Sentinel-2 imagery is an unprecedented data source with high spatial, spectral and temporal resolution in addition to free access. The objective of this paper was to evaluate the potential of using Sentinel-2 data to map winter crops in the early growth stage. Analysis of three winter crop types—winter garlic, winter canola and winter wheat—was carried out in two agricultural regions of China. We analysed the spectral characteristics and vegetation index profiles of these crops in the early growth stage and other land cover types based on Sentinel-2 images. A decision tree classification model was built to distinguish the crops based on these data. The results demonstrate that winter garlic and winter wheat can be distinguished four months before harvest, while winter canola can be distinguished two months before harvest. The overall classification accuracy was 96.62% with a kappa coefficient of 0.95. Therefore, Sentinel-2 images can be used to accurately identify these winter crops in the early growth stage, making them an important data source in the field of agricultural remote sensing.

Early-Season Mapping of Winter Crops Using Sentinel-2 Optical Imagery

Towards automation of in-season crop type mapping using spatiotemporal crop information and remote sensing data

The spatial distribution of soil organic matter (SOM) is highly significant to the assessment of the regional carbon balance, food security and cultivated land quality. Due to climate change and the increasing food demand, the intensity of cultivated land development in the Northeast China black soil region is increasing, and it is urgent to accurately map the SOM content in this region. Remote sensing technology has been widely applied in the field of soil mapping, but large-scale and high-precision soil mapping remains a significant challenge. In this study, the Google Earth Engine (GEE) platform is adopted to generate synthetic soil images based on Landsat-8 and Sentinel-2 images capturing bare soil periods at 20-d intervals. Then, the spectral index and band are adopted as input variables to evaluate the prediction accuracy of these synthetic images depicting different periods using random forest (RF) regression. Finally, two feature selection methods (Boruta and recursive feature elimination (RFE)) are employed to evaluate the performance of these two methods. The results indicate that 1) the optimal time window for SOM prediction is day of year (DOY) 120–140 for the Songnen Plain; 2) the performance of SOM prediction based on Landsat-8 synthetic images is better than that based on Sentinel-2 synthetic images; and 3) both feature selection methods improve the SOM prediction accuracy, but RFE has the highest accuracy(Landsat-8 with Coefficient of Determination (R2) of 0.702, Root Mean Square Error (RMSE) of 0.681%; Sentinel-2 with R2 of 0.5963, RMSE of 0.793%). This study provides a new model for large-scale and high-spatial resolution SOM prediction and verifies the importance of the time window to the SOM prediction accuracy. 

Regional soil organic matter mapping models based on the optimal time window, feature selection algorithm and Google Earth Engine

Crop classification based on phenology information by using time series of optical and synthetic-aperture radar images

Rice is the staple crop for more than half the world’s population, but there is a lack of high-resolution maps outlining rice areas and their growth stages. Most remote sensing studies map the rice extent; however, in tropical regions, rice is grown throughout the year with variable planting dates and cropping frequency. Thus, mapping rice growth stages is more useful than mapping only the extent. This study addressed this challenge by developing a phenology-based method. The hypothesis was that the unsupervised classification (k-means clustering) of Sentinel-1 and 2 time-series data could identify rice fields and growth stages, because (1) the presence of flooding during transplanting can be identified by Sentinel-1 VH backscatter; and (2) changes in the canopy of rice fields during growth stages (vegetative, generative, and ripening phases) up to the point of harvesting can be identified by Normalized Difference Vegetation Index (NDVI) time series. Using the proposed method, this study mapped rice field extent and cropping calendars across Peninsular Malaysia (131,598 km2) on the Google Earth Engine (GEE) platform. The Sentinel-1 and 2 monthly time series data from January 2019 to December 2020 were classified using k-means clustering to identify areas with similar phenological patterns. This approach resulted in 10-meter resolution maps of rice field extent, intensity, and cropping calendars. Validation using very high-resolution street view images from Google Earth showed that the predicted map had an overall accuracy of 95.95%, with a kappa coefficient of 0.92. In addition, the predicted crop calendars agreed well with the local government’s granary data. The results show that the proposed phenology-based method is cost-effective and can accurately map rice fields and growth stages over large areas. The information will be helpful in measuring the achievement of self-sufficiency in rice production and estimates of methane emissions from rice cultivation.

/pdf/high-resolution-mapping-of-paddy-rice-extent-and-growth-wfw5brin.pdf

High-Resolution Mapping of Paddy Rice Extent and Growth Stages across Peninsular Malaysia Using a Fusion of Sentinel-1 and 2 Time Series Data in Google Earth Engine

Monthly composites from Sentinel-1 and Sentinel-2 images for regional major crop mapping with Google Earth Engine

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Monthly composites from Sentinel-1 and Sentinel-2 images for regional major crop mapping with Google Earth Engine