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Global climate change and human activities have resulted in immense changes in the Earth’s ecosystem, and the interaction between the land surface and the atmosphere is one of the most important processes. Wind is a reference for studying atmospheric dynamics and climate change, analyzing the wind speed change characteristics in historical periods, and studying the influence of wind on the Earth-atmosphere interaction; additionally, studying the wind, contributes to analyzing and alleviating a series of problems, such as the energy crisis, environmental pollution, and ecological deterioration facing human beings. In this study, data from 697 meteorological stations in China from 2000 to 2019 were used to study the distribution and trend of wind speed over the past two decades. The relationships between wind speed and climate factors were explored using statistical methods; furthermore, combined with terrain, climate change, and human activities, we quantified the contribution of environmental factors to wind speed. The results show that a downward trend was recorded before 2011, but overall, there was an increasing trend that was not significant; moreover, the wind speed changes showed obvious seasonality and were more complicated on the monthly scale. The wind speed trend mainly increased in the western region, decreased in the eastern region, was higher in the northeastern, northwestern, and coastal areas, and was lower in the central area. Temperature, bright sunshine duration, evaporation, and precipitation had a strong influence, in which wind speed showed a significant negative correlation with temperature and precipitation and vice versa for sunshine and evapotranspiration. The influence of environmental factors is diverse, and these results could help to develop environmental management strategies across ecologically fragile areas and improve the design of wind power plants to make better use of wind energy.
Yuming Lu; Bingfang Wu; Nana Yan; Weiwei Zhu; Hongwei Zeng; Zonghan Ma; Jiaming Xu; Xinghua Wu; Bo Pang. Quantifying the Contributions of Environmental Factors to Wind Characteristics over 2000–2019 in China. ISPRS International Journal of Geo-Information 2021, 10, 515 .
AMA StyleYuming Lu, Bingfang Wu, Nana Yan, Weiwei Zhu, Hongwei Zeng, Zonghan Ma, Jiaming Xu, Xinghua Wu, Bo Pang. Quantifying the Contributions of Environmental Factors to Wind Characteristics over 2000–2019 in China. ISPRS International Journal of Geo-Information. 2021; 10 (8):515.
Chicago/Turabian StyleYuming Lu; Bingfang Wu; Nana Yan; Weiwei Zhu; Hongwei Zeng; Zonghan Ma; Jiaming Xu; Xinghua Wu; Bo Pang. 2021. "Quantifying the Contributions of Environmental Factors to Wind Characteristics over 2000–2019 in China." ISPRS International Journal of Geo-Information 10, no. 8: 515.
Cropland evapotranspiration (ET) is the major source of water consumption in agricultural systems. The precise management of agricultural ET helps optimize water resource usage in arid and semiarid regions and requires field-scale ET data support. Due to the combined limitations of satellite sensors and ET mechanisms, the current high-resolution ET models need further refinement to meet the demands of field-scale ET management. In this research, we proposed a new field-scale ET estimation method by developing an allocation factor to quantify field-level ET variations and allocate coarse ET to the field scale. By regarding the agricultural field as the object of the ET parcel, the allocation factor is calculated with combined high-resolution remote sensing indexes indicating the field-level ET variations under different crop growth and land-surface water conditions. The allocation ET results are validated at two ground observation stations and show improved accuracy compared with that of the original coarse data. This allocated ET model provides reasonable spatial results of field-level ET and is adequate for precise agricultural ET management. This allocation method provides new insight into calculating field-level ET from coarse ET datasets and meets the demands of wide application for controlling regional water consumption, supporting the ET management theory in addressing the impacts of water scarcity on social and economic developments.
Zonghan Ma; Bingfang Wu; Nana Yan; Weiwei Zhu; Hongwei Zeng; Jiaming Xu. Spatial Allocation Method from Coarse Evapotranspiration Data to Agricultural Fields by Quantifying Variations in Crop Cover and Soil Moisture. Remote Sensing 2021, 13, 343 .
AMA StyleZonghan Ma, Bingfang Wu, Nana Yan, Weiwei Zhu, Hongwei Zeng, Jiaming Xu. Spatial Allocation Method from Coarse Evapotranspiration Data to Agricultural Fields by Quantifying Variations in Crop Cover and Soil Moisture. Remote Sensing. 2021; 13 (3):343.
Chicago/Turabian StyleZonghan Ma; Bingfang Wu; Nana Yan; Weiwei Zhu; Hongwei Zeng; Jiaming Xu. 2021. "Spatial Allocation Method from Coarse Evapotranspiration Data to Agricultural Fields by Quantifying Variations in Crop Cover and Soil Moisture." Remote Sensing 13, no. 3: 343.
Accurate precipitation data at high spatiotemporal resolution are critical for land and water management at the basin scale. We proposed a downscaling framework for Tropical Rainfall Measuring Mission (TRMM) precipitation products through integrating Google Earth Engine (GEE) and Google Colaboratory (Colab). Three machine learning methods, including Gradient Boosting Regressor (GBR), Support Vector Regressor (SVR), and Artificial Neural Network (ANN) were compared in the framework. Three vegetation indices (Normalized Difference Vegetation Index, NDVI; Enhanced Vegetation Index, EVI; Leaf Area Index, LAI), topography, and geolocation are selected as geospatial predictors to perform the downscaling. This framework can automatically optimize the models’ parameters, estimate features’ importance, and downscale the TRMM product to 1 km. The spatial downscaling of TRMM from 25 km to 1 km was achieved by using the relationships between annual precipitations and annually-averaged vegetation index. The monthly precipitation maps derived from the annual downscaled precipitation by disaggregation. According to validation in the Great Mekong upstream region, the ANN yielded the best performance when simulating the annual TRMM precipitation. The most sensitive vegetation index for downscaling TRMM was LAI, followed by EVI. Compared with existing downscaling methods, the proposed framework for downscaling TRMM can be performed online for any given region using a wide range of machine learning tools and environmental variables to generate a precipitation product with high spatiotemporal resolution.
Abdelrazek Elnashar; Hongwei Zeng; Bingfang Wu; Ning Zhang; Fuyou Tian; Miao Zhang; Weiwei Zhu; Nana Yan; Zeqiang Chen; Zhiyu Sun; Xinghua Wu; Yuan Li. Downscaling TRMM Monthly Precipitation Using Google Earth Engine and Google Cloud Computing. Remote Sensing 2020, 12, 3860 .
AMA StyleAbdelrazek Elnashar, Hongwei Zeng, Bingfang Wu, Ning Zhang, Fuyou Tian, Miao Zhang, Weiwei Zhu, Nana Yan, Zeqiang Chen, Zhiyu Sun, Xinghua Wu, Yuan Li. Downscaling TRMM Monthly Precipitation Using Google Earth Engine and Google Cloud Computing. Remote Sensing. 2020; 12 (23):3860.
Chicago/Turabian StyleAbdelrazek Elnashar; Hongwei Zeng; Bingfang Wu; Ning Zhang; Fuyou Tian; Miao Zhang; Weiwei Zhu; Nana Yan; Zeqiang Chen; Zhiyu Sun; Xinghua Wu; Yuan Li. 2020. "Downscaling TRMM Monthly Precipitation Using Google Earth Engine and Google Cloud Computing." Remote Sensing 12, no. 23: 3860.