本研究提出一种统计数据空间化的方法构建多时像农作物种植格局空间数据集来解决数据缺失的问题。该方法采用两层嵌套结构实现土地利用层和农作物层模拟，其中第一层模拟的耕地数据用于控制第二层农作物种植格局空间模拟范围。第二层农作物层采用空间迭代的方法按分配规则进行农作物面积统计数据空间化，最终实现农作物空间格局动态模拟。该模型在中国黑龙江省地区进行2000-2019年农作物空间格局模拟，结果表明模型模拟精度较高，能够实现长时间序列的农作物种植面积统计数据空间化应用，未来该模型能广泛应用于农业土地系统各方面研究及生产应用。

The leaf area index (LAI) is an important vegetation parameter, which is used widely in many applications. Remote sensing techniques are known to be effective but inexpensive methods for estimating the LAI of crop canopies. During the last two decades, hyperspectral remote sensing has been employed increasingly for crop LAI estimation, which requires unique technical procedures compared with conventional multispectral data, such as denoising and dimension reduction. Thus, we provide a comprehensive and intensive overview of crop LAI estimation based on hyperspectral remote sensing techniques. First, we compare hyperspectral data and multispectral data by highlighting their potential and limitations in LAI estimation. Second, we categorize the approaches used for crop LAI estimation based on hyperspectral data into three types: approaches based on statistical models, physical models (i.e., canopy reflectance models), and hybrid inversions. We summarize and evaluate the theoretical basis and different methods employed by these approaches (e.g., the characteristic parameters of LAI, regression methods for constructing statistical predictive models, commonly applied physical models, and inversion strategies for physical models). Thus, numerous models and inversion strategies are organized in a clear conceptual framework. Moreover, we highlight the technical difficulties that may hinder crop LAI estimation, such as the “curse of dimensionality” and the ill-posed problem. Finally, we discuss the prospects for future research based on the previous studies described in this review.

Although climate change impacts and agricultural adaptations have been studied extensively, how smallholder farmers perceive climate change and adapt their agricultural activities is poorly understood. Survey-based data (presents farmers’ personal perceptions and adaptations to climate change) associated with external biophysical-socioeconomic data (presents real-world climate change) were used to develop a farmer-centered framework to explore climate change impacts and agricultural adaptations at a local level. A case study at Bin County (1980s-2010s), Northeast China, suggested that increased annual average temperature (0.6°C per decade) and decreased annual precipitation (46 mm per decade, both from meteorological datasets) were correctly perceived by 76 and 66.9%, respectively, of farmers from the survey, and that a longer growing season was confirmed by 70% of them. These reasonably correct perceptions enabled local farmers to make appropriate adaptations to cope with climate change: Longer season alternative varieties were found for maize and rice, which led to a significant yield increase for both crops. The longer season also affected crop choice: More farmers selected maize instead of soybean, as implicated from survey results by a large increase in the maize growing area. Comparing warming-related factors, we found that precipitation and agricultural disasters were the least likely causes for farmers’ agricultural decisions. As a result, crop and variety selection, rather than disaster prevention and infrastructure improvement, was the most common ways for farmers to adapt to the notable warming trend in the study region.

Rice is one of the most important grain crops in Northeast China (NEC) and its cultivation is sensitive to climate change. This study aimed to explore the spatio-temporal changes in the NEC rice planting area over the period of 1980-2010 and to analyze their relationship to climate change. To do so, the CLUE-S (conversion of land use and its effects at small region extent) model was first updated and used to simulate dynamic changes in the rice planting area in NEC to understand spatio-temporal change trends during three periods: 1980-1990, 1990-2000 and 2000-2010. The changing results in individual periods were then linked to climatic variables to investigate the climatic drivers of these changes. Results showed that the NEC rice planting area expanded quickly and increased by nearly 4.5 times during 1980-2010. The concentration of newly planted rice areas in NEC constantly moved northward and the changes were strongly dependent on latitude. This confirmed that climate change, increases in temperature in particular, greatly influenced the shift in the rice planting area. The shift in the north limit of the NEC rice planting area generally followed a 1°C isoline migration pattern, but with an obvious time-lag effect. These findings can help policy makers and crop producers take proper adaptation measures even when exposed to the global warming situation in NEC.