Analysis of spatiotemporal variability of C-factor derived from remote sensing data

Vilém Pechanec; Alexander Mráz; Antonín Benc; Pavel Cudlín

doi:10.1117/1.JRS.12.016022

14 February 2018 Analysis of spatiotemporal variability of C-factor derived from remote sensing data

Vilém Pechanec, Alexander Mráz, Antonín Benc, Pavel Cudlín

Author Affiliations +

Journal of Applied Remote Sensing, Vol. 12, Issue 1, 016022 (February 2018). https://doi.org/10.1117/1.JRS.12.016022

Abstract

Soil erosion is an important phenomenon that contributes to the degradation of agricultural land. Even though it is a natural process, human activities can significantly increase its impact on land degradation and present serious limitation on sustainable agricultural land use. Nowadays, the risk of soil erosion is assessed either qualitatively by expert assessment or quantitatively using model-based approach. One of the primary factors affecting the soil erosion assessment is a cover-management factor, C-factor. In the Czech Republic, several models are used to assess the C-factor on a long-term basis based on data collected using traditional tabular methods. This paper presents work to investigate the estimation of both long-term and short-term cover-management factors using remote sensing data. The results demonstrate a successful development of C-factor maps for each month of 2014, growing season average, and annual average for the Czech Republic. C-factor values calculated from remote sensing data confirmed expected trend in their temporal variability for selected crops. The results presented in this paper can be used for enhancing existing methods for estimating C-factor, planning future agricultural activities, and designing technical remediations and improvement activities of land use in the Czech Republic, which are also financially supported by the European Union funds.

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Vilém Pechanec, Alexander Mráz, Antonín Benc, and Pavel Cudlín "Analysis of spatiotemporal variability of C-factor derived from remote sensing data," Journal of Applied Remote Sensing 12(1), 016022 (14 February 2018). https://doi.org/10.1117/1.JRS.12.016022

Received: 7 September 2017; Accepted: 19 January 2018; Published: 14 February 2018

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