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Intensive cropland expansion for an increasing population has driven soil degradation worldwide. Modeling how agroecosystems respond to variations in soil attributes, relief and crop management dynamics can guide soil conservation. This research presents a new approach to evaluate soil loss by water erosion in cropland using the RUSLE model and Synthetic Soil Image (spectroscopy technique), which uses time series remotely sensed environmental, agricultural and anthropic variables, in the southeast region of São Paulo State, Brazil. The availability of the open-access satellite images of Tropical Rainfall Measuring Mission (TRMM) and Landsat satellite images provided ten years of rainfall data and 35 years of exposed soil surface. The bare soil surface and agricultural land use were extracted, and the multi-temporal rainfall erosivity was assessed. We predict soil maps’ attributes (texture and organic matter) through innovative soil spectroscopy techniques to assess the soil erodibility and soil loss tolerance. The erosivity, erodibility, and topography obtained by the Earth observations were adopted to estimate soil erosion in four scenarios of sugarcane (Saccharum spp.) residue coverage (0%, 50%, 75%, and 100%) in five years of the sugarcane cycle: the first year of sugarcane harvest and four subsequent harvesting years from 2013 to 2017. Soil loss tolerance means 4.3 Mg ha−1 exceeds the minimum rate in 40% of the region, resulting in a total soil loss of ~6 million Mg yr−1 under total coverage management (7 Mg ha−1). Our findings suggest that sugarcane straw production has not been sufficient to protect the soil loss against water erosion. Thus, straw removal is unfeasible unless alternative conservation practices are adopted, such as minimum soil tillage, contour lines, terracing and other techniques that favor increases in organic matter content and soil flocculating cations. This research also identifies a spatiotemporal erosion-prone area that requests an immediately sustainable land development guide to restore and rehabilitate the vulnerable ecosystem service. The high-resolution spatially distribution method provided can identify soil degradation-prone areas and the cropland expansion frequency. This information may guide farms and the policymakers for a better request of conservation practices according to site-specific management variation.

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.

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.

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

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.

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.

Made available in DSpace on 2019-10-06T15:22:10Z (GMT). No. of bitstreams: 0 Previous issue date: 2018-10-01 Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP) The mapping of soil attributes provides support to agricultural planning and land use monitoring, which consequently aids the improvement of soil quality and food production. Landsat 5 Thematic Mapper (TM) images are often used to estimate a given soil attribute (i.e., clay), but have the potential to model many other attributes, providing input for soil mapping applications. In this paper, we aim to evaluate a Bare Soil Composite Image (BSCI) from the state of São Paulo, Brazil, calculated from a multi-temporal dataset, and study its relationship with topsoil properties, such as soil class and geology. The method presented detects bare soil in satellite images in a time series of 16 years, based on Landsat 5 TM observations. The compilation derived a BSCI for the agricultural sites (242,000 hectare area) characterized by very complex geology. Soil properties were analyzed to calibrate prediction models using 740 soil samples (0-20 cm) collected of the area. Partial least squares regression (PLSR) based on the BSCI spectral dataset was performed to quantify soil attributes. The method identified that a single image represents 7 to 20% of bare soil while the compilation of the multi-temporal dataset increases to 53%. Clay content had the best soil attribute prediction estimates (R2 = 0.75, root mean square error (RMSE) = 89.84 g kg-1, and accuracy = 74%). Soil organic matter, cation exchange capacity and sandy soils also achieved moderate predictions. The BSCI demonstrates a strong relationship with legacy geological maps detecting variations in soils. From a single composite image, it was possible to use spectroscopy to evaluate several environmental parameters. This technique could greatly improve soil mapping and consequently aid several applications, such as land use planning, environmental monitoring, and prevention of land degradation, updating legacy surveys and digital soil mapping. Department of Soil Science College of Agriculture Luiz de Queiroz University of São Paulo, Rua Pádua Dias, 11, Piracicaba, Cx Postal 09 Interdisciplinary Program of Bioenergy University of São Paulo (USP) University of Campinas (UNICAMP) and São Paulo State University (UNESP), Rua Monteiro Lobato, 80, Cidade Universitária Faculty of Agronomy and Veterinary Medicine University of Brasília Campus Universitário Darcy Ribeiro, ICC Sul, Asa Norte, Cx Postal 4508 Interdisciplinary Program of Bioenergy University of São Paulo (USP) University of Campinas (UNICAMP) and São Paulo State University (UNESP), Rua Monteiro Lobato, 80, Cidade Universitária FAPESP: 2014/22262-0

Existe a necessidade de avaliar a importância do relevo associado aos parâmetros espectrais de solos como base no mapeamento. O objetivo deste trabalho foi determinar um método de detecção de limites de solos por meio da interação de dados espectrais e formas de relevo. Foram percorridas 14 topossequências representativas de uma área de 13.000 ha próxima dos municípios de São Carlos e Araraquara, SP. As amostras foram caracterizadas pelos métodos convencionais de análise química e granulométrica. Posteriormente, foram obtidos dados espectrais de 400 a 2.500 nm. As informações do relevo foram obtidas pelo emprego de técnicas de geoprocessamento. Geraram-se o modelo digital de elevação do terreno e os mapas de declividade, de curvatura, de índice topográfico composto e de Potencial de Densidade de Drenagem. Ainda, procedeu-se à validação dos métodos pontual e espacial. Na primeira validação, os pontos classificados nas topossequências foram tomados como verdadeiros e contrastados com as informações contidas no mapa de solo pré-existente, com os dados de relevo e com os dados espectrais agrupados. Na validação em nível espacial, procuraram-se avaliar em que locais os diferentes métodos indicavam mudanças nos limites dos solos e comparar com as observações reais. Verificou-se que a análise de agrupamento com cluster evidenciou-se eficiente na discriminação das unidades de solos em topossequência, quando utilizados parâmetros espectrais do solo. Já o conjunto de parâmetros de relevo isoladamente não foi o mais adequado. There is a need to evaluate the importance of soil relief together with soil spectral attributes as the basis on soil mapping. The aim of this study was to test a method for detecting soil boundaries through the interaction of spectral data and relief features. Fourteen toposequences were used, representing an area of 13,000 ha near the municipalities of São Carlos and Araraquara, SP, Brazil. The samples were described by the conventional method of chemical and particle size analysis, such as pH (H2O and KCl), size (coarse and fine sand, silt, and clay), iron content, and color. Spectral information from 400 to 2,500 nm was subsequently obtained. Relief information was obtained by geotechnics, such as the Digital Elevation Model of the terrain, slope map, Compound Topographic Index, curvature, and Drainage Density Potential. In addition, the point and spatial methods proposed were validated. In the first validation, the points classified in the toposequences were taken as true and compared to information contained in the existing soil map, as well as the relief data and spectral data, separated by cluster analysis. Validation on the spatial level sought to assess in which locations the different methods indicated changes in the soil boundaries and compare this with real observations. It was seen that cluster analysis proved to be effective in differentiating soil classes in toposequences when soil spectral attributes were used. However, the set of relief attributes alone was not very suitable.

The search for sustainable land use has increased in Brazil due to the important role that agriculture plays in the country. Soil detailed classification is related with texture attribute. How can one discriminate the same soil class with different textures using proximal soil sensing, as to reach surveys, land use planning and increase crop productivity? This study aims to evaluate soil texture using a regional spectral library and its usefulness on classification. We collected 3750 soil samples covering 3 million ha within strong soil class variations in Sao Paulo State. The spectral analyses of soil samples from topsoil and subsoil were measured in laboratory (400–2500 nm). The potential of a regional soil spectral library was evaluated on the discrimination of soil texture. We considered two types of soil texture systems, one related with soil classification and another with soil managements. The soil line technique was used to assess differentiation between soil textural groups. Soil spectra were summarized by principal component analysis (PCA) to select relevant information on the spectra. Partial least squares regression (PLSR) was used to predict texture. Spectral curves indicated different shapes according to soil texture and discriminated particle size classes from clayey to sandy soils. In the visible region, differences were small because of the organic matter, while the short wave infrared (SWIR) region showed more differences; thus, soil texture variation could be differentiated by quartz. Angulation differences are on a spectral curve from NIR to SWIR. The statistical models predicted clay and sand levels with R2 = 0.93 and 0.96, respectively. Indeed, we achieved a difference of 1.2% between laboratory and spectroscopy measurement for clay. The spectral information was useful to classify Ferralsols with different texture classification. In addition, the spectra differentiated Lixisols from Ferralsols and Arenosols. This work can help the development of computer programs that allow soil texture classification and subsequent digital soil mapping at detailed scales. In addition, it complies with requirements for sustainable land use and soil management.

A espacialização de atributos dos solos é necessária com vistas ao planejamento e monitoramento do solo. As imagens do satélite Landsat 5 Thematic Mapper (TM) são utilizadas em estudos relacionados aos recursos naturais por fornecerem informações da superfície das terras em áreas amplas e de difícil acesso. Nesse trabalho objetivou-se gerar uma imagem multitemporal de solo exposto através de imagens de satélite e, com ela, mapear atributos da superfície do solo. A área de estudo é a região de Piracicaba, SP, onde foram selecionadas treze imagens do Landsat TM. Amostras da camada mais superficial dos solos foram coletadas em 740 pontos, e nelas analisados vários atributos do solo. Por meio da reflectância espectral dos objetos das imagens de satélite foram obtidas informações de solo exposto e eliminados outros alvos. As imagens foram adquiridas em série histórica e sobrepostas, gerando uma composta final com solo exposto. Os atributos do solo que obtiveram boa correlação com as bandas dessa imagem foram quantificados por meio da técnica de regressão multivariada e espacializados. Mapas pré-existentes de geologia e pedologia auxiliaram no entendimento da variabilidade espacial da textura e cor dos solos na paisagem. A taxa de variação do solo exposto em uma imagem individual variou de 7 a 20 %, enquanto a unificada atingiu 53 % da área total. Valores de reflectância entre as bandas TM3 e TM4 contrapostos representando a linha do solo e curva espectral média de espectros de amostras de solos obtidas em laboratório apresentaram semelhança com as de satélite. Entre os atributos estudados, a argila obteve a melhor correlação com R2 de 0,75, erro baixo e RPD acima de 2. Outros atributos relacionados com a argila também obtiveram boa correlação, como matéria orgânica (MO) e capacidade de troca de cátions (CTC) com R2 de 0,4 e 0,34 respectivamente. The knowledge of spatial distribution of soil attributes is necessary for soil planning and monitoring. Landsat 5 Thematic Mapper (TM) images are used in studies related to natural resources for providing the land surface information in large areas and in areas of difficult access. This work aimed to create a multi-temporal image of bare soil through satellite scenes and map soil attributes from the surface. The study area is located in Piracicaba region, SP, where thirteen Landsat TM scenes were selected. Samples of the soil superficial layer were collected at 740 points, and several soil properties were analyzed. Spectral reflectance of different objects from satellite images was obtained and only exposed soil information was selected. Images were acquired in historical series and overlapped, generating a final composed image with bare soil. Soil attributes that presented good correlation with the bands were quantified by multivariate regression and mapped. Pre-existing maps of geology and soil helped in understanding soil texture spatial variability and color in the landscape. The soil variation rate in an individual exposed image ranged from 7 to 20%, while the unified reached 53% of the total area. Obtained values of reflectance between TM3 and TM4 bands representing the soil line and average spectral curve of laboratory soil samples were similar to the satellite ones. Among the soil attributes studied, clay presented the best correlation with R2 value of 0.75, low error and RPD value above 2.0. Other attributes related to clay also presented good correlation, such as organic matter (OM) and cation exchange capacity (CEC) with R2 values of 0.4 and 0.34 respectively.

Campinas Metropolitan Region is the third richest city in Brazil. This study assesses the urban street trees of Campinas based on data from a survey performed using satellite images in the year 2011. All public domain trees in the street system were counted and separated into trees, shrubs, palm trees, and seedlings. The density of trees was obtained using the images census and expressed as trees per linear kilometer for the perimeter of the block. The number of trees per linear kilometer was grouped into nine classes of different densities for data validation. The final number of trees was estimated based on the validation’s results. The Gini coefficient shows that the number of trees per person is very irregular in city neighborhoods (i.e., Campinas has a fairer income distribution than street trees distribution). There is a lower density of trees in the downtown area, due to the high concentration of population, and in more peripheral neighborhoods, due to the lack of design planning. The results obtained here may be used to support a new setting of local priorities for planting actions aimed at urban forestry management.

Minimizing environmental impacts and increasing crop productivity depend mainly on the knowledge of chemical, physical and mineralogical characteristics of the soil attributes. However, traditional methods are timeconsuming and costly. The objective of this study was to determine and validate a method to quantify soil attributes using UV-Vis-NIR Spectroscopy as an alternative to conventional methods of soil analyses. The work comprised two main phases: (1) creation and calibration of statistical models to determine the soil attributes derived from spectral data extracted from soil samples collected in area 1, (2) validation of statistical models in area 2 and correlations between the estimated and observed values (conventional method) for each soil attribute. The equations of the attributes Fe2O3, Al2O3, and clay reached R2 > 0.80 and may be applied to a different database than the one that was used to generate the equations, provided that they belong to the same study site.