Crop Type and Land Cover Mapping in Northern Malawi Using the Integration of Sentinel-1, Sentinel-2, and PlanetScope Satellite Data
Crop Type and Land Cover Mapping in Northern Malawi Using the Integration of Sentinel-1, Sentinel-2, and PlanetScope Satellite Data
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DOI：
10.3390/rs13040700
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发表时间：
2021-02-01
期刊：
影响因子：
5
通讯作者：
Dakishoni, Laifolo
中科院分区：
文献类型：
--
作者：
Kpienbaareh, Daniel;Sun, Xiaoxuan;Dakishoni, Laifolo
由于数据成本、高云量和卫星数据时间分辨率差，在撒哈拉以南非洲的小农农业系统中绘制作物类型和土地覆盖地图仍然是一项挑战。随着卫星技术和图像处理技术的改进，有可能整合来自不同光谱特征和时间分辨率的传感器的数据，从而有效地绘制作物类型和土地覆盖图。在我们的马拉维研究区，没有整个作物生长季节的无云图像是很常见的。本实验的目标是利用Sentinel-1 （S-1）雷达数据、Sentinel-2 （S-2）光学数据、S-2和PlanetScope数据融合、S-1 C-2矩阵和S-1 H/alpha极化分解，生成详细的农业景观作物类型和土地覆盖图。我们评估了将这些数据结合起来绘制两个小农农场作物类型和土地覆盖地图的能力。随机森林算法采用田间采集的作物和土地覆盖类型数据进行训练，并辅以谷歌Earth Pro和DigitalGlobe数字化的样本进行分类实验。结果表明，S-2和PlanetScope融合图像+ S-1协方差（C-2）矩阵+ H/alpha极化分解（一种基于熵的分解方法）融合优于所有其他图像组合，产生更高的总体精度（OAs）（>85%）和Kappa系数（>0.80）。这些oa分别比Thimalala和Edundu的Sentinel-2-only实验（oa < 80%）提高了13.53%和11.7%。该实验还提供了对该地区作物分布和土地覆盖类型的准确见解。研究结果表明，在云密集和资源贫乏的地区，将高时间分辨率雷达数据与现有光学数据融合为作物类型和土地覆盖的业务制图提供了机会，从而支持粮食安全和环境管理决策。
Mapping crop types and land cover in smallholder farming systems in sub-Saharan Africa remains a challenge due to data costs, high cloud cover, and poor temporal resolution of satellite data. With improvement in satellite technology and image processing techniques, there is a potential for integrating data from sensors with different spectral characteristics and temporal resolutions to effectively map crop types and land cover. In our Malawi study area, it is common that there are no cloud-free images available for the entire crop growth season. The goal of this experiment is to produce detailed crop type and land cover maps in agricultural landscapes using the Sentinel-1 (S-1) radar data, Sentinel-2 (S-2) optical data, S-2 and PlanetScope data fusion, and S-1 C-2 matrix and S-1 H/alpha polarimetric decomposition. We evaluated the ability to combine these data to map crop types and land cover in two smallholder farming locations. The random forest algorithm, trained with crop and land cover type data collected in the field, complemented with samples digitized from Google Earth Pro and DigitalGlobe, was used for the classification experiments. The results show that the S-2 and PlanetScope fused image + S-1 covariance (C-2) matrix + H/alpha polarimetric decomposition (an entropy-based decomposition method) fusion outperformed all other image combinations, producing higher overall accuracies (OAs) (>85%) and Kappa coefficients (>0.80). These OAs represent a 13.53% and 11.7% improvement on the Sentinel-2-only (OAs < 80%) experiment for Thimalala and Edundu, respectively. The experiment also provided accurate insights into the distribution of crop and land cover types in the area. The findings suggest that in cloud-dense and resource-poor locations, fusing high temporal resolution radar data with available optical data presents an opportunity for operational mapping of crop types and land cover to support food security and environmental management decision-making.