Cropping pattern mapping in an agro-natural heterogeneous landscape using sentinel-2 and sentinel-1 satellite datasets
The quantity of land covered by various crops in a specific time span, referred to as a cropping pattern, dictates the level of agricultural production. However, retrieval of this information at a landscape scale can be challenging, especially when high spatial resolution imagery is not available. T...
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| author | Aduvukha, Grace Rebecca Abdel-Rahman, Elfatih M. Sichangi, Arthur W. Makokha, Godfrey Ouma Landmann, Tobias Mudereri, Bester Tawona Tonnang, Henri E. Z. Dubois, Thomas |
| author_facet | Aduvukha, Grace Rebecca Abdel-Rahman, Elfatih M. Sichangi, Arthur W. Makokha, Godfrey Ouma Landmann, Tobias Mudereri, Bester Tawona Tonnang, Henri E. Z. Dubois, Thomas |
| author_sort | Aduvukha, Grace Rebecca |
| collection | DSpace |
| description | The quantity of land covered by various crops in a specific time span, referred to as a cropping pattern, dictates the level of agricultural production. However, retrieval of this information at a landscape scale can be challenging, especially when high spatial resolution imagery is not available. This study hypothesized that utilizing the unique advantages of multi-date and medium spatial resolution freely available Sentinel-2 (S2) reflectance bands (S2 bands), their vegetation indices
(VIs) and vegetation phenology (VP) derivatives, and Sentinel-1 (S1) backscatter data would improve cropping pattern mapping in heterogeneous landscapes using robust machine learning algorithms, i.e., the guided regularized random forest (GRRF) for variable selection and the random forest (RF) for classification. This study’s objective was to map cropping patterns within three sub-counties in
Murang’a County, a typical African smallholder heterogeneous farming area, in Kenya. Specifically, the performance of eight classification scenarios for mapping cropping patterns was compared, namely: (i) only S2 bands; (ii) S2 bands and VIs; (iii) S2 bands and VP; (iv) S2 bands and S1; (v) S2 bands, VIs, and S1; (vi) S2 bands, VP, and S1; (vii) S2 bands, VIs, and VP; and (viii) S2 bands, VIs, VP,
and S1. Reference data of the dominant cropping patterns and non-croplands were collected. The GRRF algorithm was used to select the optimum variables in each scenario, and the RF was used to perform the classification for each scenario. The highest overall accuracy was 94.33% with Kappa of 0.93, attained using the GRRF-selected variables of scenario (v) S2, VIs, and S1. Furthermore,
McNemar’s test of significance did not show significant differences (p 0.05) among the tested scenarios. This study demonstrated the strength of GRRF in selecting the most important variables and the synergetic advantage of S2 and S1 derivatives to accurately map cropping patterns in smallscale farming-dominated landscapes. Consequently, the cropping pattern mapping approach can be
used in other sites of relatively similar agro-ecological conditions. Additionally, these results can be used to understand the sustainability of food systems and to model the abundance and spread of crop insect pests, diseases, and pollinators. |
| format | Article |
| id | ir-11408-4711 |
| institution | My University |
| language | English |
| publishDate | 2022 |
| publisher | MDPI |
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| spelling | ir-11408-47112022-06-27T13:49:06Z Cropping pattern mapping in an agro-natural heterogeneous landscape using sentinel-2 and sentinel-1 satellite datasets Aduvukha, Grace Rebecca Abdel-Rahman, Elfatih M. Sichangi, Arthur W. Makokha, Godfrey Ouma Landmann, Tobias Mudereri, Bester Tawona Tonnang, Henri E. Z. Dubois, Thomas agricultural productivity cropping pattern Kenya multi-data analysis The quantity of land covered by various crops in a specific time span, referred to as a cropping pattern, dictates the level of agricultural production. However, retrieval of this information at a landscape scale can be challenging, especially when high spatial resolution imagery is not available. This study hypothesized that utilizing the unique advantages of multi-date and medium spatial resolution freely available Sentinel-2 (S2) reflectance bands (S2 bands), their vegetation indices (VIs) and vegetation phenology (VP) derivatives, and Sentinel-1 (S1) backscatter data would improve cropping pattern mapping in heterogeneous landscapes using robust machine learning algorithms, i.e., the guided regularized random forest (GRRF) for variable selection and the random forest (RF) for classification. This study’s objective was to map cropping patterns within three sub-counties in Murang’a County, a typical African smallholder heterogeneous farming area, in Kenya. Specifically, the performance of eight classification scenarios for mapping cropping patterns was compared, namely: (i) only S2 bands; (ii) S2 bands and VIs; (iii) S2 bands and VP; (iv) S2 bands and S1; (v) S2 bands, VIs, and S1; (vi) S2 bands, VP, and S1; (vii) S2 bands, VIs, and VP; and (viii) S2 bands, VIs, VP, and S1. Reference data of the dominant cropping patterns and non-croplands were collected. The GRRF algorithm was used to select the optimum variables in each scenario, and the RF was used to perform the classification for each scenario. The highest overall accuracy was 94.33% with Kappa of 0.93, attained using the GRRF-selected variables of scenario (v) S2, VIs, and S1. Furthermore, McNemar’s test of significance did not show significant differences (p 0.05) among the tested scenarios. This study demonstrated the strength of GRRF in selecting the most important variables and the synergetic advantage of S2 and S1 derivatives to accurately map cropping patterns in smallscale farming-dominated landscapes. Consequently, the cropping pattern mapping approach can be used in other sites of relatively similar agro-ecological conditions. Additionally, these results can be used to understand the sustainability of food systems and to model the abundance and spread of crop insect pests, diseases, and pollinators. 2022-03-18T08:38:26Z 2022-03-18T08:38:26Z 2021 Article 2077-0472 https://www.mdpi.com/2077-0472/11/6/530 http://hdl.handle.net/11408/4711 en Agriculture (Switzerland);Vol.11 , Iss.6 open MDPI |
| spellingShingle | agricultural productivity cropping pattern Kenya multi-data analysis Aduvukha, Grace Rebecca Abdel-Rahman, Elfatih M. Sichangi, Arthur W. Makokha, Godfrey Ouma Landmann, Tobias Mudereri, Bester Tawona Tonnang, Henri E. Z. Dubois, Thomas Cropping pattern mapping in an agro-natural heterogeneous landscape using sentinel-2 and sentinel-1 satellite datasets |
| title | Cropping pattern mapping in an agro-natural heterogeneous landscape using sentinel-2 and sentinel-1 satellite datasets |
| title_full | Cropping pattern mapping in an agro-natural heterogeneous landscape using sentinel-2 and sentinel-1 satellite datasets |
| title_fullStr | Cropping pattern mapping in an agro-natural heterogeneous landscape using sentinel-2 and sentinel-1 satellite datasets |
| title_full_unstemmed | Cropping pattern mapping in an agro-natural heterogeneous landscape using sentinel-2 and sentinel-1 satellite datasets |
| title_short | Cropping pattern mapping in an agro-natural heterogeneous landscape using sentinel-2 and sentinel-1 satellite datasets |
| title_sort | cropping pattern mapping in an agro-natural heterogeneous landscape using sentinel-2 and sentinel-1 satellite datasets |
| topic | agricultural productivity cropping pattern Kenya multi-data analysis |
| url | https://www.mdpi.com/2077-0472/11/6/530 http://hdl.handle.net/11408/4711 |
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