Geospatial Soil Sensing System (GEOS3): A powerful data mining procedure to retrieve soil spectral reflectance from satellite images

Soil mapping has been identified as key to environmental issues. The determination of soil attributes to achieve the best decision making on land use planning is crucial. The use of remote sensing (satellite images) can improve understanding of the surface, since it collects a spectral reflectance fingerprint related to soil properties. However, methodologies still gather spatially fragmented information on bare soil in a single image; thus, there is still room to improve information as a continuous surface. This work has the purpose of developing a procedure using multi-temporal satellite image information, aiming to construct a single synthetic image which would represent soils. The work was carried out in the state of Sao Paulo, Brazil, on a site covering 14,614 km(2). The procedure, designated as Geospatial Soil Sensing System (GEOS3), is based on the following steps: a) creation of a database with Landsat 5 legacy data.; b) filtering of the database to provide images only from the dry season in the region; c) insertion of a set of rules into the system to filter other objects besides soils; d) Each bare soil occurrence for each location along the time-series was used to calculate a Temporal Synthetic Spectral Reflectance (TESS) of the soil surface; e) aggregation of all TESS composes the Synthetic Soil Image (SYSI); f) quantitative and qualitative validation of the SYSI through the correlation between laboratory and TESS, soil line assessment and the principal component analysis (PCA). GEOS3 was able to provide the best representative reflectance of soils for each band during the historical period. Thus, TESS is not the 'true' but a synthetic spectral reflectance. The canonical correlation between laboratory and satellite data reached 0.93. A value of up to 0.88 in the Pearson's correlation between laboratory and TESS was also achieved. In a single scene, only 0.5% of area was available as isolated bare soil for spatial analysis. However, SYSI reached 68%. Considering only the sugarcane agricultural areas, a value of 92% was achieved. Our study indicates that a multi-temporal data mining procedure can retrieve soil surface representation. The key to the results was calculating the median spectral reflectance from the bare soil pixels along the period of the time series. GEOS3 products can aid soil evaluation by assisting in digital soil mapping, soil security, precision agriculture, soil attribute quantification, soil conservation, environment monitoring and soil sample allocation, among others.