Garlic is the major economic crop in China. Timely and accurate identification and mapping of garlic are significant for garlic yield prediction and garlic market management. Previous studies on garlic mapping were mainly based on all observations of the entire growing season, so the resulting maps have a hysteresis. Here, we determined the optimal identification strategy and the earliest identifiable phenophase for garlic based on all available Landsat 8/9 time series imagery in Google Earth Engine. Specifically, we evaluated the performance of different vegetation indices for each phenophase to determine the optimal classification metrics for garlic. Secondly, we identified garlic using random forest algorithm and classification metrics of different time series lengths. Finally, we determined the earliest identifiable phenophase of garlic and generated an early-season garlic distribution map. Garlic could be identified as early as March (bud differentiation period) with an F1 of 0.91. Our study demonstrates the differences in the performance of vegetation indices at different phenophases, and these differences provide a new idea for mapping crops. The generated early-season garlic distribution map provides timely data support for various stakeholders.

As the main driving force of global climate change, land use and land cover change (LUCC) can affect the surface energy balance and the interaction between the surface and atmosphere. This effect will cause further surface temperature changes. The Yellow River Basin is an important ecological security barrier in China. Therefore, exploring the impact of its LUCC on temperature changes can provide certain help for future land-use planning in the Yellow River Basin. Here, we conducted two numerical simulation experiments (Case2015 and Case1995) by using the weather research and forecasting (WRF) model to quantify the effect of LUCC in the Yellow River Basin on the summer 2 m air temperature (T2 m). The results showed that LUCC led to an overall warming trend in T2 m in the Yellow River Basin. Urban expansion caused T2 m to rise by approximately 0.3 °C to 0.6 °C. A warming effect was also identified in the areas where farmland and bare areas were converted to grassland, with T2 m increasing by around 0.4 °C.

Early crop mapping is essential in predicting crop yield, assessing agricultural disasters, and responding to food price fluctuations. Winter wheat is a major food contributor in China. Existing early season maps of winter wheat strongly depend on the shape of the time series curve, which limits applicability on large scales. Besides, the effect of garlic on winter wheat mapping is often ignored. In this study, we determined how early we could identify winter crops (winter wheat and garlic) by examining time series of different lengths, and generated annual 30-m winter wheat and garlic map of the Huaihe basin using the Random Forest classifier, Sentinel-1/2, and Landsat-7/8 time-series imagery. The results showed that garlic could be identified at the end of November by using four composite images with overall accuracy (OA) of 0.88, followed by winter wheat recognizable at the end of January by using eight composite images with an OA of 0.91. The proposed framework can also be implemented in other regions and crops to generate early season distribution maps of different crops.

Accurate garlic identification and mapping are vital for precise crop management and the optimization of yield models. However, previous understandings of garlic identification were limited. Here, we propose an automatic garlic mapping framework using optical and synthetic aperture radar (SAR) images on the Google Earth Engine. Specifically, we firstly mapped winter crops based on the phenology of winter crops derived from Sentinel-2 data. Then, the garlic was identified separately using Sentinel-1 and Sentinel-2 data based on the winter crops map. Additionally, multi-source validation data were used to evaluate our results. In garlic mapping, coupled optical and SAR images (OA 95.34% and kappa 0.91) outperformed the use of only optical images (OA 74.78% and kappa 0.50). The algorithm explored the potential of multi-source remote sensing data to identify target crops in mixed and fragmented planting regions. The garlic planting information from the resultant map is essential for optimizing the garlic planting structure, regulating garlic price fluctuations, and promoting a healthy and sustainable development of the garlic industry.