Crop type classification with satellite imageries is widely applied in support of crop production management and food security strategy. The abundant supply of these satellite data is accelerating and blooming the application of crop classification as satellite data at 10 m to 30 m spatial resolution have been made accessible easily, widely and free of charge, including optical sensors, the wide field of viewer (WFV) onboard the GaoFen (GF, high resolution in English) series from China, the MultiSpectral Instrument (MSI) onboard Sentinel 2 (S2) from Europe and the Operational Land Imager (OLI) onboard Landsat 8 (L8) from USA, thanks to the implementation of the open data policy. There are more options in using the satellite data as these three data sources are available. This paper explored the different capability of these three data sources for the crop type mapping in the same area and within the same growing season. The study was executed in a flat and irrigated area in Northwest China. Nine types of crop were classified using these three kinds of time series of data sources in 2017 and 2018, respectively. The same suites of the training samples and validation samples were applied for each of the data sources. Random Forest (RF) was used as the classifier for the crop type classification. The confusion error matrix with the OA, Kappa and F1-score was used to evaluate the accuracy of the classifications. The result shows that GF-1 relatively has the lowest accuracy as a consequence of the limited spectral bands, but the accuracy is at 93–94%, which is still excellent and acceptable for crop type classification. S2 achieved the highest accuracy of 96–98%, with 10 available bands for the crop type classification at either 10 m or 20 m. The accuracy of 97–98% for L8 is in the middle but the difference is small in comparison with S2. Any of these satellite data may be used for the crop type classification within the growing season, with a very good accuracy if the training datasets were well tuned.

As a perennial plant with long productive span of 30–50 years, grapevine may experience cross-lifespan climate change, which can modify wine quality and challenge viticultural sustainability. Therefore, it is essential to evaluate the viticultural suitability by considering both current and future climate conditions. To this end, a maximum entropy model was proposed to delimitate potentially suitable areas for viticulture based on multi-source data in a novel wine region, Ningxia, China, considering both current and future climate conditions. Firstly, we combined traditional data of climate, soil, and topography with remote sensing data to screen predictors that best characterize current geographical distribution of vineyards. Then, we used those predictors to assess current suitability (2001–2020) in Ningxia. The results indicated altitude, aridity index during April–September (K0409), precipitation during July–September (P0709), normalized difference vegetation index during July–September (NDVI0709), soil organic carbon (SOC), and precipitation in September (P09) were key predictors to assess potential suitability for viticulture, and their threshold values ranged from 1075 m to 1648 m, 2.93 to 4.83, 103.1 mm to 164.1 mm, 0.1 to 0.89, 0.07 g/kg to 11 g/kg and 28.4 mm to 45.0 mm, respectively. Suitability maps revealed a total suitable area of 12029 km², among which the highly and moderately suitable areas accounted for 6.1% and 23.1%, respectively. Finally, the alteration in proportion of potential suitable areas due to changing climate was estimated. The potential suitable areas varied from 8742 km² to 10623 km² over the next 40 years (2022–2060) and decreased to 8826–9184 km² under a short-term sustainability (suitable only during current–2040). To further consider long-term and sustainable development of the wine industry (current–2060), total suitable areas dropped by 26.7–29.2% under different climate scenarios compared with current suitable areas (2001–2020). The conclusions provide indispensable guidance for vineyard zoning considering long-term climate change.

Agricultural landscapes are characterized by diversity and complexity, which makes crop mapping at a regional scale a top priority for different purposes such as administrative decisions and farming management. Project 32194 of the Dragon 4 Program was implemented to meet the requirements of crop mapping, with the specific objective to develop suitable approaches for precise crop mapping with combined uses of European and Chinese high- and medium-resolution satellite images. Two sub-projects were involved in the project. The first was to focus on the use of time series high-resolution satellite data, including Sentinel-2 (S2, European satellite data) and Gaofen-1 (GF-1, Chinese satellite data), due to their similar spectral bands for Earth observation, while the second was to focus on medium-resolution data sources, i.e., the European Project for On-Board Autonomy–Vegetation (PROBA-V) and Chinese Fengyun-3 Medium Resolution Spectral Imager (FY-3 MERSI) satellite data, also due to their similar spectral channels. The approach of the European Space Agency (ESA) Sent2Agri project for crop mapping was adapted in the first sub-project and applied to the Yellow River irrigated district (YERID) of Ningxia in northwest China in order to assess its ability to accurately identify crop types in China. The goal of the second sub-project was to explore the potential of both European and Chinese medium-resolution satellite data for crop assessment in a large area. Methods to handle the data and retrieve the required information for the precise crop mapping were developed in the study, including the adaptation of the ESA approach to GF-1 data and the application of algorithms for classification. A scheme for the validation of the crop mapping was developed in the study. The results of implementing the scheme to the YERID in Ningxia indicated that the overall accuracies of crop mapping with S2 and GF-1 can be high, up to 94–97%, and the mapping had an accuracy of 88% with the PROBA-V and FY3B-MERSI data. The very high accuracy suggests the possibility of precise crop mapping with the combined use of time series high- and medium-resolution satellite data when suitable approaches are chosen to handle the data for the classification of crop types.

The favorable terroir of China's northwest region provides an ideal environment for the cultivation and thriving growth of grapes. However, climate change threatens to alter the optimal grape‐growing regions, presenting considerable challenges to the local wine making industry. To enhance the utilization of regional climate resources and refine the wine grape industry's spatial distribution, this study assessed the performance of nine climate models from the Coupled Model Intercomparison Project Phase 6 (CMIP6) in Northwestern China, simulated the future spatiotemporal distribution of wine grape. Results showed that EC‐Earth3 performed well in simulating temperature, precipitation, and interannual changes. Under the historical periods (1980–2014), the highly suitable areas for wine grapes were predominantly found in the eastern foothills of Helan Mountain in Ningxia, along the Yellow River in Wuhai and Linhe of Inner Mongolia, along the Qilian Mountains in Wuwei, Zhangye and Jiayuguan of Gansu, and along rivers and oases in the northern foothills of Tianshan Mountains, Ili River Valley, Tuha Basin, Yanqi Basin, Aksu, Muzart, and Kashgar of Xinjiang. Compared with historical periods, the highly and moderately suitable areas were expected to expand under SSP245 and SSP585. Nevertheless, the overall pattern of spatial distribution was not anticipated to experience substantial alterations. In the next 50 years (2055–2085), the suitable areas under SSP245 scenario would be higher than SSP585. Precipitation from July to September (pr79), soil pH (ph), elevation (dem), and near‐surface air temperature in January (tas1) were the main factors affecting the suitable areas of wine grapes. Further analysis revealed that a certain level of the near‐surface air temperature in January (tas1) contributed positively to the expansion of suitable areas. However, excessively high average temperatures in January and July tended to have a detrimental effect. A rise in winter temperature can foster a more favorable environment for wine grapes to overwinter. However, frequent summer heat waves and high winter temperatures caused by climate warming may have adverse effects.

Vineyard microclimate is a main determining factor for grape yield and quality. In the Eastern Foothills of the Helan Mountains in the Ningxia region, China, grape is a critical economic crop for wine making. The microclimate of vineyards in 2019 and 2020 was analyzed on the basis of observations from eight vineyards microclimate stations and a national reference station. The vineyard microclimate was compared with the larger-scale local weather conditions, and the vineyard microclimate was compared within and among different climatic zones. In addition, the climate was evaluated at the four study wine grape areas. The results showed that: (1) In the same wine-grape-growing area where the climate was similar, vineyard microclimate was affected by elevation, topography, and soil texture. (2) Excepting average wind velocity, there were no statistically significant (p < 0.05) differences in minimum and maximum air temperatures or relative humidity between the study field microclimate observation stations and the local national reference station. (3) Among the four climate-zone-based wine-grape-growing areas, vineyard microclimate was related to topography, geographical location, and soil type. In particular, the Yinchuan and Qingtongxia areas had an abundant heat resource, and the Hongsipu area had sufficient precipitation and high photosynthetically active radiation (PAR). By contrast, the Shizuishan area had a relatively low-to-moderate amount of heat, precipitation, and radiation resources for grape. (4) According to the climate assessment of agricultural products—wine grape (QX/T 557—2020), the climate was superior in 2019 than in 2020 for wine grapes. In particular, the climate for grape was better in the Yinchuan area than in other study areas. The results of this study are evidence-based and could be used to assist the local wine grape community in making decisions about cultivars, management practices, area expansions, marketing strategies, etc.