Synergistic Use of TanDEM-X and Landsat-8 Data for Crop-Type Classification and Monitoring

Classification of crop types using Earth Observation (EO) data is a challenging task. The challenge increases many folds when we have diverse crops within a resolution cell. In this regard, optical and Synthetic Aperture Radar (SAR) data provide complementary information to characterize a target. Therefore, we propose to leverage the synergy between multispectral and Synthetic Aperture Radar (SAR) data for crop classification. We aim to use the newly developed model-free three-component scattering power components to quantify changes in scattering mechanisms at different phenological stages. By incorporating interferometric coherence information, we consider the morphological characteristics of the crops that are not available with only polarimetric information. We also utilize the reflectance values from Landsat-8 spectral bands as complementary biochemical information of crops. The classification accuracy is enhanced by using these two pieces of information combined using a neural network-based architecture with an attention mechanism. We utilize the time series dual co-polarimetric (i.e., HH–VV) TanDEM-X SAR data and the multispectral Landsat-8 data acquired over an agricultural area in Seville, Spain. The use of the proposed attention mechanism for fusing SAR and optical data shows a significant improvement in classification accuracy by 6.0% to 9.0% as compared to the sole use of either the optical or SAR data. Besides, we also demonstrate that the utilization of single-pass interferometric coherence maps in the fusion framework enhances the overall classification accuracy by ≈ 3.0%. Therefore, the proposed synergistic approach will facilitate accurate and robust crop mapping with high-resolution EO data at larger scales.This work was supported in part by the German Aerospace Center (DLR) which provided all the TanDEM-X data under project POLI6736, in part by the State Research Agency (AEI), in part by the Spanish Ministry of Science and Innovation, and in part by the EU EFDR funds under Project TEC2017-85244-C2-1-P. The work of N. Bhogapurapu and S. Dey was supported by the Ministry of Education (formerly Ministry of Human Resource and Development-MHRD), Government of India