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.

Rice planting patterns have changed dramatically over the past several decades in northeast China (NEC) due to the combined influence of global change and agricultural policy.  Except for its great implications for environmental protection and climate change adaption, the spatio-temporal changes of rice cultivation in NEC are not clear.  In this study, we conducted spatio-temporal analyses of NEC’s major rice production region, Heilongjiang Province, by using satellite-derived rice cultivation maps.  We found that the total cultivated area of rice in Heilongjiang Province increased largely from 1993 to 2011 and it expanded spatially to the northern and eastern part of the Sanjiang Plain.  The results also showed that rice cultivation areas experienced a larger increase in the region managed by the Reclamation Management Bureau (RMB) than that managed by the local provincial government.  Rice cultivation changes were closely related with those geographic factors over the investigated periods, represented by the geomorphic (slope), climatic (accumulated temperature), and hydrological (watershed) variables.  These findings provide clear evidence that crop cultivation in NEC has been modified to better cope with the global change.

Rice area has been expanding rapidly during the past 30 years under the influence of global change in northeastern China, which is the northernmost region of rice cultivation in China.  However, the spatio-temporal dynamic changes in rice area are still unclear, although they may have important policy implications for environmental protection and adaptation to climate change.  In this study, we aimed to identify the dynamic changes of the rice area in Heilongjiang Province of northeastern China by extracting data from multiple Landsat images.  The study used ground quadrats selected from Google Earth and the extraction of a confusion matrix to verify the accuracy of extraction.  The overall accuracy of the extracted rice area was higher than 95% as a result of using the artificial neural network (ANN) classification method.  The results showed that the rice area increased by approximately 2.4×10⁶ ha during the past 30 years at an annual rate of 8.0×10⁴ ha, and most of the increase occurred after 2000.  The central latitude of the rice area shifted northwards from 46 to 47°N during the study period, and moved eastwards from 130 to 133°E.  The rice expansion area accounted for 98% of the total change in rice area, and rice loss was notably rare.  The rice expansion was primarily from dryland.  In addition, rice cultivation in marshland and grassland played a minor role in the rice expansion in this region.

By employing the unique phenological feature of winter wheat extracted from peak before winter (PBW) and the advantages of moderate resolution imaging spectroradiometer (MODIS) data with high temporal resolution and intermediate spatial resolution, a remote sensing-based model for mapping winter wheat on the North China Plain was built through integration with Landsat images and land-use data.  First, a phenological window, PBW was drawn from time-series MODIS data.  Next, feature extraction was performed for the PBW to reduce feature dimension and enhance its information.  Finally, a regression model was built to model the relationship of the phenological feature and the sample data.  The amount of information of the PBW was evaluated and compared with that of the main peak (MP).  The relative precision of the mapping reached up to 92% in comparison to the Landsat sample data, and ranged between 87 and 96% in comparison to the statistical data.  These results were sufficient to satisfy the accuracy requirements for winter wheat mapping at a large scale.  Moreover, the proposed method has the ability to obtain the distribution information for winter wheat in an earlier period than previous studies.  This study could throw light on the monitoring of winter wheat in China by using unique phenological feature of winter wheat.

The successful launched Gaofen satellite no. 1 wide field-of-view (GF-1 WFV) camera is characterized by its high spatial resolution and may provide some potential for regional crop mapping.  This study, taking the Bei’an City, Northeast China as the study area, aims to investigate the potential of GF-1 WFV images for crop identification and explore how to fully use its spectral, textural and temporal information to improve classification accuracy.  In doing so, an object-based and Random Forest (RF) algorithm was used for crop mapping.  The results showed that classification based on an optimized single temporal GF-1 image can achieve an overall accuracy of about 83%, and the addition of textural features can improve the accuracy by 8.14%.  Moreover, the multi-temporal GF-1 data can produce a classification map of crops with an overall accuracy of 93.08% and the introduction of textural variables into multi-temporal GF-1 data can only increase the accuracy by about 1%, which suggests the importance of temporal information of GF-1 for crop mapping in comparison with single temporal data.  By comparing classification results of GF-1 data with different feature inputs, it is concluded that GF-1 WFV data in general can meet the mapping efficiency and accuracy requirements of regional crop.  But given the unique spectral characteristics of the GF-1 WFV imagery, the use of textual and temporal information is needed to yield a satisfactory accuracy.

How to fully use spectral and temporal information for efficient identification of crops becomes a crucial issue since each crop has its specific seasonal dynamics.  A thorough understanding on the relative usefulness of spectral and temporal features is thus essential for better organization of crop classification information.  This study, taking Heilongjiang Province as the study area, aims to use time-series moderate resolution imaging spectroradiometer (MODIS) surface reflectance product (MOD09A1) data to evaluate the importance of spectral and temporal features for crop classification.  In doing so, a feature selection strategy based on separability index (SI) was first used to rank the most important spectro-temporal features for crop classification.  Ten feature scenarios with different spectral and temporal variable combinations were then devised, which were used for crop classification using the support vector machine and their accuracies were finally assessed with the same crop samples.  The results show that the normalized difference tillage index (NDTI), land surface water index (LSWI) and enhanced vegetation index (EVI) are the most informative spectral features and late August to early September is the most informative temporal window for identifying crops in Heilongjiang for the observed year 2011.  Spectral diversity and time variety are both vital for crop classification, and their combined use can improve the accuracy by about 30% in comparison with single image.  The feature selection technique based on SI analysis is superior for achieving high crop classification accuracy (producers’ accuracy of 94.03% and users’ accuracy of 93.77%) with a small number of features.  Increasing temporal resolution is not necessarily important for improving the classification accuracies for crops, and a relatively high classification accuracy can be achieved as long as the images associated with key phenological phrases are retained.

 Using simultaneously collected remote sensing data and field measurements, this study firstly assessed the consistency and applicability of China high-resolution earth observation system satellite 1 (GF-1) wide field of view (WFV) camera, environment and disaster monitoring and forecasting satellite (HJ-1) charge coupled device (CCD), and Landsat-8 operational land imager (OLI) data for estimating the leaf area index (LAI) of winter wheat via reflectance and vegetation indices (VIs).  The accuracies of these LAI estimates were then assessed through comparison with an empirical model and the PROSAIL radiative transfer model.  The effects of radiation calibration, spectral response functions, and spatial resolution on discrepancies in the LAI estimates between the different sensors were also analyzed.  The results yielded the following observations: (1) The correlation between reflectance from different sensors is relative good, with the adjusted coefficients of determination (R²) between 0.375 to 0.818.  The differences in reflectance are ranging from 0.002 to 0.054.  The correlation between VIs from different sensors is high with the R² between 0.729 and 0.933.  The differences in the VIs are ranging from 0.07 to 0.156.  These results show the three sensors’ images can all be used for cross calibration of the reflectance and VIs.  (2) The four VIs from the three sensors are all demonstrated to be highly correlated with LAI (R² between 0.703 and 0.849).  The linear models associated with the 2-band enhanced vegetation index (EVI2), which feature the highest R² (higher than 0.746) and the lowest root mean square errors (RMSE) (less than 0.21), were selected to estimate the winter wheat LAI.  The accuracy of the estimated LAI from Landsat-8 was the highest, with the relative errors (RE) of 2.18% and an RMSE of 0.13, while the HJ-1 was the lowest, with the RE of 2.43% and the RMSE of 0.15.  (3) The inversion errors in the different sensors’ LAI estimates using the PROSAIL model are small.  The accuracy of the GF-1 is the highest with the RE of 3.44%, and the RMSE of 0.22, whereas that of the HJ-1 is the lowest with the RE of 4.95%, and the RMSE of 0.26.  (4) The effects of the spectral response function and radiation calibration for the different sensors are small and can be ignored, but the effects of spatial resolution are significant and must be taken into consideration in practical applications.

High-resolution satellite data have been playing an important role in agricultural remote sensing monitoring.  However, the major data sources of high-resolution images are not owned by China.  The cost of large scale use of high resolution imagery data becomes prohibitive.  In pace of the launch of the Chinese “High Resolution Earth Observation Systems”, China is able to receive superb high-resolution remotely sensed images (GF series) that equalizes or even surpasses foreign similar satellites in respect of spatial resolution, scanning width and revisit period.  This paper provides a perspective of using high resolution remote sensing data from satellite GF-1 for agriculture monitoring.  It also assesses the applicability of GF-1 data for agricultural monitoring, and identifies potential applications from regional to national scales.  GF-1’s high resolution (i.e., 2 m/8 m), high revisit cycle (i.e., 4 days), and its visible and near-infrared (VNIR) spectral bands enable a continuous, efficient and effective agricultural dynamics monitoring.  Thus, it has gradually substituted the foreign data sources for mapping crop planting areas, monitoring crop growth, estimating crop yield, monitoring natural disasters, and supporting precision and facility agriculture in China agricultural remote sensing monitoring system (CHARMS).  However, it is still at the initial stage of GF-1 data application in agricultural remote sensing monitoring.  Advanced algorithms for estimating agronomic parameters and soil quality with GF-1 data need to be further investigated, especially for improving the performance of remote sensing monitoring in the fragmented landscapes.  In addition, the thematic product series in terms of land cover, crop allocation, crop growth and production are required to be developed in association with other data sources at multiple spatial scales.  Despite the advantages, the issues such as low spectrum resolution and image distortion associated with high spatial resolution and wide swath width, might pose challenges for GF-1 data applications and need to be addressed in future agricultural monitoring.

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.

For the scientific management of farmland, it is significant to understand the spatio-temporal variability of soil organic matter and to study the influences of related factors. Using geostatistical theory, GIS spatial analysis, trend analysis and a Geographically Weighted Regression (GWR) model, this study analyzed the response of soil organic matter to climate and socio-economic factors in central Heilongjiang Province during the past 25 years. Second soil survey data of China for 1979-1985, 2005 field sampling data, climate observations and socio-economic data for 1980-2005 were analyzed. First, soil organic matter in 2005 was spatially interpolated using the Co-Kriging method along with auxiliary data sets of soil type and pH. The spatio-temporal variability was then studied by comparison with the 1980s second soil census data. Next, the temporal trends in climate and socio-economic factors over the past 25 years were investigated. Finally, we examined the variation of the response of soil organic matter to climate and socio-economic factors using the GWR model spatially and temporally. The model showed that 53.82% area of the organic matter content remained constant and 29.39% has decreased during the past 25 years. The impact of precipitation on organic matter content is mainly negative, with increasing absolute values of the regression coefficient. The absolute value of regression coefficient of annual average temperature has decreased, and more areas are now under its negative effects. In addition, the areas of positive regression coefficient of annual sunshine hours have northward shifted, with the increasing absolute value of positive coefficient and decreasing absolute value of negative coefficient. The areas of positive regression coefficient of mechanized farming as a socio-economic factor have westward shifted, with the increasing absolute value of negative coefficient and decreasing absolute value of positive coefficient. The area of regions with the positive regression coefficient of irrigation has expanded. The regions with positive regression coefficient of fertilizer use have shrinked. The positive regression coefficient of mulch film consumption has significantly increased. The regression coefficient of pesticide consumption was mainly positive in the west of the study area, while it was negative to the east. Generally, GWR model is capable to investigate the influence of both climatic and socio-economic factors, avoided the insufficiency of other research based on the single perspective of climatic or socio-economic factors. Therefore, we can conclude that GWR model could provide methodological support for global change research and serve as basic reference for cultivated land quality improvement and agricultural decision making.

Remote sensing, in particular satellite imagery, has been widely used to map cropland, analyze cropping systems, monitor crop changes, and estimate yield and production. However, although satellite imagery is useful within large scale agriculture applications (such as on a national or provincial scale), it may not supply sufficient information with adequate resolution, accurate geo-referencing, and specialized biological parameters for use in relation to the rapid developments being made in modern agriculture. Information that is more sophisticated and accurate is required to support reliable decision-making, thereby guaranteeing agricultural sustainability and national food security. To achieve this, strong integration of information is needed from multi-sources, multi-sensors, and multi-scales. In this paper, we propose a new framework of satellite, aerial, and groundintegrated (SAGI) agricultural remote sensing for use in comprehensive agricultural monitoring, modeling, and management. The prototypes of SAGI agriculture remote sensing are first described, followed by a discussion of the key techniques used in joint data processing, image sequence registration and data assimilation. Finally, the possible applications of the SAGI system in supporting national food security are discussed.

To feed the increasing world population, more food needs to be produced from agricultural land systems. Solutions to produce more food with fewer resources while minimizing adverse environmental and ecological consequences require sustainable agricultural land use practices as supplementary to advanced biotechnology and agronomy. This review paper, from a land system perspective, systematically proposed and analyzed three interactive strategies that could possibly raise future food production under global change. By reviewing the current literatures, we suggest that cropland expansion is less possible amid fierce land competition, and it is likely to do less in increasing food production. Moreover, properly allocating crops in space and time is a practical way to ensure food production. Climate change, dietary shifts, and other socio-economic drivers, which would shape the demand and supply side of food systems, should be taken into consideration during the decision-making on rational land management in respect of sustainable crop choice and allocation. And finally, crop-specific agricultural intensification would play a bigger role in raising future food production either by increasing the yield per unit area of individual crops or by increasing the number of crops sown on a particular area of land. Yet, only when it is done sustainably is this a much more effective strategy to maximize food production by closing yield and harvest gaps.