Farmland Parcel Mapping in Mountain Areas Using Time-Series SAR Data and VHR Optical Images

Mapping annual 10-m soybean cropland with spatiotemporal sample migration

China, as the world's biggest soybean importer and fourth-largest producer, needs accurate mapping of its planting areas for global food supply stability. The challenge lies in gathering and collating ground survey data for different crops. We proposed a spatiotemporal migration method leveraging vegetation indices' temporal characteristics. This method uses a feature space of six integrals from the crops' phenological curves and a concavity-convexity index to distinguish soybean and non-soybean samples in cropland. Using a limited number of actual samples and our method, we extracted features from optical time-series images throughout the soybean growing season. The cloud and rain-affected data were supplemented with SAR data. We then used the random forest algorithm for classification. Consequently, we developed the 10-meter resolution ChinaSoybean10 maps for the ten primary soybean-producing provinces from 2019 to 2022. The map showed an overall accuracy of about 93%, aligning significantly with the statistical yearbook data, confirming its reliability. This research aids soybean growth monitoring, yield estimation, strategy development, resource management, and food scarcity mitigation, and promotes sustainable agriculture.

Parcel-Level Mapping of Horticultural Crop Orchards in Complex Mountain Areas Using VHR and Time-Series Images

Accurate and reliable farmland crop mapping is an important foundation for relevant departments to carry out agricultural management, crop planting structure adjustment and ecological assessment. The current crop identification work mainly focuses on conventional crops, and there are few studies on parcel-level mapping of horticultural crops in complex mountainous areas. Using Miaohou Town, China, as the research area, we developed a parcel-level method for the precise mapping of horticultural crops in complex mountainous areas using very-high-resolution (VHR) optical images and Sentinel-2 optical time-series images. First, based on the VHR images with a spatial resolution of 0.55 m, the complex mountainous areas were divided into subregions with their own independent characteristics according to a zoning and hierarchical strategy. The parcels in the different study areas were then divided into plain, greenhouse, slope and terrace parcels according to their corresponding parcel characteristics. The edge-based model RCF and texture-based model DABNet were subsequently used to extract the parcels according to the characteristics of different regions. Then, Sentinel-2 images were used to construct the time-series characteristics of different crops, and an LSTM algorithm was used to classify crop types. We then designed a parcel filling strategy to determine the categories of parcels based on the classification results of the time-series data, and accurate parcel-level mapping of a horticultural crop orchard in a complex mountainous area was finally achieved. Based on visual inspection, this method appears to effectively extract farmland parcels from VHR images of complex mountainous areas. The classification accuracy reached 93.01%, and the Kappa coefficient was 0.9015. This method thus serves as a methodological reference for parcel-level horticultural crop mapping and can be applied to the development of local precision agriculture.

Smallholder Crop Type Mapping and Rotation Monitoring in Mountainous Areas with Sentinel-1/2 Imagery

Accurate and timely crop type mapping and rotation monitoring play a critical role in crop yield estimation, soil management, and food supplies. To date, to our knowledge, accurate mapping of crop types remains challenging due to the intra-class variability of crops and labyrinthine natural conditions. The challenge is further complicated for smallholder farming systems in mountainous areas where field sizes are small and crop types are very diverse. This bottleneck issue makes it difficult and sometimes impossible to use remote sensing in monitoring crop rotation, a desired and required farm management policy in parts of China. This study integrated Sentinel-1 and Sentinel-2 images for crop type mapping and rotation monitoring in Inner Mongolia, China, with an extensive field-based survey dataset. We accomplished this work on the Google Earth Engine (GEE) platform. The results indicated that most crop types were mapped fairly accurately with an F1-score around 0.9 and a clear separation of crop types from one another. Sentinel-1 polarization achieved a better performance in wheat and rapeseed classification among different feature combinations, and Sentinel-2 spectral bands exhibited superiority in soybean and corn identification. Using the accurate crop type classification results, we identified crop fields, changed or unchanged, from 2017 to 2018. These findings suggest that the combination of Sentinel-1 and Sentinel-2 proved effective in crop type mapping and crop rotation monitoring of smallholder farms in labyrinthine mountain areas, allowing practical monitoring of crop rotations.

Fast and Accurate Terrain Image Classification for ASTER Remote Sensing by Data Stream Mining and Evolutionary-EAC Instance-Learning-Based Algorithm

Remote sensing streams continuous data feed from the satellite to ground station for data analysis. Often the data analytics involves analyzing data in real-time, such as emergency control, surveillance of military operations or scenarios that change rapidly. Traditional data mining requires all the data to be available prior to inducing a model by supervised learning, for automatic image recognition or classification. Any new update on the data prompts the model to be built again by loading in all the previous and new data. Therefore, the training time will increase indefinitely making it unsuitable for real-time application in remote sensing. As a contribution to solving this problem, a new approach of data analytics for remote sensing for data stream mining is formulated and reported in this paper. Fresh data feed collected from afar is used to approximate an image recognition model without reloading the history, which helps eliminate the latency in building the model again and again. In the past, data stream mining has a drawback in approximating a classification model with a sufficiently high level of accuracy. This is due to the one-pass incremental learning mechanism inherently exists in the design of the data stream mining algorithm. In order to solve this problem, a novel streamlined sensor data processing method is proposed called evolutionary expand-and-contract instance-based learning algorithm (EEAC-IBL). The multivariate data stream is first expanded into many subspaces, and then the subspaces, which are corresponding to the characteristics of the features are selected and condensed into a significant feature subset. The selection operates stochastically instead of deterministically by evolutionary optimization, which approximates the best subgroup. Followed by data stream mining, the model learning for image recognition is done on the fly. This stochastic approximation method is fast and accurate, offering an alternative to the traditional machine learning method for image recognition application in remote sensing. Our experimental results show computing advantages over other classical approaches, with a mean accuracy improvement at 16.62%.