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Ding Y, Zhang L, He Y, Cao S, Wei X, Guo Y, Ran L, Filonchyk M. Spatiotemporal evolution of agricultural drought and its attribution under different climate zones and vegetation types in the Yellow River Basin of China. Sci Total Environ 2024; 914:169687. [PMID: 38211870 DOI: 10.1016/j.scitotenv.2023.169687] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/24/2023] [Revised: 12/20/2023] [Accepted: 12/24/2023] [Indexed: 01/13/2024]
Abstract
Ecological protection and high-quality development of the Yellow River Basin (YRB) are major national strategies in China. Agricultural drought (AD) is one of the most important stress factors of the ecological security of the YRB. Currently, there is a lack of exploration of the spatiotemporal evolution of AD in the YRB under different climatic zones and vegetation types, and the mechanisms by the driving factors influence AD remain unclear. The Temperature Vegetation Dryness Index (TVDI) for the YRB in China during 2000-2020 was calculated using Land Surface Temperature (LST) and the Normalized Difference Vegetation Index (NDVI). We analyzed the spatiotemporal evolution of AD from the perspective of upstream of the YRB (UYRB), midstream of the YRB (MYRB), and downstream of the YRB (DYRB), as well as different climate zones and vegetation types. The driving factors were selected based on the Pearson correlation analysis, Geographical detector, and Mantel test. Structural equation modeling (SEM) was employed to quantify the direct and indirect effects of the driving factors on AD in the YRB. We found a slowing trend of AD in the YRB, mainly in the Loess Plateau, which is distributed in UYRB and MYRB, but an increasing trend for AD in DYRB. Temperature, which is the most direct influential factor, has exacerbated AD in UYRB and MYRB. However, surface solar radiation (SSR) has the greatest constraining effect on DYRB. AD increased in arid and desert zones, while a decreasing trend is observed for other climatic zones and vegetation types. In arid and semiarid zones, human activities and SSR were the largest indirect factors exacerbating AD. In humid and subhumid zones, the largest indirect factor exacerbating AD was potential evapotranspiration (PET). Temperature is the most direct factor exacerbating AD in cropland and forest, while PET is the largest indirect factor exacerbating AD in grassland. This study elucidates the driving factors and mechanisms of AD in the YRB to provide scientific decision support for mitigating regional drought and promoting regional sustainable development.
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Affiliation(s)
- Yujie Ding
- Faculty of Geomatics, Lanzhou Jiaotong University, Lanzhou 730070, China; National-Local Joint Engineering Research Center of Technologies and Applications for National Geographic State Monitoring, Lanzhou 730070, China; Gansu Provincial Engineering Laboratory for National Geographic State Monitoring, Lanzhou 730070, China
| | - Lifeng Zhang
- Faculty of Geomatics, Lanzhou Jiaotong University, Lanzhou 730070, China; National-Local Joint Engineering Research Center of Technologies and Applications for National Geographic State Monitoring, Lanzhou 730070, China; Gansu Provincial Engineering Laboratory for National Geographic State Monitoring, Lanzhou 730070, China.
| | - Yi He
- Faculty of Geomatics, Lanzhou Jiaotong University, Lanzhou 730070, China; National-Local Joint Engineering Research Center of Technologies and Applications for National Geographic State Monitoring, Lanzhou 730070, China; Gansu Provincial Engineering Laboratory for National Geographic State Monitoring, Lanzhou 730070, China.
| | - Shengpeng Cao
- Faculty of Geomatics, Lanzhou Jiaotong University, Lanzhou 730070, China; National-Local Joint Engineering Research Center of Technologies and Applications for National Geographic State Monitoring, Lanzhou 730070, China; Gansu Provincial Engineering Laboratory for National Geographic State Monitoring, Lanzhou 730070, China
| | - Xiao Wei
- Faculty of Geomatics, Lanzhou Jiaotong University, Lanzhou 730070, China; National-Local Joint Engineering Research Center of Technologies and Applications for National Geographic State Monitoring, Lanzhou 730070, China; Gansu Provincial Engineering Laboratory for National Geographic State Monitoring, Lanzhou 730070, China
| | - Yan Guo
- Faculty of Geomatics, Lanzhou Jiaotong University, Lanzhou 730070, China; National-Local Joint Engineering Research Center of Technologies and Applications for National Geographic State Monitoring, Lanzhou 730070, China; Gansu Provincial Engineering Laboratory for National Geographic State Monitoring, Lanzhou 730070, China
| | - Ling Ran
- Faculty of Geomatics, Lanzhou Jiaotong University, Lanzhou 730070, China; National-Local Joint Engineering Research Center of Technologies and Applications for National Geographic State Monitoring, Lanzhou 730070, China; Gansu Provincial Engineering Laboratory for National Geographic State Monitoring, Lanzhou 730070, China
| | - Mikalai Filonchyk
- Faculty of Geomatics, Lanzhou Jiaotong University, Lanzhou 730070, China; National-Local Joint Engineering Research Center of Technologies and Applications for National Geographic State Monitoring, Lanzhou 730070, China; Gansu Provincial Engineering Laboratory for National Geographic State Monitoring, Lanzhou 730070, China
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Khosravi Y, Homayouni S, St-Hilaire A. An integrated dryness index based on geographically weighted regression and satellite earth observations. Sci Total Environ 2024; 911:168807. [PMID: 38000741 DOI: 10.1016/j.scitotenv.2023.168807] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/08/2023] [Revised: 11/07/2023] [Accepted: 11/21/2023] [Indexed: 11/26/2023]
Abstract
Drought, characterized by the limited water availability in the atmosphere and hydrological systems, is one of the most destructive natural calamities. Defining droughts based on a single variable/index (e.g., precipitation, temperature, TCI, VCI) may not be sufficient for describing intricate conditions, impacts, and decision-making. Therefore, an integrated set of variables and indices is necessary to capture various aspects of intricate drought conditions. This paper has developed an Integrated Geographically Weighted Dryness Index (IGWDI) to model the drought. In this index, climatic parameters (CP) (i.e., precipitation, temperature, evapotranspiration) and remote-sensing-based drought indices (RSDI) (i.e., PCI, VCI, TCI, SMCI) were inputted into a GWR (Geographically Weighted Regression) model to predict the TVDI as independent variables in two distinct models, IGWDI-CP and IGWDI-RSDI, respectively. In this study, the proposed IGWDI is utilized to characterize the drought conditions in the Iranian plateau on a monthly scale from April to September over 20 years, including 2003-2022. According to adjusted R2 and AICc values, the findings revealed that IGWDI-CP is the best-fitting model for drought monitoring in all months. The IGWDI-CP model demonstrated that over the 20 years, from April to September, nearly 90 % of the examined study area experienced a range of drought severity levels. The warmest month, July, stood out, with approximately 71 % of the regions facing severe and extreme drought conditions. These adverse conditions were predominantly observed in scattered locations within Iran's middle and southern regions. Overlay, throughout all studied months, the southwestern regions of Iran emerged as the focal point for the most severe drought conditions. In most regions, an inverse relationship was discovered between TVDI and precipitation and evapotranspiration, while a positive correlation was observed between TVDI and temperature. This study employed the GWR model to combine several crucial climatic parameters and remote sensing-based indices to derive a novel index for monitoring a wider range of droughts. Consequently, these findings benefit decision-makers and authorities responsible for environmental sustainability, agriculture, and addressing the consequences of climate change.
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Affiliation(s)
- Younes Khosravi
- Environmental Science Research Laboratory, Department of Environmental Science, Faculty of Science, University of Zanjan, 45371-38791 Zanjan, Iran; Centre Eau Terre Environnement, Institut National de la Recherche Scientifique (INRS), Quebe, QC G1K 9A9 Quebec, Canada.
| | - Saeid Homayouni
- Centre Eau Terre Environnement, Institut National de la Recherche Scientifique (INRS), Quebe, QC G1K 9A9 Quebec, Canada
| | - Andre St-Hilaire
- Canada Research Chair in Statistical Hydro-Climatology, Institut national de la recherche scientifique, Centre Eau Terre Environnement, INRS-ETE, 490 De la Couronne, Qu'ebec City, QC, Canada
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Ji L, Wu Y, Ma J, Song C, Zhu Z, Zhao A. Spatio-temporal variations and drought of spring maize in Northeast China between 2002 and 2020. Environ Sci Pollut Res Int 2023; 30:33040-33060. [PMID: 36471153 DOI: 10.1007/s11356-022-24502-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/14/2022] [Accepted: 11/27/2022] [Indexed: 06/17/2023]
Abstract
A lot of spring maize is grown in Northeast China (Liaoning, Jilin, and Heilongjiang), an area that is highly susceptible to drought. Here, remote sensing indexes from 2002 to 2020 were studied using the 8-day surface reflectance and land surface temperature of Moderate-resolution Imaging Spectroradiometer data. Spring maize distribution was extracted using a decision tree classification, and the results were compared to the known distribution based on field investigation data and published statistics. The results showed that mixed pixels of spring maize and soybeans had limited influence on the study of spatio-temporal variations of spring maize, and the error was acceptable. The overall accuracy of verifying the spring maize distribution from 2018 to 2020 was above 85%. The stable, fluctuating, and low-frequency planting areas of spring maize accounted for 11.86%, 17.41%, and 34.86% of the study area, respectively. In 2015, the government directed a reduction of the planting area of spring maize in the "Liandaowan" region of Northeast China. The planting area of spring maize was characterized by a continuous increase before this change (2002-2014), exhibited changes and reductions in response to the change (2015-2017), and exhibited optimization and recovery after this change (2018-2020). Compared with the fluctuating and low-frequency planting areas, moderate and severe droughts were higher in stable planting areas. From 2002 to 2020, the most severe droughts occurred in the expanded planting areas. This rapid and large-scale monitoring of spatio-temporal variations and drought of spring maize provides a foundation for improving grain yield. This method could be easily applied to the study of other regions and combined with high-resolution and hyperspectral satellite data to improve monitoring accuracy.
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Affiliation(s)
- Lin Ji
- Institute of Environment and Sustainable Development in Agriculture, Chinese Academy of Agricultural Sciences, Beijing, 100081, China
| | - Yongfeng Wu
- Institute of Environment and Sustainable Development in Agriculture, Chinese Academy of Agricultural Sciences, Beijing, 100081, China.
| | - Juncheng Ma
- Institute of Environment and Sustainable Development in Agriculture, Chinese Academy of Agricultural Sciences, Beijing, 100081, China
| | - Chenxi Song
- Institute of Environment and Sustainable Development in Agriculture, Chinese Academy of Agricultural Sciences, Beijing, 100081, China
- College of Surveying and Mapping Engineering, East China University of Technology, Nanchang, 330013, China
| | - Zhicheng Zhu
- Institute of Environment and Sustainable Development in Agriculture, Chinese Academy of Agricultural Sciences, Beijing, 100081, China
- School of Surveying and Land Information Engineering, Henan Polytechnic University, Jiaozuo, 454000, China
| | - Aiping Zhao
- Institute of Environment and Sustainable Development in Agriculture, Chinese Academy of Agricultural Sciences, Beijing, 100081, China
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Zhang H, Ali S, Ma Q, Sun L, Jiang N, Jia Q, Hou F. Remote sensing strategies to characterization of drought, vegetation dynamics in relation to climate change from 1983 to 2016 in Tibet and Xinjiang Province, China. Environ Sci Pollut Res Int 2021; 28:21085-21100. [PMID: 33405158 DOI: 10.1007/s11356-020-12124-w] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/21/2020] [Accepted: 12/15/2020] [Indexed: 06/12/2023]
Abstract
Due to various land cover changes, vegetation dynamics, and climate, drought is the most complex climate-related disaster problem in Tibet and Xinjiang, China. The purpose of the present study is to analyze the performance of the AVHRR Normalized Vegetation Index (NDVI) and the temporal and spatial differences of seasonal vegetation dynamics by correlating the results with rainfall and temperature data of NASA's MERRA to examine the vegetation dynamics and droughts in Tibet and the Xinjiang Province of China. Our method is based on the use of AVHRR NDVI data and NASA MERRA temperature and precipitation during 1983-2016. Due to the dryness and low vegetation, NDVI is more useful to describe the drought conditions in Tibet and Xinjiang of China. The NDVI, TCI, VHI, NVSWI, VCI, TVDI, and NAP from April to October increased rapidly. While the NDVI, TCI, VHI, NVSWI, NAP, TVDI, and VCI are stable every month in September, again improve in October, and then confirm downward trend in December. The NDVI, TCI, VHI, NVSWI, NAP, VCI, and TVDI monthly values indicate that Tibet and Xinjiang province of China suffered from severe drought in 2006, 2008, and 2012 which were the most drought years. For monitoring drought in Tibet and Xinjiang province of China, the NDVI, TVDI, NAP, VCI, and NVSWI values were selected as a tool for reporting drought events during different growing seasons. Seasonal values of TVDI, NDVI, NAP, NVSWI, and VCI confirmed that Tibet and Xinjiang province of China suffered from severe drought in 2006, 2008, and 2012 and led the durations of severe drought. The correlation between NDVI, TCI, VHI, NAP, TVDI, and VCI showed a significantly positive correlation, while the significantly negative correlation between NVSWI and NDVI showed a good indication for the assessment of drought, especially for the agricultural regions of Tibet and Xinjiang province of China. This shows that the positive sign to support NAP, NVSWI, and TVDI is good monitoring of the drought indexes in Tibet and the Xinjiang province of China.
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Affiliation(s)
- Haixing Zhang
- State Key Laboratory of Grassland Agro-ecosystems, Lanzhou University, Lanzhou, 730020, People's Republic of China
- Key Laboratory of Grassland Livestock Industry Innovation, Ministry of Agriculture and Rural Affairs, Lanzhou University, Lanzhou, 730020, People's Republic of China
- Engineering Research Center of Grassland Industry, Ministry of Education, Lanzhou University, Lanzhou, 730020, People's Republic of China
- College of Pastoral Agriculture Science and Technology, Lanzhou University, Lanzhou, 730020, People's Republic of China
| | - Shahzad Ali
- State Key Laboratory of Grassland Agro-ecosystems, Lanzhou University, Lanzhou, 730020, People's Republic of China.
- Key Laboratory of Grassland Livestock Industry Innovation, Ministry of Agriculture and Rural Affairs, Lanzhou University, Lanzhou, 730020, People's Republic of China.
- Engineering Research Center of Grassland Industry, Ministry of Education, Lanzhou University, Lanzhou, 730020, People's Republic of China.
- College of Pastoral Agriculture Science and Technology, Lanzhou University, Lanzhou, 730020, People's Republic of China.
| | - Qi Ma
- State Key Laboratory of Grassland Agro-ecosystems, Lanzhou University, Lanzhou, 730020, People's Republic of China
- Key Laboratory of Grassland Livestock Industry Innovation, Ministry of Agriculture and Rural Affairs, Lanzhou University, Lanzhou, 730020, People's Republic of China
- Engineering Research Center of Grassland Industry, Ministry of Education, Lanzhou University, Lanzhou, 730020, People's Republic of China
- College of Pastoral Agriculture Science and Technology, Lanzhou University, Lanzhou, 730020, People's Republic of China
| | - Liang Sun
- State Key Laboratory of Grassland Agro-ecosystems, Lanzhou University, Lanzhou, 730020, People's Republic of China
- Key Laboratory of Grassland Livestock Industry Innovation, Ministry of Agriculture and Rural Affairs, Lanzhou University, Lanzhou, 730020, People's Republic of China
- Engineering Research Center of Grassland Industry, Ministry of Education, Lanzhou University, Lanzhou, 730020, People's Republic of China
- College of Pastoral Agriculture Science and Technology, Lanzhou University, Lanzhou, 730020, People's Republic of China
| | - Ning Jiang
- State Key Laboratory of Grassland Agro-ecosystems, Lanzhou University, Lanzhou, 730020, People's Republic of China
- Key Laboratory of Grassland Livestock Industry Innovation, Ministry of Agriculture and Rural Affairs, Lanzhou University, Lanzhou, 730020, People's Republic of China
- Engineering Research Center of Grassland Industry, Ministry of Education, Lanzhou University, Lanzhou, 730020, People's Republic of China
- College of Pastoral Agriculture Science and Technology, Lanzhou University, Lanzhou, 730020, People's Republic of China
| | - Qianmin Jia
- State Key Laboratory of Grassland Agro-ecosystems, Lanzhou University, Lanzhou, 730020, People's Republic of China.
- Key Laboratory of Grassland Livestock Industry Innovation, Ministry of Agriculture and Rural Affairs, Lanzhou University, Lanzhou, 730020, People's Republic of China.
- Engineering Research Center of Grassland Industry, Ministry of Education, Lanzhou University, Lanzhou, 730020, People's Republic of China.
- College of Pastoral Agriculture Science and Technology, Lanzhou University, Lanzhou, 730020, People's Republic of China.
| | - Fujiang Hou
- State Key Laboratory of Grassland Agro-ecosystems, Lanzhou University, Lanzhou, 730020, People's Republic of China
- Key Laboratory of Grassland Livestock Industry Innovation, Ministry of Agriculture and Rural Affairs, Lanzhou University, Lanzhou, 730020, People's Republic of China
- Engineering Research Center of Grassland Industry, Ministry of Education, Lanzhou University, Lanzhou, 730020, People's Republic of China
- College of Pastoral Agriculture Science and Technology, Lanzhou University, Lanzhou, 730020, People's Republic of China
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Kazemzadeh M, Salajegheh A, Malekian A, Liaghat A, Hashemi H. Soil moisture change analysis under watershed management practice using in situ and remote sensing data in a paired watershed. Environ Monit Assess 2021; 193:299. [PMID: 33895895 DOI: 10.1007/s10661-021-09078-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/06/2020] [Accepted: 04/15/2021] [Indexed: 06/12/2023]
Abstract
Soil moisture, vegetation cover, and land surface temperature are vital variables in water-energy balance, eco-hydrological processes, and water resources management, which can be influenced by watershed management activities. This research focused on the spatial and temporal variability of soil moisture, vegetation cover, land surface temperature, and Temperature-Vegetation Dryness Index (TVDI) under a biological watershed management practice in the Taleghan paired watershed, namely, treated (TW) and control watersheds (CW), in Alborz province, Iran. In this research, along with the remote sensing techniques, the soil moisture and vegetation cover data were measured and statistically analyzed in the three aspects of both TW and CW during a growth period from May to October 2017. The results indicated that soil moisture, vegetation cover, and land surface temperature values in the paired watershed were significantly different at the 0.01 level during the study period. The increased vegetation cover in the TW had an inverse effect on the land surface temperature and TVDI, while directly impacted the soil moisture content. The average TVDI in the CW was 0.83, while this index was found to be 0.69 in the TW. Unlike the vegetation cover and soil moisture, the results revealed that the southern aspects had the highest TVDI and land surface temperature compared to the northern and eastern aspects of both watersheds. However, the increased vegetation cover as a biological watershed management activity in the steep terrain and mountainous areas of TW led to an increased soil moisture and a decreased land surface temperature and soil dryness. As a result, decreasing soil dryness in the TW can exert vital controls on the water resources and increasing water availability. In the arid and semiarid countries such as Iran, a proper watershed management activity can effectively increase soil moisture and water availability in the watersheds. In particular, the vegetation cover protection and biological practices can be considered as practical solutions in the rehabilitation of exhausted watersheds in arid and semiarid environments.
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Affiliation(s)
- Majid Kazemzadeh
- Faculty of Natural Resources, University of Tehran, Karaj, Iran.
| | - Ali Salajegheh
- Faculty of Natural Resources, University of Tehran, Karaj, Iran
| | - Arash Malekian
- Faculty of Natural Resources, University of Tehran, Karaj, Iran
| | - Abdolmajid Liaghat
- College of Agriculture and Natural Resources, University of Tehran, Karaj, Iran
| | - Hossein Hashemi
- Department of Water Resources Engineering & Center for Advanced Middle Eastern Studies, Lund University, Lund, Sweden
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Ali S, Tong D, Xu ZT, Henchiri M, Wilson K, Siqi S, Zhang J. Characterization of drought monitoring events through MODIS- and TRMM-based DSI and TVDI over South Asia during 2001-2017. Environ Sci Pollut Res Int 2019; 26:33568-33581. [PMID: 31583522 DOI: 10.1007/s11356-019-06500-4] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/05/2019] [Accepted: 09/10/2019] [Indexed: 06/10/2023]
Abstract
South Asia is susceptible to drought due to high variation in monthly precipitation. The drought indices deriving from remote sensing data have been used to monitor drought events. To secure agricultural land in South Asia, timely and effective drought monitoring is very important. In this study, TRMM data was utilized along with remote sensing techniques for reliable drought monitoring. The Drought Severity Index (DSI), Temperature Vegetation Drought Index (TVDI), NDVI, and Normalized Vegetation Supply Water Index (NVSWI) are more helpful in describing the drought events in South Asia due to the dryness and low vegetation. To categorize drought-affected areas, the spatial maps of TRMM were used to confirm MODIS-derived TVDI, DSI, and NVSWI. The DSI, TVDI, NVSWI, and Normalized Monthly Precipitation Anomaly Percentage (NAP) indices with an integrated use of MODIS-derived ET/PET and NDVI were selected as a tool for monitoring drought in South Asia. The seasonal DSI, TVDI, NVSWI, NAP, and NDVI values confirmed that South Asia suffered an extreme drought in 2001, which continued up to 2003. The correlation was generated among DSI, NAP, NVWSI, NDVI, TVDI, and TCI on a seasonal basis. The significantly positive correlation values of DSI, TVDI, and NVSWI were in DJF, MAM, and SON seasons, which were described as good drought monitoring indices during these seasons. During summer, the distribution values of drought indicated that more droughts occurred in the southwest regions as compared to the northeast region of South Asia. From 2001 to 2017, the change trend of drought was characterized; the difference of drought trend was obviously indicated among different regions. In South Asia, generally, the frequency of drought showed declining trends from 2001 to 2017. It was verified that these drought indices are a comprehensive drought monitoring indicator and would reduce drought risk in South Asia.
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Affiliation(s)
- Shahzad Ali
- School of Computer Science and Technology, Remote Sensing and Climate Changing, Qingdao University, Shandong, 266071, China.
| | - Deming Tong
- School of Computer Science and Technology, Remote Sensing and Climate Changing, Qingdao University, Shandong, 266071, China
| | - Zhen Tian Xu
- School of Computer Science and Technology, Remote Sensing and Climate Changing, Qingdao University, Shandong, 266071, China
| | - Malak Henchiri
- School of Computer Science and Technology, Remote Sensing and Climate Changing, Qingdao University, Shandong, 266071, China
| | - Kalisa Wilson
- School of Computer Science and Technology, Remote Sensing and Climate Changing, Qingdao University, Shandong, 266071, China
| | - Shi Siqi
- University of Chinese Academy of Sciences, Beijing, 100049, China
| | - Jiahua Zhang
- School of Computer Science and Technology, Remote Sensing and Climate Changing, Qingdao University, Shandong, 266071, China.
- University of Chinese Academy of Sciences, Beijing, 100049, China.
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