GeoPard provides access to a 30+ years archive of processed, calibrated and cleaned satellite images data with comprehensive sets of views, which are calculated against every satellite image.
This allows you to monitor crop development including retrospective analysis and condition assessment, scout for areas with different levels of growth, detect anomalies and compare your farm with neighboring fields and areas.
Controls and Filters
GeoPard solution has the following controls and filters to manage satellite data:
Compare layers – allows comparing criteria analytics side by side in a split view
Cloud drop-down list with the following values:
Cloud free – shows only cloudless images
Partially cloud – adds partially cloud images
All images (default) – shows all available satellite image
Provider filter – allows filtering images by a provider (Sentinel, Planet, or Landsat)
Month and Year filters – allow filtering satellite images by month or year
Satellite monitoring section – shows satellite images, which can have the following values:
Cloud indicator (partially cloud or cloudy, if you selected this option)
For cloud free images it displays the NDVI index calculated from the image
Provider name (Sentinel, Planet, or Landsat)
On the field map you have the following controls:
Set of indices
Crop by boundary
Zoom to field
Zoom to my location
Map source (Bing or Mapbox maps)
User is able to order the Planet imagery right from the GeoPard User Interface in a field profile. (The feature should be activated for user account previously.)
1. Go to the field profile page and click the Planet icon.
2. Choose the interested date range and Request.
3. At the right bottom corner you will see the processing status.
4. As soon as the images’ meta-data will be received from the Planet. The images’ statistics will appear in Satellite Monitoring list.
5. The next step is to order the best-fit Planet satellite image. The conditions: “clear land % – 100“.
6. Downloading from Planet is ongoing.
7. The Planet image is available for further analytics.
All satellite imagery is corrected and optimized for viewing and analysis. Selecting different imagery views allows you to understand your field better by recognizing well-developed areas and areas with crop emergence anomalies. These views include:
RGB – Natural or true color (red, green, and blue).
NIR – (default) a combination of non-visible (near-infrared) and visible (red, green) parts of the spectrum. It increases interpretability of the data: vegetation is in shades of red with highly vegetated areas in bright red, and soil ranges from dark to light green or grey.
EVI2 – Enhanced Vegetation Index (from 0 to 2.4), is preferable to NDVI for fields with high canopy density where NDVI may saturate. This view can be used to analyze crops in all growth stages.
LAI – Leaf Area Index (from 0 to 5.86), dimensionless quantity that characterizes plant canopies. Distribution from bare soil to dense canopy. It ranges from the bare ground (index value 0) to the dense canopy (index value is 3.5 and higher for the peak of growing season) and shown in red to green colors.
NDVI – Normalized Difference Vegetation Index (from 0 to 1) is a good crop health indication, representing green vegetation distribution. Though there are limitations when using this view at the start of the growing season (influenced by soil) and at the peak of vegetation (saturation). It is shown from red to green on the map.
GNDVI – Green Normalized Difference Vegetation Index (from 0 to 1). It is more sensitive to chlorophyll difference than NDVI and recommended for crops in early to mid-growth stages. Distribution is from red to green on the map.
IPVI – Infrared Percentage Vegetation Index (from 0 to 1). This index is functionally the same as NDVI, but it is computationally faster.
GCI – Green Chlorophyll Index (from 0 to 7). This index is used to evaluate leaf chlorophyll content and is applicable for a wide range of plant species. It helps to measure the plant health as chlorophyll content decreases in stressed plants. Distribution is from light green to dark green on the map.
SAVI – Soil Adjusted Vegetation Index (from 0 to 1.5) minimizes the influences of soil brightness. Most useful at the beginning of the season when plants are separated or in rows and when the soil is clearly visible and to the mid-growth stage when plants are still not touching.
OSAVI – Optimized Soil Adjusted Vegetation Index (from 0 to 1). It is best used in areas with relatively sparse vegetation and for crops in early to mid-growth stages. It is shown using a red to green legend.
NDWI – Normalized Difference Water Index. It is used to differentiate water from dry land and for water body mapping. It is shown in shades of blue on the map.
WDRVI – Wide Dynamic Range Vegetation Index (from -0.6 to 0.4). This index is used for a more sophisticated analysis of the crop physiological and phenological characteristics. It uses the same bands as the NDVI but applies an enhanced dynamic range.
SBI – Soil Brightness Index. It is a proxy for soil organic matter, sands, and salinity areas, and is important for studying changes in soil conditions over time.
NDMI – Normalized Difference Moisture Index. Normalized Difference Moisture Index is used to determine vegetation water content. It is ideal for finding water stress in plants. Better vegetation has higher values. Lower moisture index values suggest plants are under stress from insufficient moisture. Interpretation:
(-1; -0.8) Bare soil;
(-0.8; -0.2) Almost no or very low canopy cover;
(-0.2; 0) Low canopy cover with high water stress OR very low canopy cover with low water stress;
(0; 0.2) Average canopy cover with high water stress OR low canopy cover with low water stress;
(0.2; 0.4) High canopy cover with high water stress OR average canopy cover with low water stress;
(0.4; 1) High and very high canopy cover with no water stress.
MSI – Moisture Stress Index. Moisture Stress Index is used for canopy stress analysis, productivity prediction, and biophysical modeling. Higher values of the index indicate greater plant water stress and less soil moisture and water content. The values of this index range from 0 to more than 3 with the common range for green vegetation being 0.2 to 2.
CCCI – Canopy Chlorophyll Content Index. The Canopy Chlorophyll Content Index (CCCI) is a two-dimensional remote sensing index that is proposed for inferring crop N status. The CCCI uses reflectances in the near-infrared (NIR) and red spectral regions to account for seasonal changes in canopy density, while reflectances in the NIR and far-red regions are used to detect relative changes in canopy chlorophyll, a surrogate for N content.
MCARI – Modified Chlorophyll Absorption Ratio Index is responsive to leaf chlorophyll concentration and ground reflectance. Generally, high MCARI values indicate low leaf chlorophyll content. MCARI shows weakness in predicting low chlorophyll concentrations, especially the impact of soil signal limits its functionality.
TCARI – Transformed Chlorophyll Absorption Reflectance Index is one of several CARI indices that indicates the relative abundance of chlorophyll. It is affected by the underlying soil reflecance, particularly in vegetation with a low LAI.
MCARI/OSAVI and TCARI/OSAVI are the integrated forms of CARI to have better linearity with chlorophyll content and resistance to leaf area index (LAI). The improvement happens because the red and nir reflectances were replaced by the green, red and red-edge reflectances. The combinations of indices can be used for canopy chlorophyll content estimation.