Crop Type and Acreage Mapping

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Product Category

• Land use
• Land cover
• Natural Disaster
• Climate Change
• Coast Management 

• Marine
• Earth's Surface Motion

Financial Domains

• Investment management 
• Risk Analysis 
• Insurance management 
• Green finance

User requirements 

UN18: Need to monitor crop productivity.

UN19: Identifying types of crops being grown is essential.

UN28: Need to classify the types of crops being grown in order to assess the sustainability and environmental impact of agricultural investments.

UN29: Need to accurately measure the planted area for crops.

Description

Crop type and acreage mapping play a crucial role in monitoring agricultural land use and making estimations of crop production. These maps provide detailed information about the agricultural species present in a specific area, including their extent, and growth stage at a particular point in time. Satellite images capture detailed data about agricultural areas, allowing for the identification and classification of different crops based on their spectral characteristics. By leveraging advanced image processing algorithms and machine learning models, crop types can be accurately determined.

Spatial Coverage Target

Individual farm level

Data Throughput

Rapid tasking 

Data availability

• High
• High

• Low
• Low

PRODUCT SPECIFICATIONS

Main processing steps

Before creating crop type and acreage maps, the initial step involves mapping the location of crops. This process utilizes machine learning-based classification models, incorporating inputs from various Earth Observation (EO) data sources such as vegetation and backscatter indices. In addition to EO data, non-EO data like local in-situ data and land use land cover maps are also incorporated. The resulting crop location maps are then combined with vegetation and backscatter indices, Digital Surface Models, existing crop type maps like ESA WorldCereal, and ground truth data. These combined inputs are then fed into machine learning models for the classification of different crop types.

Input data sources

Optical: Sentinel-2, VHR based on the availability like Pleiades 1A/1B & NEO, WorldView2&3, and SPOT6/7

Radar: Sentinel-1

Supporting data: In-situ crop type data, ESA's WorldCover layer, ESA WorldCereal, ALOS Global Digital Surface Model

Accessibility

Sentinel-1&2:  freely and publicly available from ESA.

Optical VHR imagery: commercially available on demand from EO service providers.

Spatial resolution

Sentinel-2: 10 m

Optical VHR: ≤ 1 m

Sentinel-1: 20 m

Frequency (Temporal resolution)

Sentinel-2: 6 days

Optical VHR: Sub-daily to Daily

Sentinel-1: 6 days

Latency

< 1 Day

Geographical scale coverage

Globally

Delivery/ output format

Data type: Raster, Vector

File format: GeoTIFF, Shapefile

Accuracies

Thematic accuracy: 80-90%

Spatial accuracy: 1.5-2 pixels of input data

Constraints and limitations

• The lack of local in-situ data
• Cloud presence
• Differentiating between certain crop types with similar spectral signatures can be challenging.
• Field heterogeneity with different crop types intermixed or crop rotations occurring within the same area.

User's level of knowledge and skills to extract information and perform further analysis on the EO products.

Skills: Essential

Knowledge: Essential

P02: Crop type and acreage mapping

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Maturity score

Mean: 2.5

STD: 0.5

Constraints and limitations

·  Cloud presence

·  The lack of local in-situ data to train the machine learning models.

·  Machine learning model uncertainty

Relevant user needs

UN18: Need to monitor crop productivity.

UN19: Identifying types of crops being grown is essential.

UN28: Need to classify the types of crops being grown to assess the sustainability and environmental impact of agricultural investments.

UN29: Need to accurately measure the planted area for crops.

R&D gaps

·  Limitations in discrimination of crop types with similar spectral signatures. As the crop type maps are often group classifications where crops with similar spectral signatures are grouped together.

·  Smallholder farming remains an issue because of the small size of farms where intercropping happens very often. (This comment may not apply in the case of large commercial farms).

Potential improvements drivers

The main limitations are due to the input data rather than the methodology, so the improvements include:

·  More field data worldwide.

·  Increased spatial and temporal resolution of the input EO data.

·  Increasing spectral resolution by using hyperspectral data to better discriminate between crop types.

·  Crop-type predictions using multiple datasets may allow you to differentiate between those crops that are similar spectrally.

Utilisation level review

Utilisation score

Mean: 2.6

STD: 1.02

No utilisation:

Low utilisation

Medium utilisation

Unawareness of the existence of the best available commercial EO product with better specifications.

High utilisation

Only this product satisfies the technical and usability requirements.

Critical gaps related to relevant user needs

Guideline gap

UN18: Need to monitor crop productivity