Reflections on China's food security and land use policy under rapid urbanization

Accurate mapping of winter wheat over a large area is of great significance for guiding policy formulation related to food security, farmland management, and the international food trade. Due to the complex phenological features of winter wheat, the cloud contamination in time-series imagery, and the influence of the soil/snow background on vegetation indices, there remains no effective method for mapping winter wheat at a medium spatial resolution (10–30 m). In this study, we proposed a novel method called phenology-time weighted dynamic time warping (PT-DTW) for identifying winter wheat based on Sentinel 2A/B time-series data. The main advantages of PT-DTW include (1) the use of phenological features in two periods, i.e., the greenness increase before winter and greenness decrease after heading, which are common to all winter wheat and are distinct from the features of other land cover types, and (2) the use of the normalized differential phenology index (NDPI) instead of traditional vegetation indices to provide more robust vegetation information and to suppress the adverse impacts of soil and snow cover, especially during the before-winter growth period. The proposed PT-DTW method was employed for winter wheat mapping based on Sentinel 2A/B data on the Huang-Huai Plain, China. Validation with visually interpreted samples showed that the produced winter wheat map achieved an overall classification accuracy of 89.98% and a kappa coefficient of 0.7978, outperforming previous winter wheat classification methods. Moreover, the planting area derived from PT-DTW agreed well with census data at the municipal level, with a coefficient of determination of 0.8638, indicating that the winter wheat map produced at 20 m resolution was reliable overall. Therefore, the PT-DTW method is recommended for winter wheat mapping over large areas.

Mapping Winter Wheat in North China Using Sentinel 2A/B Data: A Method Based on Phenology-Time Weighted Dynamic Time Warping

Accurate crop distribution maps provide important information for crop censuses, yield monitoring and agricultural insurance assessments. Most existing studies apply low spatial resolution satellite images for crop distribution mapping, even in areas with a fragmented landscape. Unmanned aerial vehicle (UAV) imagery provides an alternative imagery source for crop mapping, yet its spectral resolution is usually lower than satellite images. In order to produce more accurate maps without losing any spatial heterogeneity (e.g., the physical boundary of land parcel), this study fuses Sentinel-2A and UAV images to map crop distribution at a finer spatial scale (i.e., land parcel scale) in an experimental site with various cropping patterns in Heilongjiang Province, Northeast China. Using a random forest algorithm, the original, as well as the fused images, are classified into 10 categories: rice, corn, soybean, buckwheat, other vegetations, greenhouses, bare land, water, roads and houses. In addition, we test the effect of UAV image choice by fusing Sentinel-2A with different UAV images at multiples spatial resolutions: 0.03 m, 0.10 m, 0.50 m, 1.00 m and 3.00 m. Overall, the fused images achieved higher classification accuracies, ranging between 10.58% and 16.39%, than the original images. However, the fused image based on the finest UAV image (i.e., 0.03 m) does not result in the highest accuracy. Instead, the 0.10 m spatial resolution UAV image produced the most accurate map. When the spatial resolution is less than 0.10 m, accuracy decreases gradually as spatial resolution decreases. The results of this paper not only indicate the possibility of combining satellite images and UAV images for land parcel level crop mapping for fragmented landscapes, but it also implies a potential scheme to exploit optimal choice of spatial resolution in fusing UAV images and Sentinel-2A, with little to no adverse side-effects.

Finer Classification of Crops by Fusing UAV Images and Sentinel-2A Data

This study examined the effects of climate change on rice production in 30 Chinese provinces spanning 1998–2017. The study used the pooled mean group technique to capture the long-run and short-run effects of changing climatic conditions on rice production. It further employed the Dumitrescu–Hurlin panel causality test to examine the path of causality between the key variables and rice production. The study found that, in the long run, average temperature negatively influenced rice production, but average rainfall had a positive effect on rice production. The results indicated that the cultivated area and fertilizer usage were positively related to rice production in the long run. The short-run results accentuated that average temperature favourably influenced nationwide rice production, whereas average rainfall had no substantial effect on national rice production. The cultivated area had a significant positive short-term relationship with rice production, although the impact of fertilizer usage on rice production was negligible in the short run. Besides, the results established a bidirectional causality between rice output and the cultivated area, but there was a one-way causality running from fertilizer usage to rice output. Finally, the results indicated that, except for rainfall, a unidirectional causality exists between temperature and rice production. The study, therefore, recommends that in the case of crop failure due to weather conditions, policymakers could implement a new pricing policy to mitigate the deterioration of the farmers’ income. The government must also develop and implement an insurance scheme that compensates farmers for catastrophes induced by rainfall deficiency.

Impacts of climate change on rice production: evidence from 30 Chinese provinces

Changes in rice production in Southern China are crucial to national food security. This study employed Landsat images to map the distributions of paddy rice-cropping systems in Southern China in 1990 and 2015. The impact of rice multiple cropping index changes on grain production capacity was then evaluated. Three important results were obtained for the 1990 to 2015 study period. First, the multiple cropping index for rice decreased from 148.3% to 129.3%, and 253.16×104 ha of land area was converted from double-cropping to single-cropping rice, termed “double to single”. The area with the most dramatic changes is in the Middle-Lower Yangtze Plain. The rice-cropping system distribution in Southern China showed a change from north to south with double-cropping rice shrinking and single-cropping rice expanding. Second, the “double to single” conversion led to a reduction of 6.1% and 2.6% in rice and grain production, respectively. Hunan and Jiangxi Provinces, located in the main rice producing areas, and Zhejiang, which has shown better economic development, exhibited large reductions in rice production due to the “double to single” conversion, all exceeding 13%. Third, the grain production capacity of converted “double to single” paddy fields is equivalent to that of 223.3 × 104 ha of newly reclaimed cultivated land, which is 54% of the total newly cultivated land reclaimed through the 2001–2015 land consolidation project. It is also 1.7 times the target goal for newly cultivated land in the national land consolidation plan for 2016–2020. Making full use of the converted “double to single” paddy fields can save 167.44 billion yuan in newly reclaimed cultivated land costs. Therefore, instead of pursuing low-quality new arable land, it is better to make full use of the existing high-quality arable land. Based on these results, the government should change the assessment method for cultivated land balance, and incorporate the sown area increased by improving the multiple cropping index into the cultivated land compensation indicator.

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Paddy rice multiple cropping index changes in Southern China: Impacts on national grain production capacity and policy implications

Climate change continues to have a great impact on rice production in China, especially in Northeast China (NEC). Historical climate observations from the China Meteorological Administration and statistical agricultural records at the county level were utilized to evaluate the spatial and temporal effects of both climatic and socioeconomic factors on rice production between 1980 and 2010 in NEC by using a linear regression model. The results showed that a 1% increase in the rice accumulated temperature (RAT) significantly increases rice production by approximately 0.728%. Rising RAT over the past three decades increased rice production by 4.44% (equal to a relative contribution of 0.87% to production growth) in NEC, while the majority of rice production growth (79.6%) resulted from increased agricultural inputs. Furthermore, rice production has increased significantly since 2000, and its geographic centroid shifted over 320 km northeastward during the past 30 years. Historical statistical and simulated rice production data for each county were used to quantify the spatial relocation of rice production due to single climatic factors. During 1980–2010, temperature had a significant and coherent influence on moving rice production. The impact of growing season precipitation was not significant, while sunshine had a significant but less spatially coherent influence. Our findings highlight the response of the rice production system to external driving factors, both climate and socioeconomics, to target further research and provide important insights into how a rice cropping system is likely to adapt in a mid-high-latitude region in the future.

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Rice production and climate change in Northeast China: evidence of adaptation through land use shifts

Spatial distribution changes in major crops can reveal important information about cropping systems. Here, a new centroid method that applies physics and mathematics to spatial pattern analysis in agriculture is proposed to quantitatively describe the historical centroids of rice, maize and wheat in China from 1949 to 2014. The geographical centroids of the rice area moved 413.39 km in a 34.32° northeasterly (latitude 3.08°N, longitude 2.10°E) direction at a speed of 6.36 km/year from central Hunan province to Hubei province, while the geographical centroids of rice production moved 509.26 km in the direction of 45.44° northeasterly (latitude 3.22°N, longitude 3.27°E) at a speed of 7.83 km/year from central Hunan province to Henan province. The geographical centroids of the maize area and production moved 307.15 km in the direction of 34.33° northeasterly (latitude 2.29°N, longitude 1.56°E) and 308.16 km in the direction of 30.79° northeasterly (latitude 2.39°N, longitude 1.42°E), respectively. However, the geographical centroids of the wheat area and production were randomly distributed along the border of Shanxi and Henan provinces. We divided the wheat into spring wheat and winter wheat and found that the geographical centroids of the spring wheat area and production were distributed within Inner Mongolia, while the geographical centroids of winter wheat were distributed in Shanxi and Henan provinces. We found that the hotspots of crop cultivation area and production do not always change concordantly at a larger, regional scale, suggesting that the changing amplitude and rate of each crops’ yield differ between different regions in China. Thus, relevant adaptation measures should be taken at a regional level to prevent production damage in those with increasing area but decreasing production.

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Spatio-temporal analysis of the geographical centroids for three major crops in China from 1949 to 2014

Climate-mediated dynamics of the northern limit of paddy rice in China

Scientifically revealing the spatiotemporal patterns of cultivated land quality (CLQ) is crucial for increasing food production and achieving United Nations Sustainable Development Goal (SDG) 2: Zero Hunger. Although studies on the evaluation of CLQ have been conducted, an effective evaluation system that is suitable for the macro-regional scale has not yet been developed. In this study, we first defined the CLQ from four aspects: soil fertility, natural conditions, construction level, and cultivated land productivity. Then, eight indicators were selected by integrating multi-source remote sensing data to create a new CLQ evaluation system. We assessed the spatiotemporal patterns of CLQ in Guangzhou, China, from 2010 to 2018. In addition, we identified the main factors affecting the improvement of CLQ. The results showed that the CLQ continuously improved in Guangzhou from 2010 to 2018. The area of high-quality cultivated land increased by 13.7%, which was mainly distributed in the traditional agricultural areas in the northern and eastern regions of Guangzhou. The areas of medium- and low-quality cultivated land decreased by 8.1% and 5.6%, respectively, which were scattered throughout the whole study area. The soil fertility and high productivity capacity were the main obstacle factors that affected the improvement of CLQ. Simultaneously, the obstacle degree of stable productivity capacity gradually increased during the study period. Therefore, the targeted improvement measures could be put forward by applying biofertilizers, strengthening crop management and constructing well-facilitated farmland. The new CLQ evaluation system we proposed is particularly practical at the macro-regional scale, and the results provided targeted guidance for decision makers to improve CLQ and promote food security.

https://www.mdpi.com/2072-4292/14/5/1250/pdf?version=1646372788

Spatiotemporal Patterns of Cultivated Land Quality Integrated with Multi-Source Remote Sensing: A Case Study of Guangzhou, China

Rice is the most important food crop in the developing world, and more than half of the global population consumes it as a staple food. Mapping the area of rice cultivation in a timely and accurate manner is essential to ensure food safety and evaluate its environmental impact. Deep learning performs very well in high-resolution remote sensing classification; however, due to lack of high-quality training datasets and spatial semantic information of Landsat data, paddy rice mapping based on deep learning and Landsat data has received less attention. In this study, we constructed the first large-scale training dataset and a deep learning network, named full resolution network (FR-Net), for mapping paddy rice based on Landsat 8 OLI data. The pixel-wise annotated dataset is composed of 64 Landsat 8 OLI scenes covering the main areas producing rice in northeast China. To overcome the coarse segmentation borders resulting from other deep learning models, and especially with the low-resolution Landsat data, a new multi-resolution fusion unit (MRFU) was proposed to fuse different resolution streams and maintain the high-resolution streams of the model. In comparison with other models, the FR-Net acquired the highest accuracy, with MCC of 0.893 and F1 score of 0.898, and in particular, the results of different band combinations showed that the FR-Net perform better for feature extraction than other models. Without using the sensitive bands, i.e., short-wave infrared 1 and 2, the MCC of FR-Net was slightly decreased by 4.14%, smaller than other models, e.g., 8.51% for U-Net and 11.76% for RS-Net etc. The comparison showed the proposed FR-Net presented highest spatial precision among all these models, by which a fine boundary could be obtained for Landsat data to achieve high quality mapping result. The analysis of the performance in different temporal showed that FR-Net performed better at the beginning or the ending of growth stages, and the omission or false alarm main occurred in small size patches or the boundaries. In all, low-resolution characteristics of Landsat data should be more extensively investigated when developing and using deep learning in classification. 

Lingling Fan

Papers

Rice production and climate change in Northeast China: evidence of adaptation through land use shifts

Spatio-temporal analysis of the geographical centroids for three major crops in China from 1949 to 2014

Climate-mediated dynamics of the northern limit of paddy rice in China

Spatiotemporal Patterns of Cultivated Land Quality Integrated with Multi-Source Remote Sensing: A Case Study of Guangzhou, China

A full resolution deep learning network for paddy rice mapping using Landsat data