Skip to main content

Advertisement

SpringerLink
Log in

Assessing agricultural drought in summer over Oklahoma Mesonet sites using the water-related vegetation index from MODIS

  • Original Paper
  • Published:
International Journal of Biometeorology Aims and scope Submit manuscript

Abstract

Agricultural drought, a common phenomenon in most parts of the world, is one of the most challenging natural hazards to monitor effectively. Land surface water index (LSWI), calculated as a normalized ratio between near infrared (NIR) and short-wave infrared (SWIR), is sensitive to vegetation and soil water content. This study examined the potential of a LSWI-based, drought-monitoring algorithm to assess summer drought over 113 Oklahoma Mesonet stations comprising various land cover and soil types in Oklahoma. Drought duration in a year was determined by the number of days with LSWI <0 (DNLSWI) during summer months (June–August). Summer rainfall anomalies and LSWI anomalies followed a similar seasonal dynamics and showed strong correlations (r 2 = 0.62–0.73) during drought years (2001, 2006, 2011, and 2012). The DNLSWI tracked the east-west gradient of summer rainfall in Oklahoma. Drought intensity increased with increasing duration of DNLSWI, and the intensity increased rapidly when DNLSWI was more than 48 days. The comparison between LSWI and the US Drought Monitor (USDM) showed a strong linear negative relationship; i.e., higher drought intensity tends to have lower LSWI values and vice versa. However, the agreement between LSWI-based algorithm and USDM indicators varied substantially from 32 % (D 2 class, moderate drought) to 77 % (0 and D 0 class, no drought) for different drought intensity classes and varied from ∼30 % (western Oklahoma) to >80 % (eastern Oklahoma) across regions. Our results illustrated that drought intensity thresholds can be established by counting DNLSWI (in days) and used as a simple complementary tool in several drought applications for semi-arid and semi-humid regions of Oklahoma. However, larger discrepancies between USDM and the LSWI-based algorithm in arid regions of western Oklahoma suggest the requirement of further adjustment in the algorithm for its application in arid regions.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12
Fig. 13

Similar content being viewed by others

References

  • Arndt DS, Basara JB, McPherson RA, Illston BG, McManus GD, Demko DB (2009) Observations of the overland reintensification of tropical storm Erin (2007). Bull Am Meteorol Soc 90:1079–1093

    Article  Google Scholar 

  • Bajgain R, Xiao X, Wagle P, Basara J, Zhou Y (2015) Sensitivity analysis of vegetation indices to drought over two tallgrass prairie sites. ISPRS J Photogramm Remote Sens 108:151–160

    Article  Google Scholar 

  • Basara JB, Maybourn JN, Peirano CM, Tate JE, Brown PJ, Hoey JD, Smith BR (2013) Drought and associated impacts in the Great Plains of the United States—a review. Int J Geosci 4:72

    Article  Google Scholar 

  • Brock FV, Crawford KC, Elliott RL, Cuperus GW, Stadler SJ, Johnson HL, Eilts MD (1995) The Oklahoma Mesonet: a technical overview. J Atmos Ocean Technol 12:5–19

    Article  Google Scholar 

  • Ceccato P, Flasse S, Gregoire J-M (2002) Designing a spectral index to estimate vegetation water content from remote sensing data: part 2. Validation and applications. Remote Sens Environ 82:198–207

    Article  Google Scholar 

  • Ceccato P, Flasse S, Tarantola S, Jacquemoud S, Grégoire J-M (2001) Detecting vegetation leaf water content using reflectance in the optical domain. Remote Sens Environ 77:22–33

    Article  Google Scholar 

  • Chandrasekara K, Saia MS, Beheraa G (2011) Assessment of early season agricultural drought through land surface water index (LSWI) and soil water balance model. ISPRS-International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences 3820:50–55

    Google Scholar 

  • Christian J, Christian K, Basara JB (2015) Drought and pluvial dipole events within the great plains of the United States. J Appl Meteorol Climatol 54:1886–1898

    Article  Google Scholar 

  • Dong X et al. (2011) Investigation of the 2006 drought and 2007 flood extremes at the Southern Great Plains through an integrative analysis of observations. J Geophys Res Atmos 116(D3). doi:10.1029/2010JD014776

  • Gamon JA et al. (1995) Relationships between NDVI, canopy structure, and photosynthesis in three Californian vegetation types. Ecol Appl:28–41

  • Gamon JA, Field CB, Roberts DA, Ustin SL, Valentini R (1993) Functional patterns in an annual grassland during an AVIRIS overflight. Remote Sens Environ 44:239–253

    Article  Google Scholar 

  • Hoerling M et al. (2013) Anatomy of an extreme event. J Clim 26:2811–2832

    Article  Google Scholar 

  • Hulse JH, Escott VJ (1986) Drought–inevitable and unpredictable the pattern and consequences of recurrent drought interdisciplinary. Science Reviews 11:346–358

    Google Scholar 

  • Hunt ER, Rock BN, Nobel PS (1987) Measurement of leaf relative water content by infrared reflectance. Remote Sens Environ 22:429–435

    Article  Google Scholar 

  • Jackson R, Slater P, Pinter P (1983) Discrimination of growth and water stress in wheat by various vegetation indices through clear and turbid atmospheres. Remote Sens Environ 13:187–208

    Article  Google Scholar 

  • Jensen JR (2009) Remote sensing of the environment: an Earth resource perspective. Pearson Education, New Delhi

    Google Scholar 

  • Kogan F (2002) World droughts in the new millennium from AVHRR-based vegetation health indices. Eos Transactions American Geophysical Union 83:557–563

    Article  Google Scholar 

  • Kogan F, Stark R, Gitelson A, Jargalsaikhan L, Dugrajav C, Tsooj S (2004) Derivation of pasture biomass in Mongolia from AVHRR-based vegetation health indices international. Journal of Remote Sensing 25:2889–2896

    Article  Google Scholar 

  • Lillesand T, Kiefer RW, Chipman J (2014) Remote sensing and image interpretation. Wiley, New York

    Google Scholar 

  • Liu L, Hong Y, Bednarczyk CN, Yong B, Shafer MA, Riley R, Hocker JE (2012) Hydro-climatological drought analyses and projections using meteorological and hydrological drought indices: a case study in Blue River basin. Oklahoma Water Resources Management 26:2761–2779

    Article  Google Scholar 

  • Maki M, Ishiahra M, Tamura M (2004) Estimation of leaf water status to monitor the risk of forest fires by using remotely sensed data. Remote Sens Environ 90:441–450

    Article  Google Scholar 

  • Mallya G, Zhao L, Song X, Niyogi D, Govindaraju R (2013) 2012 Midwest drought in the United States. J Hydrol Eng 18:737–745

    Article  Google Scholar 

  • Mizzell HP, Lakshmi V (2003) Integration of science and policy during the evolution of South Carolina’s drought program water: science, policy, and management: challenges and opportunities 311–339

  • Nicholson SE (1989) Long-term changes in African rainfall. Weather 44:46–56

    Article  Google Scholar 

  • Nicholson SE (2000) The nature of rainfall variability over Africa on time scales of decades to millenia. Glob Planet Chang 26:137–158

    Article  Google Scholar 

  • Otkin JA, Shafer M, Svoboda M, Wardlow B, Anderson MC, Hain C, Basara J (2015) Facilitating the use of drought early warning information through interactions with agricultural stakeholders. Bull Am Meteorol Soc 96:1073–1078

    Article  Google Scholar 

  • Shahid S, Behrawan H (2008) Drought risk assessment in the western part of Bangladesh. Nat Hazards 46:391–413

    Article  Google Scholar 

  • Sönmez FK, Koemuescue AU, Erkan A, Turgu E (2005) An analysis of spatial and temporal dimension of drought vulnerability in Turkey using the standardized precipitation index. Nat Hazards 35:243–264

    Article  Google Scholar 

  • Svoboda M et al. (2002) The drought monitor. Bull Am Meteorol Soc 83:1181–1190

    Article  Google Scholar 

  • Tadesse T, Wardlow BD, Brown JF, Svoboda MD, Hayes MJ, Fuchs B, Gutzmer D (2015) Assessing the vegetation condition impacts of the 2011 drought across the US southern Great Plains using the vegetation drought response index (VegDRI). J Appl Meteorol Climatol 54:153–169

    Article  Google Scholar 

  • Tian Y, Zhou L, Romanov P, Yu B, Ek M 2013 Comparison of Amazon and central Africa tropical vegetation dynamics using SEVIRI data from 2009 to 2011. In: EGU General Assembly Conference Abstracts. p 6535

  • United States Department of agriculture farm service agency (USDA-FSA) (2015) Noninsured crop cisaster assistance program (NAP). http://www.fsa.usda.gov/Internet/FSA_File/ccc0471_nap_bp_140813v01.pdf. Acessed July 2016

  • Wagle P et al. (2014) Sensitivity of vegetation indices and gross primary production of tallgrass prairie to severe drought. Remote Sens Environ 152:1–14

    Article  Google Scholar 

  • Zhang X, Nearing M (2005) Impact of climate change on soil erosion, runoff, and wheat productivity in central Oklahoma. Catena 61:185–195

    Article  Google Scholar 

Download references

Acknowledgments

This study was supported in part by a research grant (Project No. 2012-02355) through the USDA National Institute for Food and Agriculture (NIFA)’s Agriculture and Food Research Initiative (AFRI), Regional Approached for Adaptation and Mitigation of Climate Variability and Change grant (IIA-1301789), NOAA Climate Office’s Sectoral Applications Research Program (SRP) grant NA130AR4310122, and Oklahoma’s taxpayers fund for the Oklahoma Mesonet through the Oklahoma State Regents for Higher Education and the Oklahoma Department of Public Safety. We would also like to acknowledge the National Drought Mitigation Center at the University of Nebraska-Lincoln, the US Department of Agriculture, and the National Oceanic and Atmospheric Administration for the dataset.

Author information

Authors and Affiliations

  1. Department of Microbiology and Plant Biology, Center for Spatial Analysis, University of Oklahoma, 101 David L. Boren Blvd, Norman, OK, 73019, USA

    Rajen Bajgain, Xiangming Xiao, Pradeep Wagle, Yuting Zhou & Yao Zhang

  2. Ministry of Education Key Laboratory for Biodiversity Science, and Engineering, Institute of Biodiversity of Sciences, Fudan University, Shanghai, 200433, China

    Xiangming Xiao

  3. School of Meteorology, University of Oklahoma, Norman, OK, USA

    Jeffrey Basara & Hayden Mahan

  4. Oklahoma Climate Survey, Norman, OK, USA

    Jeffrey Basara

Authors
  1. Rajen Bajgain
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Xiangming Xiao
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Jeffrey Basara
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Pradeep Wagle
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Yuting Zhou
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Yao Zhang
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. Hayden Mahan
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Xiangming Xiao.

Electronic supplementary material

Table S1

(DOCX 42 kb)

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Bajgain, R., Xiao, X., Basara, J. et al. Assessing agricultural drought in summer over Oklahoma Mesonet sites using the water-related vegetation index from MODIS. Int J Biometeorol 61, 377–390 (2017). https://doi.org/10.1007/s00484-016-1218-8

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00484-016-1218-8

Keywords

Search

Navigation