Category

•  Product Development
  

•  Product Sales

•  Underwriting
  

•  Loss Adjustment
  

•  Claims Handling

PRODUCT DESCRIPTION

Monitoring the vegetation provides an important information on crop’s development and performance of fields. Information on the vegetation status and possible deviations at certain fields is important to identify the problem areas throughout the entire insurance season so that the farmer can react with application of necessary agricultural production technology and inputs to ensure the lesser effects of possible vegetation change in time, to ensure crop’s optimal growth and yielding capacity.

Satellite based vegeation monitoring is typically done with vegetation indices, which Different vegetation indices serve as input for various applications and services of remote sensing-based analysis and products/services, such as the .  A first group represents reflectance based indices, which are  dimensionless indicators highlighting one or more surface characteristics (Normalized Difference Vegetation Index (NDVI) , Leaf Area Index (LAI), or Enhanced Vegetation Index (EVI) or )). A second group are biophysical parameters, representing a biophysical quantity such as the Fraction of Absorbed Photosynthetically Active Radiation (FAPAR) or Leaf Area Index (LAI). They are derived from satellite images using different bandsThe latter are generated with models that use the satellite reflectances as input. Consistent long time series are available on various spatial levels.

The Normalized Difference Vegetation Index (NDVI) is an indicator of a dimensionless vegetation index, which provides information on the greenness of the biomasssurface. It is closely related to the Fraction of Absorbed Photosynthetically Active Radiation (FAPAR), and is little scale-dependent. Even though it is not a physical property of the vegetation cover, its simple formulation makes it widely used for ecosystem as well as vegetation monitoring. Depending on how the plant reflects light at certain frequencies the NDVI indicates the state of plant health.

The Leaf Area Index (LAI) characterized the plant canopies. It is defined as half the total developed area of green photosynthetically active elements of the canopy vegetation per unit horizontal ground area. The LAI quantifies the thickness of the vegetation cover.

It determines the size of the interface for exchange of energy (including radiation) and mass between the canopy and the atmosphere. This is an intrinsic canopy primary variable that should not depend on observation conditions.

fAPAR corresponds to the fraction of photosynthetically active radiation absorbed by the canopy . The FAPAR value results directly from the radiative transfer model in the canopy which is computed instantaneously. It depends on canopy structure, vegetation element optical properties and illumination conditions.

Fraction of green vegetation cover (fCOVER) corresponds to the gap fraction for nadir direction. FCOVER is used to separate vegetation and soil in energy balance processes, including temperature and evapotranspiration. It is computed from the leaf area index and other canopy structural variables and does not depend on variables such as the geometry of illumination as compared to FAPAR. For this reason, it is a very good candidate for the replacement of classical vegetation indices for the monitoring of green vegetation. 

PRODUCT SPECIFICATIONS

Main processing steps

The NDVI is calculated by normalizing the spectral reflectance that are measured in the near infrared (green leaf scattering) and red (chlorophyll absorption) wavebands of satellite imagery. LAI, fCOVER and fAPAR are typically modelled.

Input data sources

Optical: Sentinel-2, MODIS, PROBA-V

Radar: n.a.

Supporting data: n.a.

Spatial resolution and coverage

Spatial resolution: >10m

Coverage: global

Availability: globally available

Accuracy / constraints

Thematic accuracy: n.a. as indices are calculated directly on the basis of raw measurements

Spatial accuracy: Absolute geolocation is constantly monitored for S2A and S2B; the long-term performance is close to 11 m at 95% for both satellites. For the geolocation accuracy for PROBA-V satellites please refer to http://proba-v.vgt.vito.be/en/quality/platform-status-information/geolocation-accuracy.

Limitations

Atmospheric effects and clouds can affect the calculations and may lead to misinterpretation.

Frequency / timeliness

Frequency: various time steps and historic archive

Timeliness: near real-time

Delivery / output format

Data type: GIS-ready data formats; Raster

File format: GeoTIFF

Accessibility

Products with higher resolution are commercially available on demand from EO service providers.

Products with higher resolution are commercially available on demand from EO service providers.

The Copernicus Global Land Service has been providing operationally global products of biophysical parameters such as NDVI, FAPAR, fcover, … from Proba-V satellite imagery. This service will switch to Sentinel 3 data (from Q2 2020 onwards) to The mission ended in June 2020. The Sentinel-3 will ensure data continuity for its users. These data provide both long historical archives (1999 – today), which could be used for historical analyses and near real time monitoring at medium resolution scale. https://land.copernicus.eu/global/. Terrascope (terrascope.be) provides operationally biophysical parameters over Europe.

CHALLENGES ADDRESSED - USE CASE(S)

Product Development:

• Market analysis
• Index insurance: Toolbox for indices
• Index insurance: Risk / crop modelling (Correlation of EO data with in-situ data)
• Index insurance: Relation between weather events and impact on crop productivity
• Index insurance: Functionalities of plants, chemical reactions, early stress detection
• Index insurance: Parcel/Field and regional yield statistics
• Index insurance: Platform for crop health products
• Elaboration of crop profile: Field crops, vegetables, horticulture, greenhouses
• Information on crop rotation
• Information on crop (seasonal) calendar
• Information on forest health and production at different temporal scales (realtime monitoring, historical development)
• Identification of specific stresses and vegetation problems and their underlying causes
• Forestry: Infrastructure & management
• High accuracy of crop-specific yield for smaller crop parcels
• Risk exposure (product design and customer communication)
• Parametrics insurance products

Product Sales:

• Client outreach
• High accuracy of crop-specific yield for smaller crop parcels (penetration)
• Pre-contractual consulting (show-case risk exposure)
• Greater acceptance of index covers by farmers
• Regular market penetration review
• Risk alerts

Underwriting:

• Seasonal portfolio monitoring
• Online platforms or easy-to-use interfaces integrating various data sources (e.g. vegetation stress, field boundary changes, comparison, etc.)
• Risk / crop zoning
• Actual crop health (vegetation)
• Global/Regional production trends (e.g. monitoring specific crop acreages of surrounding regions/countries)
• Procure better reinsurance terms/capacity from enhanced insurance practice
• Trustful historical data agro-insurance indemnity pay-outs
• Identification of farmer’s production practice (technology, infrastructure, property, machinery, etc.)
• Identification of vegetation stages (identify most sensitive stages when crop is the most vulnerable to a risk, e.g. flowering stage)
• Crop calendar and practices
• Regular assessment of risk pricing and product rating

Loss Adjustment:

• High accuracy of crop-specific yield for smaller crop / land parcels
• Regularly updated consistent long-time series of reliable data for index insurance
• Benchmark physical field observations against yield loss detection (e.g. product calibration)
• Risk-mapping against crop’s vegetation stages
• Increase credibility of loss adjustment (e.g. show EO data/visualization to support loss adjustment communication to farmer)
• Enhance field survey (better precision with EO data support)
• Detect crop damage at field level
• Assess crop damage at field level
• Distinct field heterogeneity with crop damage
• Obtain information on pasture biomass

Claims Handling:

• Identification of actual damage size (tons (volume) / ha (area) / price (yield value))
• Quality control assessment of claims before pay-out
• Fraud detection
• Obtaining timely, reliable and consistent data to speed-up the indemnity pay-outs