Soil class map of the Rio Jardim watershed in Central Brazil at 30 meter spatial resolution based on proximal and remote sensed data and MESMA method

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

Multi-Temporal Satellite Images on Topsoil Attribute Quantification and the Relationship with Soil Classes and Geology

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

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 São 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

An interlaboratory comparison of mid-infrared spectra acquisition: Instruments and procedures matter

Diffuse reflectance spectroscopy has been extensively employed to deliver timely and cost-effective predictions of a number of soil properties. However, although several soil spectral laboratories have been established worldwide, the distinct characteristics of instruments and operations still hamper further integration and interoperability across mid-infrared (MIR) soil spectral libraries. In this study, we conducted a large-scale ring trial experiment to understand the lab-to-lab variability of multiple MIR instruments. By developing a systematic evaluation of different mathematical treatments with modeling algorithms, including regular preprocessing and spectral standardization, we quantified and evaluated instruments' dissimilarity and how this impacts internal and shared model performance. We found that all instruments delivered good predictions when calibrated internally using the same instruments' characteristics and standard operating procedures by solely relying on regular spectral preprocessing that accounts for light scattering and multiplicative/additive effects, e.g., using standard normal variate (SNV). When performing model transfer from a large public library (the USDA NSSC-KSSL MIR library) to secondary instruments, good performance was also achieved by regular preprocessing (e.g., SNV) if both instruments shared the same manufacturer. However, significant differences between the KSSL MIR library and contrasting ring trial instruments responses were evident and confirmed by a semi-unsupervised spectral clustering. For heavily contrasting setups, spectral standardization was necessary before transferring prediction models. Non-linear model types like Cubist and memory-based learning delivered more precise estimates because they seemed to be less sensitive to spectral variations than global partial least square regression. In summary, the results from this study can assist new laboratories in building spectroscopy capacity utilizing existing MIR spectral libraries and support the recent global efforts to make soil spectroscopy universally accessible with centralized or shared operating procedures

The Brazilian Soil Spectral Library (BSSL): A general view, application and challenges

Made available in DSpace on 2019-10-06T16:42:11Z (GMT). No. of bitstreams: 0 Previous issue date: 2019-11-15Conselho 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 9Department of Soil Federal University of Santa Maria, Av. Roraima 1000Geographical Sciences Department Federal University of Pernambuco, Av. Ac. Hélio Ramos, s/nDepartment of Agronomy State University of Maringá, Av. Colombo 5790Department of Agriculture Biodiversity and Forestry Federal University of Santa Catarina, Rodovia Ulysses Gaboardi 3000 - Km 3Federal Rural University of Amazon, Ave. Presidente Tancredo Neves 2501Faculty of Agronomy and Veterinary Medicine University of BrasíliaEMBRAPA - Solos, R. Antônio Falcão, 402, Boa ViagemCenter of Nuclear Energy in Agriculture (CENA) USP, Av. Centenário 303CDRS/Secretary of Agriculture of São Paulo State, R. Campos Salles 507Department of Soils Federal University of Viçosa, Ave. Peter Henry Rolfs s/nEMBRAPA – Informática Agropecuária, Ave. André Tosello, 209Department of Nuclear Energy Federal University of Pernambuco, Av. Prof. Luis Freire 1000Department of Geography Federal University of Rio Grande do Norte, R. Joaquim Gregório s/nAgronomic Institute of Campinas (IAC), Ave. Barão de Itapura 1481Institute of Agricultural Sciences Federal Rural University of Amazônia, Ave. Presidente Tancredo Neves 2501, 66.077-830Department of Soil Science Federal University of LavrasFederal University of Mato Grosso, Cuiabá, Av. Fernando Corrêa da Costa 2367Department of Soils Federal Rural University of Rio de Janeiro, Rodovia BR 465, Km 07 s/nSoil and Water Sciences Department University of Florida, 2181 McCarty Hallr, PO Box 110290EMBRAPA - Solos, R. Jardim Botânico, 1024Department of Soils and Fertilizers School of Agricultural and Veterinary Studies São Paulo State University (FCAV-UNESP), Via de Acesso Prof. Paulo Donato Castellane s/nFederal University of Sergipe, Av. Marechal Rondon s/nGraduate Program in Earth Sciences (Geochemistry) Department of Geochemistry Federal Fluminense University, Outeiro São João Batista, s/nFederal Institute of the Southeast of Minas Gerais, R. Monsenhor José Augusto 204Federal University of Rio Grande do Norte, R. Joaquim Gregório s/nFederal University of PiauíEMBRAPA Milho e Sorgo, Rod MG 424 Km 45Institute of Agricultural Sciences Federal University of Jequitinhonha e Mucuri Valleys, Ave. Ver. João Narciso 1380Department of Biosystems Engineering ESALQ USP, Ave. Pádua Dias 11, Cx. Postal 9Federal University of Acre, Rodovia BR 364 Km 04Federal University of Amazonas, Av. General Rodrigo O. J. Ramos 1200EMBRAPA Clima Temperado, BR-392, km 78Department of Agronomy Federal Rural University of Pernambuco, R. Manuel de Medeiros s/nEMBRAPA Cocais, Quadra 11, Av. São Luís Rei de França 4Paraense Emílio Goeldi Museum, Av. Gov. Magalhães Barata 376Exata Laboratory, Rua Silvestre Carvalho Q 11Federal University of Rondônia, BR 364, Km 9.5Nacional Institute for Amazonian Research, Ave. André Araújo 2936Department of Forestry Sciences ESALQ-USP, Ave. Pádua Dias 11, Cx. Postal 9Department of Soils and Fertilizers School of Agricultural and Veterinary Studies São Paulo State University (FCAV-UNESP), Via de Acesso Prof. Paulo Donato Castellane s/nFAPESP: 2014/22262-0FAPESP: 2016/26176-6FAPESP: 2017/03207-