Ethiopian Crop Type 2020 (EthCT2020) dataset: Crop type data for environmental and agricultural remote sensing applications in complex Ethiopian smallholder wheat-based farming systems (Meher season 2020/21)

Abstract

Crop type observation is crucial for various environmental and agricultural remote sensing applications including land use and land cover mapping, crop growth monitoring, crop modelling, yield forecasting, disease surveillance, and climate modelling. Quality-controlled georeferenced crop type information is essential for calibrating and validating machine learning algorithms. However, publicly available field data is scarce, particularly in the highly dynamic smallholder farming systems of sub-Saharan Africa. For the 2020/21 main cropping season (Meher), the Ethiopian Crop Type 2020 (EthCT2020) dataset compiled from multiple sources provides 2,793 harmonized, quality-controlled, and georeferenced in-situ samples on annual crop types (7 crop groups; 22 crop classes) at smallholder field level across the complex and highly fragmented agricultural landscape of Ethiopia. The focus was on rainfed, wheat-based farming systems. A nationwide ground data collection campaign (GDCC; Source 1) was designed using a stratification approach based on wheat crop calendar information, and 1,263 in-situ data samples were collected in selected sampling regions. This in-situ data pool was enriched with 1,530 wheat samples extracted from a) the Wheat Rust Toolbox (WRTB; Source 2; 734 samples), a database for wheat disease surveillance data [1] and b) an inhouse farm household survey database (FHSD; Source 3; 796 samples). Obtained field data was labelled according to the Joint Experiment for Crop Assessment and Monitoring (JECAM) guidelines for cropland and crop type definition and field data collection [2] and the FAO Indicative Crop Classification [3]. The EthCT2020 dataset underwent extensive processing including data harmonization, mixed pixel assessment through visual interpretation using 5 m Planet satellite image composites, and quality-control using Sentinel-2 NDVI homogeneity analysis. The EthCT2020 dataset is unique in terms of crop diversity, pixel purity, and spatial accuracy while targeting a countrywide distribution. It is representative of Ethiopia's complex and highly fragmented agricultural landscape and can be useful for developing new machine learning algorithms for land use land cover mapping, crop type mapping, agricultural monitoring, and yield forecasting in smallholder cropping systems. The dataset can also serve as a baseline input parameter for crop models, climate models, and crop disease and pest forecasting systems.

Keywords: Agriculture; Annual cropland; Crop type mapping; Ethiopia; Field reference data; In-situ crop type observation; Machine learning; Remote sensing.

Graphical abstract

Fig. 1

(a) Location of the EthCT2020 dataset and the distribution of the fields, showing crop groups; (b) Example of circular field plots showing crop classes over a color-infrared (CIR) composition of PlanetScope Surface Reflectance Mosaics (NIR-red-green bands) from September 2020 at 4.8 m spatial resolution (West Arsi zone, Oromia region).

Fig. 2

Location of the source datasets and the distribution of the fields, showing crop classes (a) Source 1 – Ground Data Collection Campaign (GDCC); (b) Source 2 – Wheat Rust Toolbox (WRTB); and (c) Source 3 – Farm Household Survey Database (FHSD).

Fig. 3

(a) Distribution of crop groups (samples per crop group; crop groups with <35 samples are not displayed); (b) Distribution of main crop classes (samples per crop class). Note: The EthCT2020 dataset contains 13 circle field plots, having a high overlap (≥10 m) with neighboring circle field plots. The IDs (id) are: 579, 583, 585, 1064, 2113, 2119, 2161, 2201, 2287, 2336, 2340, 2350, and 2362. All overlapping polygons belong to the wheat crop class (c_class).

Fig. 4

Overview of the rainfed cropland area of Ethiopia – African MARS-JRC crop mask over Ethiopia (orange) overlayed with the vectorized, generalized cropland boundary (red dashed line).

Fig. 5

Location of sampling regions in relation to – (a) the crop calendar (planting date) sampling frame; (b) the agro-ecological zones ; and (c-f) the physical area of barley, maize, sorghum, and wheat (pixel size: 100 km²) .