Variation of Routine Soil Analysis When Compared with Hyperspectral Narrow Band Sensing Method

Abstract: The objectives of this research were to: (i) develop hyperspectral narrow-band models to determine soil variables such as organic matter content (OM), sum of cations (SC = Ca + Mg + K), aluminum saturation (m%), cations saturation (V%), cations exchangeable capacity (CEC), silt, sand and clay content using visible-near infrared (Vis-NIR) diffuse reflectance spectra; (ii) compare the variations of the chemical and the spectroradiometric soil analysis (Vis-NIR). The study area is located in São Paulo State, Brazil. The soils were sampled over an area of 473 ha divided into grids (100 × 100 m) with a total of 948 soil samples georeferenced. The laboratory RS data were obtained using an IRIS (Infrared Intelligent Spectroradiometer) sensor (400–2,500 nm) with a 2-nm spectral resolution between 450 and 1,000 nm and 4-nm between 1,000 and 2,500 nm. Satellite reflectance values were sampled from corrected Landsat Thematic Mapper (TM) images. Each pixel in the image was evaluated as its vegetation index, color compositions and soil line concepts regarding certain locations of the field in the image. Chemical and physical analysis (organic matter content, sand, silt, clay, sum of cations, cations saturation, aluminum saturation and cations exchange capacity) were performed in th

Comparação de equações de chuvas intensas para localidades do estado de São Paulo Comparing rainfall intensity duration relationships for sites of the state of São Paulo

Dada a importância das equações de chuvas intensas para o dimensionamento de obras de controle de enxurradas, este trabalho teve como objetivo comparar as alturas precipitadas geradas pelas equações de Martinez & Magni (1999) com aquelas obtidas com o programa PLUVIO 2.1, considerando as primeiras como padrão. Foram comparadas chuvas intensas de 10; 20; 30; 60; 120 e 1.440 minutos, e períodos de retorno de 2; 5; 10; 50 e 100 anos, para 30 localidades do Estado de São Paulo. Os resultados revelaram que, principalmente para as chuvas de 24 horas e período de retorno de 100 anos, houve desvios importantes para 4 postos localizados na região central e a leste do Estado. Para as demais localidades o programa apresentou bom desempenho.<br>Rainfall intensity durations relationships are extremely important in the design of systems for mitigating runoff losses. The objective of this work was to compare rainfall depths generated by the PLUVIO 2.1 software, with depths from the standard intensity duration curves developed by MARTINEZ & MAGNI (1999). It was compared rainfall intensities of 10, 20, 30, 60, 120 and 1440 minute durations for 2, 5, 10, 50 and 100 year return periods for 30 sites in the state of São Paulo. The results showed that PLUVIO was effective, except in predicting the 24 hours rainfall from 100 year return period events in four locations in the central and eastern regions of the state

Monitoring <i>Bemisia tabaci</i> (Gennadius) (Hemiptera: Aleyrodidae) Infestation in Soybean by Proximal Sensing

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

Monitoring Bemisia tabaci (Gennadius) (Hemiptera: Aleyrodidae) Infestation in Soybean by Proximal Sensing

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

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 &gt; 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

Análise discriminante dos solos por meio da resposta espectral no nível terrestre Soil discrimination analysis by spectral response in the ground level

O objetivo deste trabalho foi desenvolver e avaliar um método para discriminação das classes de solos a partir de suas respostas espectrais, utilizando-se um sensor em laboratório. Os dados espectrais foram utilizados no desenvolvimento de modelos estatísticos para discriminar as classes de solos de uma área no sudoeste do Estado de São Paulo. Equações discriminantes foram desenvolvidas para as 18 classes. A resposta espectral foi obtida em amostras da porção superficial e da porção subsuperficial dos solos da área de estudo, num total de 370 amostras. As amostras foram coletadas em 185 ha, com uma tradagem por ha. Os resultados demonstraram que as classes de solos podem ser individualizadas e distinguidas pela análise discriminante. A análise registrou índices de acerto acima de 80% de determinação da classe de solo avaliada. O acerto global foi de 90,71% quando se utilizaram todas as classes para a geração dos modelos, e 93,44% quando se utilizaram as dez classes com maior número de indivíduos. O teste estatístico simulado mostrou-se eficiente na análise discriminante, com taxa média de acerto acima de 91%, com erro global de 8,8%. A análise demonstrou redução na qualidade do modelo quando aplicado para um subconjunto de 20% das amostras, com erro global de 33,9%. O método auxilia na discriminação de classes de solos pela sua reflectância, devido às interações físicas com a energia eletromagnética.<br>The objective of this study was to develop and test a discrimination method for soil classes by their spectral response (SR), using a laboratory sensor. Spectral data were used to develop statistical model for discriminating soil classes in an area at the southwest of São Paulo State, Brazil. Discriminant equations were developed for 18 soil classes. The spectral data were obtained in superficial and subsuperficial soil samples in the study area, with a total of 370 samples. The samples were collected in 185 ha, with one borehole per ha. The results showed that soil classes can be separated and delimitated by discriminant analysis. The analysis presented a classification index higher than 80% for each soil class. The global classification index was 90.71%, when all soil classes were used to develop the model, and 93.44% when most individuals classes were used. The simulated statistical test was efficient in the discriminant analysis, presenting a classification index higher than 91%, with a global error of 8.8%. The analysis demonstrated a reduction of the model quality when applied for 20% sub-group of the samples with global error of 33.9%. The method helped in the soil classes discrimination by their spectral reflectance, based on their physical interaction with electromagnetic energy

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-