Sensors Supported: Sentinel-2The Gap-Filled NDVI Model is designed to fill in missing NDVI values caused by cloud cover, ensuring continuous assessment of farm health. The model leverages Sentinel-1 data to predict missing NDVI values. The model calculates the average NDVI with each farm boundary and fills in gaps caused by cloud cover.
Name | Description |
---|---|
Farm Boundary | Boundaries Extracted from the Farm Boundary Model |
Start date | The start date in “YYYY-MM-DD” format |
End date | The end date in “YYYY-MM-DD” format |
timestamps | num of timestamps | mean ndvi | gap filled ndvi | number of timestamps with gaps | number of timestamps gaps filled | percent of gaps filled |
---|---|---|---|---|---|---|
2021-01-03, 2021-01-04, 2021-01-08, 2021-01-11 | 4 | nan, 0.4873, nan, nan | 0.4697, 0.4873, 0.3934, 0.3561 | 3 | 3 | 100 |
2021-01-03, 2021-01-04, 2021-01-08, 2021-01-11 | 4 | nan, 0.47, nan, nan | 0.6204, 0.47, 0.4619, 0.6516 | 3 | 3 | 100 |
Along with Farm Boundary output, Time frame or Date range also need to be provided as an additional input since with this the mean NDVI values will be estimated.
Name | Description |
---|---|
Start date | The start date in “YYYY-MM-DD” format |
End date | The end date in “YYYY-MM-DD” format |
Model generate two outputs i.e. Classified map and its statistics. These files can be downloaded as .geojson and tabular format respectively.On the platform we can see the NDVI graphs for the selected farm.