National Crop Mapping Using Sentinel-1 Time Series: A Knowledge-Based Descriptive Algorithm

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.

Living Earth: Implementing national standardised land cover classification systems for Earth Observation in support of sustainable development

“…For this, a number of methods have been used to generate the categories, including well-developed machine learning approaches (e.g. Vegetation Fractional Cover, Gill et al, 2017;Hill & Guerschman, 2020;Woody Cover Fraction, Liao et al, 2020), or inherent qualities of sensors such as C-band backscatter characteristics (Planque et al, 2021). However, based on the FAO LCCS definitions of lifeform, the best approach is to use continuous raster height products derived from, for example, airborne or spaceborne interferometric SAR or Lidar (e.g.…”

“…In particular, Sentinel-1 provides a very useful temporal dataset for Wales and the broader UK due to data collection independent of cloud cover. Retrieval of environmental descriptors relevant to Living Earth have recently been demonstrated, including semi-natural vegetation extent (Punalekar et al, 2020), identification of water bodies and water seasonality (Planque et al, 2020), and species-level crop type classification (Planque et al, 2021).…”

“…Wales represents a highly modified and complex landscape dominated by pastureland, woodland and urbanised settings (Lucas et al, 2006). Seasonality and episodic events, such as flooding and severe storms, as well as forestry and clear-cutting activities, are common pressures of landscape change in Wales (Planque et al, 2021). Identifying lifeform is of primary importance and facilitates differentiation of environmental descriptors important for major natural resource (e.g.…”

Density Estimates as Representations of Agricultural Fields for Remote Sensing-Based Monitoring of Tillage and Vegetation Cover

“…We assume the pixels of a given raster image area to be drawn from a probability distribution of pixel band values. Rough aggregate summaries, such as mean and median of the pixel values are still in active use in remote sensing due to simplicity and robustness [10][11][12][13], but our interest lies in the advantages of characterising subtle differences in the whole distribution. Histograms of pixel values have a long history as a natural representation of spectral distribution in computer vision [14][15][16], and they have also been considered in remote sensing [17][18][19].…”

Improving Parcel-Level Mapping of Smallholder Crops from VHSR Imagery: An Ensemble Machine-Learning-Based Framework

“…Fine-scale remote sensing (RS) mapping of smallholder crops remains challenging, due to the high fragmentation and heterogeneity of agricultural landscapes. Over the past decade, many studies have been conducted using RS technology to objectively identify and map crop types and planting intensity at national, regional, and other spatial scales [14][15][16][17][18]. Despite its diverse uses, RS technology has not been widely used in parcel-level smallholder crop mapping, which is critical to better predicting grain yields and determining area-based subsidies [19][20][21].…”