Skip to main content

Advertisement

SpringerLink
Log in

Estimation of Crop Water Requirement Based on Planting Structure Extraction from Multi-Temporal MODIS EVI

  • Published:
Water Resources Management Aims and scope Submit manuscript

Abstract

Estimation of crop water requirement (CWR) is key to the rational water use and agricultural water conservation in arid regions. Using remote sensing data to obtain long-term CWR over large areas helps water resources management in water-scarce areas. This study, taking the Kaidu-Kongqi River basin in arid northwest China as the study area, investigated the feasibility of synergistically using phenological characteristics, Savitzky-Golay filter, harmonic analysis and decision tree to extract crop planting structures (CPS) from MODIS EVI, and meanwhile analyzed the spatiotemporal variation in the estimated CWR. The results show that the integrated method for CPS identification and extraction is feasible and reliable with the classification accuracy over 80%. The mid-season stage requires the most water and cash crops need more water than cereal crops. Summer accounts for 69% the total growing season water use. The significant increase in the area of high water demand crops such as cotton raised the total CWR of the basin surging from 14.91× 108m3 in 2000 to 34.92×108m3 in 2017. The spatial distribution of CWR was more related to crop types and area than to climatic conditions. Controlling the expansion of arable land and optimizing the agricultural planting structure remain important tasks for the sustainable management of water resources in the basin.

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

Similar content being viewed by others

References

  • Allen RG, Pereira LS, Raes D, Smith M (1998) Crop evapotranspiration: guidelines for computing crop water requirements. FAO Irrigation and drainage paper 56. FAO, Rome

    Google Scholar 

  • Bansouleh BF, Karimi AR, Hesadi H (2015) Evaluation of SEBAL and SEBS algorithms in the estimation of maize evapotranspiration. Int J Plant Soil Sci 6:350–358. https://doi.org/10.9734/IJPSS/2015/15711

    Article  Google Scholar 

  • Bargiel D (2017) A new method for crop classification combining time series of radar images and crop phenology information. Remote Sens Environ 198:369–383. https://doi.org/10.1016/j.rse.2017.06.022

    Article  Google Scholar 

  • Bastiaanssen WGM, Menenti M, Feddes RA, Holtslag AAM (1998) A remote sensing surface energy balance algorithm for land (SEBAL): 1. Formulation. J Hydrol 212–213:198–212a. https://doi.org/10.1016/S0022-1694(98)00253-4

    Article  Google Scholar 

  • Cao R, Chen Y, Shen M, Chen J, Zhou J, Wang C, Yang W (2018) A simple method to improve the quality of NDVI time - series data by integrating spatiotemporal information with the Savitzky - Golay filter. Remote Sens Environ 217:244–257. https://doi.org/10.1016/j.rse.2018.08.022

    Article  Google Scholar 

  • Chen J, Jönsson P, Tamura M, Gu Z, Matsushita B, Eklundh L (2004) A simple method for reconstructing a high-quality NDVI time-series data set based on the Savitzky–Golay filter. Remote Sens Environ 91:332–344. https://doi.org/10.1016/j.rse.2004.03.014

    Article  Google Scholar 

  • Conrad C, Colditz RR, Dech S, Klein D, Vlek PLG (2011) Temporal segmentation of MODIS time series for improving crop classification in Central Asian irrigation systems. Int J Remote Sens 32:8763–8778. https://doi.org/10.1080/01431161.2010.550647

    Article  Google Scholar 

  • Dalezios NR, Dercas N, Spyropoulos NV, Psomiadis E (2019) Remotely sensed methodologies for crop water availability and requirements in precision farming of vulnerable agriculture. Water Resour Manag 33:1499–1519. https://doi.org/10.1007/s11269-018-2161-8

    Article  Google Scholar 

  • Doorenbos J, Pruitt WO (1977) Guidelines for predicting crop water requirements. Irrigation and Drainage Paper 24. FAO, Rome

    Google Scholar 

  • Duan A, Sun J, Liu Y, Xiao J, Liu Q, Qi X (2004) Irrigation quota of major crops for Northern China. China Agricultural Science and Technology Press, Beijing

    Google Scholar 

  • Durdiev K, Chen X, Huang Y, Ilkhom M, Liu T, Friday O, Abdullaev F, Gafforov K, Omurakunova G (2021) Investigation of crop evapotranspiration and irrigation water requirement in the lower Amu Darya River Basin, Central Asia. J Arid Land 13:23–39. https://doi.org/10.1007/s40333-021-0054-9

    Article  Google Scholar 

  • Feyisa GL, Palao LK, Nelson A, Gumma MK, Paliwal A, Win KT, Nge KH, Johnson DE (2020) Characterizing and mapping cropping patterns in a complex agro-ecosystem: an interactive participatory mapping procedure using machine learning algorithms and MODIS vegetation indices. Comput Electron Agric 175:105595. https://doi.org/10.1016/j.compag.2020.105595

    Article  Google Scholar 

  • Gong X, Wang S, Xu C, Zhang H, Ge J (2020) Evaluation of several reference evapotranspiration models and determination of crop water requirement for tomato in a solar greenhouse. Am Soc Horticult Sci 55(2):244–250. https://doi.org/10.21273/HORTSCI14514-19

    Article  Google Scholar 

  • Gontia NK, Tiwari KN (2010) Estimation of crop coefficient and evapotranspiration of wheat (Triticum aestivum) in an irrigation command using remote sensing and GIS. Water Resour Manag 24:1399–1414. https://doi.org/10.1007/s11269-009-9505-3

    Article  Google Scholar 

  • Hagemann S, Chen C, Clark DB, Folwell S, Gosling SN, Haddeland I, Hanasaki N, Heinke J, Ludwig F, Voss F, Wiltshire AJ (2013) Climate change impact on available water resources obtained using multiple global climate and hydrology models. Earth Syst Dynam 4:129–144. https://doi.org/10.5194/esd-4-129-2013

    Article  Google Scholar 

  • Hao P, Zhan Y, Wang L, Niu Z, Shakir M (2015) Feature selection of time series MODIS data for early crop classification using random forest: a case study in Kansas, USA. Remote Sens 7(5):5347–5369. https://doi.org/10.3390/rs70505347

    Article  Google Scholar 

  • Hutchinson MF, Xu T (2013) ANUSPLIN version 4.4 user guide. The Australia National University, Fenner Schoolof Environment and Society, Canberra

    Google Scholar 

  • Jakubauskas ME, Legates DR, Kastens JH (2001) Harmonic analysis of time-series AVHRR NDVI data. Photogramm Eng Remote Sens. 67:461–470

    Google Scholar 

  • Kamali MI, Nazari R (2018) Determination of maize water requirement using remote sensing data and SEBAL algorithm. Agric Water Manag 209:197–205. https://doi.org/10.1016/j.agwat.2018.07.035

    Article  Google Scholar 

  • Li Z, Tang H, Yang P, Zhou Q, Wu W, Zou J, Zhang L, Zhang X (2011) Responses of cropland phenophases to agricultural thermal resources change in Northeast China. Acta Geogr Sin 66(7):928–939

    Google Scholar 

  • Li R, Xu M, Chen Z, Gao B, Cai J, Shen F, He X, Zhang Y, Chen D (2021) Phenology-based classification of crop species and rotation types using fused MODIS and Landsat data: the comparison of a random-forest-based model and a decision-rule-based mole. Soil Tillage Res 206:104838. https://doi.org/10.1016/j.still.2020.104838

    Article  Google Scholar 

  • Liu L, Xiao X, Qin Y, Wang J, Xu X, Hu Y, Qiao Z (2020a) Mapping cropping intensity in China using time series Landsat and Sentinel-2 images and Google Earth Engine. Remote Sens Environ 239:111624. https://doi.org/10.1016/j.rse.2019.111624

    Article  Google Scholar 

  • Liu X, Zhai H, Shen Y, Lou B, Jiang C, Li T, Hussain SB, Shen G (2020b) Large-scale crop mapping from multisource remote sensing images in google earth engine. IEEE J Sel Top Appl Earth Obs Remote Sens 13:414–427

    Article  Google Scholar 

  • Mahour M, Stein A, Sharifi A, Tolpekin V (2015) Integrating super resolution mapping and SEBS modeling for evapotranspiration mapping at the field scale. Precision Agric 16:571–586. https://doi.org/10.1007/s11119-015-9395-8

    Article  Google Scholar 

  • Maina M, Amin MSM, Wayayok A, Asha TS (2012) Evaluation of different ET0 calculation methods: a case study in Kano state, Nigeria. Philipp Agric Scientist 95(4):378–382

    Google Scholar 

  • Massey R, Sankey TT, Congalton RG, Yadav K, Thenkabail PS, Ozdogan M, Meador AJS (2017) MODIS phenology-derived, multi-year distribution of conterminous U.S. crop types. Remote Sens Environ 198:490–503. https://doi.org/10.1016/j.rse.2017.06.033

    Article  Google Scholar 

  • Mcnally A, Hustak GJ, Brown M et al (2015) Calculating crop water requirement satisfaction in the West Africa Sahel with remotely sensed soil moisture. J Hydrometeorol 16:295–305. https://doi.org/10.1175/JHM-D-14-0049.1

    Article  Google Scholar 

  • Menenti M, Azzali S, Verhoef W, Swol RV (1993) Mapping agroecological zones and time lag in vegetation growth by means of Fourier analysis of time series of NDVI images. Adv Space Res 5:233–237. https://doi.org/10.1016/0273-1177(93)90550-U

    Article  Google Scholar 

  • Muhammad S, Niu Z, Wang L, Aablikim A, Hao P, Wang C (2015) Crop classification based on time series MODIS EVI and ground observation for three adjoining years in Xinjiang. Spectrosc Spectr Anal 35(5):1345–1350

    Google Scholar 

  • Ngongondo C, Xu C, Tallaksen L, Alemaw BF (2013) Evaluation of the FAO Penman-Monteith, Priestley-Taylor and Hargreaves models for estimating reference evapotranspiration in southern Malawi. Hydrol Res 4:706–722. https://doi.org/10.2166/nh.2012.224

    Article  Google Scholar 

  • Pakhale G, Gupta P, Nale J (2010) Crop and irrigation water requirement estimation by remote sensing and GIS: a case study of Karnal District, Haryana, India. Int J Eng Technol 2(4):207–211

    Google Scholar 

  • Roerink GJ, Menenti M, Verhoef W (2000) Reconstructing cloud free NDVI composites using Fourier analysis of time series. Int J Remote Sens 21:1911–1917. https://doi.org/10.1080/014311600209814

    Article  Google Scholar 

  • Ruan H, Yu J, Wang P, Wang T (2020) Increased crop water requirement have exacerbated water stress in the arid transboundary rivers of Central Asia. Sci Total Environ 713:136585. https://doi.org/10.1016/j.scitotenv.2020.136585

    Article  Google Scholar 

  • Samuel A, Girma A, Zenebe A, Ghebreyohannes T (2018) Spatio-temporal variability of evapotranspiration and crop water requirement from space. J Hydrol 567:732–742. https://doi.org/10.1016/j.jhydrol.2018.01.058

    Article  Google Scholar 

  • Schewe J, Heinke J, Gerten D et al (2014) Multimodel assessment of water scarcity under climate change. PNAS 9:3245–3250. https://doi.org/10.1073/pnas.1222460110

    Article  Google Scholar 

  • Sharifi A, Dinpashoh Y (2014) Sensitivity analysis of the Penman-Monteith reference crop evapotranspiration to climatic variables in Iran. Water Resour Manag 28:5465–5476. https://doi.org/10.1007/s11269-014-0813-x

    Article  Google Scholar 

  • Shen Y, Li S, Chen Y, Qi Y, Zhang S (2013) Estimation of regional irrigation water requirement and water supply risk in the arid region of Northwestern China 1989–2010. Agric Water Manage 128:55–64. https://doi.org/10.1016/j.agwat.2013.06.014

    Article  Google Scholar 

  • Son NT, Chen CF, Chen CR, Guo HY (2020) Classification of multitemporal Sentinel-2 data for field-level monitoring of rice cropping practices in Taiwan. Adv Space Res 65:1910–1921. https://doi.org/10.1016/j.asr. 2020.01.028

    Article  Google Scholar 

  • Su Z (2002) The surface energy balance system (SEBS) for estimation of turbulent heat fluxes. Hydrol Earth Syst Sci 6:85–100. https://doi.org/10.5194/hess-6-85-2002

    Article  Google Scholar 

  • Supriyasilp T, Pongput K, Boonyanupong S, Suwanlertcharoen T (2020) Enhance water management for Muang Fai irrigation system through remote sensing and SWOT analysis. Water Resour Manag. https://doi.org/10.1007/s11269-020-02724-z

    Article  Google Scholar 

  • Wang P, Xun L, Li L, Xie Y, Wang L (2017) Extraction of planting areas of main crops based on Fourier transformed characteristics of time series leaf area index products. Transactions of the Chinese Society of Agricultural Engineering 21:207–215

    Google Scholar 

  • Wang J, Liu X, Cheng K, Zhang X, Li L, Pan G (2018) Winter wheat water requirement and utilization efficiency under simulated climate change conditions: a Penman-Monteith model evaluation. Agric Water Manage 197:100–109. https://doi.org/10.1016/j.agwat.2017.11.015

    Article  Google Scholar 

  • Wu W, Yang P, Tang H (2009) Comparison of two fitting methods of NDVI time series datasets. Trans Chin Soc Agric Eng 11:183–188

    Google Scholar 

  • Xu Q, Yang G, Long H, Wang C, Li X, Huang D (2014) Crop information identification based on MODIS NDVI time-series data. Trans Chin Soc Agric Eng 11:134–144

    Google Scholar 

  • Zhang J, Feng L, Yao F (2014) Improved maize cultivated area estimation over a large scale combining MODIS-EVI time series data and crop phenological information. ISRS J Photogramm Remote Sens 94:102–113

    Article  Google Scholar 

  • Zhou J, Li J, Massimo M (2015) Reconstruction of global MODIS NDVI time series: Performance of Harmonic ANalysis of Time Series (HANTS). Remote Sens Environ 15:217–228. https://doi.org/10.1016/j.rse.2015.03.018

    Article  Google Scholar 

Download references

Acknowledgements

This work was jointly supported by the National Natural Science Foundation of China (Nos. 41561023, 42067062); the China Scholarship Council Program (No. 201808655036). Thanks to China Meteorological Administration and NASA for providing free data. Thanks to the editors and anonymous reviewers for their detailed and constructive comments.

Author information

Authors and Affiliations

  1. Key Laboratory of Oasis Ecology, College of Resource and Environmental Sciences, Xinjiang University, Urumqi, Xinjiang, China

    Changchun Xu, Xicheng Zhang & Jinxia Zhang

  2. State Key Laboratory of Desert and Oasis Ecology, Xinjiang Institute of Ecology and Geography, Chinese Academy of Sciences, Urumqi, Xinjiang, China

    Yapeng Chen

  3. Hydrometeorology and Remote Sensing Laboratory, School of Civil Engineering and Environmental Sciences, The University of Oklahoma, Norman, OK, USA

    Teshome L. Yami & Yang Hong

Authors
  1. Changchun Xu
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Xicheng Zhang
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Jinxia Zhang
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Yapeng Chen
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Teshome L. Yami
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Yang Hong
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

All authors contributed to the study conception and design. Data collection and processing was performed by Xicheng Zhang and Jinxia Zhang. Artworks were modified by Yapeng Chen. The language was polished by Teshome L. Yami and Yang Hong. The draft of the manuscript was written by Changchun Xu. All authors commented on previous versions of the manuscript and approved the final manuscript.

Corresponding author

Correspondence to Changchun Xu.

Ethics declarations

Conflict of Interest

The authors declare that they have no conflict of interest.

Additional information

Publisher’s Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Xu, C., Zhang, X., Zhang, J. et al. Estimation of Crop Water Requirement Based on Planting Structure Extraction from Multi-Temporal MODIS EVI. Water Resour Manage 35, 2231–2247 (2021). https://doi.org/10.1007/s11269-021-02838-y

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11269-021-02838-y

Keywords

Search

Navigation