SpringerLink
Log in

UAV-based multispectral image analytics for generating crop coefficient maps for rice

  • Original Paper
  • Published:
Arabian Journal of Geosciences Aims and scope Submit manuscript

    We’re sorry, something doesn't seem to be working properly.

    Please try refreshing the page. If that doesn't work, please contact support so we can address the problem.

Abstract

Crop coefficients are important to determine crop evapotranspiration (ETc), seasonal crop water requirement, and irrigation water depth. Similarities between the curve of crop coefficient and remote sensing-based vegetation indices of a crop during its growth period suggested the possibility of modeling crop coefficient (Kc) as a function of vegetation indices. In this study, high-resolution multispectral (MS) images of a rice crop acquired using a quadcopter unmanned aerial vehicle (UAV) were processed to generate Kc maps. Lysimeter experiments were conducted for estimating daily ETc of rice using the water balance concept for conventionally irrigated paddy during kharif (monsoon) 2018/2019, rabi 2018/2019 (non-monsoon), and kharif (monsoon) 2019/2020 seasons. The average lysimeter-based crop coefficients for all three seasons were observed to be 1.04, 1.24, 1.36, and 1.18 for initial, crop development, reproductive, and late growth stages of the rice crop season, respectively. The mean Kc was significantly different for the growth stage, but it was not significantly different for the season. Among the eight vegetation indices tested, the ratio vegetation index (RVI) and normalized difference red edge (NDRE) were found to be correlated well with the lysimeter-based Kc for the kharif 2019/2020 season. The developed linear regression equations (RVI vs. Kc; NDRE vs. Kc) were tested using kharif 2018/2019 and rabi 2018/2019 seasons and found acceptable levels of different performance indices. These regression models will be helpful in generating Kc maps and estimating irrigation water requirement of paddy crop (MTU 1010 variety) in the sub-humid subtropical region.

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

Access this article

Log in via an institution

Subscribe and save

Springer+ Basic
EUR 32.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or Ebook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

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

Similar content being viewed by others

References

  • Acorsi MG, Gimenez LM (2021) Predicting soil water content on rainfed maize through aerial thermal imaging. Agric Eng 3(4):942–953

    Google Scholar 

  • Ahmet ERTEK (2011) Importance of pan evaporation for irrigation scheduling and proper use of crop-pan coefficient (Kcp), crop coefficient (Kc) and pan coefficient (Kp). Afr J Agric Res 6(32):6706–6718

    Google Scholar 

  • Alberto MCR, Wassmann R, Hirano T, Miyata A, Kumar A, Padre A, Amante M (2011) CO2/heat fluxes in rice fields: comparative assessment of flooded and non-flooded fields in the Philippines. Agric for Meteorol 149(10):1737–1750

    Google Scholar 

  • 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 300(9):D05109

    Google Scholar 

  • Alvino A, Marino S (2017) Remote sensing for irrigation of horticultural crops. Horticulturae 3(2):40

    Google Scholar 

  • Anthony D, Elbaum S, Lorenz A, Detweiler C (2014) On crop height estimation with UAV’s. In: IEEE/RSJ International Conference on Intelligent Robots and Systems. Chicago, Illinois, pp 4805–4812

  • Ashfaq M, Razzaq A, Ali Q (2020) Comparison of water use efficiency, profitability, and consumer preferences of different rice varieties in Punjab, Pakistan. Paddy Water Environ 18(1):273–282

    Google Scholar 

  • Barker R, Dawe D, Tuong TP, Bhuiyan SI, Guerra LC (1999) The outlook for water resources in the year 2020: challenges for research on water management in rice production. Southeast Asia 1:1–5

    Google Scholar 

  • Barnes EM, Clarke TR, Richards SE, Colaizzi PD, Haberland J, Kostrzewski M, Waller P, Choi C, Riley E, Thompson T, Lascano RJ (2000) Coincident detection of crop water stress, nitrogen status, and canopy density using ground-based multispectral data. In: Fifth International Conference on Precision Agriculture. Bloomington, MN

  • Bausch WC, Neale CM (1987) Crop coefficients derived from reflected canopy radiation: a concept. Trans ASAE 30(3):703–0709

    Google Scholar 

  • Belder P, Bouman BAM, Cabangon R, Guoan L, Quilang EJP, Yuanhua L, Spiertz JHJ, Tuong TP (2004) Effect of water-saving irrigation on rice yield and water use in typical lowland conditions in Asia. Agric Water Manag 65(3):193–210

  • Bendig J, Bolten A, Bareth G (2012) Introducing a low-cost mini-UAV for thermal-and multispectral-imaging. Int Arch Photogramm Remote Sens Spat Inf Sci 39:345–349

    Google Scholar 

  • Bouman BAM, Tuong TP (2001) Field water management to save water and increase its productivity in irrigated lowland rice. Agric Water Manag 49(1):11–30

    Google Scholar 

  • Bueno CS, Bucourt M, Kobayashi N, Inubush K, Lafarge T (2010) Water productivity of contrasting rice genotypes grown under water-saving conditions in the tropics and investigation of morphological traits for adaptation. Agric Water Manag 98(2):241–250

    Google Scholar 

  • Buschmann C, Nagel E (1993) In vivo spectroscopy and internal optics of leaves as the basis for remote sensing of vegetation. Int J Remote Sens 14(4):711–722

    Google Scholar 

  • Cai W, Ullah S, Yan L, Lin Y (2021) Remote sensing of ecosystem water use efficiency: a review of direct and indirect estimation methods. Remote Sens 13(12):2393

    Google Scholar 

  • Calera A, Campos I, Osann A, D’Urso G, Menenti M (2017) Remote sensing for crop water management: from ET modelling to services for the end users. Sensors 17(50):1104

    Google Scholar 

  • Delegido J, Verrelst J, Meza CM, Rivera JP, Alonso L, Moreno J (2013) A red-edge spectral index for remote sensing estimation of green LAI over agroecosystems. Eur J Agron 46:42–52

    Google Scholar 

  • Djaman K, Rudnick DR, Moukoumbi YD, Sow A, Irmak S (2019) Actual evapotranspiration and crop coefficients of irrigated lowland rice (Oryza sativa L.) under semiarid climate. Italian J Agron 14(9):1059

  • Drerup P, Brueck H, Scherer HW (2017) Evapotranspiration of winter wheat estimated with the FAO 56 approach and NDVI measurements in a temperate humid climate of NW Europe. Agric Water Manag 192:180–188

    Google Scholar 

  • El-Shirbeny MA, Ali AM, Badr MA, Bauomy EM (2014) Assessment of wheat crop coefficient using remote sensing techniques. World J Agric Res 1(2):12–17

    Google Scholar 

  • Espinosa ERRJ, David DL, Cummings G, Peters LA (2020) Single crop coefficients for agricultural irrigation in Guyana. Trop Agric 97(1):1–8

  • Feng A, Zhang M, Sudduth KA, Vories ED, Zhou J (2019) Cotton yield estimation from UAV-based plant height. Trans ASABE 62(2):393–404

    Google Scholar 

  • Foley WJ, McIlwee A, Lawler I, Aragones L, Woolnough AP, Berding N (1998) Ecological applications of near infrared reflectance spectroscopy–a tool for rapid, cost-effective prediction of the composition of plant and animal tissues and aspects of animal performance. Oecologia 116(3):293–305

    Google Scholar 

  • Ghamarnia H (2019) Estimation of rice cultivar (Amberbo) water requirement and crop coefficients using lysimeter under non-flooding irrigation conditions. J Rice Sci 1(2):1–6

    Google Scholar 

  • Giordano M, Barron J, Ünver O (2019) Water scarcity and challenges for smallholder agriculture. In: Sustainable food and agriculture. Academic Press, Cambridge, pp 75–94

  • Gitelson AA, Kaufman YJ, Merzlyak MN (1996) Use of a green channel in remote sensing of global vegetation from EOS-MODIS. Remote Sens Environ 58(3):289–298

    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 Manage 24(7):1399–1414

    Google Scholar 

  • Guan Y, Grote K, Schott J, Leverett K (2022) Prediction of soil water content and electrical conductivity using random forest methods with UAV multispectral and ground-coupled geophysical data. Remote Sens 14(4):1023

    Google Scholar 

  • Han WT, Shao GM, Ma DJ (2018) Estimating method of crop coefficient of maize based on UAV multispectral remote sensing. Trans Chin Soc Agric Mach 49:134–143

    Google Scholar 

  • Hargreaves GH, Samani ZA (1985) Reference crop evapotranspiration from temperature. Appl Eng Agric 1(2):96–99

    Google Scholar 

  • Hassan DF, Abdalkadhum AJ, Mohammed RJ, Shaban A (2022) Integration remote sensing and meteorological data to monitoring plant phenology and estimation crop coefficient and evapotranspiration. J Ecol Eng 23(4):325–335

  • Hassan-Esfahani L, Torres-Rua A, Jensen A, McKee M (2015) Assessment of surface soil moisture using high-resolution multi-spectral imagery and artificial neural networks. Remote Sens 7(3):2627–2646

    Google Scholar 

  • Hoffmann H, Nieto H, Jensen R, Guzinski R, Zarco-Tejada PJ, Friborg T (2015) Estimating evapotranspiration with thermal UAV data and two source energy balance models. Hydrol Earth Syst Sci Discuss 12(8):7469–7502

    Google Scholar 

  • Hossen MA, Diwakar PK, Ragi S (2021) Total nitrogen estimation in agricultural soils via aerial multispectral imaging and LIBS. Sci Rep 11(1):1–11

    Google Scholar 

  • Howell T A, Evett SR (2002) The Penman-Monteith method. Available online: http://www.cprl.ars.usda.gov/wmru/pdfs/PM%20COLO%20Bar%202004%20cor-rected%209apr04.pdf. Accessed 21 Nov 2020

  • Huang Y, Hoffmann WC, Lan Y, Wu W, Fritz BK (2009) Development of a spray system for an unmanned aerial vehicle platform. Appl Eng Agric 25(6):803–809

    Google Scholar 

  • Hunsaker DJ, Pinter PJ, Kimball BA (2005) Wheat basal crop coefficients determined by normalized difference vegetation index. Irrig Sci 24(1):1–14

    Google Scholar 

  • Inman-Bamber NG, McGlinchey MG (2003) Crop coefficients and water use estimates for sugarcane based on long-term Bowen ratio energy balance measurements. Field Crops Res 83(2):125–138

    Google Scholar 

  • Jacovides CP, Kontoyiannis H (1995) Statistical procedures for the evaluation of evapotranspiration computing models. Agric Water Manag 27(3–4):365–371

    Google Scholar 

  • Javed MA, Rashid Ahmad S, Awan WK, Munir BA (2020) Estimation of crop water deficit in lower Bari Doab, Pakistan using reflection-based crop coefficient. ISPRS Int J Geoinf 9(3):173

    Google Scholar 

  • Jensen ME, Burman RD, Allen RG (1990) Evaporation and irrigation water requirements. In: ASCE Manuals and Reports on Eng Practices No 70. American Society of Civil Engineers, New York, NY, pp 360

  • Jordan CF (1969) Derivation of the leaf-area index from quality of light on the forest floor. Ecol 50(4):663–666

    Google Scholar 

  • Kamble B, Kilic A, Hubbard K (2013) Estimating crop coefficients using remote sensing-based vegetation index. Remote Sens 5(4):1588–1602

    Google Scholar 

  • Kang Y, Meng Q, Liu M, Zou Y, Wang X (2021) Crop classification based on red edge features analysis of GF-6 WFV data. Sensors 21(13):4328

    Google Scholar 

  • Kanke Y, Tubana B, Dalen M, Harrell D (2016) Evaluation of red and red-edge reflectance-based vegetation indices for rice biomass and grain yield prediction models in paddy fields. Precis Agric 17(5):507–530

    Google Scholar 

  • Knipper KR, Kustas WP, Anderson MC, Alsina MM, Hain CR, Alfieri JG, Prueger JH, Gao F, McKee LG, Sanchez LA (2019) Using high-spatiotemporal thermal satellite ET retrievals for operational water use and stress monitoring in a California vineyard. Remote Sens 11(18):2124

  • Kustas WP, Hatfield JL, Prueger JH (2005) The soil moisture–atmosphere coupling experiment (SMACEX): Background, hydrometeorological conditions, and preliminary findings. J Hydrometeorol 6(6):791–804

    Google Scholar 

  • Li X, Giles DK, Niederholzer FJ, Andaloro JT, Lang EB, Watson LJ (2021) Evaluation of an unmanned aerial vehicle as a new method of pesticide application for almond crop protection. Pest Manag Sci 77(1):527–537

    Google Scholar 

  • Mancosu N, Snyder RL, Kyriakakis G, Spano D (2015) Water scarcity and future challenges for food production. Water 7(3):975–992

    Google Scholar 

  • Marcial-Pablo MDJ, Ontiveros-Capurata RE, Jiménez-Jiménez SI, Ojeda-Bustamante W (2021) Maize crop coefficient estimation based on spectral vegetation indices and vegetation cover fraction derived from UAV-based multispectral images. Agronomy 11(4):668

    Google Scholar 

  • Mateos L, González-Dugo MP, Testi L, Villalobos FJ (2013) Monitoring evapotranspiration of irrigated crops using crop coefficients derived from time series of satellite images. I Method Validation. Agric Water Manag 125:81–91

    Google Scholar 

  • McCabe MF, Wood EF (2006) Scale influences on the remote estimation of evapotranspiration using multiple satellite sensors. Remote Sens Environ 105(4):271–285

    Google Scholar 

  • Me SM, Maguteeswaran R, Be NG, Srinivasan G (2016) Quadcopter UAV based fertilizer and pesticide spraying system. Int Acad Res J Eng Sci 1:8–12

    Google Scholar 

  • Mohan S, Arumugam N (1994) Irrigation crop coefficients for lowland rice. Irrig Drain Syst 8(3):159–176

    Article  Google Scholar 

  • Mokhtari A, Noory H, Vazifedoust M, Bahrami M (2018) Estimating net irrigation requirement of winter wheat using model-and satellite-based single and basal crop coefficients. Agric Water Manag 208:95–106

    Google Scholar 

  • Moorhead JE (2018) Field-scale estimation of evapotranspiration. In: Advanced Evapotranspiration Methods and Applications. IntechOpen, London, pp 3–20

  • Motohka T, Nasahara KN, Oguma H, Tsuchida S (2010) Applicability of green-red vegetation index for remote sensing of vegetation phenology. Remote Sens 2(10):2369–2387

    Google Scholar 

  • Nash JE, Sutcliffe JV (1970) River flow forecasting through conceptual models part I—A discussion of principles. J Hydrol 10(3):282–290

    Google Scholar 

  • Nielsen HHM (2016) Evapotranspiration from UAV Images: A New Scale of Measurements. Dissertation, University of Copenhagen

  • Niu H, Zhao T, Wang D, Chen Y (2019) Estimating evapotranspiration with UAV’s in agriculture: a review. In: 2019 ASABE Annual International Meeting. Boston, Massachusett

  • Pereira LS, Alves I (2013) Crop water requirements, reference module in earth systems and environmental sciences. In: Encyclopedia of Soils in the Environment. pp 322–334

  • Pereira LS, Paredes P, López-Urrea DJ, Jovanovic N (2021) Updates and advances to the FAO56 crop water requirements method. Agric Water Manag 248:106697

  • Prasad PVV, Staggenborg SA, Ristic Z (2008) Impacts of drought and/or heat stress on physiological, developmental, growth, and yield processes of crop plants. In: Response of crops to limited water: understanding and modeling water stress effects on plant growth processes. ASA-CSSA: Madison, WI, USA, pp 301–355

  • Primicerio J, Di Gennaro SF, Fiorillo E, Genesio L, Lugato E, Matese A, Vaccari FP (2012) A flexible unmanned aerial vehicle for precision agriculture. Precis Agric 13(4):517–523

    Google Scholar 

  • Quan Z, **anfeng Z, Miao J (2011) Eco-environment variable estimation from remote sensed data and eco-environment assessment: models and system. Acta Botanica Sinica, Bot Sin 47:1073–1080

    Google Scholar 

  • Rao SS, Naik BB, Ramulu V, Devi MU, Shivani D (2019) Impact of irrigation practices on production and water productivity of transplanted rice under NSP canal command area. Int J Bio-Resour Stress Manag 10(6):621–627

    Google Scholar 

  • Reyes-Gonzalez A, Hay C, Kjaersgaard J, Neale C (2015) Use of remote sensing to generate crop coefficient and estimate actual crop evapotranspiration. In: 2015 ASABE Annual International Meeting. New Orleans, LA

  • Reyes-González A, Trooien T, Kjaersgaard J, Hay C, Reta-Sánchez DG (2016) Development of crop coefficients using remote sensing-based vegetation index and growing degree days. In: 2016 ASABE Annual International Meeting. Orlando, Florida

  • Reyes-González A, Kjaersgaard J, Trooien T, Hay C, Ahiablame L (2018) Estimation of crop evapotranspiration using satellite remote sensing-based vegetation index. Adv Meteorol 2018:1–12

  • Rosegrant MW, Ringler C (1998) Impact on food security and rural development of transferring water out of agriculture. Water Policy 1(6):567–586

    Google Scholar 

  • Rouse J Jr, Haas RH, Schell JA, Deering DW (1974) Monitoring vegetation systems in the Great Plains with ERTS, NASA AP-351. Third ERTS-1 Symposium 1:309–317

    Google Scholar 

  • Rouze GS, Morgan CL, Neely H L, Kustas W, McKee L, Prueger JH, Yang C, Cope D, Thomasson JA, Jung J (2017) Assessing the Efficacy of Unmanned Aerial Vehicles (UAV’s) in Monitoring Crop Evapotranspiration within a Heterogeneous Soil. In: ASA, CSSA, and SSSA International Annual Meetings. Tampa, FL

  • Sandham LA, Zietsman HL (1997) Surface temperature measurement from space: a case study in the South Western Cape of South Africa. S Afr J Enol Vitic 18(2):25–30

    Google Scholar 

  • Shaheen SM, Antoniadis V, Shahid M, Yang Y, Abdelrahman H, Zhang T, ..., Rinklebe J (2022) Sustainable applications of rice feedstock in agro-environmental and construction sectors: a global perspective. Renew Sust Energ Rev 153:111791

  • Shekhar S, Tamilarasan R, Mailapalli DR, Raghuwanshi NS (2021) Estimation of evapotranspiration for paddy under alternate wetting and drying irrigation practice. Irrig Drain 70(2):195–206

    Google Scholar 

  • Shekhar S, Mailapalli DR, Raghuwanshi NS (2022) Effect of alternate wetting and drying irrigation practice on rice crop growth and yield: a lysimeter study. ACS Agric Sci Technol 2(5):919–931

    Google Scholar 

  • Sheng H, Chao H, Coopmans C, Han J, McKee M, Chen Y (2010) Low-cost UAV-based thermal infrared remote sensing: platform, calibration, and applications. In: IEEE/ASME International Conference on Mechatronic and Embedded Systems and Applications. QingDao, pp 38–43

  • Shibayama M, Akiyama T (1989) Seasonal visible, near-infrared and mid-infrared spectra of rice canopies in relation to LAI and above-ground dry phytomass. Remote Sens Environ 27(2):119–127

    Google Scholar 

  • Shibayama M, Sakamoto T, Takada E, Inoue A, Morita K, Takahashi W, Kimura A (2009) Continuous monitoring of visible and near-infrared band reflectance from a rice paddy for determining nitrogen uptake using digital cameras. Plant Prod Sci 12(3):293–306

    Google Scholar 

  • Shuttleworth WJ, Wallace JS (2009) Calculating the water requirements of irrigated crops in Australia using the Matt-Shuttleworth approach. Trans ASABE 52(6):1895–1906

    Google Scholar 

  • Singh S, Pandey P, Khan MS, Semwal M (2021) Multi-temporal high resolution unmanned aerial vehicle (UAV) Multispectral imaging for menthol mint crop monitoring. In: 6th International Conference for Convergence in Technology (I2CT). IEEE, Pune, pp 1–4

  • Sulik JJ, Long DS (2020) Automated detection of phenological transitions for yellow flowering plants such as Brassica oilseeds. Agrosyst Geosci Environ 3(1):e20125

    Google Scholar 

  • Tabbal DF, Bouman BAM, Bhuiyan SI, Sibayan EB, Sattar MA (2002) On-farm strategies for reducing water input in irrigated rice. Agric Water Manag 56(2):93–112

    Google Scholar 

  • Tanda G, Chiarabini V (2019) Use of multispectral and thermal imagery in precision viticulture. J Phys Conf 1224(1):012034

  • Tang J, Han W, Zhang L (2019) UAV multispectral imagery combined with the FAO-56 dual approach for maize evapotranspiration map** in the North China Plain. Remote Sens 11(21):2519

    Google Scholar 

  • Ten Harkel J, Bartholomeus H, Kooistra L (2020) Biomass and crop height estimation of different crops using UAV-based LiDAR. Remote Sens 12(1):17

    Google Scholar 

  • Thenkabail PS, Smith RB, De Pauw E (2000) Hyperspectral vegetation indices for determining agricultural crop characteristics. Remote Sens Environ 71(2):158–182

    Google Scholar 

  • Tucker CJ (1979) Red and photographic infrared linear combinations for monitoring vegetation. Remote Sens Environ 8(2):127–150

    Google Scholar 

  • Tuong TP, Bouman BAM (2003) Rice production in water-scarce environments. Water Prod Agric: Limits and Opportunities for Improvement 1:13–42

    Google Scholar 

  • Turner D, Lucieer A, Malenovský Z, King DH, Robinson SA (2014) Spatial co-registration of ultra-high resolution visible, multispectral, and thermal images acquired with a micro-UAV over Antarctic moss bed. Remote Sens 6(5):4003–4024

    Google Scholar 

  • Tyagi NK, Sharma DK, Luthra SK (2000) Determination of evapotranspiration and crop coefficients of rice and sunflower with lysimeter. Agric Water Manag 45(1):41–54

    Google Scholar 

  • Verma HC, Ahmed T, Rajan S (2020) Map** and area estimation of mango orchards of Lucknow region by applying knowledge based decision tree to Landsat 8 OLI Satellite Images. Int J Innov Technol Explor Eng 9:3627–3635

    Google Scholar 

  • Wang C, He X, Wang X, Wang Z, Wang S, Li L, Wang Z (2018) Testing method and distribution characteristics of spatial pesticide spraying deposition quality balance for unmanned aerial vehicle. Int J Agric Biol Eng 11(2):18–26

    Google Scholar 

  • Wang LP, Ochoa-Rodriguez S, Van Assel J, Pina, RD, Pessemier M, Kroll S, ..., On of C (2015) Enhancement of radar rainfall estimates for urban hydrology through optical flow temporal interpolation and Bayesian gauge-based adjustment. J Hydrol 531:408–426

  • Willmott CJ (1981) On the validation of models. Phys Geogr 2(2):184–194

    Google Scholar 

  • Wu K, Rodriguez GA, Zajc M, Jacquemin E, Clément M, De Coster A, Lambot S (2019) A new drone-borne GPR for soil moisture map**. Remote Sens Environ 235:111456

    Google Scholar 

  • ** evapotranspiration with high-resolution aircraft imagery over vineyards using one-and two-source modeling schemes. Hydrol Earth Syst Sci 20:1523

    Google Scholar 

  • **: a review. J Plant Ecol 1(1):9–23

    Google Scholar 

  • ** crop key phenological stages in the North China Plain using NOAA time series images. Int J Appl Earth Obs Geoinf 4(2):109–117

    Google Scholar 

  • Yan H, Huang S, Zhang J, Zhang C, Wang G, Li L, Zhao S, Li M, Zhao B (2022) Comparison of Shuttleworth-Wallace and dual crop coefficient method for estimating evapotranspiration of a tea field in Southeast China. Agriculture 12(9):1392

    Google Scholar 

  • Yao H, Qin R, Chen X (2019) Unmanned aerial vehicle for remote sensing applications-a review. Remote Sens 11(12):1443

    Google Scholar 

  • Yu B, Shang S (2020) Estimating growing season evapotranspiration and transpiration of major crops over a large irrigation district from HJ-1A/1B Data Using a Remote Sensing-Based Dual Source Evapotranspiration Model. Remote Sens 1:865

    Google Scholar 

  • Zhang Y, Han W, Niu X, Li G (2019) Maize crop coefficient estimated from UAV-measured multispectral vegetation indices. Sensors 19(23):5250

    Google Scholar 

  • Zhou K, Guo Y, Geng Y, Zhu Y, Cao W, Tian Y (2014) Development of a novel bidirectional canopy reflectance model for row-planted rice and wheat. Remote Sens 6(8):7632–7659

    Google Scholar 

Download references

Funding

This work was financially supported by a national initiative of the Ministry of Human Resource Development (MHRD) under the Government of India (GoI) and also supported by the Ministry of Agriculture under GoI under the scheme of IMPacting Research, INnovation and Technology (IMPRINT, project no.5682).

Author information

Authors and Affiliations

  1. Agricultural and Food Engineering Department, Indian Institute of Technology Kharagpur, Kharagpur, West Bengal, India

    Suyog Balasaheb Khose, Damodhara Rao Mailapalli, Sudarsan Biswal & Chandranath Chatterjee

Authors
  1. Suyog Balasaheb Khose
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Damodhara Rao Mailapalli
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Sudarsan Biswal
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Chandranath Chatterjee
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Damodhara Rao Mailapalli.

Ethics declarations

Conflict of interest

The authors declare that they have no competing interests.

Additional information

Responsible Editor: Biswajeet Pradhan

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Khose, S.B., Mailapalli, D.R., Biswal, S. et al. UAV-based multispectral image analytics for generating crop coefficient maps for rice. Arab J Geosci 15, 1681 (2022). https://doi.org/10.1007/s12517-022-10961-2

Download citation

  • Received:

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1007/s12517-022-10961-2

Keywords

Search

Navigation