Mapping annual crops in Portugal with Sentinel-2 data

Abstract

This paper presents an annual crop classification exercise considering the entire area of continental Portugal for the 2020 agricultural year. The territory was divided into landscape units, i.e. areas of similar landscape characteristics for independent training and classification. Data from the Portuguese Land Parcel Identification System (LPIS) was used for training. Thirty-one annual crops were identified for classification. Supervised classification was undertaken using Random Forest. A time-series of Sentinel-2 images was gathered and prepared. Automatic processes were applied to auxiliary datasets to improve the training data quality and lower class mislabelling. Automatic random extraction was employed to derive a large amount of sampling units for each annual crop class in each landscape unit. An LPIS dataset of controlled parcels was used for results validation. An overall accuracy of 85% is obtained for the map at national level indicating that the methodology is useful to identify and characterize most of annual crop types in Portugal. Class aggregation of the annual crop types by two types of growing season, autumn/winter and spring/summer, resulted in large improvements in the accuracy of almost all annual crops, and an overall accuracy improvement of 2%. This experiment shows that LPIS dataset can be used for training a supervised classifier based on machine learning with highresolution remote sensing optical data, to produce a reliable crop map at national level.

Airborne Hyperspectral Images and Machine Learning Algorithms for the Identification of Lupine Invasive Species in Natura 2000 Meadows

“…However, these small, solitary patches of lupine are indicated as the main factor causing the spread of this species into new areas. Low accuracies for white and yellow lupine (F1 score below 0.04) were also obtained when mapping annual crops in Portugal using Sentinel-2 data and the random forest method [74]. This confirms that, in the case of lupine identification, high spatial and spectral resolutions of the images are important, especially if the beginning of the invasion is to be detected.…”

Improving the spatial and temporal variability of ammonia emissions from agriculture in Western Europe

“…The advent of the Sentinel-2 instruments heralds possibilities for more refined differentiation amongst diverse crop categories, attributed to their sensor and orbit features, high-resolution capabilities, and relatively rapid revisit intervals (Belgiu and Csillik, 2018;Kobayashi et al, 2020;Sonobe et al, 2018;Yi et al, 2020). Benevides et al (2022) derived a parcel-level crop map for the entire area of continental Portugal for 2020 using multitemporal Sentinel-2 data and the Portuguese Land Parcel Identification System (LPIS), achieving an overall accuracy of 85% for the map at the national level. Blickensdörfer et al (2022) used the assimilation of dense time series data from Sentinel-2 and Landsat 8 in combination with monthly Sentinel-1 composites and environmental data to derive national crop maps for Germany.…”