4.7 Article

Mapping Crop Types and Cropping Systems in Nigeria with Sentinel-2 Imagery

期刊

REMOTE SENSING
卷 13, 期 17, 页码 -

出版社

MDPI
DOI: 10.3390/rs13173523

关键词

spectral-temporal metrics; time series; smallholder agriculture; intercropping; SkySat; classification; random forest; maize; potato; sub-Saharan Africa

资金

  1. Nigerian-German Postgraduate Training Programme PhD, 2019 [57473408]
  2. National Centre for Remote Sensing, Jos Plateau State, Nigeria
  3. Humboldt university of Berlin, Germany
  4. Leibniz Centre for Agricultural Landscape Research (ZALF), Brandenburg, Germany

向作者/读者索取更多资源

This study used Sentinel-2A/B and SkySat data to map crop types in the Jos Plateau, Nigeria, showing maize as the dominant crop, followed by mixed cropping systems, and potato as the least prevalent class. Analyses of mixed crop classes were conducted, revealing regional variations in the distribution of crop types.
Reliable crop type maps from satellite data are an essential prerequisite for quantifying crop growth, health, and yields. However, such maps do not exist for most parts of Africa, where smallholder farming is the dominant system. Prevalent cloud cover, small farm sizes, and mixed cropping systems pose substantial challenges when creating crop type maps for sub-Saharan Africa. In this study, we provide a mapping scheme based on freely available Sentinel-2A/B (S2) time series and very high-resolution SkySat data to map the main crops-maize and potato-and intercropping systems including these two crops on the Jos Plateau, Nigeria. We analyzed the spectral-temporal behavior of mixed crop classes to improve our understanding of inter-class spectral mixing. Building on the Framework for Operational Radiometric Correction for Environmental monitoring (FORCE), we preprocessed S2 time series and derived spectral-temporal metrics from S2 spectral bands for the main temporal cropping windows. These STMs were used as input features in a hierarchical random forest classification. Our results provide the first wall-to-wall crop type map for this key agricultural region of Nigeria. Our cropland identification had an overall accuracy of 84%, while the crop type map achieved an average accuracy of 72% for the five relevant crop classes. Our crop type map shows distinctive regional variations in the distribution of crop types. Maize is the dominant crop, followed by mixed cropping systems, including maize-cereals and potato-maize cropping; potato was found to be the least prevalent class. Plot analyses based on a sample of 1166 fields revealed largely homogeneous mapping patterns, demonstrating the effectiveness of our classification system also for intercropped classes, which are temporally and spatially highly heterogeneous. Moreover, we found that small field sizes were dominant in all crop types, regardless of whether or not intercropping was used. Maize-legume and maize exhibited the largest plots, with an area of up to 3 ha and slightly more than 10 ha, respectively; potato was mainly cultivated on fields smaller than 0.5 ha and only a few plots were larger than 1 ha. Besides providing the first spatially explicit map of cropping practices in the core production area of the Jos Plateau, Nigeria, the study also offers guidance for the creation of crop type maps for smallholder-dominated systems with intercropping. Critical temporal windows for crop type differentiation will enable the creation of mapping approaches in support of future smart agricultural practices for aspects such as food security, early warning systems, policies, and extension services.

作者

我是这篇论文的作者
点击您的名字以认领此论文并将其添加到您的个人资料中。

评论

主要评分

4.7
评分不足

次要评分

新颖性
-
重要性
-
科学严谨性
-
评价这篇论文

推荐

暂无数据
暂无数据