Clouds in satellite imagery pose a significant challenge for downstream applica-
tions. A major challenge in current cloud removal research is the absence of a
comprehensive benchmark and a sufficiently large and diverse training dataset.
To address this problem, we introduce the largest public dataset — AllClear for
cloud removal, featuring 23,742 globally distributed regions of interest (ROIs) with
diverse land-use patterns, comprising 4 million images in total. Each ROI includes
complete temporal captures from the year 2022, with (1) multi-spectral optical im-
agery from Sentinel-2 and Landsat 8/9, (2) synthetic aperture radar (SAR) imagery
from Sentinel-1, and (3) auxiliary remote sensing products such as cloud masks
and land cover maps. We validate the effectiveness of our dataset by benchmarking
performance, demonstrating the scaling law — the PSNR rises from 28.47 to 33.87
with 30× more data, and conducting ablation studies on the temporal length and the
importance of individual modalities. This dataset aims to provide comprehensive
coverage of the Earth’s surface and promote better cloud removal results.
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Crop Type and Land Cover Mapping in Northern Malawi Using the Integration of Sentinel-1, Sentinel-2, and PlanetScope Satellite Data
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 C2 matrix and S-1 H/α 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 (C2) matrix + H/α 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.
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- Award ID(s):
- 1852587
- PAR ID:
- 10319791
- Date Published:
- Journal Name:
- Remote Sensing
- Volume:
- 13
- Issue:
- 4
- ISSN:
- 2072-4292
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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