Determine risk factors associated with reducing or eliminating crop yield based on historical weather, geohazards, and crop health.
Intro
In the face of increased climate variation and extreme weather, industrial agriculture companies are neutralizing risk through crop insurance. The pricing of crop insurance policies can be remotely informed by historical analysis and predictions of crop health, weather events, and water-related geohazards, such as flooding or irrigation contamination.
Overview
Access years of high-resolution archive imagery
Leverage the breadth and depth of UP42's archive data library, with satellite imagery, weather data, aerial data, and more - some of which have data going back over a decade.
Task satellites for imagery to assess insurance claims
React to insurance claims remotely by tasking high-resolution satellites for specific areas of interest, ensuring low cloud cover and specific incidence angle ranges.
Automate analysis with machine learning
Process your data with out-of-the-box machine-learning algorithms focused on crop health, geohazard predictions, and more.
The technical details
Data
High-resolution optical and radar imagery
Access SAR data from Sentinel-1 and optical data from Sentinel-2 to power different machine-learning models. Augment the machine-learning models with high-resolution Pleiades imagery, for which an extensive archive of imagery can be called upon for historical analysis. At the same time, you can also commission imagery for a given future period.
Historical analysis
Analyze historical crop health
Apply NDVI or other band math processing blocks on your historical Pleiades imagery captured on multiple dates to build up a view of historical crop health and ascertain the likelihood for that level of crop health to continue in the absence of geohazards or extreme weather variability. Additionally, use flood mapping to determine the historical prevalence of flooding using Sentinel-1 SAR images over time.
Forecasting risk
Machine-learning-backed risk assessments
Predict geohazards month in advance using Platoi's Advanced Water-Related Geohazards Predictor. This machine-learning algorithm leverages Sentinel-2 data to predict instances of flooding or exposure to water bodies. This can be a crucial input into a decision whether or not to approve a crop insurance policy.
API
Automate, embed and scale your analytics
Through the UP42, and supported by the Python SDK, it is easier than ever to integrate the outputs of UP42 workflows into your end-users' products. Trigger your analysis through the Python SDK and access your outputs via download or integrated into your products, cloud storage, or legacy insurance claims management software.
import up42
up42.authenticate(project_id="12345", project_api_key="12345")
project=up42.initialize_project()
workflow = project.create_workflow(name="Crop Insurance", use_existing=True)
input_tasks=['oneatlas-pleiades-fullscene','pansharpen', 'ndvi']
workflow.add_workflow_tasks(input_tasks=input_tasks)
parameter = workflow.construct_parameters(
geometry=[13.4466, 52.495598, 13.453016, 52.499216],
geometry_operation="bbox",
scene_ids="DS_PHR1A_202005061018418_FR1_PX_E013N52_0513_01179")
job = workflow.run_job(input_parameters=parameter, track_status=True)
job.download_results()