Integration of Multiple Spectral Data via a Logistic Regression Algorithm for Detection of Crop Residue Burned Areas: A Case Study of Songnen Plain, Northeast China

The burning of crop residues in fields is a significant global biomass burning activity which is a key element of the terrestrial carbon cycle, and an important source of atmospheric trace gasses and aerosols. Accurate estimation of cropland burned area is both crucial and challenging, especially for the small and fragmented burned scars in China. Here we developed an automated burned area mapping algorithm that was implemented using Sentinel-2 Multi Spectral Instrument (MSI) data and its effectiveness was tested taking Songnen Plain, Northeast China as a case using satellite image of 2020. We employed a logistic regression method for integrating multiple spectral data into a synthetic indicator, and compared the results with manually interpreted burned area reference maps and the Moderate-Resolution Imaging Spectroradiometer (MODIS) MCD64A1 burned area product. The overall accuracy of the single variable logistic regression was 77.38% to 86.90% and 73.47% to 97.14% for the 52TCQ and 51TYM cases, respectively. In comparison, the accuracy of the burned area map was improved to 87.14% and 98.33% for the 52TCQ and 51TYM cases, respectively by multiple variable logistic regression of Sentind-2 images. The balance of omission error and commission error was also improved. The integration of multiple spectral data combined with a logistic regression method proves to be effective for burned area detection, offering a highly automated process with an automatic threshold determination mechanism. This method exhibits excellent extensibility and flexibility taking the image tile as the operating unit. It is suitable for burned area detection at a regional scale and can also be implemented with other satellite data.