**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 Name | Description               |
| ---------- | ------------------------- |
| Raster     | Sentinel-2 raster image.  |

<img src="../../modelDetailImg/cloudgapfillndvi1.png"/>

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## 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. | Output              | Description                                                                                                                          | Download output as |
| ----- | ------------------- | -------------------------------------------------------------------------------------------------------------------------------------- | ------------------ |
| 1     | Reconstructed NDVI  | Reconstructed 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

<div style={{ maxWidth: "440px" }}>

| AOI Size Range (Sq KMs) | Estimated Model Run Time* |
| ----------------------- | ------------------------- |
| 5 to 100                | 45 minutes                |
| 100 to 250              | 2 hours                   |
| 250 to 500              | 4 hours 30 minutes        |
| 500 to 1000             | 9 hours                   |
| 1000 to 2000            | 11 hours 15 minutes       |
| 2000 to 3000            | 21 hours                  |
| 3000 to 5000            | 36 hours                  |

</div>

*Actual time may vary.

<img src="../../modelDetailImg/cloudgapfillndvi2.png"/>

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## Additional Details

| Parameter             | Details          |
| --------------------- | ---------------- |
| Minimum AOI Size      | 5 sq km          |
| Maximum AOI Size      | 5000 sq km       |
| Geographies Supported | All geographies  |
| Sensors Supported     | Sentinel-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.