Sentinel-1 Time Series for Crop Identification in the Framework of the Future CAP Monitoring

Abstract

In this upcoming Common Agricultural Policy (CAP) reform, the use of satellite imagery is taking an increasing role for improving the Integrated Administration and Control System (IACS). Considering the operational aspect of the CAP monitoring process, the use of Sentinel-1 SAR (Synthetic Aperture Radar) images is highly relevant, especially in regions with a frequent cloud cover, such as Belgium. Indeed, SAR imagery does not depend on sunlight and is barely affected by the presence of clouds. Moreover, the SAR signal is particularly sensitive to the geometry and the water content of the target. Crop identification is often a pre-requisite to monitor agriculture at parcel level (ploughing, harvest, grassland mowing, intercropping, etc.) The main goal of this study is to assess the performances and constraints of a SAR-based crop classification in an operational large-scale application. The Random Forest object-oriented classification model is built on Sentinel-1 time series from January to August 2020 only. It can identify crops in the Walloon Region (south part of Belgium) with high performance: 93.4% of well-classified area, representing 88.4% of the parcels. Among the 48 crop groups, the six most represented ones get a F1-score higher or equal to 84%. Additionally, this research documents how the classification performance is affected by different parameters: the SAR orbit, the size of the training dataset, the use of different internal buffers on parcel polygons before signal extraction, the set of explanatory variables, and the period of the time series. In an operational context, this allows to choose the right balance between classification accuracy and model complexity. A key result is that using a training dataset containing only 3.2% of the total number of parcels allows to correctly classify 91.7% of the agricultural area. The impact of rain and snow is also discussed. Finally, this research analyses how the classification accuracy depends on some characteristics of the parcels like their shape or size. This allows to assess the relevance of the classification depending on those characteristics, as well as to identify a subset of parcels for which the global accuracy is higher.

Mapping Crop Types of Germany by Combining Temporal Statistical Metrics of Sentinel-1 and Sentinel-2 Time Series with LPIS Data

“…In this context, and particularly since the availability of Sentinel-1 acquisitions, advances have been made in the analysis of radar data for crop mapping, and in its combination with optical images, to create robust classification approaches for agricultural areas [29]. Various studies have shown the potential to map crop types based on Sentinel-1 time series without additional optical data input in Europe (e.g., [30][31][32][33][34][35][36][37][38][39][40][41]) and in Germany (e.g., [42][43][44]). However, particularly promising are approaches combining optical and SAR data (e.g., for Asia [45], Africa [46,47], Europe [48][49][50][51][52][53][54][55], and Germany [56][57][58]).…”

Crop Mapping in the Sanjiang Plain Using an Improved Object-Oriented Method Based on Google Earth Engine and Combined Growth Period Attributes

“…By combining Figures 4 and 5, we found that for the features that conformed with the separability requirements, the growth stage was distributed from the 251st to 280th days, that is, mid-September to early October; this stage was the maturity period of the crops [39], which means that the difference of the spectral features reached the maximum level at the maturity stage of the crops. In addition, of the features that conformed with the separability requirements, the top five features in order of importance were RE1, NDVI, Red, SWIR2, and Aerosols, and the growth stage associated with these five features was between September 17 and September 28, i.e., within this 10-day growth stage, the differences in the spectral features of the three crops were the most significant and the easiest to extract [65]. In addition, it can be thus determined that in addition to using the conventional wavebands (visible light and near infrared (NIR)) [66], the addition of 703.9 nm (S2A)/703.8 nm (S2B) from RE1 and 2202.4 nm (S2A)/2185.7 nm (S2B) from SWIR2 had important significance for crop classification [60].…”

Filtering Lpis Data for Building Trustworthy Training Datasets for Crop Type Mapping: A Case Study in Greece

“…Aiming at reducing the uncertainty and error rates of the declaration procedures, the new rules for 2020+ CAP payments provide the increased use of modern technologies during verification phases and in particular the integration of Sentinel data for monitoring and cross-compliance assessment (Beriaux et al, 2021;Campos-Taberner et al, 2019;David et al, 2021). In this regard, the development of efficient remote sensing techniques towards monitoring and regularly updated crop type mapping arises as essential to achieve the new CAP goals.…”

“…In this regard, the development of efficient remote sensing techniques towards monitoring and regularly updated crop type mapping arises as essential to achieve the new CAP goals. To this end many recent crop mapping studies have documented the employment of Sentinel data along with LPIS data for developing crop classification models in Austria, Belgium, Germany, France, Spain and the Netherlands (Beriaux et al, 2021;Blickensdörfer et al, 2022;Campos-Taberner et al, 2019;David et al, 2021;Griffiths et al, 2019;Reuß et al, 2021;Sitokonstantinou et al, 2018). In most cases LPIS data * Corresponding author were utilised after the application of some basic preparation steps in order to fit the desired nomenclature and to exclude erroneous geometries and field boundaries from the analysis.…”