Abstract
The convergence of new EO data flows, new methodological developments and cloud computing infrastructure calls for a paradigm shift in operational agriculture monitoring. The Copernicus Sentinel-2 mission providing a systematic 5-day revisit cycle and free data access opens a completely new avenue for near real-time crop specific monitoring at parcel level over large countries. This research investigated the feasibility to propose methods and to develop an open source system able to generate, at national scale, cloud-free composites, dynamic cropland masks, crop type maps and vegetation status indicators suitable for most cropping systems. The so-called Sen2-Agri system automatically ingests and processes Sentinel-2 and Landsat 8 time series in a seamless way to derive these four products, thanks to streamlined processes based on machine learning algorithms and quality controlled in situ data. It embeds a set of key principles proposed to address the new challenges arising from countrywide 10 m resolution agriculture monitoring. The full-scale demonstration of this system for three entire countries (Ukraine, Mali, South Africa) and five local sites distributed across the world was a major challenge met successfully despite the availability of only one Sentinel-2 satellite in orbit. In situ data were collected for calibration and validation in a timely manner allowing the production of the four Sen2-Agri products over all the demonstration sites. The independent validation of the monthly cropland masks provided for most sites overall accuracy values higher than 90%, and already higher than 80% as early as the mid-season. The crop type maps depicting the 5 main crops for the considered study sites were also successfully validated: overall accuracy values higher than 80% and F1 Scores of the different crop type classes were most often higher than 0.65. These respective results pave the way for countrywide crop specific monitoring system at parcel level bridging the gap between parcel visits and national scale assessment. These full-scale demonstration results clearly highlight the operational agriculture monitoring capacity of the Sen2-Agri system to exploit in near real-time the observation acquired by the Sentinel-2 mission over very large areas. Scaling this open source system on cloud computing infrastructure becomes instrumental to support market transparency while building national monitoring capacity as requested by the AMIS and GEOGLAM G-20 initiatives.
Highlights
Satellite remote sensing was promoted as a key data source for operational agriculture monitoring
The objective of this paper is to investigate whether the generic time series analysis methods provided by the Sen2-Agri platform can be successfully applied to various cropping systems to deliver key agriculture information in a timely and accurate manner at 10 m resolution over large areas
Except Sudan, the 300-m European Space Agency (ESA) Climate Change Initiative (CCI) land cover map proved to be enough to allow getting a cropland mask with an Overall Accuracy (OA) higher than 80% and F1Score for cropland and non-cropland classes higher than 0.75
Summary
Satellite remote sensing was promoted as a key data source for operational agriculture monitoring. The advanced crop monitoring system developed over India (Ray et al, 2016) is only possible thanks to a combination of the Indian Resourcesat-2 AWiFS and LISS-IV instruments (i.e. spatial resolution of 56 m and 5,8 m respectively) Commercial satellite constellations such as RapidEye allow monitoring agricultural landscapes with a spatial resolution higher than 5 m (Lussem et al, 2016; Xu et al, 2016). Their small scene footprint combined with rather poor radiometric and atmospheric correction were identified as significant issues preventing operational wall-to-wall consistent coverage over large areas in addition to the cost constraint (Davidson et al, 2017). SPOT 6&7 imagery is used over targeted areas for commercial crop advice programs (e.g. FARMSTAR service in France (WWW4)) or specific applications like nitrogen management (e.g. MasAgro GreenSat project in Mexico (WWW6)), but cannot be generalized due to their significant cost and still limited scene footprint
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