Caio Troula Fongaro

Remote Sensing from Ground to Space Platforms Associated with Terrain Attributes as a Hybrid Strategy on the Development of a Pedological Map

There is a consensus about the necessity to achieve a quick soil spatial information with few human resources. Remote/proximal sensing and pedotransference are methods that can be integrated into this approach. On the other hand, there is still a lack of strategies indicating on how to put this in practice, especially in the tropics. Thus, the objective of this work was to suggest a strategy for the spatial prediction of soil classes by using soil spectroscopy from ground laboratory spectra to space images platform, as associated with terrain attributes and spectral libraries. The study area is located in Sao Paulo State, Brazil, which was covered by a regular grid (one per ha), with 473 boreholes collected at top and undersurface. All soil samples were analyzed in laboratory (granulometry and chemical), and scanned with a VIS-NIR-SWIR (400–2500 nm) spectroradiometer. We developed two traditional pedological maps with different detail levels for comparison: TFS-1 regarding orders and subgroups; and TFS-2 with additional information such as color, iron and fertility. Afterwards, we performed a digital soil map, generated by models, which used the following information: (i) predicted soil attributes from undersurface layer (diagnostic horizon), obtained by using a local spectral library; (ii) spectral reflectance of a bare soil surface obtained by Landsat image; and (iii) derivative of terrain attributes. Thus, the digital map was generated by a combination of three variables: remote sensing (Landsat data), proximal sensing (laboratory spectroscopy) and relief. Landsat image with bare soil was used as a first observation of surface. This strategy assisted on the location of topossequences to achieve soil variation in the area. Afterwards, spectral undersurface information from these locations was used to modelize soil attributes quantification (156 samples). The model was used to quantify samples in the entire area by spectra (other 317 samples). Since the surface was bare soil, it was sampled by image spectroscopy. Indeed, topsoil spectral laboratory information presented great similarity with satellite spectra. We observed angle variation on spectra from clayey to sandy soils as differentiated by intensity. Soil lines between bands 3/4 and 5/7 were helpful on the link between laboratory and satellite data. The spectral models of soil attributes (i.e., clay, sand, and iron) presented a high predictive performance (R2 0.71 to 0.90) with low error. The spatial prediction of these attributes also presented a high performance (validations with R2 > 0.78). The models increased spatial resolution of soil weathering information using a known spectral library. Elevation (altitude) improved mapping due to correlation with soil attributes (i.e., clay, iron and chemistry). We observed a close relationship between soil weathering index map and laboratory spectra + image spectra + relief parameters. The color composite of the 5R, 4G and 3B had great performance on the detection of soils along topossequences, since colors went from dark blue to light purple, and were related with soil texture and mineralogy of the region. The comparison between the traditional and digital soil maps showed a global accuracy of 69% for the TFS-1 map and 62% in the TFS-2, with kappa indices of 0.52 and 0.45, respectively. We randomly validated both digital and traditional maps with individual plots at field. We achieve a 75% and 80% agreement for digital and traditional maps, respectively, which allows us to conclude that traditional map is not necessarily the truth and digital is very close. The key of the strategy was to use bare soil image as a first step on the indication of soil variation in the area, indicating in-situ location for sample collection in all depths. The current strategy is innovative since we linked sensors from ground to space in addition with relief parameters and spectral libraries. The strategy indicates a more accurate map with less soil samples and lower cost.

Tropical Texture Determination by Proximal Sensing Using a Regional Spectral Library and Its Relationship with Soil Classification

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.

Hyperspectral remote sensing as an alternative to estimate soil attributes

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 time- consuming 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 R 2 > 0.80 and may be applied to a different database than the one that was used to generate the

Detecção de limites de solos por dados espectrais e de relevo

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 Sao 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.

Improvement of Clay and Sand Quantification Based on a Novel Approach with a Focus on Multispectral Satellite Images

Soil mapping demands large-scale surveys that are costly and time consuming. It is necessary to identify strategies with reduced costs to obtain detailed information for soil mapping. We aimed to compare multispectral satellite image and relief parameters for the quantification and mapping of clay and sand contents. The Temporal Synthetic Spectral (TESS) reflectance and Synthetic Soil Image (SYSI) approaches were used to identify and characterize texture spectral signatures at the image level. Soil samples were collected (0-20 cm depth, 919 points) from an area of 14,614 km2 in Brazil for reference and model calibration. We compared different prediction approaches: (a) TESS and SYSI; (b) Relief-Derived Covariates (RDC); and (c) SYSI plus RDC. The TESS method produced highly similar behavior to the laboratory convolved data. The sandy textural class showed a greater increase in average spectral reflectance from Band 1 to 7 compared with the clayey class. The prediction using SYSI produced a better result for clay (R2 = 0.83; RMSE = 65.0 g kg-1) and sand (R2 = 0.86; RMSE = 79.9 g kg-1). Multispectral satellite images were more stable for the identification of soil properties than relief parameters.

Espectroscopia VIS-NIR-SWIR na avaliação de solos ao longo de uma topossequência em Piracicaba (SP)

Objetivou-se neste trabalho caracterizar diferentes solos por espectrorradiometria de reflectância ao longo de uma topossequencia na regiao de Piracicaba, SP. Amostras de solo foram coletadas e analisadas em campo, em laboratorio de analises quimicas e por sensores Vis-NIR (400-2500 nm). Alteracoes nos solos da topossequencia foram identificaveis nas informacoes espectrais. Constituintes dos solos, tais como, materia orgânica, mineralogia, formas de oxidos de ferro e granulometria foram determinantes nas variacoes das feicoes de absorcao e intensidades de reflectância. Cada perfil mostrou caracteristicas espectrais diferenciadoras entre horizontes, relacionadas a intensidade, feicoes de absorcao e morfologia da curva. A avaliacao morfologica nao pode ser avaliada pelo sensor, sendo uma de suas limitacoes. Existe relacao entre grau de intemperismo (indices ki, relacao silte/argila e mineralogia) e dados espectrais. Isso foi observado nos solos originados de basalto, onde houve aumento do ferro extraido pelo ditionito (cristalino e amorfo) na sequencia Nitossolo Vermelho Latossolico (NVL) em direcao ao Cambissolo (C) e, aumento do ferro amorfo nesta mesma sequencia. Na avaliacao da topossequencia completa observou-se a sequencia de absorcao centrada em 500 e 850 nm decrescente do Nitossolo Vermelho Latossolico em direcao ao Chernossolo, ou seja, na sequencia de decrescimo dos teores de ferro cristalino (hematita e goethita) e aumento de ferro amorfo, corroborado pelo aumento dos valores do indice ki. Houve relacao entre os dados espectrais, o indice ki e a posicao do solo na paisagem. Esses resultados mostram que a espectrorradiometria e uma ferramenta promissora para auxiliar o levantamento de solos. Entretanto, ha necessidade do suporte a implantacao de bibliotecas de dados espectrais de solos com acesso irrestrito aos usuarios.