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

Demattê, José A.M.; Galdos, Marcelo Valadares; Guimaraes, Rodrigo; Genú, Aline Marques; Nanni, Marcos Rafael; Zullo, Jurandir

doi:10.1080/01431160601121469

Cited by 61 publications

(35 citation statements)

References 15 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…CAMPOS et al (2007), SANTOS et al (2011), andCAMPOS et al (2012b) obtained similar results by studying soil chemical properties in different landscape compartments. DEMATTÊ et al (2007), SANTOS et al (2011), NOVAES FILHO et al (2012) and CAMPOS et al (2012b highlight a numerical classification of models that take into account spatial distribution of soil attributes, to identify boundaries within soil taxonomic classes, and provide information on relationship between soil genesis and landscape evolution. Despite GS I is older and more weathered (Figure 2), it is the most homogeneous layer and has lower spatial variability than GS III, which is younger and with great heterogeneity (larger number of classes within same compartment), especially at 0.60-0.80 m depth.…”

Section: Resultsmentioning

confidence: 99%

“…However, precision and accuracy of these procedures are related to the number of observation points, which increase mapping costs (DEMATTÊ et al, 2007;CAMPOS et al, 2012a). In South America, procedures that require a large number of samples is not widely used due to high cost of soil analysis.…”

Section: Introductionmentioning

confidence: 99%

“…However, the absence of detailed information on soil and its attributes is further aggravated with the lack of experienced soil scientists (mappers) (DEMATTÊ et al, 2007;SANCHEZ et al, 2012). Additionally, there is deficiency in cartographic works and soil taxonomic (Radam Brasil and state survey) compared to new advances in soil knowledge, its attributes, and further studies on variability (CAMPOS et al, 2012a;SANCHEZ et al, 2012).…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Outlining precision boundaries among areas with different variability standards using magnetic susceptibility and geomorphic surfaces

et al. 2014

View full text Add to dashboard Cite

ABSTRACT:There is an increasing demand for detailed maps that represent in a simplified way the knowledge of the variability of a particular area or region maps. The objective was to outline precision boundaries among areas with different accuracy variability standards using magnetic susceptibility and geomorphic surfaces. The study was conducted in an area of 110 ha, which identified three compartment landscapes based on the geomorphic surfaces model. To determinate pH, organic matter, phosphorus, potassium and magnesium, the total sand and clay, 514 soil samples were collected at depths of 0-0.20 m and 0.60-0.80 m. The sum of base, cationic exchange capacity and base saturation were calculated and the magnetic susceptibility was evaluated in the laboratory using a system based on a balance of analytical precision method. Geomorphic surfaces identification allowed setting specific management areas (locations with maximum homogeneity of soil attributes). The map of spatial variability of magnetic susceptibility can be used to validate the precise boundaries among geomorphic surfaces identified in the field and infer the variability of clay content and soil base saturation.KEYWORDS: detailed mapping, pedometrics, accuracy boundaries. DELINEAMENTO DE LIMITES DE PRECISÃO ENTRE ÁREAS COM DIFERETES PADRÕES DE VARIABILIDADE UTILIZANDO SUSCETIBILIDADE MAGNÉTICA E SUPERFÍCIES GEOMÓRFICASRESUMO: Existe uma demanda cada vez maior por mapas detalhados que representem de maneira simplificada o conhecimento da variabilidade de determinada área ou região. O objetivo do trabalho foi delinear limites de precisão entre áreas com diferentes padrões de variabilidade utilizando suscetibilidade magnética e superfícies geomórficas. O trabalho foi realizado em uma área de 110 ha, onde se identificaram três compartimentos de paisagem com base no modelo de superfícies geomórficas. Foram coletadas 514 amostras de solo nas profundidades de 0-0,20 m e 0,60-0,80 m, para a determinação de pH, matéria orgânica, fósforo, potássio e magnésio, areia total e argila. A soma de base, capacidade de troca catiônica e saturação por bases foram calculadas. A suscetibilidade magnética foi avaliada em laboratório, utilizando-se de um método baseado em uma balança de precisão analítica. A identificação das superfícies geomórficas permitiu o reconhecimento de áreas específicas de manejo (locais com máxima homogeneidade dos atributos do solo). O mapa de variabilidade espacial da suscetibilidade magnética pode ser utilizado para validar com precisão os limites entre as superfícies geomórficas identificadas no campo e inferir a variabilidade do teor de argila e saturação por base do solo.

show abstract

Section: Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Outlining precision boundaries among areas with different variability standards using magnetic susceptibility and geomorphic surfaces

et al. 2014

View full text Add to dashboard Cite

show abstract

“…Although the Landsat bands have limited capacity for predicting soil properties (Section 4.4), the prediction results for SOM and clay are moderate and spatial patterns were predicted as expected. Other studies that predicted soil properties based on Landsat data [9,11,12] have shown similar or slightly better results, although comparison is difficult with only R 2 as a performance indicator. Moreover, when discussing these results, we must be aware that these are exclusively based on Landsat data while most digital soil maps used many more covariates.…”

Section: Discussion Of the Resultsmentioning

confidence: 89%

Barest Pixel Composite for Agricultural Areas Using Landsat Time Series

et al. 2017

View full text Add to dashboard Cite

Many soil remote sensing applications rely on narrow-band observations to exploit molecular absorption features. However, broadband sensors are invaluable for soil surveying, agriculture, land management and mineral exploration, amongst others. These sensors provide denser time series compared to high-resolution airborne imaging spectrometers and hold the potential of increasing the observable bare-soil area at the cost of spectral detail. The wealth of data coming along with these applications can be handled using cloud-based processing platforms such as Earth Engine. We present a method for identifying the least-vegetated observation, or so called barest pixel, in a dense time series between January 1985 and March 2017, based on Landsat 5, 7 and 8 observations. We derived a Barest Pixel Composite and Bare Soil Composite for the agricultural area of the Swiss Plateau. We analysed the available data over time and concluded that about five years of Landsat data were needed for a full-coverage composite (90% of the maximum bare soil area). Using the Swiss harmonised soil data, we derived soil properties (sand, silt, clay, and soil organic matter percentages) and discuss the contribution of these soil property maps to existing conventional and digital soil maps. Both products demonstrate the substantial potential of Landsat time series for digital soil mapping, as well as for land management applications and policy making.

show abstract

“…It is not possible to have a correct assess of the cation exchange capacity for the region if this equation with the obtained R-square for the entire data is used. It should be noted that other researchers like Dematte et al (2007) also reported weak correlations between some soil chemical properties such as Ca, Mg, Al, pH and K values in soil. Although, the correlation coefficient was between 0.3 and 0.4 when organic matter, sand and clay percentage were considered for the analysis.…”

Section: Resultsmentioning

confidence: 99%

Using satellite data for soil cation exchange capacity studies

Ghaemi¹,

Astaraei²,

Nejad³

et al. 2013

International Agrophysics

View full text Add to dashboard Cite

A b s t r a c t. This study was planned to examine the use of LandSat ETM + images to develop a model for monitoring spatial variability of soil cation exchange capacity in a semi-arid area of Neyshaboor. 300 field data were collected from specific GPS registered points, 277 of which were error free, to be analysed in the soil laboratory. The statistical analysis showed that there was a small R-Squared value, 0.17, when we used the whole data set. Visual interpretation of the graphs showed a trend among some of the data in the data set. Forty points were filtered based on the trends, and the statistical analysis was repeated for those data. It was discovered that the 40 series were more or less in the same environmental conditions; most of them were located in disturbed soils or abandoned lands with sparse vegetation cover. The soil was classified into high and medium salinity, with variable carbon (1.0 to 1.6%), heavy textured and with high silt and clay. Finally it was concluded that two different models could be fitted in the data based on their spatial dependency. The current models are able to explain spatial variability in almost 45 to 65% of the cases.K e y w o r d s: soil cation exchange capacity, remote sensing, soil properties, soil spatial variability

show abstract

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

Cited by 61 publications

References 15 publications

Outlining precision boundaries among areas with different variability standards using magnetic susceptibility and geomorphic surfaces

Outlining precision boundaries among areas with different variability standards using magnetic susceptibility and geomorphic surfaces

Barest Pixel Composite for Agricultural Areas Using Landsat Time Series

Using satellite data for soil cation exchange capacity studies

Contact Info

Product

Resources

About