Version 1
: Received: 25 October 2020 / Approved: 26 October 2020 / Online: 26 October 2020 (11:44:04 CET)
How to cite:
Aduvukha, G.; Abdel-Rahman, E.; Sichangi, A.; Makokha, G.; Landmann, T.; Mudereri, B.; Tonnang, H.; Dubois, T. Sophisticated Cropping Patterns Mapping in Kenya Using Vegetation Indices and Phenology from Sentinel-2 and Sentinel-1 Radar Backscatter Data. Preprints2020, 2020100517. https://doi.org/10.20944/preprints202010.0517.v1
Aduvukha, G.; Abdel-Rahman, E.; Sichangi, A.; Makokha, G.; Landmann, T.; Mudereri, B.; Tonnang, H.; Dubois, T. Sophisticated Cropping Patterns Mapping in Kenya Using Vegetation Indices and Phenology from Sentinel-2 and Sentinel-1 Radar Backscatter Data. Preprints 2020, 2020100517. https://doi.org/10.20944/preprints202010.0517.v1
Aduvukha, G.; Abdel-Rahman, E.; Sichangi, A.; Makokha, G.; Landmann, T.; Mudereri, B.; Tonnang, H.; Dubois, T. Sophisticated Cropping Patterns Mapping in Kenya Using Vegetation Indices and Phenology from Sentinel-2 and Sentinel-1 Radar Backscatter Data. Preprints2020, 2020100517. https://doi.org/10.20944/preprints202010.0517.v1
APA Style
Aduvukha, G., Abdel-Rahman, E., Sichangi, A., Makokha, G., Landmann, T., Mudereri, B., Tonnang, H., & Dubois, T. (2020). Sophisticated Cropping Patterns Mapping in Kenya Using Vegetation Indices and Phenology from Sentinel-2 and Sentinel-1 Radar Backscatter Data. Preprints. https://doi.org/10.20944/preprints202010.0517.v1
Chicago/Turabian Style
Aduvukha, G., Henri Tonnang and Thomas Dubois. 2020 "Sophisticated Cropping Patterns Mapping in Kenya Using Vegetation Indices and Phenology from Sentinel-2 and Sentinel-1 Radar Backscatter Data" Preprints. https://doi.org/10.20944/preprints202010.0517.v1
Abstract
The proportion of area under various crops at a given point in time, known as a cropping pattern, plays an essential role in determining the level of agricultural production. In this study, cropping patterns of three sub-counties in Murang’a County, a typical African smallholder farming area in Kenya, were mapped. Specifically, we compared the performance of eight classification scenarios for mapping cropping patterns; namely using (i) only Sentinel-2 reflectance bands (S2), (ii) S2 and S2 derived vegetation indices (VIs); (iii) S2 and S2 vegetation phenology (VP); (iv) S2 and Sentinel-1 radar backscatter data (S1); (v) S2, VIs, and S1; (vi) S2, VP, and S1; (vii) S2, VIs and VP, and (viii) S2, VIs, VP and S1. Reference data of the dominant cropping patterns and non-croplands were collected. The guided regularized random forest (GRRF) algorithm was used to select the optimum variables and to perform the respective classification for each scenario. The most accurate result of the overall accuracy of 93.16% was attained from the scenario (viii) S2, VIs, VP, and S1. The McNemar’s test of significance did not show significant differences (p≤0.05) among the tested scenarios. Our study demonstrated the strength of GRRF and the synergetic advantage of S2 and S1 derivatives to map cropping patterns in a heterogeneous landscape where high resolution imagery are inaccessible. Our cropping pattern mapping approach can be used in other sites of relatively similar agro-ecological conditions.
Environmental and Earth Sciences, Atmospheric Science and Meteorology
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
,
,
,
,
,
