| Abstrakt: |
The determination of soil properties, in addition to requiring great human effort, also involves a number of technical activities of high financial cost. Seeking to show that it is possible to reduce these high financial costs, this study presents a methodology that combines remote sensing techniques and mathematical modeling for estimating the concentration of soil organic matter (SOM). Technological advances have provided great improvements in equipment for capturing terrestrial information, allowing a greater availability of quality data for spatial analysis, which has brought about better estimates in the models. Thus, this study evaluated the capacity of the Sentinel-1 Synthetic Aperture Radar (SAR) satellite to determine the concentration of SOM in areas with different types of agricultural use in a hydrographic basin localted in the souutheastern region of the state of São Paulo, Brazil. The partial least squares regression method was used to build models for estimating the SOM, taking into account the SAR backscattering values and soil samples obtained in situ, which were collected at a depth of 0–10 cm. The results obtained indicated that the accuracy of the model adjusted based on backscattering values in the vertical/vertical (VV), vertical/horizontal (VH) polarizations, product of VHxVV and soil moisture presented a coefficient of determination of 0.502 with the SOM for independent data, at p <0.0001. These results indicate that the model derived from the SAR data for the mapping of SOM presented potential for prediction, but only within acceptable limits for predicting SOM in areas with similar characteristics of land use. [ABSTRACT FROM AUTHOR] |
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