WorldView-2 sensor for the detection of hematite and goethite in tropical soils

Abstract

-The objective of this work was to simulate the bands of the WorldView-2 sensor from laboratory specters, in order to study its potential to detect iron oxides, besides proposing a spectral index based on the depth of the spectral feature (RHGt Pf ). The iron index (IFe) and the hematite index (IHm), developed by Madeira Netto for bands of Landsat TM-5 sensor, were adjusted for WorldView-2 to test the potential of the new yellow band. The results showed that the yellow band degrades to 2% compared to the red one. This is due to fact that mineral quantification and identification are in the absorption feature, not in the reflectance one, and it is only improved in the visualization of color. A new spectral index, the RHGt PF , based on the depth feature with the continuum-removed spectra, was proposed, since the new bands of the WorldView-2 sensorthe coastal blue and yellow bands -allow a better individualization of the absorption features for goethite and hematite separately. The new index is statistically similar to the ratio Hm / Gt + Hm; however, it is not similar to the mineralogical relation obtained with Munsell colors.Index terms: mineralogical relation, remote sensing, spectral feature depth, spectral indices. Sensor WorldView-2 para detecção de hematita e goethita em solos tropicaisResumo -O objetivo deste trabalho foi simular as bandas do sensor WorldView-2 a partir de espectros obtidos em laboratório, para estudar seu potencial de detectar óxidos de ferro, além de propor um índice espectral com base na profundidade da feição espectral (RHGt PF ). O índice férrico IFe e o índice hematita IHm, desenvolvidos por Madeira Netto para as bandas do sensor Landsat-TM5, foram ajustados para o WorldView-2 para testar o potencial da nova banda do amarelo. Os resultados mostraram que a banda do amarelo degrada em 2% comparada à do vermelho. Isso se deve ao fato de a quantificação e identificação do mineral estarem localizadas na feição de absorção e não na de reflectância, e isso só é melhorado na visualização da cor. Um novo índice espectral, RHGt PF , baseado na profundidade de feição com o contínuo espectral removido, foi proposto, pois as novas bandas do sensor WorldView-2 -azul costal e amarelo -permitem individualizar melhor as feições de absorção da goethita e da hematita separadamente. O novo índice é estatisticamente similar à relação Hm / Hm + Gt; porém, ele não é similar à relação mineralógica obtida por meio das cores de Munsell.Termos para indexação: relação mineralógica, sensoriamento remoto, profundidade da feição espectral, índices espectrais.

Digital Soil Mapping Using Multispectral Modeling with Landsat Time Series Cloud Computing Based

“…The FRro-I presented stronger absorption features than FRro-II due to the smaller particle size in the first endmember, especially those related to kaolinite features. Baptista and Teobaldo [45] examined iron oxides in the region using spectroscopy, and they found hematite percentages ranging from 0 to 20%, gibbsite from 0.8 to 6.8% for tropical Rhodic Ferralsols. These results agree with [3], who state that iron oxide content varies by 15% in the study area.…”

“…It was possible to observe iron oxide features at specific points on the curve, with a predominance of goethite over hematite, in addition to kaolinite absorption features (1.4 µm and 2.205 µm), hydroxyl groups (1.4 µm and 1.9 µm), and gibbsite (2.265 µm). Previous studies [10,17,20,45] revealed that the soil mineral composition, which has a low content of primary minerals and a high content of oxides, indicates a high degree of weathering processes influencing soil colors. Hydromorphic soils usually exhibit a lower albedo due to the higher OM and residual moisture in the soil solution [4].…”

Estimating water erosion from the brightness index of orbital images: A framework for the prognosis of degraded pastures

“…Accordingly, uncovered soils or soils with sparse vegetation are perceived by high brightness values (Gleriani, 2003;Healey et al, 2005;Vorovencii, 2007). The brightness index is able to discriminate the vegetation from minerals in the soil, such as quartz or carbonates, especially when they are dry (Bannari et al, 2016;Baptista and Teobaldo, 2017;Demattê et al, 2014). In the EPA of Uberaba River basin, Rodrigues et al (2019) reported a spatial correlation between brightness and soil loss, more evident in the dry season, using cross tabulation.…”