Remote Sensing Monitoring of Agricultural Drought and Vegetation Sensitivity Analysis in the Middle and Lower Reaches of the Yangtze River from 2001 to 2019

YIN Guoying; ZHANG Hongyan; ZHANG Liangpei

doi:10.13203/j.whugis20210172

Volume 47 Issue 8

Aug. 2022

Turn off MathJax

Article Contents

Abstract

References

Geomatics and Information Science of Wuhan University > 2022 > 47(8): 1245-1256. > DOI: 10.13203/j.whugis20210172

YIN Guoying, ZHANG Hongyan, ZHANG Liangpei. Remote Sensing Monitoring of Agricultural Drought and Vegetation Sensitivity Analysis in the Middle and Lower Reaches of the Yangtze River from 2001 to 2019[J]. Geomatics and Information Science of Wuhan University, 2022, 47(8): 1245-1256. DOI: 10.13203/j.whugis20210172

Citation:

PDF (22167 KB)

Remote Sensing Monitoring of Agricultural Drought and Vegetation Sensitivity Analysis in the Middle and Lower Reaches of the Yangtze River from 2001 to 2019

1.
State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University, Wuhan 430079, China
2.
Collaborative Innovation Center for Geospatial Technology, Wuhan 430079, China

Funds:

The National Natural Science Foundation of China 61871298

The National Natural Science Foundation of China 42071322

the Natural Science Foundation of Hubei Province 2020CFA053

More Information

Author Bio:
YIN Guoying, PhD candidate, specializes in agricultural remote sensing. E-mail: yin_gy@whu.edu.cn
Corresponding author:
ZHANG Hongyan, PhD, professor. E-mail: zhanghongyan@whu.edu.cn
Received Date: April 08, 2021
Available Online: August 15, 2022
Published Date: August 04, 2022

Graphical Abstract

Abstract

Abstract

Objectives The middle and lower reaches of the Yangtze River, including Hubei, Hunan, Jiangxi, Anhui, Jiangsu, Zhejiang and Shanghai, are important planting bases of commercial grain in China. However, at present, there are relatively few studies on agricultural drought in this region, and there is a lack of attention to the response of land cover types to drought. Moreover, in the context of climate change, the evolution and trend of agricultural drought in the middle and lower reaches of the Yangtze River need further discussion.
Methods This study used MODIS (moderate resolution imaging spectroradiometer) V6 products to construct vegetation condition index (VCI), temperature condition index (TCI) and vegetation health index (VHI) to monitor the temporal and spatial evolution of agricultural drought in the middle and lower reaches of the Yangtze River from 2001 to 2019, and further explored the drought sensitivity of different vegetative types.Based on the concept of climate change, this study analyzed the drought trends in six provinces and one city in the middle and lower reaches of the Yangtze River. The results of the above three indices were further evaluated by standardized precipitation index (SPI) on different time scales, obtained and calculated from the CHIRPS V2.0 dataset.
Results The results show that the VCI and TCI could monitor the long-term abnormal vegetation growth and heat anomalies, respectively, but neither index could provide comprehensive overview of drought conditions. Combining the advantages of both indices with the weights of 0.7 and 0.3 for VCI and TCI, respectively, the VHI, was more effective in agricultural drought monitoring in the middle and lower reaches of the Yangtze River. Different vegetation showed different drought sensitivity in study. Crops have the highest sensitivity to drought, forests are the lowest, and grasslands are somewhere in between. In the context of climate change, Jiangxi, Hunan, Hubei, Zhejiang, and Anhui show an intense wet trend in the past 20 years, while the Jiangsu and Shanghai show a weak wet trend.
Conclusions Drought indices should be integrated to provide comprehensive evaluation of agricultural drought in the middle and lower reaches of the Yangtze River. Jiangsu province and Shanghai city are still at drought risk due to the weak wet trend and the local agricultural department should take drought mitigation measures to prevent economic losses. In the middle and lower reaches of the Yangtze River, croplands have the most obvious response to drought, indicating that crops are most sensitive to drought than grasses and forests, more attention should be paid to agriculture management. The relevant results can provide reference for the early warning of drought in various provinces and cities in the middle and lower reaches of the Yangtze River and help the management of regional agricultural production.

FullText(HTML)

References (39)

References

[1]	Zhang L F, Jiao W Z, Zhang H M, et al. Studying Drought Phenomena in the Continental United States in 2011 and 2012 Using Various Drought Indices[J]. Remote Sensing of Environment, 2017, 190: 96-106 doi: 10.1016/j.rse.2016.12.010
[2]	Cong D M, Zhao S H, Chen C, et al. Characterization of Droughts During 2001—2014 Based on Remote Sensing: A Case Study of Northeast China [J]. Ecological Informatics, 2017, 39: 56-67 doi: 10.1016/j.ecoinf.2017.03.005
[3]	Ford T W, Labosier C F. Meteorological Conditions Associated with the Onset of Flash Drought in the Eastern United States[J]. Agricultural and Forest Meteorology, 2017, 247: 414-423 doi: 10.1016/j.agrformet.2017.08.031
[4]	Vinod K, Dharssi I. Evaluation and Calibration of a High-Resolution Soil Moisture Product for Wildfire Prediction and Management[J]. Agricultural and Forest Meteorology, 2019, 264: 27-39 doi: 10.1016/j.agrformet.2018.09.012
[5]	于人杰, 康平, 任远刘瑞, 等. 成都市降水对大气颗粒物湿清除作用的观测研究[J]. 环境污染与防治, 2020, 42(8): 990-995 https://www.cnki.com.cn/Article/CJFDTOTAL-HJWR202008011.htm Yu Renjie, Kang Ping, Ren Yuanliurui, et al. Observational Study on Precipitation Wet Removal of Atmospheric Particulate Matter in Chengdu[J]. Environmental Pollution & Control, 2020, 42(8): 990-995 https://www.cnki.com.cn/Article/CJFDTOTAL-HJWR202008011.htm
[6]	杨丽萍, 韩德彪, 姜宝法. 干旱对人类健康影响的研究进展[J]. 环境与健康杂志, 2013, 30(5): 453-455 https://www.cnki.com.cn/Article/CJFDTOTAL-HJYJ201305027.htm Yang Liping, Han Debiao, Jiang Baofa. Drought and Human Health: A Review of Recent Studies [J]. Journal of Environment and Health, 2013, 30 (5): 453-455 https://www.cnki.com.cn/Article/CJFDTOTAL-HJYJ201305027.htm
[7]	The Intergovernmental Panel on Climate Change (IPCC). Global Warming of 1.5 ℃: An IPCC Special Report[M]. Cambridge, UK: Cambridge University Press, 2018
[8]	杨绍锷, 闫娜娜, 吴炳方. 农业干旱遥感监测研究进展[J]. 遥感信息, 2010, 25(1): 103-109 doi: 10.3969/j.issn.1000-3177.2010.01.021 Yang Shao'e, Yan Nana, Wu Binfang, et al. Advances in Agricultural Drought Monitoring by Remote Sensing[J]. Remote Sensing Information, 2010, 25 (1): 103-109 doi: 10.3969/j.issn.1000-3177.2010.01.021
[9]	Esfahanian E, Nejadhashemi A P, Abouali M, et al. Development and Evaluation of a Comprehensive Drought Index[J]. Journal of Environmental Management, 2017, 185: 31-43
[10]	钱莉莉, 贺中华, 梁虹, 等. 农业干旱监测方法与指标研究进展[J]. 绿色科技, 2019(6): 5-8 https://www.cnki.com.cn/Article/CJFDTOTAL-LVKJ201906004.htm Qian Lili, He Zhonghua, Liang Hong, et al. Research Progress of Agricultural Drought Monitoring Methods and Indicator[J]. Journal of Green Science and Technology, 2019(6): 5-8 https://www.cnki.com.cn/Article/CJFDTOTAL-LVKJ201906004.htm
[11]	黄友昕, 刘修国, 沈永林, 等. 农业干旱遥感监测指标及其适应性评价方法研究进展[J]. 农业工程学报, 2015, 31(16): 186-195 doi: 10.11975/j.issn.1002-6819.2015.16.025 Huang Youxin, Liu Xiuguo, Shen Yonglin, et al. Advances in Remote Sensing Derived Agricultural Drought Monitoring Indices and Adaptability Evaluation Methods[J]. Transactions of the Chinese Society of Agricultural Engineering, 2015, 31(16): 186-195 doi: 10.11975/j.issn.1002-6819.2015.16.025
[12]	董婷, 孟令奎, 张文. MODIS短波红外水分胁迫指数及其在农业干旱监测中的适用性分析[J]. 遥感学报, 2015, 19(2): 319-327 https://www.cnki.com.cn/Article/CJFDTOTAL-YGXB201502016.htm Dong Ting, Meng Lingkui, Zhang Wen. Analysis of the Application of MODIS Shortwave Infrared Water Stress Index in Monitoring Agricultural Drought[J]. Journal of Remote Sensing, 2015, 19 (2): 319-327 https://www.cnki.com.cn/Article/CJFDTOTAL-YGXB201502016.htm
[13]	Sadri S, Pan M, Wada Y, et al. A Global Near-Real-Time Soil Moisture Index Monitor for Food Security Using Integrated SMOS and SMAP[J]. Remote Sensing of Environment, 2020, 246: 111864 doi: 10.1016/j.rse.2020.111864
[14]	Du J, Kimball J S, Velicogna I, et al. Multicomponent Satellite Assessment of Drought Severity in the Contiguous United States from 2002 to 2017 Using AMSR-E and AMSR2[J]. Water Resources Research, 2019, 55(7): 5394-5412 doi: 10.1029/2018WR024633
[15]	Zhang Z, Wang D G, Wang G L, et al. Use of SMAP Soil Moisture and Fitting Methods in Improving GPM Estimation in near Real Time[J]. Remote Sensing, 2019, 11(3): 368 doi: 10.3390/rs11030368
[16]	Tucker C J. Red and Photographic Infrared Linear Combinations for Monitoring Vegetation[J]. Remote Sensing of Environment, 1979, 8(2): 127-150 doi: 10.1016/0034-4257(79)90013-0
[17]	宋怡, 马明国. 基于GIMMS AVHRR NDVI数据的中国寒旱区植被动态及其与气候因子的关系[J]. 遥感学报, 2008, 12 (3): 499-505 https://www.cnki.com.cn/Article/CJFDTOTAL-YGXB200803018.htm Song Yi, Ma Mingguo. Variation of AVHRR NDVI and Its Relationship with Climate in Chinese Arid and Cold Regions[J]. Journal of Remote Sensing, 2008, 12(3): 499-505 https://www.cnki.com.cn/Article/CJFDTOTAL-YGXB200803018.htm
[18]	Kogan F N. Application of Vegetation Index and Brightness Temperature for Drought Detection[J]. Advances in Space Research, 1995, 15(11): 91-100 doi: 10.1016/0273-1177(95)00079-T
[19]	Bokusheva R, Kogan F, Vitkovskaya I, et al. Satellite-Based Vegetation Health Indices as a Criteria for Insuring Against Drought-Related Yield Losses[J]. Agricultural and Forest Meteorology, 2016, 220: 200-206 doi: 10.1016/j.agrformet.2015.12.066
[20]	Pei F S, Wu C J, Liu X P, et al. Monitoring the Vegetation Activity in China Using Vegetation Health Indices[J]. Agricultural and Forest Meteorology, 2018, 248: 215-227 doi: 10.1016/j.agrformet.2017.10.001
[21]	Jiao W Z, Wang L X, Novick K A, et al. A New Station-Enabled Multi-sensor Integrated Index for Drought Monitoring[J]. Journal of Hydrology, 2019, 574: 169-180 doi: 10.1016/j.jhydrol.2019.04.037
[22]	蔡斌, 陆文杰, 郑新江. 气象卫星条件植被指数监测土壤状况[J]. 国土资源遥感, 1995, 7 (4): 45-50 https://www.cnki.com.cn/Article/CJFDTOTAL-GTYG504.010.htm Cai Bin, Lu Wenjie, Zheng Xinjiang. Using Meteorological Satellite's Vic to Monitor Soil State[J]. Remote Sensing for Land & Resources, 1995, 7(4): 45-50 https://www.cnki.com.cn/Article/CJFDTOTAL-GTYG504.010.htm
[23]	王思琪, 张翔, 陈能成, 等. 基于多种干旱指数的长江中下游五省干旱监测与对比[J]. 干旱气象, 2019, 37(2): 209-217 https://www.cnki.com.cn/Article/CJFDTOTAL-GSQX201902003.htm Wang Siqi, Zhang Xiang, Chen Nengcheng, et al. Monitoring and Comparison of Drought in Five Provinces of the Middle and Lower Reaches of the Yangtze River Based on the Multiple Drought Indices[J]. Journal of Arid Meteorology, 2019, 37(2): 209-217 https://www.cnki.com.cn/Article/CJFDTOTAL-GSQX201902003.htm
[24]	罗彪, 刘潇, 郭萍. 基于MODIS数据的河套灌区遥感干旱监测[J]. 中国农业大学学报, 2020, 25(10): 44-54 doi: 10.11841/j.issn.1007-4333.2020.10.05 Luo Biao, Liu Xiao, Guo Ping. Remote Sensing Drought Monitoring of Hetao Irrigation Area Based on MODIS Data[J]. Journal of China Agricultural University, 2020, 25(10): 44-54 doi: 10.11841/j.issn.1007-4333.2020.10.05
[25]	王锦杰, 陈昊, 张莹, 等. 基于植被健康指数的2001-2018年间江苏省农业干旱时空分析[J]. 江苏农业科学, 2020, 48(6): 223-231 https://www.cnki.com.cn/Article/CJFDTOTAL-JSNY202006044.htm Wang Jinjie, Chen Hao, Zhang Ying, et al. Spatial and Temporal Analysis of Agricultural Drought in Jiangsu Province from 2001 to 2018 Based on Vegetation Health Index[J]. Jiangsu Agricultural Sciences, 2020, 48(6): 223-231 https://www.cnki.com.cn/Article/CJFDTOTAL-JSNY202006044.htm
[26]	孙洪泉, 杨晓静, 张泽天. 2019年下半年长江中下游旱情分析及未来趋势预判[J]. 中国防汛抗旱, 2020, 30(2): 18-20 https://www.cnki.com.cn/Article/CJFDTOTAL-FHKH202002012.htm Sun Hongquan, Yang Xiaojing, Zhang Zetian. Analysis of Drought in the Middle and Lower Reaches of the Yangtze River in the Second Half of 2019 and Prediction of the Future Trend[J]. China Flood & Drought Management, 2020, 30(2): 18-20 https://www.cnki.com.cn/Article/CJFDTOTAL-FHKH202002012.htm
[27]	吕星玥, 荣艳淑, 石丹丹. 长江中下游地区2010/2011年秋冬春连旱成因再分析[J]. 干旱气象, 2019, 37(2): 198-208 https://www.cnki.com.cn/Article/CJFDTOTAL-GSQX201902002.htm Lü Xingyue, Rong Yanshu, Shi Dandan. Reanalysis on the Causes of Continuous Drought from Autumn 2010 to Spring 2011 in the Middle and Lower Reaches of the Yangtze River[J]. Journal of Arid Meteorology, 2019, 37(2): 198-208 https://www.cnki.com.cn/Article/CJFDTOTAL-GSQX201902002.htm
[28]	Li R H, Chen N C, Zhang X, et al. Quantitative Analysis of Agricultural Drought Propagation Process in the Yangtze River Basin by Using Cross Wavelet Analysis and Spatial Autocorrelation[J]. Agricultural and Forest Meteorology, 2020, 280: 107809 doi: 10.1016/j.agrformet.2019.107809
[29]	Berry J A, Beerling D J, Franks P J. Stomata: Key Players in the Earth System, Past and Present[J]. Current Opinion in Plant Biology, 2010, 13(3): 232-239 doi: 10.1016/j.pbi.2010.04.013
[30]	Bayarjargal Y, Karnieli A, Bayasgalan M, et al. A Comparative Study of NOAA-AVHRR Derived Drought Indices Using Change Vector Analysis[J]. Remote Sensing of Environment, 2006, 105(1): 9-22 doi: 10.1016/j.rse.2006.06.003
[31]	Karnieli A, Agam N, Pinker R T, et al. Use of NDVI and Land Surface Temperature for Drought Assessment: Merits and Limitations[J]. Journal of Climate, 2010, 23(3): 618-633 doi: 10.1175/2009JCLI2900.1
[32]	Zhang A Z, Jia G S. Monitoring Meteorological Drought in Semiarid Regions Using Multi-Sensor Microwave Remote Sensing Data[J]. Remote Sensing of Environment, 2013, 134: 12-23 doi: 10.1016/j.rse.2013.02.023
[33]	李斌, 李丽娟, 李海滨, 等. 澜沧江流域干旱变化的时空特征[J]. 农业工程学报, 2011, 27(5): 87-92 doi: 10.3969/j.issn.1002-6819.2011.05.014 Li Bin, Li Lijuan, Li Haibin, et al. Spatial and Temporal Variability of Droughts in the Lancang River Basin[J]. Transactions of the Chinese Society of Agricultural Engineering, 2011, 27(5): 87-92 doi: 10.3969/j.issn.1002-6819.2011.05.014
[34]	王先伟, 刘梅, 柳林. MODIS光谱指数在中国西南干旱监测中的应用[J]. 遥感学报, 2014, 18(2): 432-452 https://www.cnki.com.cn/Article/CJFDTOTAL-YGXB201402013.htm Wang Xianwei, Liu Mei, Liu Lin. Responses of MODIS Spectral Indices to Typical Drought Events from 2000 to 2012 in Southwest China[J]. Journal of Remote Sensing, 2014, 18(2): 432-452 https://www.cnki.com.cn/Article/CJFDTOTAL-YGXB201402013.htm
[35]	Rhee J, Im J, Carbone G J. Monitoring Agricultural Drought for Arid and Humid Regions Using Multi-Sensor Remote Sensing Data[J]. Remote Sensing of Environment, 2010, 114(12): 2875-2887 doi: 10.1016/j.rse.2010.07.005
[36]	Dai A. Increasing Drought Under Global Warming in Observations and Models[J]. Nature Climate Change, 2013, 3(1): 52-58 doi: 10.1038/nclimate1633
[37]	Wang A H, Kong X H. Regional Climate Model Simulation of Soil Moisture and Its Application in Drought Reconstruction Across China from 1911 to 2010[J]. International Journal of Climatology, 2021, DOI: 10.1002/joc.6748
[38]	Liang L, Zhao S H, Qin Z H, et al. Drought Change Trend Using MODIS TVDI and Its Relationship with Climate Factors in China from 2001 to 2010[J]. Journal of Integrative Agriculture, 2014, 13(7): 1501-1508 doi: 10.1016/S2095-3119(14)60813-3
[39]	Zeng Z Q, Wu W X, Li Y M, et al. Spatiotemporal Variations in Drought and Wetness from 1965 to 2017 in China[J]. Water, 2020, 12(8): 2097 doi: 10.3390/w12082097

Cited By

Get Citation

PDF

XML

Article views (1882) PDF downloads (265)

Remote Sensing Monitoring of Agricultural Drought and Vegetation Sensitivity Analysis in the Middle and Lower Reaches of the Yangtze River from 2001 to 2019

Abstract

References

Catalog

Related

Export File

Citation

Format

Content