Characterising maize and intercropped maize spectral signatures for cropping pattern classification

Intercropping – the planting of more than one crop in the same plot of land – is a prevalent agricultural management practice which can be used for risk reduction. Despite its widespread use, intercropping is not commonly reported in agricultural statistics, resulting to very limited spatially disaggregated information about its prevalence. Remote sensing-based approaches to detect and estimate the area of cropping patterns like intercropping require good understanding of the spectral response of (intercropped) crops at different crop growth phases. This study integrates field surveys, farmer interviews and temporal Sentinel-2 data from four crop growth phases and the post-harvest period of maize and intercropped maize (imaize). The goal is to identify the optimal crop growth phases, spectral regions and vegetation indices (VIs) that can accurately discriminate the two cropping patterns. We computed p-values for the spectral bands using Mann-Whitney U test and identified critical crop growth phases. Classification of maize and imaize cropping patterns was performed using random forest classifier. Our spectral analysis revealed effective discrimination between maize and imaize cropping patterns during the vegetative (in all spectral bands) and flowering-yield phases (in Blue, Green, Red, RE704, RE783, NIR833, NIR865). The most suitable VIs contained red-edge and near-infrared spectal bands. Utilizing spectral data and VIs from vegetative and flowering-yield phases, we achieved optimal discrimination during the vegetative phase (user’s accuracy of 100 % and producer’s accuracy of 100 %). However, accuracy decreased during the flowering yield phase (overall accuracy of 87 % for all spectral bands). The highest classification results using all spectral bands at the flowering yield phase resulted in 80 % producer’s accuracy for maize and 100 % for imaize. This study illustrates the utility of temporal Sentinel-2 spectral data for identifying the critical crop growth phase, spectral regions and VIs for cropping patterns classification, particularly for intercropping

The Importance of Agronomic Knowledge for Crop Detection by Sentinel-2 in the CAP Controls Framework: A Possible Rule-Based Classification Approach

Farmers are supported by European Union (EU) through contributions related to the common agricultural policy (CAP). To obtain grants, farmers have to apply every year according to the national/regional procedure that, presently, relies on the Geo-Spatial Aid Application (GSAA). To ensure the properness of applications, national/regional payment agencies (PA) operate random controls through in-field surveys. EU regulation n. 809/2014 has introduced a new approach to CAP controls based on Copernicus Sentinel-2 (S2) data. These are expected to better address PA checks on the field, suggesting eventual inconsistencies between satellite-based deductions and farmers’ declarations. Within this framework, this work proposed a hierarchical (HI) approach to the classification of crops (soya, corn, wheat, rice, and meadow) explicitly aimed at supporting CAP controls in agriculture, with special concerns about the Piemonte Region (NW Italy) agricultural situation. To demonstrate the effectiveness of the proposed approach, a comparison is made between HI and other, more ordinary approaches. In particular, two algorithms were considered as references: the minimum distance (MD) and the random forest (RF). Tests were operated in a study area located in the southern part of the Vercelli province (Piemonte), which is mainly devoted to agriculture. Training and validation steps were performed for all the classification approaches (HI, MD, RF) using the same ground data. MD and RF were based on S2-derived NDVI image time series (TS) for the 2020 year. Differently, HI was built according to a rule-based approach developing according to the following steps: (a) TS standard deviation analysis in the time domain for meadows mapping; (b) MD classification of winter part of TS in the time domain for wheat detection; (c) MD classification of summer part of TS in the time domain for corn classification; (d) selection of a proper summer multi-spectral image (SMSI) useful for separating rice from soya with MD operated in the spectral domain. To separate crops of interest from other classes, MD-based classifications belonging to HI were thresholded by Otsu’s method. Overall accuracy for MD, RF, and HI were found to be 63%, 80%, and 89%, respectively. It is worth remarking that thanks to the SMSI-based approach of HI, a significant improvement was obtained in soya and rice classification