Improving cover and management factor (C-factor) estimation using remote sensing approaches for tropical regions

• Published in: International Soil and Water Conservation Research Vol. 7; no. 4; pp. 325 - 334
• Main Authors: Almagro, André, Thomé, Thais Caregnatto, Colman, Carina Barbosa, Pereira, Rodrigo Bahia, Marcato Junior, José, Rodrigues, Dulce Buchala Bicca, Oliveira, Paulo Tarso Sanches
• Format: Journal Article
• Language: English
• Published: Elsevier B.V 01.12.2019; KeAi Communications Co., Ltd
• Subjects: basins; Brazil; Land use/land cover; Landsat 8; monitoring; NDVI; normalized difference vegetation index; prediction; rain; remote sensing; Revised Universal Soil Loss Equation; RUSLE; sediment yield; soil; Soil erosion; tropics; water conservation; Brazil; RUSLE; Soil erosion; Landsat 8; NDVI; Land use/land cover
• DOI: 10.1016/j.iswcr.2019.08.005
• ISSN: 2095-6339

The Revised Universal Soil Loss Equation (RUSLE)'s cover and management factor (C-factor) is one of the most difficult factors to obtain, mainly because long-term monitoring soil erosion plots under natural rainfall are needed. Therefore, remote sensing approaches have been used as an alternative for obtaining this factor. However, there is a lack of studies comparing values of this factor computed from remote sensing approaches with measured data. In this study, we compare two widely used remote sensing approaches (CrA and CVK) to estimate the C-factor based on the Normalized Difference Vegetation Index (NDVI) with the literature (CLIT) and field experimental data. We also investigated the influence of C-factor methods on the prediction of soil loss and sediment yield (SY) using measured data in the Guariroba basin, Central-West Brazil. We obtained mean C-factor values of 0.032, 0.023 and 0.137 for CLIT, CrA and CVK, respectively. We found an average annual soil loss of 2.20 t ha−1 yr−1, 2.02 t ha−1 yr−1 and 10.07 t ha−1 yr−1 and SY values of 6875 t yr−1, 6468 t yr−1 and 33,435 t yr1, for CLIT, CrA and CVK, respectively. Our results indicated a significant improvement in soil loss and SY estimations by using the CrA approach developed for tropical regions, with a bias of 13% to the measured SY (5709 t yr−1). We conclude that the CrA method present the most suitable alternative to compute soil loss and SY in tropical regions. Furthermore, this approach allows large-scale evaluation and temporal monitoring, therefore enhancing multi spatial and temporal assessment of soil erosion processes. [Display omitted]