Investigating the Potential of Crop Discrimination in Early Growing Stage of Change Analysis in Remote Sensing Crop Profiles

Currently, remote sensing crop identification is mostly based on all available images acquired throughout growth. However, the image and data resources in early growth stage are limited, which makes challenging. Different types have different phenological characteristics seasonal rhythm characteristics, their rates at times. Therefore, making full use of to augment difference information times key identification. In this study, we first calculated differential features between periods as new during stage. Secondly, multi-temporal each period were constructed by combination, then a feature optimization method was used obtain optimal set possible combinations crops, well change explored. Finally, performance classification regression tree (Cart), Random Forest (RF), Gradient Boosting Decision Tree (GBDT), Support Vector Machine (SVM) classifiers recognizing crops analyzed. The results show that: (1) There differences with rice changing significantly F, corn E, M, L, H, soybean N, H. (2) For rice, land surface water index (LSWI), simple ratio (SR), B11, normalized tillage (NDTI) contributed most, while red-edge3 (NDRE3), LSWI, green vegetation (VIgreen), red-edge spectral (RESI), red-edge2 (NDRE2) greatly (3) Rice could be identified 13 May, PA UA high 95%. Corn soybeans 7 July, 97% 94%, respectively. (4) With addition more temporal features, recognition accuracy increased. GBDT RF performed best identifying three This study demonstrates feasibility using for recognition, can provide idea recognition.