A Comprehensive Benchmarking of the Available Spectral Indices Based on Sentinel-2 for Large-Scale Mapping of Plastic-Covered Greenhouses

Plastic-covered greenhouses (PCG) have been extensively used in agricultural practices around the world. Remote sensing based on spectral indices is a key asset to monitor the spatial distribution of these structures on a large scale. The primary objective of this research was to conduct a comprehensive benchmarking of the available spectral indices based on Sentinel-2 data for large-scale PCG mapping. For that, eight PCG indices were thoroughly analyzed by systematically investigating their optimal thresholds in five study sites located in Almeria (Spain), Antalya (Turkey), Agadir (Morocco), Weifang (China), and Nantong (China), including also different growing seasons. The experimental results demonstrated that the Plastic GreenHouse Index (PGHI) achieved the best PCG mapping accuracy in almost all study sites and growing seasons tested. From the visual analysis carried out on the PGHI mapping results, it was made out that the main misclassification between PCG and background classes took place in water bodies and industrial building land covers, particularly in the Weifang and Nantong study areas. Based on this fact, the original version of PGHI was modified by adding two processes aimed at masking water bodies and industrial buildings. This new composite index, called Improved PGHI (IPGHI), attained better accuracy results in all study sites, especially in Chinese PCG areas. The average F1 score calculated for all the study cases improved from 86.05% using PGHI to 90.51% applying IPGHI. The new approach provided a significant and robust improvement in PCG large-scale mapping for several types of PCG sites, even considering different growing seasons.