Digital soil mapping with limited data

There has been considerable expansion in the use of digital soil mapping technologies and development of methodologies that improve digital soil mapping at all scales and levels of resolution. These developments have occurred in all parts of the world in the past few years and also in countries where it was previously absent. There is almost always a shortage of data in soil research and its applications and this may lead to unsupported statements, poor statistics, misrepresentations and ultimately bad resource management. In digital soil mapping, maximum use is made of sparse data and this book contains useful examples of how this can be done. This book focuses on digital soil mapping methodologies and applications for areas where data are limited, and has the following sections (i) introductory papers, (ii) dealing with limited spatial data infrastructures, (iii) methodology development, and (iv) examples of digital soil mapping in various parts of the globe (including USA, Brazil, UK, France, Czech Republic, Honduras, Kenya, Australia). The final chapter summarises priorities for digital soil mapping

Componentes principais como preditores no mapeamento digital de classes de solos Principal components as predictor variables in digital mapping of soil classes

Tecnologias disponíveis para a observação da Terra oferecem uma grande gama de informações sobre componentes ambientais que, por estarem relacionadas com a formação dos solos, podem ser usadas como variáveis preditoras no Mapeamento Digital de Solos (MDS). No entanto, modelos com um grande número de preditores, bem como a existência de multicolinearidade entre os dados, podem ser ineficazes no mapeamento de classes e propriedades do solo. O objetivo deste estudo foi empregar a Análise de Componentes Principais (ACP) visando a selecionar e diminuir o número de preditores na regressão logística múltipla multinomial (RLMM) utilizada no mapeamento de classes de solos. Nove covariáveis ambientais, ligadas ao fator de formação relevo, foram derivadas de um Modelo Digital de Elevação e denominadas variáveis originais, estas foram submetidas à ACP e transformadas em Componentes Principais (CP). As RLMM foram desenvolvidas utilizando-se atributos de terreno e as CP como variáveis explicativas. O mapa de solos gerado a partir de três CP (65,6% da variância original) obteve um índice kappa de 37,3%, inferior aos 48,5% alcançado pelo mapa de solos gerado a partir de todas as nove variáveis originais.<br>Available technologies for Earth observation offer a wide range of predictors relevant to Digital Soil Mapping (DSM). However, models with a large number of predictors, as well as, the existence of multicollinearity among the data, may be ineffective in the mapping of classes and soil properties. The aim of this study was to use the Principal Component Analysis (PCA) to reduce the number of predictors in the multinomial logistic regression (MLR) used in soil mapping. Nine environmental covariates, related to the relief factor of soil formation, were derived from a digital elevation model and named the original variables, which were submitted to PCA and transformed into principal components (PC). The MLR were developed using the terrain attributes and the PC as explanatory variables. The soil map generated from three PC (65.6% of the original variance) had a kappa index of 37.3%, lower than the 48.5% achieved by the soil map generated from all nine original variables

Resolução espacial de um modelo digital de elevação definida pela função wavelet

O objetivo deste trabalho foi definir a resolução espacial mais apropriada para representar a variabilidade da elevação, declividade, curvatura em perfil e índice de umidade topográfica de um terreno, por meio de avaliações com a transformada wavelet. Os dados utilizados no estudo têm sua origem em três transectos de 27 km, posicionados em áreas do Planalto, Rebordo do Planalto e Depressão Central na região central do Estado do Rio Grande do Sul. As variáveis - elevação, declividade, curvatura em perfil e índice de umidade topográfica - foram derivadas de um modelo digital de elevação Topodata com resolução de 30 m. A avaliação da resolução com a máxima variabilidade foi realizada pela aplicação da wavelet-mãe, denominada Morlet. Os resultados foram analisados a partir do isograma e do escalograma dos coeficientes wavelet e indicaram que sensores remotos com resolução espacial próxima a 32 e 40 m podem ser utilizados em pesquisas que considerem os atributos de terreno, como declividade, curvatura em perfil e índice de umidade topográfica, ou, ainda, fenômenos ambientais correlacionados a eles. No entanto, não foi possível estabelecer um valor conclusivo para a resolução espacial mais adequada para a variável elevação

Digital Soil Map of the World

Soils are increasingly recognized as major contributors to ecosystem services such as food production and climate regulation (1, 2), and demand for up-to-date and relevant soil information is soaring. But communicating such information among diverse audiences remains challenging because of inconsistent use of technical jargon, and outdated, imprecise methods. Also, spatial resolutions of soil maps for most parts of the world are too low to help with practical land management. While other earth sciences (e.g., climatology, geology) have become more quantitative and have taken advantage of the digital revolution, conventional soil mapping delineates space mostly according to qualitative criteria and renders maps using a series of polygons, which limits resolution. These maps do not adequately express the complexity of soils across a landscape in an easily understandable wa

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-