Finding a suitable sensing time period for crop identification using heuristic techniques with multi-temporal satellite images

Abstract

Satellite crop identification processes are increasingly being used on a large scale, both to verify the crop and to improve production. As it is necessary to study phenological data over a period of time across a large territory, a lot of storage space is needed to save the satellite images and a lot of calculation time to analyse all this information. Sensing periods are usually established based on subjective expert criteria or previous experience. However, this decision may cause several differences when discriminating crop patterns, besides not guaranteeing good precision. These processes would greatly improve if the appropriate time periods could be found systematically using the minimum number of satellite images in the shortest possible time. In this paper, we propose a new methodology to determine a suitable sensing period for crop identification using Sentinel-2 images, applying hill climbing algorithms to the training sets of neural network models. We have used the method successfully in the 2020 Common Agricultural Policy campaign in the Extremadura region, Spain. The article also describes the use of the method in a case on tobacco detection in this region.

Evaluation of the Use of the 12 Bands vs. NDVI from Sentinel-2 Images for Crop Identification

“…One of the phases included in the method is the search for the optimal analysis period. In [ 17 ], this process is described, and crop identification comparison data are offered using data from different periods, where the analysis period is gradually modified through heuristic searches.…”