José Alexandre Melo Demattê

SOIL SPECTRAL LIBRARY AND ITS USE IN SOIL CLASSIFICATION

SUMMARY Soil science has sought to develop better techniques for the classification of soils, one of which is the use of remote sensing applications. The use of ground sensors to obtain soil spectral data has enabled the characterization of these data and the advancement of techniques for the quantification of soil attributes. In order to do this, the creation of a soil spectral library is necessary. A spectral library should be representative of the variability of the soils in a region. The objective of this study was to create a spectral library of distinct soils from several agricultural regions of Brazil. Spectral data were collected (using a Fieldspec sensor, 350–2,500 nm) for the horizons of 223 soil profiles from the regions of Matao, Paraguacu Paulista, Andradina, Ipaussu, Mirandopolis, Piracicaba, Sao Carlos, Araraquara, Guararapes, Valparaiso (SP); Navirai, Maracaju, Rio Brilhante, Tres Lagoas (MS); Goianesia (GO); and Uberaba and Lagoa da Prata (MG). A Principal Component Analysis (PCA) of the data was then performed and a graphic representation of the spectral curve was created for each profile. The reflectance intensity of the curves was principally influenced by the levels of Fe2O3, clay, organic matter and the presence of opaque minerals. There was no change in the spectral curves in the horizons of the Latossolos, Nitossolos, and Neossolos Quartzarenicos. Argissolos had superficial horizon curves with the greatest intensity of reflection above 2,200 nm. Cambissolos and Neossolos Litolicos had curves with greater reflectance intensity in poorly developed horizons. Gleisols showed a convex curve in the region of 350–400 nm. The PCA was able to separate different data collection areas according to the region of source material. Principal component one (PC1) was correlated with the intensity of reflectance samples and PC2 with the slope between the visible and infrared samples. The use of the Spectral Library as an indicator of possible soil classes proved to be an important tool in profile classification.

Quantification of tropical soil attributes from ETM+/LANDSAT-7 data

The characterization of physical and chemical soils attributes is a pressing necessity for the agricultural land management optimization in many countries. Currently, soil analyses are performed by chemical treatments in a laboratory, generating environmental and time‐consuming problems. Remote sensing techniques can be faster and cheaper than conventional methods, do not generate chemical residues and are non‐destructive to the samples. The objective of the present work was to determine a remote sensing technique to estimate soil physical and chemical attributes in the regions of Paraguaçu Paulista and Rio Brilhante, in the States of São Paulo and Mato Grosso do Sul, respectively, Brazil, using reflectance data obtained by a sensor located in orbit. Fieldwork was performed to validate orbital data. A total of 110 soil samples were collected representing 43,000 h for the development of spectral models. Landsat‐7 ETM+images were atmospherically corrected and transformed to reflectance. The soil samples observed in the field were located by GPS and evaluated in the orbital image. The method used consists in a detailed investigation of the spectral data, in which the spectral curve, the position of the data in a graphic dispersion, the colour compositions and the pixel cursor values are evaluated. Spectral models were determined to quantify soil attributes. Soil samples from a different area had their attribute contents determined by the models. The attributes studied were sand, silt, clay, pH (CaCl2), organic matter, phosphorus, potassium, calcium, magnesium, aluminium, hydrogen, cation exchange capacity (CEC), sum of cations (SC) base saturation (BS) and aluminium saturation (AS). The results showed weak correlations with some soil attributes, such as SC, K, Ca, Mg, Al and P. High correlations (reaching 0.86) were obtained with sand, clay, silt, OM, CEC and H. On the other hand, validation procedure indicated that the best attributes for quantification were clearly clay (0.61) and sand (0.5). Therefore, there is strong evidence that these attributes can be predicted in similar landscapes, using the multiple‐linear equations developed in this study. This knowledge can be useful in many ways in agriculture, such as soil mapping, determining soil attributes, determining soil information in difficult access regions, and diminishing traditional soil analyses with environmental protection.

Caracterização e discriminação de solos pela sua energia eletromagnética refletida

(2) Abstract - The objective of this work was to verify if reflected energy of soils can characterize and discriminate them. A spectroradiometer (Spectral reflectance between: 400-2,500 nm) was utilized in laboratory. The soils evaluated are located in Bauru region, SP, Brazil, and are classified as Typic Argiudoll (TR), Typic Eutrorthox (LR), Typic Argiudoll (PE), Typic Haplortox (LE), Typic Paleudalf (PV) and Typic Quartzipsamment (AQ). They were characterized by their spectral reflectance as for descriptive conventional methods (Brazilian and International) according to the types of spectral curves. A method for the spectral descriptive evaluation of soils was established. It was possible to characterize and discriminate the soils by their spectral reflectance, with exception for LR and TR. The spectral differences were better identified by the general shape of spectral curves, by the intensity of band absorption and angle tendencies. These characteristics were mainly influenced by organic matter, iron, granulometry and mineralogy constituents. A reduction of iron and clay contents, which influ- enced higher reflectance intensity and shape variations, occurred on the soils LR/TR, PE, LE, PV and AQ, on that sequence. Soils of the same group with different clay textures could be discriminated. The conventional descriptive evaluation of spectral curves was less efficient on discriminating soils. Simu- lated orbital data discriminated soils mainly by bands 5 and 7.

Optimum size in grid soil sampling for variable rate application in site-specific management

The importance of understanding spatial variability of soils is connected to crop management planning. This understanding makes it possible to treat soil not as a uniform, but a variable entity, and it enables site-specific management to increase production efficiency, which is the target of precision agriculture. Questions remain as the optimum soil sampling interval needed to make site-specific fertilizer recommendations in Brazil. The objectives of this study were: i) to evaluate the spatial variability of the main attributes that influence fertilization recommendations, using georeferenced soil samples arranged in grid patterns of different resolutions; ii) to compare the spatial maps generated with those obtained with the standard sampling of 1 sample ha -1 , in order to verify the appropriateness of the spatial resolution. The attributes evaluated were phosphorus (P), potassium (K), organic matter (OM), base saturation (V%) and clay. Soil samples were collected in a 100 × 100 m georeferenced grid. Thinning was performed in order to create a grid with one sample every 2.07, 2.88, 3.75 and 7.20 ha. Geostatistical techniques, such as semivariogram and interpolation using kriging, were used to analyze the attributes at the different grid resolutions. This analysis was performed with the Vesper software package. The maps created by this method were compared using the kappa statistics. Additionally, correlation graphs were drawn by plotting the observed values against the estimated values using cross-validation. P, K and V%, a finer sampling resolution than the one using 1 sample ha -1 is required, while for OM and clay coarser resolutions of one sample every two and three hectares, respectively, may be acceptable.

Comportamento da linha do solo obtida por espectrorradiometria laboratorial para diferentes classes de solo

Com vistas em avaliar o comportamento da linha do solo para diferentes classes de solos, este trabalho foi realizado, utilizando-se dados de reflectância por meio de um espectrorradiometro em laboratorio. Amostras representativas dos horizontes superficiais e subsuperficiais de 18 classes coletadas em uma area piloto no Municipio de Rafard, sudoeste de Sao Paulo, foram secas, peneiradas e acondicionadas para leitura espectral. Os dados foram tabulados, para simularem as bandas 3 e 4 do TM-Landsat e construcao dos graficos. Os resultados demonstraram que as linhas de solo obtidas para as amostras de ambas as camadas comportaram-se de maneira semelhante, estando dispostas ortogonalmente aos eixos de um grafico, com valores de R2 proximos a 1,0 para todas as classes avaliadas. Nao se observou uma unica linha de solo, embora os dados revelem que cada solo apresenta uma linha individual e caracteristica. De modo complementar, destacou-se a utilizacao deste indice como metodo auxiliar na discriminacao de classes de solos, uma vez que os solos com textura arenosa e com menores teores de Fe total discriminaram-se daqueles mais argilosos e com maiores teores de Fe, alem de apresentarem valores de reflectância mais elevados. Por fim, constatou-se que a materia orgânica nao constituiu fator determinante no comportamento espectral em relacao a linha do solo.

Comparisons of chemical properties of forest (Amazon region) and savanna soils (central region) of Brazil

Foi estudada uma serie de propriedades dos solos das regioes Amazonica e de cerrado. Para a regiao de cerrado, selecionou-se 17 perfis do Triângulo Mineiro, 6 perfis da regiao de Goias e 5 perfis do Sudeste de MT, todos publicados em boletins de solo da EMBRAPA, sendo a maioria latossolos. Na Amazonia, selecionaram-se dados dos Boletins do Projeto RADAM BRASIL, sendo 76 perfis ao todo, retirados desde a regiao do Para ate o Acre; destes, 38 sao latossolos e 38 podzolicos vermelho amarelo (PVA). Para fins de comparacao entre os solos as seguintes profundidades foram padronizadas: 0-10 cm; 10-40 cm; 40-80 cm; 80-100 cm. Nao foi considerada a camada de serapilheira. Os solos da regiao do cerrado sao mais intemperizados do que os da regiao Amazonica, havendo dominância de mineralogia oxidica, mais estavel do que a dos solos da regiao Amazonica, onde a mineralogia e caulinitica com contribuicao de minerais 2:1. Apesar disso, a CTC dos latossolos da Amazonia nao difere da CTC dos solos de cerrado. Na regiao Amazonica os latossolos apresentam Ki diferenciado, sendo um grupo de solos com Ki na faixa de 0,8-1,5 tipico dos latossolos e um grupo na faixa de 2,0-2,2. O grupo dos PVA possui Ki superior a 2,0. Na regiao de cerrado o Ki varia de 0,6 a 1,5. Os solos da regiao Amazonica sao mais acidos e apresentam valores de Al trocavel significativamente superiores do que os da regiao de cerrado, o que requer mais corretivos de que o Cerrado. Ha grande diferenca entre a saturacao por bases (V) e o pH nos solos das duas regioes. Na regiao Amazonica a elevacao do pH segue a elevacao do V enquanto que no cerrado isto nao ocorre. Em consequencia deste fato, o manejo quimico em relacao a quantidade de calcario deve ser diferenciado entre as regioes. O teor de carbono orgânico nos solos de textura arenosa, a media e sempre maior para a regiao Amazonica. Entretanto, para todas as profundidades estudadas e para os solos argilosos a muito argilosos, o teor de carbono orgânico e sempre mais elevado na regiao de cerrado. Nao houve diferencas significativas entre o teor de carbono orgânico dos solos com vegetacao de cerrado e cerradao. Por outro lado, o teor de carbono orgânico dos latossolos da regiao Amazonica e mais elevado do que dos PVA.

Influência da matéria orgânica e de formas de ferro na reflectância de solos tropicais

ABSTRACT INFLUENCE OF ORGANIC MATTER AND IRON OXIDES ON THE SPECTRALREFLECTANCE OF TROPICAL SOILS The influence of organic matter and amorphous and crystalline iron contents on the reflectance(400 to 2500 nm) of various soils was evaluated. Studied soils were Typic Argiudoll (TR), Typic Eutrorthox(LR), Typic Argiudoll (PE), Typic Haplortox (LE), Typic Quartzipsamment (AQ), and Typic Paleudalf (PV).Surface and subsurface soil samples were used in four conditions: a) as sampled; b) after removal oforganic matter; c) after removal of the organic matter and the amorphous iron; and d) after removingorganic matter and amorphous iron and crystalline iron. Reflectance factor was acquired from 400 nmto 2.450 nm and TM/Landsat spectral bands were simulated. Removal of organic and amorphous ironcaused an overall increase of the reflectance along the spectrum, for all soils. Removal of crystallineiron caused the reflectance to increase in the first portion of the spectrum (400 to 1.000 nm), but not inintermediate (1.000 to 1.900 nm) and final portion of the spectrum (after 1.900 nm). Crystalline iron wasresponsible for concavities in the spectral region near 400 nm and 850 nm. Use of laboratoryspectroradiometry proved to be a fast, non-destructive, and easy-to-prepare technique for analyzingabsorption features caused by organic matter and iron forms in soils.

Prediction of soil properties using imaging spectroscopy: Considering fractional vegetation cover to improve accuracy

Spectroscopic techniques have become attractive to assess soil properties because they are fast, require little labor and may reduce the amount of laboratory waste produced when compared to conventional methods. Imaging spectroscopy (IS) can have further advantages compared to laboratory or field proximal spectroscopic approaches such as providing spatially continuous information with a high density. However, the accuracy of IS derived predictions decreases when the spectral mixture of soil with other targets occurs. This paper evaluates the use of spectral data obtained by an airborne hyperspectral sensor (ProSpecTIR-VS – Aisa dual sensor) for prediction of physical and chemical properties of Brazilian highly weathered soils (i.e., Oxisols). A methodology to assess the soil spectral mixture is adapted and a progressive spectral dataset selection procedure, based on bare soil fractional cover, is proposed and tested. Satisfactory performances are obtained specially for the quantification of clay, sand and CEC using airborne sensor data (R2 of 0.77, 0.79 and 0.54; RPD of 2.14, 2.22 and 1.50, respectively), after spectral data selection is performed; although results obtained for laboratory data are more accurate (R2 of 0.92, 0.85 and 0.75; RPD of 3.52, 2.62 and 2.04, for clay, sand and CEC, respectively). Most importantly, predictions based on airborne-derived spectra for which the bare soil fractional cover is not taken into account show considerable lower accuracy, for example for clay, sand and CEC (RPD of 1.52, 1.64 and 1.16, respectively). Therefore, hyperspectral remotely sensed data can be used to predict topsoil properties of highly weathered soils, although spectral mixture of bare soil with vegetation must be considered in order to achieve an improved prediction accuracy.

Estimation of soil properties by orbital and laboratory reflectance means and its relation with soil classification.

Wet chemistry methods to extract soil properties such as Fe2O3, TiO2, MnO and clay are cost effective, time consuming and environmental polluter. Moreover, a large set of samples has to be collected for precise spatial mapping. Ordinary surface soil mapping is a problematic method. Accordingly, non destructive technologies, such as remote sens- ing methods can provide important vantages. The objective of the present work was to estimate soil attributes by labora- tory and orbital sensors and compare these results with soil classification. The study area is a 473 ha bare soil field located in the region of Barra Bonita, Brazil. A sampling grid of 100 by 100 m was established and the exact position of each point was georeferenced, and sent to traditional (wet) laboratory analyses. The soil samples reflectance were also acquired by a laboratory sensor using artificial illumination (450 to 2500 nm). Over the same selected ground area reflectance data were extracted from the TM-Landsat-5 image. Prediction equations between the satellite and laboratory reflectance data and the wet chemistry were generated for each attribute. Most of the generated equations presented high and significant R 2 such as for the Fe2O3 with 0.82 for laboratory and 0.67 for the orbital reflectance data. The comparison between reflec- tance estimates and laboratory wet measurements for iron presented 92.2% success for the laboratory and 91.3% for the orbital sensors. The comparison for the texture intervals, showed 65% and 50% success for laboratory and orbital data re- spectively. The iron contents obtained by the sensors allowed to better remotely classify soil classes. Soil extractions to determine these attributes can be substitute by spectral reflectance models based on the present methodology.

Análise discriminante dos solos por meio da resposta espectral no nível terrestre

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 Sao 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.