Fang Liu

Improving farmland yield is one of the most critical options for increasing grain production and ensuring food security worldwide. The High‐Standard Farmland (HSF) project is China's largest investment in farmland infrastructure, designed to enhance grain productivity, promote rural equity, and close regional yield gaps by improving farmland quality and resilience to environmental stress. However, the HSF project's effects on national‐scale productivity trends and spatial heterogeneity remain poorly characterized. Here, we analyzed spatiotemporal changes in the farmland productivity in the 2330 HSF project areas using time‐series remote sensing images and the Vegetation Photosynthesis Model and evaluated the effectiveness of HSF projects on productivity enhancement. We found that 92% of the HSF project areas experienced farmland productivity improvement, and 38% of the HSF project areas experienced an average annual farmland productivity increase of more than 1500 kg/ha (the target threshold in the HSF projects). Spatially, productivity improvements in HSF project areas were predominantly concentrated in North China, while South China showed no significant gains. At the provincial scale, the more HSF project regions were implemented, the higher the rate of increase in farmland productivity. Previous studies found that the HSF projects improved low‐ and medium‐yield farmland productivity, while we found that high‐yield farmland had higher productivity improvement than low‐ and medium‐yield farmland, suggesting high‐yield farmland was more efficient for enhancing yield and grain production. For future HSF projects in China, we proposed three distinct project area types, each requiring region‐specific implementation strategies based on local agricultural conditions. This study can provide guidance for future strategies to increase farmland yield and grain production.

The Tarim Basin is a typical arid area and has the world’s most severe desertification of natural and semi-natural land due to limited water resources. However, knowledge about the impacts of changes in water resources on the spatio-temporal dynamics of natural and semi-natural land is still limited. We analyzed the spatio-temporal changes in natural and semi-natural land and the associations with desertification in the Tarim Basin during the period 1990–2015. We then investigated the changes in water resources and the consequent impacts on the spatio-temporal changes of natural and semi-natural land by integrating Gravity Recovery and Climate Experiment territorial water storage data and field observations. The results showed that a total area of 10.32 × 10³ km² of natural and semi-natural land was converted to desert during the period 1990–2015. Desert vegetation type and saline type were the natural and semi-natural land types most sensitive to conversion to desert. The area of natural and semi-natural land decreased by 0.83% every year, and the proportion of desertified land was 34.79% on average during the period 2000–2010; this is less than for the period 1990–2000 (1.14% yr⁻¹ and 52.01%) due to increased availability of water resources from the water conveyance program. However, the rate of decrease of natural and semi-natural land area (0.93% yr⁻¹) and the proportion of desertified land (58.88%) rose again during the period 2010–2015 due to the rapid decrease in water resources. During the period 2000–2015, the rate of loss of natural and semi-natural land area (7.89%) in the region with decreased water resources was about twice that in the region with increased water resources (3.88%), highlighting the critical role of water resources in maintaining natural and semi-natural land and slowing desertification.

As the country with the highest food consumption in the world, China’s food security has been drawing global attention. The inter-annual variability of agricultural productivity and its predominant driving factors play important roles in food security and sustainable agricultural development. Here, we used gross primary productivity (GPP), which was simulated using the vegetation photosynthesis model (VPM), to quantify the spatial-temporal heterogeneity of cropland productivity from 2000 to 2015. The results showed that the cropland GPP significantly increased in northern China and markedly decreased in southern China. Socioeconomic and climatic factors jointly promoted a rise in GPP in the Northeast region, Inner Mongolia and Great Wall region, Huang-Huai-Hai region, and Loess Plateau region, with contribution rates of 93.6%, 67.9%, 73.8%, and 78.1%, respectively. The negative GPP trend in southern China was mainly attributed to the decreasing cropping intensity, with direct contributions of 54.1%, 53.9%, and 48.7% for the Yangtze River region, Southwest region, and South China region, respectively. Despite the decline in cropping intensity, the policies of Cang-liang-yu-di and Cang-liang-yu-ji can help in ensuring food security in China.

Spatial data of regional populations are indispensable in studying the impact of human activities on resource utilization and the ecological environment. Because the differences between datasets and their spatial distribution are still unclear, this has become a puzzle in data selection and application. This study is based on four mainstream spatialized population datasets: the History Database of the Global Environment version 3.2.000 (HYDE), Gridded Population of the World version 4 (GPWv4), Global Human Settlement Layer (GHSL), and WorldPop. In view of possible influences of geographical factors, this study analyzes the differences in accuracy of population estimation by computing relative errors and population spatial distribution consistency in different regions by comparing datasets pixel by pixel. The results demonstrate the following: (1) Source data, spatialization methods, and case area features affect the precision of datasets. As the main data source is statistical data and the spatialization method maintains the population in the administrative region, the populations of GPWv4 and GHSL are closest to the statistical data value. (2) The application of remote sensing, mobile communication, and other geospatial data makes the datasets more accurate in the United Kingdom, with rich information, and the absolute value of relative errors is less than 4%. In the Tibet Autonomous Region of China, where data are hard to obtain, the four datasets have larger relative errors. However, the area where the four datasets are completely consistent is as high as 84.73% in Tibet, while in the UK it is only 66.76%. (3) The areas where the spatial patterns of the four datasets are completely consistent are mainly distributed in areas with low population density, or with developed urbanization and concentrated population distribution. Areas where the datasets have poor consistency are mainly distributed in medium population density areas with high urbanization levels. Therefore, in such areas, a more careful assessment should be made during the data application process, and more emphasis should be placed on improving data accuracy when using spatialization methods.

Field net primary productivity (NPP) is useful in research modeling of regional and global carbon cycles and for validating results by remote sensing or process-based models. In this study, we used multiple models of NPP estimation and vegetation classification methods to study Chinese vegetation NPP characteristics, trends, and drivers using 7618 field measurements from the 1960s, 1980s, and 2000s. The values of other relevant NPP models, as well as process-based simulation and remote sensing models, were compared. Our results showed that NPP ranged from 3 to 12,407 gC·m⁻²·year⁻¹ with a mean value of 571 gC·m⁻²·year⁻¹. Vegetation NPP gradually decreased from the southeast to the northwest. Forest, farmland, and grassland NPP was 1152, 294, and 518 gC·m⁻²·year⁻¹, respectively. Total NPP of grassland was higher than that of farmland. Total terrestrial NPP decreased from 3.58 to 3.41 Pg C·year⁻¹ from the 1960s to the 2000s, a decadal decrease of 4.7%. Total NPP in forests and grasslands consistently showed a decreasing trend and decreased by 0.46 Pg C·year⁻¹and 0.16 Pg C·year⁻¹, respectively, whereas NPP for farmland showed an opposite trend, with a growth of 0.45 Pg C·year⁻¹. Our research findings filled gaps in the information regarding NPP for the entire landmass of China based on field data from a long-term time series and provide valuable information and a basis for validation analyses by remote sensing models, as well as a robust quantification of carbon estimation to anticipate future development at the national and global scale.

Drought index plays an important role in the assessment of drought severity as one sensitive indicator of land drought status. A simple and accurate method of expressing ground drought from remote sensing data is in urgent. Firstly, a brand-new frame has been developed by coordinate system transformation, with (x', y') expressing (soil water content, vegetation coverage), which made the meaning more clearly. Secondly, a new drought index - the modified Shortwave Infrared Perpendicular Water Stress Index (MSPSI) has been proposed, with additive of vegetation fraction f, (expressed as a distance from LAI contour to soil baseline) as a vegetation coverage adjusting factor, which makes ground drought status with vegetation coverage variance be considered as belonging to the new coordinate system. To evaluate the new index, the correlation analysis between the in-situ observation data from the meteorological sites and simulated values from MOD09A1 has been performed on a regional scale. We found them to be in good agreement. To further confirm that the drought index is feasible with higher precision than original drought indices (DIs), comparison experiment has been done.