Liang He
China Meteorological Administration
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Featured researches published by Liang He.
Remote Sensing | 2016
Ning Jin; Bo Tao; Wei Ren; Meichen Feng; Rui Sun; Liang He; W. Zhuang; Qiang Yu
Irrigation is crucial to agriculture in arid and semi-arid areas and significantly contributes to crop development, food diversity and the sustainability of agro-ecosystems. For a specific crop, the separation of its irrigated and rainfed areas is difficult, because their phenology is similar and therefore less distinguishable, especially when there are phenology shifts due to various factors, such as elevation and latitude. In this study, we present a simple, but robust method to map irrigated and rainfed wheat areas in a semi-arid region of China. We used the Normalized Difference Vegetation Index (NDVI) at a 30 × 30 m spatial resolution derived from the Chinese HJ-1A/B (HuanJing(HJ) means environment in Chinese) satellite to create a time series spanning the whole growth period of wheat from September 2010 to July 2011. The maximum NDVI and time-integrated NDVI (TIN) that usually exhibit significant differences between irrigated and rainfed wheat were selected to establish a classification model using a support vector machine (SVM) algorithm. The overall accuracy of the Google-Earth testing samples was 96.0%, indicating that the classification results are accurate. The estimated irrigated-to-rainfed ratio was 4.4:5.6, close to the estimates provided by the agricultural sector in Shanxi Province. Our results illustrate that the SVM classification model can effectively avoid empirical thresholds in supervised classification and realistically capture the magnitude and spatial patterns of rainfed and irrigated wheat areas. The approach in this study can be applied to map irrigated/rainfed areas in other regions when field observational data are available.
Science of The Total Environment | 2018
Ning Jin; Wei Ren; Bo Tao; Liang He; Qingfu Ren; Shiqing Li; Qiang Yu
The Loess Plateau, the largest arid and semi-arid zone in China, has been confronted with more severe water resource pressure and a growing demand for food production under global changes. For developing sustainable agriculture in this region, it is critical to learn spatiotemporal variations in water use efficiency (WUE) of main crops (e.g. winter wheat in this region) under various water management practices. In this study, we classified irrigated and rainfed wheat areas based on MODIS data, and calculated the winter wheat yield by using an improved light use efficiency model. The actual evapotranspiration (ETa) of winter wheat and the evapotranspiration drought index (EDI) were also investigated. Then we mainly examined the synergistic relationship between crop yield, ETa, and WUE, and analyzed the variations in WUE of irrigated and rainfed wheat under water stress during the 2010-2011 growing season. The results suggested that winter wheat in the Loess Plateau was primarily dominated by rainfed wheat. The average yield of irrigated wheat was 3928.4 kg/ha, 22.2% more than that of rainfed wheat. High spatial heterogeneities of harvest index (HI) and maximum light use efficiency (εmax) were found in the Loess Plateau. The ETa of irrigated wheat was 10.2% more than that of rainfed wheat. The ratio of irrigated and rainfed wheat under no water stress was 31.55% and 17.16%, respectively. With increasing water stress, the WUE of rainfed wheat decreased more quickly than that of irrigated wheat. The WUE variations in winter wheat under water stress depended strongly on the synergistic effects of two WUE components (crop yield and ETa) and their response to environmental conditions as well as water management practices (irrigated or rainfed). Our findings enhance our current understanding of the variations in WUE as affected by water stress under various water use conditions in arid and semi-arid areas.
Journal of Geophysical Research | 2018
Yue Li; Hao Shi; Lei Zhou; Derek Eamus; Alfredo R. Huete; Longhui Li; James Cleverly; Zhongmin Hu; Mahrita Harahap; Qiang Yu; Liang He; Shaoqiang Wang
Water use efficiency (WUE), the ratio of gross primary productivity (GPP) over evapotranspiration (ET), is a critical ecosystem function. However, it is difficult to distinguish the individual effects of climatic variables and leaf area index (LAI) on WUE, mainly due to the high collinearity among these factors. Here we proposed a partial least squares regression-based sensitivity algorithm to confront the issue, which was first verified at seven ChinaFlux sites and then applied across China. The results showed that across all biomes in China, monthly GPP (0.42–0.65), ET (0.33–0.56), and WUE (0.01–0.31) showed positive sensitivities to air temperature, particularly in croplands in northeast China and forests in southwest China. Radiation exerted stronger effects on ET (0.55–0.78) than GPP (0.19–0.65), resulting in negative responses ( 0.44 to 0.04) of WUE to increased radiation among most biomes. Increasing precipitation stimulated both GPP (0.06–0.17) and ET (0.05–0.12) at the biome level, but spatially negative effects of excessive precipitation were also found in some grasslands. Both monthly GPP ( 0.01 to 0.29) and ET (0.02–0.12) showed weak or moderate responses to vapor pressure deficit among biomes, resulting in weak response of monthly WUE to vapor pressure deficit ( 0.04 to 0.08). LAI showed positive effects on GPP (0.18–0.60), ET (0–0.23), and WUE (0.13–0.42) across biomes, particularly on WUE in grasslands (0.42 ± 0.30). Our results highlighted the importance of LAI in influencingWUE against climatic variables. Furthermore, the sensitivity algorithm can be used to inform the design of manipulative experiments and compare with factorial simulations for discerning effects of various variables on ecosystem functions.
Agricultural and Forest Meteorology | 2015
Liang He; Senthold Asseng; Gang Zhao; Dingrong Wu; Xiaoya Yang; W. Zhuang; Ning Jin; Qiang Yu
Agronomy Journal | 2014
Liang He; James Cleverly; Chao Chen; Xiaoya Yang; Jun Li; Wenzhao Liu; Qiang Yu
Theoretical and Applied Climatology | 2018
Liang He; James Cleverly; Bin Wang; Ning Jin; Chunrong Mi; De Li Liu; Qiang Yu
AMBIO: A Journal of the Human Environment | 2016
Xueling Li; Joshua N Philp; Roger Cremades; Anna Roberts; Liang He; Longhui Li; Qiang Yu
European Journal of Agronomy | 2018
Qin Zu; Chunrong Mi; De Li Liu; Liang He; Zhaomin Kuang; Quanxiao Fang; Daniel Ramp; Li Li; Bin Wang; Yanli Chen; Jun Li; Ning Jin; Qiang Yu
International Journal of Plant Production | 2016
W. Zhuang; Lei Cheng; Rhys Whitley; Hao Shi; Jason Beringer; Ying-Ping Wang; Liang He; James Cleverly; Derek Eamus; Qiang Yu
International Journal of Plant Production | 2017
C.R. Mi; Q. Zu; Liang He; F. Huettmann; N. Jin; Jun Li