Crop Phenology, Rotation, and Number of Seasons

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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

Crop phenology refers to the study and observation of the timing and sequence of distinct stages in the life cycle of a crop from germination to the final maturity and senescence. Phenological stages are anticipated to happen consistently at the same time every year within a particular bioclimatic region. Nevertheless, it is important to acknowledge that certain advancements or delays (measured in weeks) might be observed due to varying climatic conditions.  Understanding the seasonal patterns in satellite time-series data and their correlation with dynamic vegetation characteristics, like phenology (growth stages) and temporal growth patterns, is a crucial process for developing more advanced crop-specific products. These products may include information related to crop rotation, the number of growing seasons, tillage practices, green biomass, and yield estimation. Crop type and acreage maps, along with vegetation indices, serve as the primary inputs for calculating land surface phenology (LSP) metrics, including the start of the season (SOS), end of the season (EOS), length of the season (LOS), and the peak of the season (POS). Additionally, local observations of crop phenology can be utilised for calibration purposes.

Spatial Coverage Target

Individual farm level

Data Throughput

Rapid tasking 

Data availability

• High
• High

• Low
• Low

PRODUCT SPECIFICATIONS

Main processing steps

After generating the vegetation indices, linear interpolation will be used to fill in the gaps in the time series of vegetation indices.  Afterwards, a temporal smoothing algorithm is employed to average matched images that fall within a predetermined time window. Then LSP metrics will be calculated based on the concept and methodology behind the Timesat software which has been used widely for phenological analysis and was applied to the generation of European high-resolution vegetation phenology and productivity data for Copernicus (HR-VPP). 

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

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 limited temporal resolution can make it challenging to capture specific phenological changes or detect short-duration crops accurately.
• Capture complex phenological events.
• May not directly capture the underlying physiological processes driving phenological stages, limiting the understanding of crop responses to environmental stressors.

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

Skills: Essential

Knowledge: Essential

 P03: Crop phenology, rotation, and number of seasons

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Maturity score

Mean: 2.9

STD: 0.3

Constraints and limitations

·  Cloud presence

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

Relevant user needs

UN18: Need to monitor crop productivity.

R&D gaps

·  The limited temporal resolution can make it challenging to capture specific phenological changes or detect short-duration crops accurately.

·  May not directly capture the underlying physiological processes driving phenological stages, limiting the understanding of crop responses to environmental stressors.

Potential improvements drivers

·  Higher temporal resolution EO data with adequate spectral bands.

Utilisation level review

Utilisation score

Mean: 2

STD: 0.58

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.

Medium utilisation

High utilisation

Critical gaps related to relevant user needs

Guideline gap

UN18: Need to monitor crop productivity