Pixxel

Model Overview

Satellite Data: Sentinel-2

Optical satellite imagery is an important resource for agricultural monitoring. However, cloud cover can limit the availability of usable optical data and interrupt temporal monitoring for applications such as crop health monitoring, crop classification, and yield estimation, particularly across tropical and temperate regions.

Synthetic Aperture Radar (SAR) provides complementary information because it can observe the Earth regardless of atmospheric conditions.

The Cloud Gap Fill NDVI model combines contextual information from optical and SAR data to reconstruct cloud-affected NDVI values. It uses a deep learning architecture to fuse Sentinel-2 optical data with Sentinel-1 SAR data and reconstruct missing NDVI values using historical temporal dependencies.

The reconstructed NDVI enables more continuous assessment of vegetation status and can provide information related to vegetation health, phenological development, and land cover dynamics.

Model Inputs

The following input is required:

Input NameDescription
RasterSentinel-2 raster image.

Model Output

The model reconstructs NDVI values at cloud-masked locations within the selected Area of Interest and generates the resulting raster file.

Output Files

S.No.OutputDescriptionDownload output as
1Reconstructed NDVIReconstructed NDVI raster with values filled at cloud-masked locations in the AOI using fused Sentinel-1 and Sentinel-2 temporal information.GeoTIFF

Estimated Model Run Time

AOI Size Range (Sq KMs)Estimated Model Run Time*
5 to 10045 minutes
100 to 2502 hours
250 to 5004 hours 30 minutes
500 to 10009 hours
1000 to 200011 hours 15 minutes
2000 to 300021 hours
3000 to 500036 hours

*Actual time may vary.


Additional Details

ParameterDetails
Minimum AOI Size5 sq km
Maximum AOI Size5000 sq km
Geographies SupportedAll geographies
Sensors SupportedSentinel-2

Limitations

Sentinel-1 data should be available for the selected date or within ±2 days of the selected date.

The model currently works for the NDVI index.

Model Accuracy

The model has been tested across different geographical locations in the UK, USA, Africa, and India.

The average R² value is 0.75, which varies depending on the temporal extent of cloud cover.