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In-Season Potato Crop Nitrogen Status Assessment from Satellite and Meteorological Data


For a conventional potato crop, splitting nitrogen (N) application is recognised as an efficient strategy to improve tuber yield and quality and to mitigate N losses to the environment. This approach requires the assessment of in-season crop N status for decisions on supplemental mineral N fertiliser application. This study focuses on the assessment of potato crop biophysical variables useful to establish crop N status. Field, satellite and meteorological data were collected in farmer’s fields during 3 years (2017–2019) with contrasted meteorological conditions. Degree days (DD) and water balance from planting date were computed from meteo data, and a selection of relevant vegetation indices (VIs) was derived from Sentinel-2 reflectance. Multiple linear regression (MLR) and random forest regression (RFR) models predicting shoots biomass, shoots N content and shoots N uptake from a combination of meteo and/or satellite-based variables were defined and evaluated. The best combinations integrate DD and two to four VIs and perform with cross-validation RMSE of about 0.38 DM t ha−1, 0.41%, 21 kg ha−1 for MLR and 0.32 DM t ha−1, 0.31%, 19 kg ha−1 for RFR. Despite these performances, MLR was shown to be more robust. From these estimated variables, two methods are proposed to derive total N uptake and nitrogen nutrition index. The most relevant method uses shoots N uptake and biomass. It allows future estimation of in-season supplemental N fertiliser to be applied to reach a targeted tuber yield.

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Data Availability

Data is stored on Walloon Agricultural Research Center (CRA-W) servers and can be made available on demand through specific agreement.


  • Alva L (2004) Potato Nitrogen Management. Journal of Vegetable Crop Production 10:97–132.

    Article  Google Scholar 

  • B-CGMS team Belgian Crop Growth Monitoring System, 2017, 2018 and 2019 reports (n.d.) In: Belgian Crop Growth Monitoring System (B-CGMS). Accessed 3 Mar 2020

  • Bélanger G, Walsh JR, Richards JE et al (2001) Critical nitrogen curve and nitrogen nutrition index for potato in Eastern Canada. Am J Pot Res 78:355–364.

    Article  Google Scholar 

  • Ben Abdallah F, Olivier M, Goffart JP, Minet O (2016) Establishing the nitrogen dilution curve for potato cultivar Bintje in Belgium. Potato Res 59:241–258.

    CAS  Article  Google Scholar 

  • Bernardi M, Deline J, Durand W, Zhang N (2016) Crop yield forecasting: methodological and institutional aspects. FAO, Rome

    Google Scholar 

  • Bontemps S, Bajec K, Cara C et al (2020) Sen4CAP System Software User Manual v1.1

  • Breiman L (2001) Random forests. Mach Learn 45:5–32.

    Article  Google Scholar 

  • Brostaux Y (2005) “Etude du classement par forêts aléatoires d'échantillons perturbés à forte structure d'interaction.” Unpublished doctoral thesis, ULiège. GxABT - Liège Université. Gembloux Agro-Bio Tech

  • Chambenoit C, Laurent F, Machet JM, Boizard H (2004) Development of a decision support system for nitrogen management on potatoes. In: MacKerron DKL, Haverkort AJ (eds) Decision support systems in potato production: bringing models to practice. Wageningen Academic Publishers, pp 55–67

  • Clevers JGPW (1989) Application of a weighted infrared-red vegetation index for estimating leaf area index by correcting for soil moisture. Remote Sensing of Environment 29:25–37.

    Article  Google Scholar 

  • Clevers J, Kooistra L, van den Brande M (2017) Using Sentinel-2 data for retrieving LAI and leaf and canopy chlorophyll content of a potato crop. Remote Sensing 9:405.

    Article  Google Scholar 

  • Daughtry CST, Walthall CL, Kim MS et al (2000) Estimating corn leaf chlorophyll concentration from leaf and canopy reflectance. Remote Sensing of Environment 74:229–239.

    Article  Google Scholar 

  • Delegido J, Verrelst J, Alonso L, Moreno J (2011) Evaluation of Sentinel-2 red-edge bands for empirical estimation of green LAI and chlorophyll content. Sensors 11:7063–7081

    Article  Google Scholar 

  • Delloye C, Weiss M, Defourny P (2018) Retrieval of the canopy chlorophyll content from Sentinel-2 spectral bands to estimate nitrogen uptake in intensive winter wheat cropping systems. Remote Sensing of Environment 216:245–261.

    Article  Google Scholar 

  • Drusch M, Del Bello U, Carlier S et al (2012) Sentinel-2: ESA’s optical high-resolution mission for GMES operational services. Remote Sensing of Environment 120:25–36.

    Article  Google Scholar 

  • Duchenne T, Machet JM, Martin M (1997) Potatoes. In: Lemaire G (ed) Diagnosis of the nitrogen status in crops. Springer, Berlin, Heidelberg, pp 119–130

    Chapter  Google Scholar 

  • Errebhi M, Rosen CJ, Gupta SC, Birong DE (1998) Potato yield response and nitrate leaching as influenced by nitrogen management. Agronomy Journal 90:10–15.

    Article  Google Scholar 

  • Fox RH, Walthall CL (2008) Crop monitoring technologies to assess nitrogen status. In: Nitrogen in agricultural systems. John Wiley & Sons, Ltd, pp 647–674

  • Frampton WJ, Dash J, Watmough G, Milton EJ (2013) Evaluating the capabilities of Sentinel-2 for quantitative estimation of biophysical variables in vegetation. ISPRS Journal of Photogrammetry and Remote Sensing 82:83–92.

    Article  Google Scholar 

  • Giletto CM, Echeverría HE (2012) Critical nitrogen dilution curve for processing potato in Argentinean humid pampas. Am J Pot Res 89:102–110.

    Article  Google Scholar 

  • Giletto CM, Echeverría HE (2015) Critical nitrogen dilution curve in processing potato cultivars. Am J Plant Sci 6:3144–3156.

    CAS  Article  Google Scholar 

  • Giletto CM, Reussi Calvo NI, Sandaña P et al (2020) Shoot- and tuber-based critical nitrogen dilution curves for the prediction of the N status in potato. Eur J Agron 119:126114.

    CAS  Article  Google Scholar 

  • Gitelson AA, Zur Y, Chivkunova OB, Merzlyak MN (2002) Assessing carotenoid content in plant leaves with reflectance spectroscopy. Photochem Photobiol 75:272–281

    CAS  Article  Google Scholar 

  • Gitelson AA, Gritz †Y, Merzlyak MN (2003) Relationships between leaf chlorophyll content and spectral reflectance and algorithms for non-destructive chlorophyll assessment in higher plant leaves. Journal of Plant Physiology 160:271–282.

    CAS  Article  PubMed  Google Scholar 

  • Goffart J-P, Olivier M (2004) Management of N-fertilization of the potato crop using total N-advice software and in-season chlorophyll meter measurements. In: Haverkort AJ, MacKerron DKL (eds). Wageningen Academic Publishers, Decision support systems in potato production, pp 68–83

    Google Scholar 

  • Goffart J-P, Olivier M, MacKerron DKL et al (2000) Spatial and temporal aspects of sampling of potato crops for nitrogen analysis. In: Haverkort AJ, MacKerron DKL (eds) Management of nitrogen and water in potato production. Wageningen Academic Publishers, Wageningen, pp 83–102

    Google Scholar 

  • Goffart J-P, Olivier M, Destain J-P (2005) Presentation of a decision support system (DSS) for nitrogen management in potato production to improve the use of resources. In: Haverkort AJ, Struik PC (eds) Potato in progress: science meets practice. Wageningen Academic Publishers, pp 134–142

  • Goffart J-P, Olivier M, Frankinet M (2008) Potato crop nitrogen status assessment to improve n fertilization management and efficiency: past–present–future. Potato Res 51:355–383.

    CAS  Article  Google Scholar 

  • Goffart J-P, Gobin A, Delloye C, Curnel Y (2017) Crop spectral reflectance to support decision making on crop nutrition. International Fertiliser Society, Colchester, p 28

    Google Scholar 

  • Greenwood DJ, Lemaire G, Gosse G et al (1990) Decline in percentage N of C3 and C4 crops with increasing plant mass. Annals of Botany 66:425–436

    CAS  Article  Google Scholar 

  • Haboudane D, Miller JR, Tremblay N et al (2002) Integrated narrow-band vegetation indices for prediction of crop chlorophyll content for application to precision agriculture. Remote Sensing of Environment 81:416–426.

    Article  Google Scholar 

  • Hack H, Gall H, Klemke T, et al (1993) The BBCH scale for phenological growth stages of potato (Solanum tuberosum L.). In: Proceedings of the 12th annual congress of the European Association for Potato Research, pp 153–154

  • Hatfield JL, Gitelson AA, Schepers JS, Walthall CL (2008) Application of spectral remote sensing for agronomic decisions. Agronomy Journal 100:S-117–S-131.

    CAS  Article  Google Scholar 

  • Haverkort AJ, Franke AC, Steyn JM et al (2015) A robust potato model: LINTUL-POTATO-DSS. Potato Research 58:313–327.

    Article  Google Scholar 

  • Herrmann I, Karnieli A, Bonfil DJ et al (2010) SWIR-based spectral indices for assessing nitrogen content in potato fields. International Journal of Remote Sensing 31:5127–5143.

    Article  Google Scholar 

  • Herrmann I, Pimstein A, Karnieli A et al (2011) LAI assessment of wheat and potato crops by VENμS and Sentinel-2 bands. Remote Sensing of Environment 115:2141–2151.

    Article  Google Scholar 

  • Jacques DC, Kergoat L, Hiernaux P et al (2014) Monitoring dry vegetation masses in semi-arid areas with MODIS SWIR bands. Remote Sensing of Environment 153:40–49.

    Article  Google Scholar 

  • Jaramaz D, Perovic V, Belanovic S (2013) The ESA Sentinel-2 mission vegetation variables for remote sensing of plant monitoring

  • Jongschaap REE, Booij R (2004) Spectral measurements at different spatial scales in potato: relating leaf, plant and canopy nitrogen status. International Journal of Applied Earth Observation and Geoinformation 5:205–218.

    Article  Google Scholar 

  • Lacaux JP, Tourre YM, Vignolles C et al (2007) Classification of ponds from high-spatial resolution remote sensing: application to Rift Valley fever epidemics in Senegal. Remote Sensing of Environment 106:66–74.

    Article  Google Scholar 

  • le Maire G, François C, Dufrêne E (2004) Towards universal broad leaf chlorophyll indices using PROSPECT simulated database and hyperspectral reflectance measurements. Remote Sensing of Environment 89:1–28.

    Article  Google Scholar 

  • MacKerron DKL (2000) Perspectives for use in practice—How can assessment of plant and CNS be used in practice. In: Management of nitrogen and water in potato production. Wageningen Academic Publishers, pp 103–110

  • Meier U (1997) Growth stages of mono-and dicotyledonous plants. Blackwell Wissenschafts-Verlag

    Google Scholar 

  • Morier T, Cambouris AN, Chokmani K (2015) In-season nitrogen status assessment and yield estimation using hyperspectral vegetation indices in a potato crop. Agronomy Journal 107:1295–1309.

    Article  Google Scholar 

  • Olivier M, Goffart J-P, Ledent J-F (2006) Threshold value for chlorophyll meter as decision tool for nitrogen management of potato. Agronomy Journal 98:496–506.

    CAS  Article  Google Scholar 

  • Perry EM, Roberts DA (2008) Sensitivity of narrow-band and broad-band indices for assessing nitrogen availability and water stress in an annual crop. Agronomy Journal 100:1211–1219.

    CAS  Article  Google Scholar 

  • Radoux J, Chome G, Jacques DC et al (2016) Sentinel-2’s potential for sub-pixel landscape feature detection. Remote Sensing 8.

  • Schleicher TD, Bausch WC, Delgado JA, Ayers PD (2001) Evaluation and refinement of the nitrogen reflectance index (NRI) for site-specific fertilizer management. In: 2001 ASABE Annual Meeting. American Society of Agricultural and Biological Engineers, p 1

    Google Scholar 

  • Shenk JS, Westerhaus MO (1993) Analysis of agriculture and food products by near infrared reflectance spectroscopy. Infrasoft International, Port Matilda, PA 116

  • Vincini M, Frazzi E, D’Alessio P (2008) A broad-band leaf chlorophyll vegetation index at the canopy scale. Precision Agric 9:303–319.

    Article  Google Scholar 

  • Wang L, Zhou X, Zhu X et al (2016) Estimation of biomass in wheat using random forest regression algorithm and remote sensing data. The Crop Journal 4:212–219.

    Article  Google Scholar 

  • Weiss M, Baret F (2016) S2ToolBox Level 2 products: LAI, FAPAR, FCOVER, Version 1.1

  • Wu J, Wang D, Rosen CJ, Bauer ME (2007) Comparison of petiole nitrate concentrations, SPAD chlorophyll readings, and QuickBird satellite imagery in detecting nitrogen status of potato canopies. Field Crops Research 101:96–103.

    Article  Google Scholar 

  • Zhou Z, Jabloun M, Plauborg F, Andersen MN (2018) Using ground-based spectral reflectance sensors and photography to estimate shoot N concentration and dry matter of potato. Computers and Electronics in Agriculture 144:154–163.

    Article  Google Scholar 

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Many thanks to all the farmers that allowed us to sample their fields, to the CRA-W crew who contributed to the field work (Amaury Leclef, Gregory Cloux, Daniel Deloze, William Philippe), to Alban Jago (CRA-W) for the processing of the L2A and L3B level Sentinel-2 images, to Yves Brostaux for its expert advices on random forest algorithm and to Belgian Scientific Policy (BELSPO) for the funding of this work. R (R Core Team, 2018) was used for the data processing. Thanks to the whole team and community for developing such a powerful and versatile tool.

Author Contribution (CRediT)

Dimitri Goffart: conceptualisation, methodology, software, formal analysis, investigation, data curation, writing—original draft, writing—review and editing, visualisation. Feriel Ben Abdallah: conceptualisation, methodology, resources, writing—review and editing. Yannick Curnel: conceptualisation, methodology, writing—review and editing, supervision, funding acquisition. Viviane Planchon: conceptualisation, methodology, resources, project administration, funding acquisition. Jean-Pierre Goffart: conceptualisation, methodology, resources, writing—review and editing, supervision, funding acquisition. Pierre Defourny: conceptualisation, writing—review and editing, supervision, funding acquisition

Code Availability

R code used for the data processing, analysis and figures production is a custom code written for the only purpose of this paper.


This work was supported by the SPP Politique scientifique–BELSPO under Grants SR/00/300–BELCAM and SR/42/203–STARGATE.

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Table 3 For each explained shoots variables and each regression type, classification of the ten best combinations of explanatory variables used to build empirical models according to the predictive RMSE from a tenfold cross-validation procedure (RMSECV)

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Goffart, D., Abdallah, F.B., Curnel, Y. et al. In-Season Potato Crop Nitrogen Status Assessment from Satellite and Meteorological Data. Potato Res. (2022).

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  • Biomass
  • Degree days
  • Nitrogen content
  • Nitrogen nutrition index
  • Nitrogen uptake
  • Sentinel-2