Temporal Pattern Analysis of Cropland Phenology in Shandong Province of China Based on Two Long-Sequence Remote Sensing Data

被引:6
作者
Ren, Shilong [1 ]
An, Shuai [2 ]
机构
[1] Shandong Univ, Environm Res Inst, Qingdao 266237, Peoples R China
[2] Beijing Union Univ, Coll Appl Arts & Sci, Beijing 100191, Peoples R China
关键词
vegetation phenology; variation trend; cropland; Global Inventory Modeling and Mapping Studies; Vegetation Index and Phenology; TIME-SERIES DATA; WINTER-WHEAT; CLIMATE-CHANGE; VEGETATION PHENOLOGY; DATA SET; TEMPERATURE; TRENDS; MANAGEMENT; IMPACTS; INDEX;
D O I
10.3390/rs13204071
中图分类号
X [环境科学、安全科学];
学科分类号
08 ; 0830 ;
摘要
Vegetation phenology dynamics have attracted worldwide attention due to its direct response to global climate change and the great influence on terrestrial carbon budgets and ecosystem productivity in the past several decades. However, most studies have focused on phenology investigation on natural vegetation, and only a few have explored phenology variation of cropland. In this study, taking the typical cropland in the Shandong province of China as the target, we analyzed the temporal pattern of the Normalized Difference Vegetation Index (NDVI) and phenology metrics (growing season start (SOS) and end (EOS)) derived from the Global Inventory Monitoring and Modeling System (GIMMS) 3-generation version 1 (1982-2015) and the Vegetation Index and Phenology (VIP) version 4 (1981-2016), and then investigated the influence of climate factors and Net Primary Production (NPP, only for EOS) on SOS/EOS. Results show a consistent seasonal profile and interannual variation trend of NDVI for the two products. Annual average NDVI has significantly increased since 1980s, and hugely augmentations of NDVI were detected from March to June for both NDVI products (p < 0.01), which indicates a consistent greening tendency of the study region. SOSs from both products are correlated well with the ground-observed wheat elongation and spike date and have significantly advanced since the 1980s, with almost the same changing rate (0.65/0.64 days yr-1, p < 0.01). EOS also exhibits an earlier but weak advancing trend. Due to the significant advance of SOS, the growing season duration has significantly lengthened. Spring precipitation has a relatively stronger influence on SOS than temperature and shortwave radiation, while a greater correlation coefficient was diagnosed between EOS and autumn temperature/shortwave radiation than precipitation/NDVI. Autumn NPP exhibits a nonlinear effect on EOS, which is first earlier and then later with the increase of autumn NPP. Overall, we highlight the similar capacity of the two NDVI products in characterizing the temporal patterns of cropland phenology.</p>
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页数:13
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[21]   Remote Sensing Inversion for Simulation of Soil Salinization Based on Hyperspectral Data and Ground Analysis in Yinchuan, China [J].
Wu, Dan ;
Jia, Keli ;
Zhang, Xiaodong ;
Zhang, Junhua ;
Abd El-Hamid, Hazem T. .
NATURAL RESOURCES RESEARCH, 2021, 30 (06) :4641-4656
[22]   Analysis of spatial-temporal variations of grassland gross ecosystem product based on machine learning algorithm and multi-source remote sensing data: A case study of Xilinhot, China [J].
Wang, Haiwen ;
Wu, Nitu ;
Han, Guodong ;
Li, Wu ;
Batunacun ;
Bao, Yuhai .
GLOBAL ECOLOGY AND CONSERVATION, 2024, 51
[23]   Phenology shift from 1989 to 2008 on the Tibetan Plateau: an analysis with a process-based soil physical model and remote sensing data [J].
Jin, Zhenong ;
Zhuang, Qianlai ;
He, Jin-Sheng ;
Luo, Tianxiang ;
Shi, Yue .
CLIMATIC CHANGE, 2013, 119 (02) :435-449
[24]   Spatio-Temporal Evolution of a Typical Sandstorm Event in an Arid Area of Northwest China in April 2018 Based on Remote Sensing Data [J].
Wu, Zhiyu ;
Jiang, Qun'ou ;
Yu, Yang ;
Xiao, Huijie ;
Freese, Dirk .
REMOTE SENSING, 2022, 14 (13)
[25]   Spatio-temporal distribution and trends monitoring of land desertification based on time-series remote sensing data in northern China [J].
Zhao, XiangWei ;
Yu, MengLi ;
Pan, Shun ;
Jin, FengXiang ;
Zou, DeXu ;
Zhang, LiXing .
ENVIRONMENTAL EARTH SCIENCES, 2023, 82 (11)
[26]   Temporal and spatial distributions and influencing factors of HABs outbreaks around the north of Shandong Peninsula during 2000-2019: based on remote sensing images and field monitoring data [J].
Zhou, Min ;
Wu, Mengquan ;
Zhao, Lianjie ;
Zheng, Longxiao ;
Li, Binyu .
GEOCARTO INTERNATIONAL, 2022, 37 (25) :8440-8455
[27]   Spatio-temporal analysis of shoreline changes and erosion risk assessment along Jerba island (Tunisia) based on remote-sensing data and geospatial tools [J].
Boussetta, Amina ;
Niculescu, Simona ;
Bengoufa, Soumia ;
Zagrarni, Mohamed Faouzi .
REGIONAL STUDIES IN MARINE SCIENCE, 2022, 55
[28]   Spatio temporal analysis trend of land use and land cover change against temperature based on remote sensing data in Malang City [J].
Purwanto ;
Utomo, Dwiyono Hari ;
Kurniawan, Bharadhian Rizki .
CITIES 2015: INTELLIGENT PLANNING TOWARDS SMART CITIES, 2016, 227 :232-238
[29]   Urban development trend analysis and spatial simulation based on time series remote sensing data: A case study of Jinan, China [J].
Zhang, Yanghua ;
Zhao, Liang ;
Zhao, Hu ;
Gao, Xiaofeng .
PLOS ONE, 2021, 16 (10)
[30]   Coastline Monitoring and Prediction Based on Long-Term Remote Sensing Data-A Case Study of the Eastern Coast of Laizhou Bay, China [J].
Mu, Ke ;
Tang, Cheng ;
Tosi, Luigi ;
Li, Yanfang ;
Zheng, Xiangyang ;
Donnici, Sandra ;
Sun, Jixiang ;
Liu, Jun ;
Gao, Xuelu .
REMOTE SENSING, 2024, 16 (01)