Abstract
Many indices are used to monitor drought events. However, different indices have different data requirements and applications. Hence, evaluating their applicability will help to characterize drought events and refine the development of effective drought indices. We constructed different drought indices based on multisource remote sensing data and comprehensively evaluated and compared their applicability for drought monitoring throughout China. The characteristics of drought events in 2009 and 2011 were compared using various drought indices. The different time scales of the Palmer Drought Severity Index (PDSI) and the Standardized Precipitation Index (SPI) were used to evaluate remote sensing drought indices in different regions. Single drought indices, including the Vegetation Condition Index (VCI), the Temperature Condition Index (TCI) derived from Moderate Resolution Imaging Spectroradiometer (MODIS) data, the Precipitation Condition Index (PCI) derived from Tropical Rainfall Measurement Mission (TRMM) data, and the TCI and Soil Moisture Condition Index (SMCI) derived from Advanced Microwave Scanning Radiometer for the Earth Observing System (AMSR-E) data, as well as combined drought indices, including the Microwave Integrated Drought Index (MIDI), Optimized Vegetation Drought Index (OVDI), Optimized Meteorological Drought Index (OMDI), Scale Drought Conditions Index (SDCI), and Synthesized Drought Index (SDI), were analyzed and compared to evaluate their applicability. The results showed that different drought indices have specific characteristics under different land use types in China. The VCI and TCI can better monitor long-term drought conditions, but they have a weak correlation with the in situ drought index in forestland and grassland areas. The correlation of SPI-1 with the PCI is higher than that with other single indices, which indicates that the PCI is a good short-term drought index. The SMCI has a better correlation with the short-term in situ drought index, but it is not conducive to drought monitoring in areas such as densely forested land and grassland. The correlations of the in situ drought index with the combined drought indices (the MIDI, OVDI, OMDI, SDCI, and SDI) are better than those with the single drought indices.
Similar content being viewed by others
Data availability
Not applicable.
References
Badgley G, Field CB, Berry JA (2017) Canopy near-infrared reflectance and terrestrial photosynthesis. Sci Adv 3(3):e1602244
Badgley G, Field CB, Berry JA (2019) Terrestrial gross primary production: using NIRV to scale from site to globe. Glob Chang Biol 25(11):3731–3740
Bayarjargal Y, Karnieli A, Bayasgalan M, Khudulmur S, Gandush C, Tucker C (2006) A comparative study of NOAA–AVHRR derived drought indices using change vector analysis. Remote Sens Environ 105:9–22
Bhuiyan C, Singh RP, Kogan FN (2006) Monitoring drought dynamics in the Aravalli region (India) using different indices based on ground and remote sensing data. Int J Appl Earth Obs Geoinf 8(4):289–302
Bindlish R, Jackson TJ, Wood E, Gao HL, Starks P, Bosch D, Lakshmi V (2003) Soil moisture estimates from TRMM microwave imager observations over the southern United States. Remote Sens Environ 85(4):507–515
Brown JF, Wardlow BD, Tadesse T, Hayes MJ, Reed BC (2008) The vegetation drought response index (VegDRI): a new integrated approach for monitoring drought stress in vegetation. IEEE Geosci Remote Sens Lett 45:16–46
Buckley TN (2019) How do stomata respond to water status? New Phytol 224:21–36
Caccamo G, Chisholm LA, Bradstock RA, Puotinen ML (2011) Assessing the sensitivity of MODIS to monitor drought in high biomass ecosystems. Remote Sens Environ 115:2626–2639
De Jeu RAM, Wagner W, Holmes TRH, Dolman AJ, Van de Giesen NC, Riesen J (2008) Global soil moisture patterns observed by space borne microwave radiometers and scatterometers. Surv Geophys 29:399–420
Deng JS, Wang K, Deng YH, Qi GJ (2008) PCA-based land-use change detection and analysis using multitemporal and multisensor satellite data. Int J Remote Sens 29(16):4823–4838
Dracup JA, Lee KS, Paulson EG (1980) On the statistical characteristics of drought events. Water Resour Res 16:289–296
Draper CS, Walker JP, Steinle PJ, De Jeu RAM, Holmes RHT (2009) An evaluation of AMSR-E derived soil moisture over Australia. Remote Sens Environ 113:703–710
Du L, Tian Q, Yu T, Meng Q, Jancso T, Udvardy P, Huang Y (2013) A comprehensive drought monitoring method integrating MODIS and TRMM data. Int J Appl Earth Obs Geoinf 23:245–253
Dubovyk O, Ghazaryan G, González J, Graw V, Löw F, Schreier J (2019) Drought hazard in Kazakhstan in 2000-2016: a remote sensing perspective. Environ Monit Assess 191:510
Feng L, Li T, Yu W (2014) Cause of severe droughts in Southwest China during 1951–2010. Clim Dyn 43:2033–2042
Frankenberg C, Fisher JB, Worden J et al (2011) New global observations of the terrestrial carbon cycle from GOSAT: patterns of plant fluorescence with gross primary productivity. Geophys Res Lett 38:L17706
Gitelson AA, Kogan F, Zakarin E, Spivak L, Lebed L (1998) Using AVHRR data for quantitive estimation of vegetation conditions: calibration and validation. Adv Space Res 22(5):673–676
Guanter L, Alonso L, GómezChova L, AmorósLópez J, Vila J, Moreno J (2007) Estimation of solar induced vegetation fluorescence from space measurements. Geophys Res Lett 34:L08401
Guanter L, Frankenberg C, Dudhia A, Lewis PE, Gómez‐Dans J, Kuze A, et al (2012) Retrieval and global assessment of terrestrial chlorophyll fluorescence from GOSAT space measurements. Remote Sens of Environ 121:236–251
Guanter L, Zhang Y, Jung M, Joiner J, Voigt M, Berry JA, Frankenberg C, Huete AR, Zarco-Tejada P, Lee JE, Moran MS, Ponce-Campos G, Beer C, Camps-Valls G, Buchmann N, Gianelle D, Klumpp K, Cescatti A, Baker JM, Griffis TJ (2014) Global and time-resolved monitoring of crop photosynthesis with chlorophyll fluorescence. Proc Natl Acad Sci 111(14):E1327–E1333
Guttman NB (1998) Comparing the Palmer drought index and the standardized precipitation index. J Am Water Resour As 34:113–121
Guttman NB (1999) Accepting the standardized precipitation index: a calculation algorithm. J Am Water Resour As 35:311–322
Hagman G (1984) Prevention better than cure: report on human and natural disasters in the third world. Swedish Red Cross, Stockholm
Halwatura D, McIntyre N, Lechner AM, Arnold S (2017) Capability of meteorological drought indices for detecting soil moisture droughts. J Hydrol Reg Stud 12:396–412
Hao Z, Singh VP (2015) Drought characterization from a multivariate perspective: a review. J Hydrol 527:668–678
Hao C, Zhang J, Yao F (2015) Combination of multi-sensor remote sensing data for drought monitoring over Southwest China. Int J Appl Earth Obs Geoinf 35(Part B):270–283
Harris I, Jones PD, Osborn TJ (2014) Updated high-resolution grids of monthly climatic observations-the CRU TS3.10 dataset. Int J Climatol 34(3):623–642
Hayes MJ, Svoboda MD, Wilhite DA, Vanyarkho OV (1999) Monitoring the 1996 drought using the standardized precipitation index. Bull Am Meteorol Soc 80(80):429–438
He Y, Lee E, Warner TA (2017) A time series of annual land use and land cover maps of China from 1982 to 2013 generated using AVHRR GIMMS NDVI3g data. Remote Sens Environ 199:201–217
Huang RH, Cai RS, Chen JL, Zhou LT (2006) Interdecadal variations of drought and flooding disasters in China and their association with the East Asian Climate System. Chin J Atmos Sci 30:730–743 (in Chinese)
Iguchi T, Kozu T, Meneghini R, Awaka J, Okamoto K (2000) Rain-profiling algorithm for the TRMM precipitation radar. J Appl Meteorol 39:2038–2052
Jackson TJ, Schmugge TJ (1991) Vegetation effects on the microwave emission of soils. Remote Sens Environ 36:203–212
Jiao W, Tian C, Chang Q, Novick KA, Wang L (2019a) A new multi-sensor integrated index for drought monitoring. Agric For Meteorol 268:74–85
Jiao W, Wang L, Novick KA, Chang Q (2019b) A new station-enabled multi-sensor integrated index for drought monitoring. J Hydrol 574:169–180
Jiao W, Chang Q, Wang L (2019c) The sensitivity of satellite solar-induced chlorophyll fluorescence to meteorological drought. Earth’s Future 7:558–573
Karnieli A, Agam N, Pinker RT, Anderson M, Imhoff ML, Gutman GG, Panov N, Goldberg A (2010) Use of NDVI and land surface temperature for drought assessment: merits and limitations. J Clim 23:618–633
Kawanishi T, Sezai T, Ito Y, Imaoka K, Takeshima T, Ishido Y, Shibata A, Miura M, Inahata H, Spencer RW (2003) The advanced microwave scanning radiometer for the earth observing system (AMSR-E), NASDA's contribution to the EOS for global energy and water cycle studies. IEEE Trans Geosci Remote Sens 41(2):184–194
Keyantash JA, Dracup JA (2004) An aggregate drought index: assessing drought severity based on fluctuations in the hydrologic cycle and surface water storage. Water Resour Res 40(9)
Kimball BA (2016) Crop responses to elevated CO2and interactions with H2O, N, and temperature. Curr Opin Plant Biol 31:36–43
Kogan FN (1995) Droughts of the late 1980s in the United States as derived from NOAA polar-orbiting satellite data. Bull Am Meteorol Soc 76:655–668
Kogan FN (1997) Global drought watch from space. Bull Am Meteorol Soc 78(4):621–636
Liu Q, Zhang S, Zhang HR, Bai Y, Zhang JH (2020) Monitoring drought using composite drought indices based on remote sensing. Sci Total Environ 711:134585
Manderscheid R, Erbs M, Burkart S, Wittich K-P, Lopmeier F-J, Weigel H-J (2016) Effects of free-air carbon dioxide enrichment on sap flow and canopy microclimate of maize grown under different water supply. J Agron Crop Sci 202:255–268
McKee TB, Doesken NJ, Kleist J (1993) The relationship of drought frequency and duration to time scales. In: 8th Conference on Applied Climatology, Anaheim, pp 179–184
Meroni M, Rossini M, Guanter L, Alonso L, Rascher U, Colombo R, Moreno J (2009) Remote sensing of solar-induced chlorophyll fluorescence: review of methods and applications. Remote Sens Environ 113:2037–2051
Ministry of Natural Resources of the People’s Republic of China. Current Land Use Classification (GB/T 21010–2011) [EB/OL]. (2011-07-01)
Mitchell SW, Remmel TK, Csillag F, Wulder MA (2008) Distance to second cluster as a measure of classification confidence. Remote Sens Environ 112:2615–2626
National Bureau of Statistics of China (NBSC) (2009) China Energy Statistical Yearbook. China Statistics Press, Beijing
National Bureau of Statistics of China (NBSC) (2011) China Energy Statistical Yearbook. China Statistics Press, Beijing
Njoku E, Jackson TJ, Lakshmi V, Chane T, Nghiem S (2003) Soil moisture retrieval from AMSR-E. IEEE T Geosci Remote 41:215–229
Orville H (1990) AMS statement on meteorological drought. Amer Meteorological Soc, Boston, pp 02108–03693
Owe M, Jeu RD, Holmes T (2008) Multisensor historical climatology of satellite-derived global land surface moisture. J Geophys Res Earth Surf 113(F1):196–199
Palmer WC (1965) Meteorological drought: US Department of Commerce. US Department of Commerce, Weather Bureau, Washington, DC
Parinussa RM, De Jeu RAM, Holmes TRH, Walker JP (2008) Comparison of microwave and infrared land surface temperature products over the NAFE'06 research sites. IEEE Geosci Remote Sens Lett 5:783–787
Park S, Im J, Park S, Rhee J (2017) Drought monitoring using high resolution soil moisture through multi–sensor satellite data fusion over the Korean peninsula. Agric For Meteorol 237–238:257–269
Parkinson CL (2003) Aqua: an earth-observing satellite mission to examine water and other climate variables. IEEE Trans Geosci Remote Sens 41(2):173–183
Piao S, Ciais P, Huang Y, Shen Z, Peng S, Li JS, Zhou LP, Liu HY, Ma YC, Ding YH, Friedlingstein P, Liu CZ, Tan K, Yu YQ, Zhang TY, Fang JY (2010) The impacts of climate change on water resources and agriculture in China. Nature 467(7311):43–51
Pouya A, Hadigheh B, Ozgur K (2020) Comparison of three different bio-inspired algorithms to improve ability of neuro fuzzy approach in prediction of agricultural drought, based on three different indexes. Comput Electron Agric 170:105279
Powell MJD (1978) A fast algorithm for nonlinearly constrained optimization calculations. In: Numerical analysis. Springer, Berlin, Heidelberg, pp 144–157
Powell MJD (1983) Variable metric methods for constrained optimization mathematical programming the state of the art. Springer, pp 288–311
Quiring SM, Ganesh S (2010) Evaluating the utility of the vegetation condition index (VCI) for monitoring meteorological drought in Texas. Agric For Meteorol 150:330–339
Rhee J, Im J, Carbone GJ (2010) Monitoring agricultural drought for arid and humid regions using multi-sensor remote sensing data. Remote Sens Environ 114:2875–2887
Seiler R, Kogan F, Sullivan J (1998) AVHRR-based vegetation and temperature condition indices for drought detection in Argentina. Adv Space Res 21(3):481–484
Shahabfar A, Ghulam A, Eitzinger J (2012) Drought monitoring in Iran using the perpendicular drought indices. Int J Appl Earth Obs Geoinf 18:119–127
Shen ZX, Zhang Q, Singh VP, Sun P, Song CQ, Yu HQ (2019) Agricultural drought monitoring across Inner Mongolia, China: model development, spatiotemporal patterns and impacts. J Hydrol 571:793–804
Singh RP, Roy S, Kogan F (2003) Vegetation and temperature condition indices from NOAA AVHRR data for drought monitoring over India. Int J Remote Sens 24:4393–4402
Song LS, Li Y, Ren YH, Wu XC, Guo B, Tang XG, Shi W, Ma M, Han X, Zhao L (2019) Divergent vegetation responses to extreme spring and summer droughts in southwestern China. Agric For Meteorol 279:107703
Sun Y, Fu R, Dickinson R, Joiner J, Frankenberg C, Gu LH, Xia YL, Fernando N (2015) Drought onset mechanisms revealed by satellite solar-induced chlorophyll fluorescence: insights from two contrasting extreme events. J Geophys Res-Biogeo 120:2427–2440
Trenberth KE, Dai A, Rasmussen RM, Parsons DB (2003) The changing character of precipitation. B Am Meteorol Soc 84:1205–1217
Um MJ, Kim Y, Park D (2018) Evaluation and modification of the drought severity index (DSI) in East Asia. Remote Sens Environ 209:66–76
van Dijk AIJM, Beck HE, Crosbie RS, de Jeu RAM, Liu YY, Podger GM, Timbal B, Viney NR (2013) The millennium drought in Southeast Australia (2001–2009): natural and human causes and implications for water resources, ecosystems, economy, and society. Water Resour Res 49:1040–1057
Vicente-Serrano SM, Begueria S, Lopez-Moreno JI (2010) A multiscalar drought index sensitive to global warming: the standardized precipitation evapotranspiration index. J Clim 23:1696–1718
Wang A, Lettenmaier DP, Sheffield J (2011) Soil moisture drought in China, 1950–2006. J Clim 24:3257–3271
Wang L, D'Odorico P, Evans J, Eldridge D, McCabe M, Caylor K, King E (2012) Dryland ecohydrology and climate change: critical issues and technical advances. Hydrol Earth Syst Sci 16:2585–2603
Wang SH, Huang CP, Zhang LF, Lin Y, Cen Y, Wu T (2016) Monitoring and assessing the 2012 drought in the great plains: analyzing satellite-retrieved solar-induced chlorophyll fluorescence, drought indices, and gross primary production. Remote Sens 8(2):61
Wei W, Pang SF, Wang XF, Zhou L, Xie BB, Zhou JJ, Li CH (2020) Temperature vegetation precipitation dryness index (TVPDI)-based dryness-wetness monitoring in China. Remote Sens Environ 248:111957. https://doi.org/10.1016/j.rse.2020.111957
Wells N, Goddard S, Hayes M J (2004) A Self-Calibrating Palmer drought severity index. J Clim 17:2335–2351
Wilhelmi OV, Wilhite DA (2002) Assessing vulnerability to agricultural drought: a Nebraska case study. Nat Hazards 25(1):37–58
Wu J, Zhou L, Liu M, Zhang J, Leng S, Diao C (2013) Establishing and assessing the integrated surface drought index (ISDI) for agricultural drought monitoring in mid-eastern China. Int J Appl Earth Obs Geoinf 23:397–410
Yuan X, Ma ZG, Pan M, Shi CX (2015) Microwave remote sensing of short–term droughts during crop growing seasons. Geophys Res Lett 42(11):4394–4401
Zhang AZ, Jia GS (2013) Monitoring meteorological drought in semiarid regions using multi-sensor microwave remote sensing data. Remote Sens Environ 134:12–23
Zhang X, Chen N, Li J, Chen Z, Niyogi D (2017) Multi-sensor integrated framework and index for agricultural drought monitoring. Environ Remote Sens 188:141–163
Zhou L, Zhang J, Wu J, Zhao L, Liu M, Lü A, Wu Z (2012) Comparison of remotely sensed and meteorological data-derived drought indices in mid-eastern China. Int J Remote Sens 33:1755–1779
Acknowledgments
We gratefully acknowledge the support from the National Natural Science Foundation of China (grant numbers 41861040 and 41761047) and Natural Science Foundation of Gansu Province (grant number 1506RJZA129).
Funding
This study was supported in part by grants from the National Natural Science Foundation of China (grant numbers 41861040 and 41761047) and Natural Science Foundation of Gansu Province (grant number 1506RJZA129).
Author information
Authors and Affiliations
Contributions
Wei Wei and Jing Zhang participated in the design of this study, and they both performed the statistical analysis. Wei Wei carried out the study and collected important background information. Jing Zhang drafted the manuscript. All authors read and approved the final manuscript. Liang Zhou and Binbin Xie carried out the concepts, definition of intellectual content, literature search, data acquisition, data analysis, and manuscript preparation. Junju Zhou and Chuanhua Li provided assistance for data acquisition, data analysis, and statistical analysis. Liang Zhou and Binbin Xie carried out data acquisition and manuscript editing. Liang Zhou performed manuscript review. All authors have read and approved the content of the manuscript.
Corresponding author
Ethics declarations
Competing interests
The authors declare that they have no competing interests.
Ethics approval and consent to participate
Not applicable.
Consent for publication
Not applicable.
Additional information
Responsible Editor: Philippe Garrigues
Publisher’s note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Rights and permissions
About this article
Cite this article
Wei, W., Zhang, J., Zhou, L. et al. Comparative evaluation of drought indices for monitoring drought based on remote sensing data. Environ Sci Pollut Res 28, 20408–20425 (2021). https://doi.org/10.1007/s11356-020-12120-0
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s11356-020-12120-0