Detection of Cover Crop Using Time-Series Remote Sensing Observations

Vegetation cover plays a crucial role in enriching the soil carbon content. The sequestered CO
2
gets released due to exposure of soil to the atmosphere by the process of volatilization. Therefore, there is a need to monitor sustainable farm management practises like cover cropping. Remote Sensing coupled with artificial intelligence helps in non-invasive monitoring of vegetation cover. The main objective of this study is to detect the presence of cover crop using the time-series of remote sensing observations. The study was carried out on selected fields from the Europe region. A total of 60 fields from four countries, namely, France, Germany, Poland, and Spain during 2019–2021 were selected. In this study we proposed a two-step approach for cover crop detection. The first step involves separating vegetation period from fallow/bare soil and snow cover. The second step has mainly focused on the vegetation period to initially separate main crop period and subsequent detection of cover crop for the remainder of the vegetation period. Combination of index based thresholding and phenology indicators was used for cover crop detection. The proposed approach was validated using the ground reference data on presence or absence of a cover crop. Results showed an overall accuracy of 91.7% with an F1 score of 91.2%. Moreover, cover crop detection rate was found to be 92.9%. One field was misclassified as cover crop, whereas it had a dense cover of weeds. This was mainly due to higher peak NDVI value of dense weeds than NDVI threshold.