the BSSL spectra database, it was possible to estimate important soil attributes, such as clay, sand, soil organic carbon, cation exchange capacity, pH and base saturation, resulting in differences among the multi-scale models taking Brazil (overall), regional and state scale. In general, spectral descriptive and quantitative behavior indicated important relationship with physical, chemical and mineralogical properties. Statistical analyses showed that six basic patterns of spectral signatures represent the Brazilian soils types and that environmental conditions explain the differences in spectra. This study demonstrates that spectroscopy analyses along with the establishment of soil spectral libraries are a powerful technique for providing information on a national and regional levels. We also developed an interactive online platform showing soil sample locations and their contributors. As soil spectroscopy is considered a fast, simple, accurate and nondestructive analytical procedure, its application may be integrated with wet analysis as an alternative to support the sustainable management of soils. Department of Soil Science Luiz de Queiroz College of Agriculture (ESALQ) University of São Paulo (USP), Ave. Pádua Dias 11, Cx. Postal 9 Department of Soil Federal University of Santa Maria, Av. Roraima 1000 Geographical Sciences Department Federal University of Pernambuco, Av. Ac. Hélio Ramos, s/n Department of Agronomy State University of Maringá, Av. Colombo 5790 Department of Agriculture Biodiversity and Forestry Federal University of Santa Catarina, Rodovia Ulysses Gaboardi 3000 - Km 3 Federal Rural University of Amazon, Ave. Presidente Tancredo Neves 2501 Faculty of Agronomy and Veterinary Medicine University of Brasília EMBRAPA - Solos, R. Antônio Falcão, 402, Boa Viagem Center of Nuclear Energy in Agriculture (CENA) USP, Av. Centenário 303 CDRS/Secretary of Agriculture of São Paulo State, R. Campos Salles 507 Department of Soils Federal University of Viçosa, Ave. Peter Henry Rolfs s/n EMBRAPA – Informática Agropecuária, Ave. André Tosello, 209 Department of Nuclear Energy Federal University of Pernambuco, Av. Prof. Luis Freire 1000 Department of Geography Federal University of Rio Grande do Norte, R. Joaquim Gregório s/n Agronomic Institute of Campinas (IAC), Ave. Barão de Itapura 1481 Institute of Agricultural Sciences Federal Rural University of Amazônia, Ave. Presidente Tancredo Neves 2501, 66.077-830 Department of Soil Science Federal University of Lavras Federal University of Mato Grosso, Cuiabá, Av. Fernando Corrêa da Costa 2367 Department of Soils Federal Rural University of Rio de Janeiro, Rodovia BR 465, Km 07 s/n Soil and Water Sciences Department University of Florida, 2181 McCarty Hallr, PO Box 110290 EMBRAPA - Solos, R. Jardim Botânico, 1024 Department of Soils and Fertilizers School of Agricultural and Veterinary Studies São Paulo State University (FCAV-UNESP), Via de Acesso Prof. Paulo Donato Castellane s/n Federal University of Sergipe, Av. Marechal Rondon s/n Graduate Program in Earth Sciences (Geochemistry) Department of Geochemistry Federal Fluminense University, Outeiro São João Batista, s/n Federal Institute of the Southeast of Minas Gerais, R. Monsenhor José Augusto 204 Federal University of Rio Grande do Norte, R. Joaquim Gregório s/n Federal University of Piauí EMBRAPA Milho e Sorgo, Rod MG 424 Km 45 Institute of Agricultural Sciences Federal University of Jequitinhonha e Mucuri Valleys, Ave. Ver. João Narciso 1380 Department of Biosystems Engineering ESALQ USP, Ave. Pádua Dias 11, Cx. Postal 9 Federal University of Acre, Rodovia BR 364 Km 04 Federal University of Amazonas, Av. General Rodrigo O. J. Ramos 1200 EMBRAPA Clima Temperado, BR-392, km 78 Department of Agronomy Federal Rural University of Pernambuco, R. Manuel de Medeiros s/n EMBRAPA Cocais, Quadra 11, Av. São Luís Rei de França 4 Paraense Emílio Goeldi Museum, Av. Gov. Magalhães Barata 376 Exata Laboratory, Rua Silvestre Carvalho Q 11 Federal University of Rondônia, BR 364, Km 9.5 Nacional Institute for Amazonian Research, Ave. André Araújo 2936 Department of Forestry Sciences ESALQ-USP, Ave. Pádua Dias 11, Cx. Postal 9 Department of Soils and Fertilizers School of Agricultural and Veterinary Studies São Paulo State University (FCAV-UNESP), Via de Acesso Prof. Paulo Donato Castellane s/n FAPESP: 2014/22262-0 FAPESP: 2016/26176-6 FAPESP: 2017/03207-6

Pedological assessment and mapping by hyperspectral proximal and multispectral remote sensing comes up as an important alternative for large extent areas with high soil variability. Strategies indicating how to integrate these multi-source data for digital soil mapping are emerging. In this paper, we used proximal and remote sensed data to perform pedological assessments and to support a detailed soil class mapping by MESMA. The study area is located in Southeast of Federal District, Brazil. Six toposequences were defined according to pedomorphogeological assessments and 34 sites were collected for laboratory analysis (texture and chemical) and VIS-NIR-SWIR (350–2500 nm) spectroscopy reflectance analysis. We assessed soil mineralogy based on derivative analysis of topsoil reflectance and we grouped observations to recognize topsoil spectral patterns by clustering method. Topsoil patterns (mean spectral curve for each cluster) were convolved using a Gaussian function of Landsat 5-TM spectral bands to obtain endmembers. Then, we used a Landsat 5 TM time series to produce a bare soil composite denominated Synthetic Soil Image (SYSI). Endmembers and SYSI were used as input data for Multiple Endmember Spectral Mixture Analysis (MESMA) to map the soil classes. Topsoil spectra clustered soil samples that were similar in texture, mineralogy and color, and identified 13 topsoil patterns (endmembers). SYSI retrieved 74% of bare soil area coverage and presented very similar spectral patterns to endmembers. The RGB 543 composite highlighted the mineralogy (i.g. sesquioxides, kaolinite and quartz), texture and color of the soils. MESMA modeled almost 100% of SYSI from endmembers, with low global RMSE of 0.86% and high global fraction of 62%. Soil classes were mapped using topsoil reflectance patterns and satellite images by MESMA method. We validated the digital soil map by using independent field-visited sites, which reached a Kappa coefficient of 0.73. We efficiently assessed soil mineralogy and recognized patterns from laboratory topsoil spectra. We successfully mapped soil classes using topsoil reflectance patterns and a bare soil composite image by MESMA method.

Soil minerals are usually quantified by the conventional laboratory soil analyses. However, developments in interpretations and analyses of the visible, near-infrared, and short-infrared (Vis-NIR-SWIR) diffuse reflectance have allowed the quantification of some soil minerals. In this study, we aimed to implement a novel framework using Vis-NIR-SWIR spectroscopy to quantify the main soil minerals. We also assessed the application of this framework to create new environmental variables for digital soil mapping (DSM). The soil spectra database comprised 2701 samples from 1008 sites in the spectral range of 350–2500 nm at 0–20, 40–60, and 80–100 cm depths. The specific bands in the Vis-NIR-SWIR spectra that identify the presence of soil mineral were selected based on the literature with the United States Geological Survey Spectral Library Version 7 and in the strong maxima and minima of the second-derivative curves of the soil mineral standards using the Savitzky-Golay method. We proposed an estimation and conversion of the measurement unit of soil minerals in amplitude to g kg−1 using a small dataset of mineral content quantified via X-Ray Powder Diffraction. We selected randomly 85 samples out of 2701 available at 0–20 cm depth and sent to conventional laboratory analyses to calibrate the final estimation, using the kaolinite soil mineral as an example. Therefore, a constant factor was determined to estimate mineral content in soils displaying RMSE, R2adj, the Lin's concordance coefficient (CCC), Bias, and RPIQ values of 7612 g kg−1, 0.28, 0.50, 13.09 g kg−1, and 0.56, respectively. This evaluation was assessed by splitting 85 samples into 80% to determine and 20% to validate the constant factor. For the DSM procedure, we used 2701 samples split into 80% and 20% for calibration and validation, respectively, of the models for each of the nine minerals. This study showed that the proposed framework using Vis-NIR-SWIR spectroscopy to estimate soil minerals is promising due to higher CCC and lower RMSE values obtained. Furthermore, the spectral amplitude for each mineral provides important information to be used as environmental variables for the prediction of soil attributes, soil types, and soil properties.

The understanding of attributes and magnetic susceptibility (χ) at soil surface, mainly subsurface, is crucial due to their role to identify climate changes, soil degradation, soil classification systems, soil fertility, and pedogenesis. The integration of proximal sensing (PS) and remote sensing (RS) data sources could increase the efficiency of Digital Soil Mapping. Nevertheless, products of this integration need to be evaluated in hybrid, stochastic, and deterministic models to predict soil attributes and χ at surface and subsurface. This study investigates the PS and RS integration by applying four deterministic (e.g. Bayesian Regularised Neural Network, Generalised Linear Model, Random Forest and Cubist) and hybrid models (e.g. Regression Kriging of residuals of the best-fitted model) to create a new environmental variable, the Best Synthetic Soil Image (BSSI), at three soil depths (e.g. 0 – 20, 40 – 60 and 80 – 100 cm) that quantitatively represent the soil spectral signature. We also used the BSSI in a comparison with bare soil surface (e.g. SYSI - Synthetic Soil Image) to predict soil attributes and χ by performing the deterministic and hybrid models. We hypothesize that the BSSI, which integrates PS and RS data, enhances soil modelling predictions at subsurface by selecting the best model approach. The BSSI demonstrated original and valuable contribution to increase the predictive model power at deeper layers, while SYSI was effective at upper layers. The PS and RS integration helped to identify the main soil patterns horizontally and vertically, which traditional soil surveys have not been capable of representing.

Soil presents a high vulnerability to the environmental degradation processes especially in arid and semiarid regions, requiring research that leads to its understanding. To date, there are no detailed soil maps covering a large extension of the Middle East region, especially for calcium carbonate content. Thus, we used topsoil data (0–20 cm) from more than 5000 sites for mapping near 3,338,000 square km of the Middle East. To do this, we used covariates obtained from remote sensing data and random forest (RF) algorithm. Around 65% of the soil information was acquired from Iranian datasets and the remaining from the World Soil Information Service dataset. By using 30 covariates layers—soil, climate, relief, parent material and age features— we then trained and tuned RF regression models—in R software— and used the optimal ones (according to the minimum root mean square error) for making spatial predictions—within Google Earth Engine— of topsoil attributes and associated uncertainties at 30 m resolution. All covariates were relatively important for mapping topsoil attributes, ranging from 4% to 98%. Annual precipitation, temperature annual range and elevation were the most important ones (>31%). Overall, the prediction models trained by RF explained around 40–66% of the variation present in topsoil attributes. The ratio of the performance to interquartile distance (RPIQ) ranged between 1.59 and 2.83, suggesting accurate models. Our predicted maps indicated that sandy and loamy soils with poor organic carbon levels, alkaline reaction and high calcium carbonate content were widespread in middle eastern topsoils. Our framework overcomes some limitations related to high computational requirements and enables accurate predictions of topsoil attributes. Our maps presented correct pedological correspondences and had realistic spatial representations and interesting levels of uncertainties.

Soil maps are an important tool for agricultural planning and land management. Digital techniques have been used to create soil maps. However, most studies did not explore drainage network (DN) information on prediction models, which are related to soil variability. Thus, this study aims to evaluate the contribution of DN to predict soil classes using digital soil mapping techniques. We used a conventional soil class map (1:20,000) and environmental variables, such as drainage and relief attributes and satellite images, aiming to extrapolate the soil map to a larger area. The work was conducted in Sao Paulo State, Brazil. We created a point grid with 30 × 30 m resolution to extract the soil and variables information. We used these data to calibrate a random forest model along with cross-validation to optimize the model selection. The predicted soil classes for the 53,800-ha study area were determined on two levels according to the World Reference Base (WRB) soil classification system. The first level considered only soil groups (i.e. Acrisol and Ferralsol), while the second level considered the soil group and a qualifier (i.e. Chromic Acrisol and Rhodic Acrisol). We validated the maps using other conventional soils maps (internal validation) and field sampling points (external validation). After extrapolating the soil map, we validated the model s performance using field observations. In this case, the method reached an accuracy of 0.56 and kappa of 0.31 for the soil’s first level, and 0.38 and 0.25 for the second level. Regosols and Cambisols prediction was underestimated, lowering the accuracy and kappa results on the validation. However, Ferralsols reached accuracy and Acrisols reached around 70% accuracy. The drainage related attributes had the highest contribution to the model’s performance (accuracy = 56%) and improved the soil map extrapolation.

There is a worldwide need for detailed spatial information to support soil mapping, mainly in the tropics, where main agricultural areas are concentrated. In this line, satellite images are useful tools that can assist in obtaining soil information from a synoptic point of view. This study aimed at evaluating how satellite images at different resolutions (spatial, spectral and temporal) can influence the representation of soil variability over time, the percentage of bare soil areas and spatial predictions of soil properties in southeastern Brazil. We used single-date and multi-temporal images (SYSI, Synthetic Soil Images) of bare soil pixels from the Sentinel2-MultiSpectral Instrument (S2-MSI) and the Landsat-8 Operational Land Imager (L8-OLI) to conduct this research. Two SYSIs were obtained from images acquired in four years (2016–2019) for each satellite (SYSI S2-MSI and SYSI L8-OLI) and a third SYSI, named SYSI Combined, was obtained by combining the images from both satellites. The single-date images for each satellite was acquired in September, when the influence of clouds was low and bare soil pixels was predominant. Single-date images and SYSIs were compared by means of their spectral patterns and ability to predict topsoil properties (clay, sand, silt, and organic matter contents and soil color) using the Cubist algorithm. We found that the SYSIs outperformed single-date images and that the SYSI Combined and SYSI L8-OLI provided the best prediction performances. The SYSIs also had the highest percentage of areas with bare soil pixels (~30–50%) when compared to the single-date images (~20%). Our results suggest that bare soil images obtained by combining Landsat-8 and Sentinel-2 images are more important for soil mapping than spatial or spectral resolutions. Soil maps obtained via satellite images are important tools for soil survey, land planning, management and precision agriculture.

Determinations of soil carbon and nitrogen stocks are important to evaluate land fertility and agricultural potential and because of their influence on the global climate. Spectroscopic determinations are faster, cheaper and less pollutant than traditional methods. The potential use of spectroscopy in the visible (Vis), near infrared (NIR) and mid-infrared (MIR) regions and their combination to estimate total C and N concentrations were evaluated using seven different pre-processing and two regression models and comparing to the concentrations determined by dry combustion of 701 soil samples from different soil classes and land uses in Northeast Brazil. Better C and N concentration predictions were obtained with the MIR region than with the Vis-NIR region and no significant improvement occurred when the two spectra were combined. The support vector machine (SVM) and the partial least squares (PLSR) models had similar performances both for C and N. The multiplicative scatter correction pre-processing is recommended for C and the standard normal transformation technique for N. Equations to estimate soil C and N concentrations of the predominant soil classes in the region and of the set of all classes are provided. Their high accuracy confirm the potential of reflectance spectroscopy as a useful and rapid tool to quantify C and N concentrations in different tropical soils and under different land uses.

Conventional soil mapping is costly and time consuming. Therefore, the development of quick, cheap, but accurate methods is required. Several studies highlight the importance of developing regional soil spectral libraries for digital soil mapping, but few studies report on the use of these libraries to aid digital mapping of soil types. This study aims to produce a digital soil map using as training set Visible and Near Infra-Red (Vis–NIR) spectra from local soil samples, a regional spectral library and terrain attributes. The soils were sampled in 162 locations on a 270-ha farm in the municipality of Piracicaba, Sao Paulo, Brazil. Spectra from topsoil and subsoil were measured in laboratory (400–2500 nm) and arranged as multi-depth spectra. Information was summarized by principal component analysis. Regression tree models were calibrated to predict principal components (PC) scores based on terrain attributes. After calibration, the models were applied to the entire study site, resulting in PC score maps. Fuzzy c-means and PC maps were used to define the soil mapping units (MU). Based on fuzzy centroids, representative samples (RS) were defined to the MU. Munsell soil color and soil order were predicted from soil spectra and used to characterize the MU. The regression tree model had a good fit for PC1, with an r2 of 0.92, and a satisfactory r2 for PC3, PC4, and PC5, respectively 0.58, 0.66 and 0.53. The fuzzy clustering defined seven MU. The R2 for Munsell color predictions were 0.94 (hue), 0.96 (value) and 0.73 (chroma). Soil order had good agreement in validation, with kappa coefficient of 0.41. The methodology indicates the potential of Vis–NIR spectra to improve soil mapping campaigns and consequently provides a product similar to a conventional soil map.

Soil provides ecosystemservices, supports human health and habitation, stores carbon and regulatesemissions ofgreenhouse gases. Unprecedented pressures on soil from degradation and urbanization are threatening agroecologicalbalances and food security. It is important that we learn more about soil to sustainably manage andpreserve it for future generations. To this end, we developed and analyzed a global soil visible–near infrared(vis–NIR) spectral library. It is currently the largest and most diverse database of its kind. We show that the informationencoded in the spectra can describe soil composition and be associated to land cover and its global geographic distribution, which acts as a surrogate for global climate variability. We also show the usefulness ofthe global spectra for predicting soil attributes such as soil organic and inorganic carbon, clay, silt, sand andiron contents, cation exchange capacity, and pH. Usingwavelets to treat the spectra, whichwere recorded in differentlaboratories using different spectrometers and methods, helped to improve the spectroscopic modelling.We found that modelling a diverse set of spectra with a machine learning algorithm can find the local relationshipsin the data to produce accurate predictions of soil properties. The spectroscopic models that we derivedare parsimonious and robust, and using them we derived a harmonized global soil attribute dataset, whichmight serve to facilitate research on soil at the global scale. This spectroscopic approach should help to dealwith the shortage of data on soil to better understand it and to meet the growing demand for information to assessandmonitor soil at scales ranging fromregional to global.New contributions to the library are encouraged sothat this work and our collaboration might progress to develop a dynamic and easily updatable database withbetter global coverage. We hope that this work will reinvigorate our community's discussion towards larger,more coordinated collaborations. We also hope that use of the database will deepen our understanding of soilso that we might sustainably manage it and extend the research outcomes of the soil, earth and environmentalsciences towards applications that we have not yet dreamed of.

Portable X-ray fluorescence (pXRF) spectrometry and visible near-infrared diffuse reflectance spectroscopy (Vis-NIR DRS), used separately or in tandem, have become important techniques for determination and prediction of soil attributes worldwide. However, there is little information available regarding the effectiveness of their combined use in tropical soils. This study aimed to predict soil texture using pXRF and Vis-NIR DRS, evaluating the efficiency of using these proximal sensors separately and in tandem. A total of 315 soil samples were collected from A and B horizons in Brazil. Soil samples were submitted to analyses of texture, pXRF and Vis-NIR DRS. Vis-NIR DRS spectral data pre-processing was evaluated by comparing results delivered by the derivative smoothing methods Savitzky-Golay (WT), Savitzky-Golay with Binning (WB), and data without the pre-processing treatment (WOT). Four algorithms were utilized for predictions: Gaussian Process (Gaussian), Support Vector Machine with linear (SVM-L) and radial (SVM-R) kernels, and Random Forest (RF). In general, models using only pXRF data slightly outperformed models using Vis-NIR DRS (WT, WB, WOT) data alone. Models combining data from both sensors achieved similar results to those obtained by pXRF alone. The best predictions of sand, silt, and clay contents were obtained via pXRF + RF using B horizon data, reaching R2 values of 0.91, 0.81, and 0.83, respectively. Although pXRF alone provided slightly better results, soil texture can be accurately predicted via both pXRF and Vis-NIR DRS data, separately and in tandem. These sensors can contribute to reduce costs and time required for tropical soil texture determination.

The land surface temperature (LST) provides important information about energy exchange processes, which are influenced by land use/land cover (LULC). Thus, our objective was to evaluate LST patterns driven by LULC changes, detected over a time series of Landsat images. The study area of 2990 km2 is located in the Piracicaba region, state of Sao Paulo, Brazil. We acquired Landsat images from 1985 to 2019, in dry and moist seasons. Six LULC classes (agriculture, bare soil, straw, forest, water, and pasture) were identified by maximum-likelihood supervised classification every five years and then LST was estimated using the inversion of Planck’s function in the thermal band. Spectral indices representing vegetation, water, bare soil, and straw were calculated and correlated to LST in specific years. Bare soil images and their respective LST in both seasons were used annually to approach the influence of bare soil areas on the LST, considering soil class, time and rainfall. LULC alterations over 1985–2015 were an important factor on the LST change, which varied on average from 21.46 °C to 41.31 °C in the moist season and 17.05 °C to 31.67 °C in the dry one. Water bodies and vegetation had the lowest LST values, whereas bare soil and straw had the highest ones. The correlation between LST and spectral indices somewhat agreed with such patterns. Arenosols presented the highest LST mean values in both seasons and differed from Acrisols in the dry season, which is probably related to their texture and mineralogical composition. In the moist season, LST was negatively correlated to rainfall, suggesting the influence of soil moisture content on its surface temperature. In the dry season, the LST of bare soil areas increased by an average of 0.13 °C per year, indicating a warming trend. In general, LST increased in the studied period, probably due to the increase of anthropic activity, such as the expansion of agricultural areas. These findings can assist future studies on the influence of soils and land use on climate alterations.

Soil magnetic susceptibility (κ) has potential to be used as a pedoenvironmental indicator from which mineralogy, pedogeochemical, pedogeomorphological and pedogenic processes can be inferred. It can be used in pedosphere studies, as an auxiliary information for appropriate and sustainable soil use and management. This research aimed to analyze how pedogenesis and geochemical processes affect the κ and some of its attributes, as well as its potential use in discriminating soil great groups, following the digital soil mapping approach. The study area is located in Sao Paulo State - Brazil. Soil samples were collected for physical–chemical analysis from 79 locations (0–20 cm depth). At these sites, magnetic susceptibility was measured with a portable field instrument and analyzed in terms of geology, relief and soil class. The results showed that geology strongly affects κ, mainly in diabase derived soils, followed by metamorphosed siltstone and siltstone. In fluvial sediments, the κ exhibits different behaviors due to different sediments deposited by the Capivari River. In less evolved soils, such as Cambisols, lithology is a more important contributor to κ than pedogenesis. In more evolved soils, pedogenesis increases κ, whereas argilluviation/ferralitization reduces it. The κ values did not decrease significantly or even increase downslope, due to the presence of diabase on the lower parts. Differences in κ where observed between diabase bedrock located in different parts of the study area, indicating more of an influence by geomorphic processes rather than lithology. With respect to soil attributes, positive correlations between κ and base saturation, cation exchange capacity, organic matter, and iron and clay content were found, whereas a negative correlation was found between κ and sand content. The κ correlates with changes in lithology and soil class demonstrating its application as a potential tool for the discrimination of soil great groups and digital soil mapping.

Quantification of soil organic carbon (SOC) is a low-cost and necessary practice to meet increasing agricultural demands. Studies show that remote sensing (RS) is important for SOC prediction and its use has become crucial in agricultural management. In this study, a Multiple Linear Regression (MLR) model was constructed to predict SOC in a site in Piracicaba, Sao Paulo, Brazil. As predictor variables, we used the optical-satellite data of OLI/Landsat-8 sensor (bands 5 and 7, specifically), clay concentration, and the Normalized Difference Vegetation Index (NDVI). We collected 218 samples at the sampling points in the field to quantify clay and SOC in the laboratory as a calibration procedure. An Exposed Soil Mask (ESM) was created using the method GEOS3 technology, which showed pixels with greater variability of bare soil. The pixels were evaluated with their respective surface reflectance values obtained by the satellite sensor and their respective NDVI index values. We evaluated the model predictive performance based on the adjusted coefficient of determination (R2), the Root Mean-Squared Error (RMSE), and the Ratio of Performance to Interquartile Range (RPIQ) obtained in data validation. The MLR model presented R2 values 0.79 and 0.81 for calibration and validation, respectively. We obtained important RMSE and RPIQ values, 0.14 and 2.32, respectively. The high RPIQ indicated significative sampling distribution around the trendline. After construction, the model was applied to the C spatial distribution using the predictive variables as layers, predominant concentrations of 0.65 to 0.79 g. Kg−1 in 51 (23.4%) soil samples. The analysis presented here offer possibilities for SOC prediction using Geographic Information Systems (GIS) tools.