Tillage and Crop Residue Cover Practices

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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.

Description

An EO product designed for monitoring tillage and crop residue cover practices involves the use of remote sensing technology to assess the extent and intensity of tillage operations, as well as the amount of crop residue left on agricultural fields. This product provides valuable insights into sustainable farming practices and their relation to crop productivity. Tracking tillage and residue cover, enables farmers and agricultural policymakers to optimize land management strategies, reducing soil erosion and conserving moisture. The maintenance of crop residues on fields can improve soil health, reduce weed growth, and enhance nutrient retention, ultimately promoting higher crop yields and increased agricultural sustainability.

Spatial Coverage Target

Individual farm level

Data Throughput

Rapid tasking 

Data availability

• High
• High

• Low
• Low

PRODUCT SPECIFICATIONS

Main processing steps

Based on calculating phenology metrics, monitoring activities following the conclusion of the first growing season, such as ploughing and residue management, can be accomplished by utilizing soil, senescence, and tillage indices. Soil indices can help determine if the soil remains fallow or is being actively managed. Additionally, the use of senescence and tillage indices allows for distinguishing between different levels of intensity in ploughing and residue management scenarios. To achieve this, it is essential to have sampling points with known tillage practices and residue management to train and validate the modelling process.

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: Crop phenology observations, ground truth tillage and crop residue practices

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

File format: GeoTIFF

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
• The effectiveness of the product can be affected by environmental conditions such as heavy rain, snow cover, or flooding, which can obscure the view of the land surface or modify tillage and residue patterns.
• Different crops and crop varieties may have varying residue cover practices, making it challenging to establish a one-size-fits-all monitoring system.

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

Skills: Essential

Knowledge: Essential

P04: Tillage and crop residue cover practices

Download the product sheet gap analysis 

Maturity score

Mean: 2.1

STD: 0.83

Constraints and limitations

·  Cloud presence

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

·  Machine learning model uncertainty

·  The effectiveness of the product can be affected by environmental conditions such as heavy rain, snow cover, or flooding, which can obscure the view of the land surface or modify tillage and residue patterns.

·  Different crops and crop varieties may have varying residue cover practices, making it challenging to establish a one-size-fits-all monitoring system.

Relevant user needs

UN18: Need to monitor crop productivity.

R&D gaps

·  Limited training data.

·  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

·  Methodology for the retrieval of ground data for model training and validation.

·  Increasing spectral resolution by using hyperspectral data.

Utilisation level review

Utilisation score

Mean: 1.8

STD: 0.40

No utilisation:

Users’ lack of EO knowledge and skills to utilize the EO product.

Low utilisation

·  Unawareness of the existence of commercial EO products with better specifications

·  Higher cost of using the commercial EO product

·  Higher cost in terms of internal training and resources to use the data that comes from this process/data source.

Medium utilisation

High utilisation

Critical gaps related to relevant user needs

Guideline gap

UN18: Need to monitor crop productivity