Abstract
National-level mapping of crop types is important to monitor food security, understand environmental conditions, inform optimal use of the landscape, and contribute to agricultural policy. Countries or economic regions currently and increasingly use satellite sensor data for classifying crops over large areas. However, most methods have been based on machine learning algorithms, with these often requiring large training datasets that are not always available and may be costly to produce or collect. Focusing on Wales (United Kingdom), the research demonstrates how the knowledge that the agricultural community has gathered together over past decades can be used to develop algorithms for mapping different crop types. Specifically, we aimed to develop an alternative method for consistent and accurate crop type mapping where cloud cover is quite persistent and without the need for extensive in situ/ground datasets. The classification approach is parcel-based and informed by concomitant analysis of knowledge-based crop growth stages and Sentinel-1 C-band SAR time series. For 2018, crop type classifications were generated nationally for Wales, with regional overall accuracies ranging between 85.8% and 90.6%. The method was particularly successful in distinguishing barley from wheat, which is a major source of error in other crop products available for Wales. This study demonstrates that crops can be accurately identified and mapped across a large area (i.e., Wales) using Sentinel-1 C-band data and by capitalizing on knowledge of crop growth stages. The developed algorithm is flexible and, compared to the other methods that allow crop mapping in Wales, the approach provided more consistent discrimination and lower variability in accuracies between classes and regions.
Highlights
By using knowledge gathered by the agricultural community to inform a descriptive By using knowledge gathered by the agricultural community to inform a descriptive decision algorithm based on the Sentinel-1 C-band Synthetic Aperture Radar (SAR) time series, eight different crop decision algorithm based on the Sentinel-1 C-band SAR time series, eight different crop types were able to be mapped across Wales for 2018 with overall accuracies of between types were able to be mapped across Wales for 2018 with overall accuracies of between
For crop mapping over large areas, a consistent and flexible descriptive knowledgebased (K-based) algorithm that solely used Sentinel-1 C-band SAR annual time series has been developed, with this being informed by knowledge gathered by the agricultural community on crop growth stages
The method was developed for Wales and the accuracies of classification are comparable or exceed those generated in previous efforts, with these based on optical or combined SAR-optical data
Summary
The cover of crops and other agricultural land covers (e.g., grasslands under pastoral management) ranges from 80% (e.g., Uruguay) [1]. Highquality crop mapping has become a requirement for most nations given its importance in national and international economics, trade, and food security [2] and is a major topic of interest in the domains of policy, economics, land management, and conservation. Monitoring agricultural practices is essential as demand for food has placed huge pressures on landscapes and natural ecosystems, with these impacting (often adversely) on soils, air, water, and biodiversity [3,4,5,6,7,8,9]. By knowing and understanding the distributions, types, and management regimes (e.g., rotational cycles) of crops, changes in management
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