Using Time Series Sentinel-1 Images for Object-Oriented Crop Classification in Google Earth Engine

Abstract

The purpose of this study was to evaluate the feasibility and applicability of object-oriented crop classification using Sentinel-1 images in the Google Earth Engine (GEE). In this study, two study areas (Keshan farm and Tongnan town) with different average plot sizes in Heilongjiang Province, China, were selected. The research time was two consecutive years (2018 and 2019), which were used to verify the robustness of the method. Sentinel-1 images of the crop growth period (May to September) in each study area were composited with three time intervals (10 d, 15 d and 30 d). Then, the composite images were segmented by simple noniterative clustering (SNIC) according to different sizes and finally, the training samples and processed images were input into a random forest classifier for crop classification. The results showed the following: 1) the overall accuracy of using the object-oriented classification method combined composite Sentinel-1 image represented a great improvement compared with the pixel-based classification method in areas with large average plots (increase by 10%), the applicable scope of the method depends on the plot size of the study area; 2) the shorter time interval of the composite Sentinel-1 image was, the higher the crop classification accuracy was; 3) the features with high importance of composite Sentinel-1 images with different time intervals were mainly distributed in July, August and September, which was mainly due to the large differences in crop growth in these months; and 4) the optimal segmentation size of crop classification was closely related to image resolution and plot size. Previous studies usually emphasize the advantages of object-oriented classification. Our research not only emphasizes the advantages of object-oriented classification but also analyzes the constraints of using object-oriented classification, which is very important for the follow-up research of crop classification using object-oriented and synthetic aperture radar (SAR).

Fusion of Multi-Temporal PAZ and Sentinel-1 Data for Crop Classification

“…When comparing examples of crops classifications using X-band and C-band SAR data, it should be first mentioned that each study has unique features, including the SAR data available and the number and type of crops present. In this study, and in terms of overall accuracy, the results obtained from the experiments using only eight PAZ and eight S1 images were not as good as expected with regard to previous results found in the literature with time series of X-and C-band SAR data [23,[41][42][43][44]. For comparison purposes, the OA and kappa values obtained in all five experiments are depicted in Figure 6.…”

Google Earth Engine and Artificial Intelligence (AI): A Comprehensive Review

“…(2) SAR + optical RS images for better model performance: In addition, many studies reported [17,57,68,72,74,165,166,182,212,214] or suggested in future work [46,56,107,215] that SAR combined with optical RS images would improve model performance. Three classification methods (SVM, RF, and decision fusion) were used in [52] for the pixel-wise classification for crop mapping.…”

“…However, it is often blocked by clouds limiting its utility. SAR imagery works day or night regardless of cloud cover, so [74] used it for crop classification while testing input composite image length and ML classification performance. The authors compared an object-oriented classification method combining the SNIC algorithm with a RF with that of a pixel-based method of just the RF by itself.…”

Evaluation of Sentinel-1 and Sentinel-2 Feature Sets for Delineating Agricultural Fields in Heterogeneous Landscapes

“…Although Landsat images have proven useful for mapping agricultural fields over large areas, the spatial resolution of 30 m is often unable to resolve individual agricultural fields thereby inhibiting field-based applications in many cropping systems around the world [28]. Building on the experiences of the Landsat and SPOT missions, Sentinel-2 (S2) was designed within the framework of the European Copernicus program for land surface and agriculture monitoring [28] at a temporal resolution of 5 days and a spatial resolution of 10 m. As opposed to optical sensors, which are inhibited by clouds, Sentinel-1 (S1), which is also part of the Copernicus program enables the continuous monitoring of the earth's surface in all weather conditions at a temporal resolution of 6 days and a spatial resolution of 20 m. Various researchers have used S1 [29], [30], and predominantly S2 [31]- [40] for segmenting agricultural fields. In using the S1 or S2 images, most of those authors used existing segmentation algorithms (e.g., [29], [30], [32]- [34], [36]- [40]), some proposed new segmentation algorithms (e.g., [31], [35]), and others proposed new segmentation parameter optimization approaches (e.g., [36], [37]).…”

“…Building on the experiences of the Landsat and SPOT missions, Sentinel-2 (S2) was designed within the framework of the European Copernicus program for land surface and agriculture monitoring [28] at a temporal resolution of 5 days and a spatial resolution of 10 m. As opposed to optical sensors, which are inhibited by clouds, Sentinel-1 (S1), which is also part of the Copernicus program enables the continuous monitoring of the earth's surface in all weather conditions at a temporal resolution of 6 days and a spatial resolution of 20 m. Various researchers have used S1 [29], [30], and predominantly S2 [31]- [40] for segmenting agricultural fields. In using the S1 or S2 images, most of those authors used existing segmentation algorithms (e.g., [29], [30], [32]- [34], [36]- [40]), some proposed new segmentation algorithms (e.g., [31], [35]), and others proposed new segmentation parameter optimization approaches (e.g., [36], [37]). One area that is yet to be comprehensively explored is the determination of the optimal feature set from S1 and S2 images for segmenting agricultural fields given that both sensors come with different bands and additional features like band indices can be calculated as well.…”