, 44:139 | Cite as

Streamlining multitemporal vegetation indices for dependable crop growth monitoring in Himalayan foothill region

  • Sandeep Kumar SinglaEmail author
  • Rahul Dev Garg
  • Om Prakash Dubey


Satellite data in conjunction with geoinformatics are used to study the land cover change dynamics, extraction of crop information and the monitoring of crop growth. The information derived from the satellite may contain contaminated values due to the atmospheric effects, geometric errors, snow and clouds. These contaminated values can be identified and eliminated using time series analysis to further streamline for agricultural monitoring and prognostic applications. The inherent advantages and disadvantages of existing streamlining methods limit their usability in particular situation. The method proposed in this study synergizes the use of interpolation, running median and moving average. This has clearly shown the proposal’s capability in preserving the trend in the series in addition to streamlining the temporal profile of satellite data in the Himalayan foothills. This will make the road map for satisfactory crop growth monitoring and crop yield estimation. Analysis based on the root mean square error and F-Test has been presented to deduce the results and interpretations.


Remote sensing NDVI streamlining crop yield estimation crop growth monitoring temporal data 


  1. 1.
    MacDonald R B, Bauer M E, Allen R D, Clifton J W, Erickson J D and Landgrebe D A 1972 Results of the 1971 Corn Blight Watch Experiment. LARS Tech. Rep. 107-107Google Scholar
  2. 2.
    Murthy R S, Venkataratnam L and Saxena R K 1983 Application of remote sensing techniques for land evaluation and classification for agriculture. Proc. Indian Acad. Sci. (Eng. Sci.) 6: 177–188Google Scholar
  3. 3.
    Laxman S and Sastry P S 2006 A survey of temporal data mining. Sadhana 31: 173–198MathSciNetCrossRefGoogle Scholar
  4. 4.
    Bauer M E, Cary T K, Davis B J and Swain P H 1975 Crop identification technology assessment for remote sensing (CITARS). NASA-CR-147389, LARS-INFORM-NOTE-072175, pp. 1–59Google Scholar
  5. 5.
    Bauer M E, McEwen M C, Malila W A and Harlan J C 1979 Design, implementation and results of LACIE field research. LARS Tech. Rep. 102579, pp. 1037–1066Google Scholar
  6. 6.
    Dempewolf J, Adusei B, Inbal B R, Hansen M, Potapov P, Khan A and Barker B 2104 Wheat yield forecasting for Punjab province from vegetation index time series and historic crop statistics. Remote Sens. 6: 9653–9675CrossRefGoogle Scholar
  7. 7.
    Dadhwal V K, Singh R P, Dutta S and Parihar J S 2002 Remote sensing based crop inventory: a review of Indian experience. Trop. Ecol. 43: 107–122Google Scholar
  8. 8.
    Silleos N G, Alexandridis T K, Gitas I Z and Perakis K 2006 Vegetation indices: advances made in biomass estimation and vegetation monitoring in the last 30 years. Geocarto Int. 21: 21–28CrossRefGoogle Scholar
  9. 9.
    Deekshatulu B L and Krishnan R 1983 Basic research problem in remote sensing. Proc. Indian Acad. Sci. (Eng. Sci.) 6: 337–354Google Scholar
  10. 10.
    Mulianga B, Bégué A, Clouvel P and Todoroff P 2015 Mapping cropping practices of a sugarcane-based cropping system in Kenya using remote sensing. Remote Sens. 7: 14428–14444CrossRefGoogle Scholar
  11. 11.
    Wei W, Wu W, Li Z, Yang P and Zhou Q 2016 Selecting the optimal NDVI time-series reconstruction technique for crop phenology detection. Intell. Autom. Soft. Comput. 22: 237–247CrossRefGoogle Scholar
  12. 12.
    Holben B N 1986 Characteristics of maximum-value composite images from temporal AVHRR data. Int. J. Remote Sens. 7: 1417–1434CrossRefGoogle Scholar
  13. 13.
    van Dijk A, Callis S L, Sakamoto C M and Decker W L 1987 Smoothing vegetation index profiles: an alternative method for reducing radiometric disturbance in NOAA/AVHRR data. Photogramm. Eng. Remote Sens. 53: 1059–1067Google Scholar
  14. 14.
    Jonsson P and Eklundh L 2002 Seasonality extraction by function fitting to time-series of satellite sensor data. IEEE Trans. Geosci. Remote Sens. 40: 1824–1832CrossRefGoogle Scholar
  15. 15.
    Chen J, Jonsson P, Tamura M, Gu Z, Matsushita B and 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: 332–344CrossRefGoogle Scholar
  16. 16.
    Menenti M, Azzali S, Verhoef W and Swol R 1993 Mapping agroecological zones and time lag in vegetation growth by means of fourier analysis of time series of NDVI images. Adv. Space Res. 13: 233–237CrossRefGoogle Scholar
  17. 17.
    Kosarev E L and Pantos E 1983 Optimal smoothing of ’noisy’ data by fast Fourier transform. J. Phys. E: Sci. Instrum. 16: 537–543CrossRefGoogle Scholar
  18. 18.
    Geng L, Ma M, Wang X, Yu W, Jia S and Wang H 2014 Comparison of eight techniques for reconstructing multi-satellite sensor time-series NDVI data sets in the Heihe river basin, China. Remote Sens. 6: 2024–2049CrossRefGoogle Scholar
  19. 19.
    Velleman P F and Hoanglin D C 1981 Applications, basics and computing of exploratory data analysis. Boston, MA: Duxbury PressGoogle Scholar
  20. 20.
    Reed B C, Brown J F, VanderZee D, Loveland T R, Merchant J W and Ohlen D O 1994 Measuring phenological variability from satellite imagery. J. Veg. Sci. 5: 703–714CrossRefGoogle Scholar
  21. 21.
    Hird J N and McDermid G J 2009 Noise reduction of NDVI time series: an empirical comparison of selected techniques. Remote Sens. Environ. 113: 248–258CrossRefGoogle Scholar
  22. 22.
    Atzberger C and Eilers P H C 2011 Evaluating the effectiveness of smoothing algorithms in the absence of ground reference measurements. Int. J. Remote Sens. 32: 3689–3709CrossRefGoogle Scholar
  23. 23.
    Tucker C J, Townshend J R and Goff T E 1985 African land-cover classification using satellite data. Science 227: 369–375CrossRefGoogle Scholar
  24. 24.
    Ahamed T, Tian L, Zhang Y and Ting K C 2011 A review of remote sensing methods for biomass feedstock production. Biomass Bioenergy 35: 2455–2469CrossRefGoogle Scholar
  25. 25.
    Morel J, Todoroff P, Bégué A, Bury A, Martiné J F and Petit M 2014 Toward a satellite-based system of sugarcane yield estimation and forecasting in smallholder farming conditions: a case study on Reunion Island. Remote Sens. 6: 6620–6635CrossRefGoogle Scholar
  26. 26.
    Dangwal N, Patel N R, Kumari M and Saha S K 2016 Monitoring of water stress in wheat using multispectral indices derived from Landsat-TM. Geocarto Int. 31: 682–693CrossRefGoogle Scholar
  27. 27.
    Gers C J 2003 Relating remotely sensed multi-temporal Landsat 7 ETM+ imagery to sugarcane characteristics. In: Proceedings of the South African Sugar Technology Association, pp. 1–7Google Scholar
  28. 28.
    Rao P V K, Rao V V and Venkataratnam L 2002 Remote sensing: a technology for assessment of sugarcane crop acreage and yield. Sugar Tech. 4: 97–101CrossRefGoogle Scholar
  29. 29.
    Ke Y, Im J, Lee J, Gong H and Ryu Y 2015 Characteristics of Landsat 8 OLI-derived NDVI by comparison with multiple satellite sensors and in-situ observations. Remote Sens. Environ. 164: 298–313CrossRefGoogle Scholar
  30. 30.
    Jia K, Wei X, Gu X, Yao Y, Xie X and Li B 2014 Land cover classification using Landsat 8 Operational Land Imager data in Beijing, China. Geocarto Int. 29: 941–951CrossRefGoogle Scholar
  31. 31.
    Viovy N, Arino O and Belward A S 1992 The best index slope extraction BISE: a method for reducing noise in NDVI time-series. Int. J. Remote Sens. 13: 1585–1590CrossRefGoogle Scholar
  32. 32.
    Lovell J L and Graetz R D 2001 Filtering pathfinder AVHRR land NDVI data for Australia. Int. J. Remote Sens. 22: 2649–2654CrossRefGoogle Scholar
  33. 33.
    Roerink G J, Menenti M and Verhoef W 2000 Reconstructing cloudfree NDVI composites using Fourier analysis of time series. Int. J. Remote Sens. 21: 1911–1917CrossRefGoogle Scholar
  34. 34.
    Savitzky A and Golay M J E 1964 Smoothing and differentiation of data by simplified least squares procedures. Anal. Chem. 36: 1627–1639CrossRefGoogle Scholar
  35. 35.
    Ma M and Veroustraete F 2006 Reconstructing pathfinder AVHRR land NDVI time-series data for the Northwest of China. Adv. Space Res. 37: 835–840CrossRefGoogle Scholar
  36. 36.
    Beck P S A, Atzberger C, Hogda K A, Johansen B and Skidmore A K 2006 Improved monitoring of vegetation dynamics at very high latitudes: a new method using MODIS NDVI. Remote Sens. Environ. 100: 321–334CrossRefGoogle Scholar
  37. 37.
    Galford G L, Mustard J F, Melillo J, Gendrin A, Cerri C C and Cerri C E P 2008 Wavelet analysis of MODIS time series to detect expansion and intensification of row-crop agriculture in Brazil. Remote Sens. Environ. 112: 576–587CrossRefGoogle Scholar
  38. 38.
    Zhang S, Lei Y, Wang L, Li H and Zhao H 2011 Crop classification using MODIS NDVI data denoised by wavelet: a case study in Hebei plain, China. Chin. Geogr. Sci. 21: 322–333CrossRefGoogle Scholar
  39. 39.
    Julien Y and Sobrino J A 2010 Comparison of cloud-reconstruction methods for time series of composite NDVI data. Remote Sens. Environ. 114: 618–625CrossRefGoogle Scholar
  40. 40.
    Zhu W, Pan Y, He H, Wang L, Mou M and Liu J 2012 A changing-weight filter method for reconstructing a high-quality NDVI time series to preserve the integrity of vegetation phenology. IEEE Trans. Geosci. Remote Sens. 50: 1085–1094CrossRefGoogle Scholar
  41. 41.
    Yang G, Shen H, Zhang L, He Z and Li X 2015 A moving weighted harmonic analysis method for reconstructing high-quality spot vegetation NDVI time-series data. IEEE Trans. Geosci. Remote Sens. 53: 6008–6021CrossRefGoogle Scholar
  42. 42.
    Atkinson P M, Jeganathan C, Dash J and Atzberger C 2012 Inter-comparison of four models for smoothing satellite sensor time-series data to estimate vegetation phenology. Remote Sens. Environ. 123: 400–417CrossRefGoogle Scholar
  43. 43.
    Chander G, Markham B L and Helder D L 2009 Summary of current radiometric calibration coefficients for Landsat MSS, TM, ETM+, and EO-1 ALI sensors. Remote Sens. Environ. 113: 893–903CrossRefGoogle Scholar
  44. 44.
    Basso B, Cammarano D and Carfagna E 2013 Review of crop yield forecasting methods and early warnings. In: Proceedings of the First Meeting of the Scientific Advisory Committee of the Global Strategy to Improve Agricultural and Rural Statistics, FAO Headquarters, Rome, Italy, pp. 1–56Google Scholar
  45. 45.
    Rouse J W, Haas R H, Schell J A and Deering D W 1974 Monitoring vegetation systems in the great plains with ERTS. In: Technical Presentations, Proceedings of the Third Earth Resources Technology Satellite-1 Symposium, vol. I, p. 309Google Scholar
  46. 46.
    Tukey J W 1977 Exploratory data analysis. Reading: Addison-WesleyzbMATHGoogle Scholar
  47. 47.
    Jin Z and Xu B 2013 A novel compound smoother—RMMEH to reconstruct MODIS NDVI time series. IEEE Geosci. Remote Sens. Lett. 10: 942–946CrossRefGoogle Scholar
  48. 48.
    Shabani A, Sepaskhah A R, Kamgar-Haghighi A A and Honar T 2018 Using double logistic equation to describe the growth of winter rapeseed. J. Agric. Sci. 156: 37–45CrossRefGoogle Scholar

Copyright information

© Indian Academy of Sciences 2019

Authors and Affiliations

  • Sandeep Kumar Singla
    • 1
    Email author
  • Rahul Dev Garg
    • 1
  • Om Prakash Dubey
    • 1
  1. 1.Geomatics Engineering Group, Department of Civil EngineeringIndian Institute of Technology (IIT) RoorkeeRoorkee India

Personalised recommendations