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Abstract. Topsoil (0–20 cm) and subsoil (60–100 cm) properties are compared at agricultural and nearby natural vegetation sites in São Paulo State. Differences are related to land use and climate, in order to estimate soil carbon storage under various ecosystems and also to study the effects of high‐input agriculture on the chemical composition of soils with low activity clays. Within each land use, organic carbon in the topsoil is most strongly related to clay + silt content. This relationship is stronger for cropped, short savannah (cerrado) and tall savannah (cerradão) sites than for semi‐deciduous and evergreen forest sites. Losses of topsoil carbon with cropping can be predicted if the initial carbon and the clay+silt contents are known. The greatest carbon losses after long term cultivation occurred in forest mineral topsoils, ranging from 6% for perudic clayey soils to 37% for ustic sandy soils. No significant difference in carbon content was found between the paired savannah‐cultivated sites. In most of the originally less fertile soils cation exchange capacity was greater in the cultivated topsoil (Ap) than in the topsoil under savannah or forest (A1), probably because of liming and phosphate fertilization. Most subsoils at agricultural sites show increases in exchangeable bases (mainly Ca) and base saturation, but no significant change in pH.

( l ) Recebido para publicação a 26 de agosto de 1981. No solo, o teor de carbono em equilíbrio com determinado tipo de vegetação é função das adições e da taxa de decomposição da matéria orgânica. Essa relação pode ser expressa pela fórmula: c -bm/k, onde c é a quantidade de carbono orgânico em equilíbrio no solo (t/ha) ; b, a quantidade anual de matéria orgânica (%) adicionada ao solo; m, a taxa de conversão, do material orgânico fresco em carbono orgânico do solo (t/ha), e k, a taxa de decomposição anual da matéria orgânica [%) (5).A taxa de decomposição da matéria orgânica varia em função das condições de temperatura e umidade. Para idênticas adições, quanto mais frio e úmido for o local, menor é essa taxa e, portanto, maiores teores de matéria orgânica deverão ser encontrados no solo. Contudo, mesmo em condições semelhantes de clima, drenagem e manejo, é comum observar considerável variação nos teores de húmus, isto porque os solos têm diferentes capacidades de retenção e proteção à decomposição da matéria orgânica, em função de suas superfícies específicas, ou seja, quanto maior for esta superfície, mais húmus poderão adsorver e proteger (1). Como GROH-MANN (3) encontrou, para solos de São Paulo, positiva relação entre superfície específica e quantidades de argila, é de esperar que os teores de matéria orgânica estejam relacionados com a textura do solo, desde que sejam semelhantes clima, tipo (ou atividade) de argila, vegetação, manejo e drenagem. De fato, LEPSCH (4) encontrou

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.

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SUMMARYSince different pedologists will draw different soil maps of a same area, it is important to compare the differences between mapping by specialists and mapping techniques, as for example currently intensively discussed Digital Soil Mapping. Four detailed soil maps (scale 1:10.000) of a 182-ha sugarcane farm in the county of Rafard, São Paulo State, Brazil, were compared. The area has a large variation of soil formation factors. The maps were drawn independently by four soil scientists and compared with a fifth map obtained by a digital soil mapping technique. All pedologists were given the same set of information. As many field expeditions and soil pits as required by each surveyor were provided to define the mapping units (MUs). For the Digital Soil Map (DSM), spectral data were extracted from Landsat 5 Thematic Mapper (TM) imagery as well as six terrain attributes from the topographic map of the area. These data were summarized by principal component analysis to generate the map designs of groups through Fuzzy K-means clustering. Field observations were made to identify the soils in the MUs and classify them according to the Brazilian Soil Classification System (BSCS). To compare the conventional and digital (DSM) soil maps, they were crossed pairwise to generate confusion matrices that were mapped. The categorical analysis at each classification level of the BSCS showed that the agreement between the maps (1) Part of the Dissertation of the first author, submitted to the Escola Superior de Agricultura "Luiz de Queiroz" -ESALQ/USP.Received for publication on November 1st, 2012 and approved on July 11, 2013. (2 RESUMO: COMPARAÇÃO ENTRE MAPAS DE SOLOS DETALHADOS OBTIDOS PELOS MÉTODOS CONVENCIONAL E DIGITAL EM