Surface reflectance and pXRF for assessing soil weathering indexes

Abstract

The use of satellite images is a widespread technique for estimating soil attributes. However, what is the potential of satellite data on the prediction of soil weathering indices? How much in their performance is affected by spectral mixing and data resolution? There is no consensus for answering these questions. Hence, the main objectives of this work were: i) to show the difference of spectral signature between prepared and field-condition samples; ii) to compare the convoluted data from laboratory spectroradiometer with the spectral signatures from Sentinel-2 and Landsat-8 satellites; and iii) to build and evaluate predictive models for soil weathering indices using diffuse reflectance spectroscopy from samples under natural conditions, air-dried-sieved and from satellite images. Soil samples were collected from the first 1-cm depth in 27 locations along seven catenas located in a 163-ha area in São Paulo State, Brazil. The samples were analyzed by a Vis-NIR-SWIR spectrometer without sample preparation (field conditions) and after being air-dried and sieved (prepared). The air-dried-sieved samples were also analyzed by an X-ray fluorescence equipment. Reflectance data were obtained from Sentinel and Landsat for the same 27 locations. The spectral data from the lab were convoluted to respective bands of the real satellite data. Predictive models for soil weathering indices using the convoluted laboratory data and the satellite data were also built and compared. The reflectance of samples under field conditions contrasted significantly between soils derived from basalt and sedimentary rocks. In general, the reflectance intensity of prepared samples was 80% higher than that observed for field-conditions, which can be explained by the roughness and the structure effect. However, some cases showed an opposite trend, with a smaller relative reflectance from the air-dried-sieved samples. These trends support the hypothesis of aggregates heterogeneity and clay occlusion. The sample preparation can increase or decrease the reflectance of the soil surface material, depending, mainly, on roughness, soil structure and particles occlusion by the structure. The Landsat spectral signatures were more similar to those from convoluted laboratory than to the Sentinel ones. The satellite performance on the estimation of the soil weathering indices is comparable to those from convoluted laboratory data, but it is limited by spectral resolution. Sentinel and Landsat achieved ratio of performance to interquartile distance (RPIQ) from 1.53 to 2.38 and from 1.56 to 2.42 on soil weathering indices estimation, respectively. The models for Plagioclase Index of Alteration (PIA) and Si/Ti [Si and (Ti and Al) ratio] indices were considered the best ones (RPIQ from 2.17 to 3.6; and RPIQ from 1.90 to 5.56; respectively) for both laboratory and satellite data; in turn, Si/Al, Si (Al + Fe) and Chemical Index of Alteration (CIA) indices performed poorly/reasonably (RPIQ <1.92).