P02: Crop type and acreage mapping | |
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 | |
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