Crop planting patterns are an important component of agricultural land systems.  These patterns have been significantly changed due to the combined impacts of climatic changes and socioeconomic developments.  However, the extent of these changes and their possible impacts on the environment, terrestrial landscapes and rural livelihoods are largely unknown due to the lack of spatially explicit datasets including crop planting patterns.  To fill this gap, this study proposes a new method for spatializing statistical data to generate multitemporal crop planting pattern datasets.  This method features a two-level model that combines a land-use simulation and a crop pattern simulation.  The output of the first level is the spatial distribution of the cropland, which is then used as the input for the second level, which allocates crop censuses to individual gridded cells according to certain rules.  The method was tested using data from 2000 to 2019 from Heilongjiang Province, China, and was validated using remote sensing images.  The results show that this method has high accuracy for crop area spatialization.  Spatial crop pattern datasets over a given time period can be important supplementary information for remote sensing and thus support a wide range of application in agricultural land systems.

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.