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Soil apparent electrical conductivity (ECa) is related to various soil attributes and processes. This research aimed to understand the relationship between ECa and paedogenesis, lithology and attributes. The study area is located in S~ao Paulo State, Brazil. Soil samples were collected for physical-chemical analysis from 79 locations (0–20 cm layer). A geophysical field-portable equipment (Geonics EM38-MK2 conductivity meter) was used to measure soil ECa. For that, four toposequences were selected on landscape. Sixteen soil profiles were allocated in the landscape for soil characterization and classification. The ECa values were measured across toposequences, soil types and lithology variations, at the same 79 locations used for physico-chemical characterization. The statistical analysis and Kruskal–Wallis rank sum test and Cluster analysis by k-means and Wilcoxon–Mann–Whitney test was performed to verify differences in ECa values over lithology, relief and soil types. The values of ECa measurements were related to soil attributes variation. The results showed that lithology strongly affects ECa values, mainly in diabase-derived soils, followed by metamorphosed siltstone. In fluvial sediments, the ECa exhibits different behaviours. In less evolved soils, the lithology contributed more to ECa than paedogenesis, as the opposite occurs in more evolved ones. The ECa decreases downslope and correlates with lithology alteration and soil types. This study demonstrates that soil ECa can be a tool to differentiate between various soil types and lithological transitions across the landscape, supporting the soil survey and mapping at finer scales. [ABSTRACT FROM AUTHOR]

Detailed soil maps contribute to the understanding of agricultural and food value chains. PlanetScope satellite constellations have high spatial and temporal resolution and could be used to develop Synthetic Soil Image (SYSI) at specific dates of field crops calendar. This work aimed to derive a SYSI from PlanetScope time series and estimate soil clay content, an important attribute related to several soil functions. The normalization process was applied to make the radiometries of the different Doves' images compatible and, subsequently, land cover classification was performed to select the bare soil class, obtaining the SYSI-Planet. The clay content was predicted for 2337 topsoil samples using the SYSI-Planet and Random Forest algorithm. The normalization of PlanetScope images were based on reference images, which mitigated the radiometric differences from acquisition dates and different radiometric calibrations. SYSI-Planet resulted in an excellent clay content prediction, resulting in a coefficient of determination (R2) equal to 0.93 and Root-Mean-Square Error (RMSE) equal to 70.90. The predicted clay mapped revealed spatial patterns with a high spatial resolution (~3 m), which can enhance precise agricultural management. [ABSTRACT FROM AUTHOR]

Terrain analysis is an important tool for modeling environmental systems. Aiming to use the cloud-based computing capabilities of Google Earth Engine (GEE), we customized an algorithm for calculating terrain attributes, such as slope, aspect, and curvatures, for different resolution and geographical extents. The calculation method is based on geometry and elevation values estimated within a 3 × 3 spheroidal window, and it does not rely on projected elevation data. Thus, partial derivatives of terrain are calculated considering the great circle distances of reference nodes of the topographic surface. The algorithm was developed using the JavaScript programming interface of the online code editor of GEE and can be loaded as a custom package. The algorithm also provides an additional feature for making the visualization of terrain maps with a dynamic legend scale, which is useful for mapping different extents: from local to global. We compared the consistency of the proposed method with an available but limited terrain analysis tool of GEE, which resulted in a correlation of 0.89 and 0.96 for aspect and slope over a near-global scale, respectively. In addition to this, we compared the slope, aspect, horizontal, and vertical curvature of a reference site (Mount Ararat) to their equivalent attributes estimated on the System for Automated Geospatial Analysis (SAGA), which achieved a correlation between 0.96 and 0.98. The visual correspondence of TAGEE and SAGA confirms its potential for terrain analysis. The proposed algorithm can be useful for making terrain analysis scalable and adapted to customized needs, benefiting from the high-performance interface of GEE. [ABSTRACT FROM AUTHOR]