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Research Article

Sentinel-2 estimation of CNC and LAI in rice cropping system through hybrid approach modelling

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Article: 2117651 | Received 05 Mar 2022, Accepted 23 Aug 2022, Published online: 20 Sep 2022

References

  • Adeluyi, O., Harris, A., Verrelst, J., Foster, T., & Clay, G. D. (2021). Estimating the phenological dynamics of irrigated rice leaf area index using the combination of PROSAIL and gaussian process regression. International Journal of Applied Earth Observation and Geoinformation, 102, 102454. https://doi.org/10.1016/J.JAG.2021.102454
  • Agri-food Data Portal. (2019). DG AGRI - Rice Production. https://agridata.ec.europa.eu/extensions/DashboardRice/RiceProduction.html
  • Baret, F., Houlès, V., & Guérif, M. (2007). Quantification of plant stress using remote sensing observations and crop models: The case of nitrogen management. Journal of Experimental Botany, 58(4), 869–20. https://doi.org/10.1093/jxb/erl231
  • Berger, K., Rivera Caicedo, J. P., Martino, L., Wocher, M., Hank, T., & Verrelst, J. (2021). A survey of active learning for quantifying vegetation traits from terrestrial earth observation data. Remote Sensing, 13(2), 287. https://doi.org/10.3390/rs13020287
  • Berger, K., Verrelst, J., Féret, J.-B., Hank, T., Wocher, M., Mauser, W., & Camps-Valls, G. (2020a). Retrieval of aboveground crop nitrogen content with a hybrid machine learning method. International Journal of Applied Earth Observation and Geoinformation, 92, 102174. https://doi.org/10.1016/j.jag.2020.102174
  • Berger, K., Verrelst, J., Féret, J. B., Hank, T., Wocher, M., Mauser, W., & Camps-Valls, G. (2020b). Retrieval of aboveground crop nitrogen content with a hybrid machine learning method. International Journal of Applied Earth Observation and Geoinformation, 92, 102174. https://doi.org/10.1016/j.jag.2020.102174
  • Berger, K., Wang, Z., Danner, M., Wocher, M., Mauser, W., & Hank, T. (2018, July). Simulation of spaceborne hyperspectral remote sensing to assist crop nitrogen content monitoring in agricultural crops. International Geoscience and Remote Sensing Symposium, 3801–3804. https://doi.org/10.1109/IGARSS.2018.8518537
  • Boschetti, M., Bocchi, S., Stroppiana, D., & Brivio, P. A. (2006). Estimation of parameters describing morpho-physiological features of Mediterranean rice varieties for modelling purposes. The Italian Journal of Agronomy, 49, 40–49. http://www.agrometeorologia.it/wp-content/uploads/2010/11/5.pdf
  • Boschetti, M., Busetto, L., Ranghetti, L., Haro, J. G., & Bassini, V. (2018). TESTING MULTI-SENSORS TIME SERIES OF LAI ESTIMATES TO MONITOR RICE PHENOLOGY: PRELIMINARY RESULTS institute for electromagnetic sensing of the environment, Italian national research council, department of earth physics and thermodynamics, faculty of P. IGARSS 2018-2018 IEEE. International Geoscience and Remote Sensing Symposium, 8221–8224. doi: 10.1109/IGARSS.2018.8518494.
  • Campos-Taberner, M., García-Haro, F. J., Camps-Valls, G., Grau-Muedra, G., Nutini, F., Busetto, L., Katsantonis, D., Stavrakoudis, D., Minakou, C., Gatti, L., Barbieri, M., Holecz, F., Stroppiana, D., & Boschetti, M. (2017). Exploitation of SAR and optical sentinel data to detect rice crop and estimate seasonal dynamics of leaf area index. Remote Sensing, 9(3), 248. https://doi.org/10.3390/rs9030248
  • Campos-Taberner, M., Garcia-Haro, F. J., Camps-Valls, G., Grau-Muedra, G., Nutini, F., Crema, A., & Boschetti, M. (2016a). Multitemporal and multiresolution leaf area index retrieval for operational local rice crop monitoring. Remote Sensing of Environment, 187, 102–118. https://doi.org/10.1016/j.rse.2016.10.009
  • Campos-Taberner, M., García-Haro, F. J., Camps-Valls, G., Grau-Muedra, G., Nutini, F., Crema, A., & Boschetti, M. (2016). Multitemporal and multiresolution leaf area index retrieval for operational local rice crop monitoring. Remote Sensing of Environment, 187, 102–118. https://doi.org/10.1016/j.rse.2016.10.009
  • Campos-Taberner, M., Garcia-Haro, F. J., Confalonieri, R., Martinez, B., Moreno, A., Sanchez-Ruiz, S., Gilabert, M. A., Camacho, F., Boschetti, M., & Busetto, L. (2016b). Multitemporal monitoring of plant area index in the Valencia rice district with PocketLAI. Remote Sensing, 8(3), 202. https://doi.org/10.3390/rs8030202
  • Candiani, G., Tagliabue, G., Panigada, C., Verrelst, J., Picchi, V., Rivera Caicedo, J. P., & Boschetti, M. (2022). Evaluation of hybrid models to estimate chlorophyll and nitrogen content of maize crops in the framework of the future CHIME mission. Remote Sensing, 14(8), 1792. https://doi.org/10.3390/rs14081792
  • Clevers, J. G. P. W., & Gitelson, A. A. (2013). Remote estimation of crop and grass chlorophyll and nitrogen content using red-edge bands on Sentinel-2 and −3. International Journal of Applied Earth Observation and Geoinformation, 23, 344–351. https://doi.org/10.1016/J.JAG.2012.10.008
  • Colorado, J. D., Cera-Bornacelli, N., Caldas, J. S., Petro, E., Rebolledo, M. C., Cuellar, D., Calderon, F., Mondragon, I. F., & Jaramillo-Botero, A. (2020). Estimation of nitrogen in rice crops from UAV-captured images. Remote Sensing, 12(20), 1–31. https://doi.org/10.3390/rs12203396
  • Confalonieri, R., Foi, M., Casa, R., Aquaro, S., Tona, E., Peterle, M., Boldini, A., De Carli, G., Ferrari, A., Finotto, G., Guarneri, T., Manzoni, V., Movedi, E., Nisoli, A., Paleari, L., Radici, I., Suardi, M., Veronesi, D., Bregaglio, S., … Acutis, M. (2013). Development of an app for estimating leaf area index using a smartphone. Trueness and precision determination and comparison with other indirect methods. Computers and Electronics in Agriculture, 96, 67–74. https://doi.org/10.1016/j.compag.2013.04.019
  • Danner, M., Berger, K., Wocher, M., Mauser, W., & Hank, T. (2019). Fitted PROSAIL parameterization of leaf inclinations, water content and brown pigment content for winter wheat and maize canopies. Remote Sensing, 11(10), 1150. https://doi.org/10.3390/rs11101150
  • 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. https://doi.org/10.1016/j.rse.2018.06.037
  • Fava, F., Colombo, R., Bocchi, S., Meroni, M., Sitzia, M., Fois, N., & Zucca, C. (2009). Identification of hyperspectral vegetation indices for Mediterranean pasture characterization. International Journal of Applied Earth Observation and Geoinformation, 11, 233–243. https://doi.org/10.1016/j.jag.2009.02.003
  • Féret, J. B., Berger, K., de Boissieu, F., & Malenovský, Z. (2021). PROSPECT-PRO for estimating content of nitrogen-containing leaf proteins and other carbon-based constituents. Remote Sensing of Environment, 252, 112173. https://doi.org/10.1016/j.rse.2020.112173
  • Gilardelli, C., Stella, T., Confalonieri, R., Ranghetti, L., Campos-Taberner, M., García-Haro, F. J., & Boschetti, M. (2019). Downscaling rice yield simulation at sub-field scale using remotely sensed LAI data. European Journal of Agronomy, 103, 108–116. https://doi.org/10.1016/j.eja.2018.12.003
  • Guanter, L., Kaufmann, H., Segl, K., Foerster, S., Rogass, C., Chabrillat, S., Kuester, T., Hollstein, A., Rossner, G., Chlebek, C., Straif, C., Fischer, S., Schrader, S., Storch, T., Heiden, U., Mueller, A., Bachmann, M., Mühle, H., Müller, R., … Sang, B. (2015). The EnMAP spaceborne imaging spectroscopy mission for earth observation. Remote Sensing, 7(7), 8830–8857. https://doi.org/10.3390/rs70708830
  • Hank, T. B., Berger, K., Bach, H., Clevers, J. G. P. W., Gitelson, A., Zarco-Tejada, P., & Mauser, W. (2019). Spaceborne imaging spectroscopy for sustainable agriculture: contributions and challenges, surveys in geophysics. Springer Netherlands. https://doi.org/10.1007/s10712-018-9492-0
  • Hansen, P. M., & Schjoerring, J. K. (2003). Reflectance measurement of canopy biomass and nitrogen status in wheat crops using normalized difference vegetation indices and partial least squares regression. Remote Sensing of Environment, 86(4), 542–553. https://doi.org/10.1016/S0034-4257(03)
  • Inoue, Y., Sakaiya, E., Zhu, Y., & Takahashi, W. (2012). Diagnostic mapping of canopy nitrogen content in rice based on hyperspectral measurements. Remote Sensing of Environment, 126, 210–221. https://doi.org/10.1016/j.rse.2012.08.026
  • Jacquemoud, S., & Baret, F. (1990). PROSPECT: A model of leaf optical properties spectra. Remote Sensing of Environment, 34(2), 75–91. https://doi.org/10.1016/0034-4257(90)90100-Z
  • Lassaletta, L., Billen, G., Garnier, J., Bouwman, L., Velazquez, E., Mueller, N. D., & Gerber, J. S. (2016). Nitrogen use in the global food system: Past trends and future trajectories of agronomic performance, pollution, trade, and dietary demand. Environmental Research Letters, 11(9), 095007. https://doi.org/10.1088/1748-9326/11/9/095007
  • Lemaire, G., Tang, L., Bélanger, G., Zhu, Y., & Jeuffroy, M. H. (2021). Forward new paradigms for crop mineral nutrition and fertilization towards sustainable agriculture. European Journal of Agronomy, 125, 126248. https://doi.org/10.1016/J.EJA.2021.126248
  • Liu, N., Townsend, P. A., Naber, M. R., Bethke, P. C., Hills, W. B., & Wang, Y. (2021). Hyperspectral imagery to monitor crop nutrient status within and across growing seasons. Remote Sensing of Environment, 255, 112303. https://doi.org/10.1016/J.RSE.2021.112303
  • Loizzo, R., Daraio, M., Guarini, R., Longo, F., Lorusso, R., Dini, L., & Lopinto, E., 2019. Prisma mission status and perspective, in: IGARSS 2019-2019 IEEE International geoscience and remote sensing symposium. pp. 4503–4506. https://doi.org/10.1109/IGARSS.2019.8899272
  • Matsunaga, T., Iwasaki, A., Tachikawa, T., Tanii, J., & Kashimura, O. (2020). HYPERSPECTRAL IMAGER SUITE (HISUI): ITS LAUNCH AND CURRENT STATUS national institute for environmental studies (NIES), Japan The University of Tokyo, Japan Japan Space Systems, Japan National Institute of Advanced Industrial Science and Technology. International Geoscience and Remote Sensing Symposium, 3, 3272–3273. https://doi.org/10.1109/IGARSS39084.2020.9323376
  • NOAA, Global Positioning Laboratory. (n.d.). NOAA Solar Calculator. https://gml.noaa.gov/grad/solcalc/index.html
  • Nutini, F., Confalonieri, R., Paleari, L., Pepe, M., Criscuolo, L., Porta, F., Ranghetti, L., Busetto, L., & Boschetti, M. (2021). Supporting operational site‐specific fertilization in rice cropping systems with infield smartphone measurements and Sentinel-2 observations. Precision Agriculture, 22(4), 1284–1303. https://doi.org/10.1007/s11119-021-09784-0
  • Paleari L, Movedi E, Vesely FM, Thoelke W, Tartarini S, Foi M, Boschetti M, Nutini F, Confalonieri R. Estimating Crop Nutritional Status Using Smart Apps to Support Nitrogen Fertilization. A Case Study on Paddy Rice. Sensors. 2019; 19(4):981. https://doi.org/10.3390/s19040981
  • Pipia, L., Amin, E., Belda, S., Salinero-Delgado, M., & Verrelst, J. (2021). Green lai mapping and cloud gap-filling using Gaussian process regression in google earth engine. Remote Sensing, 13(3), 1–25. https://doi.org/10.3390/rs13030403
  • Ranghetti, L., Boschetti, M., Nutini, F., & Busetto, L. (2020). “sen2r”: An R toolbox for automatically downloading and preprocessing Sentinel-2 satellite data. Computers & Geosciences, 139, 104473. https://doi.org/10.1016/j.cageo.2020.104473
  • Rast, M., Ananasso, C., Bach, H., Dor, E., Chabrillat, S., Colombo, R., Bello, U., Feret, J., Giardino, C., Green, R., Guanter, L., Marsh, S., Nieke, J., Ong, C., Rum, G., Schaepman, M., Schlerf, M., Skidmore, A., & Strobl, P. (2019). Copernicus hyperspectral imaging mission for the environment-mission requirements document. The European Space Agency. ESA-EOPSM-CHIM-MRD-3216. https://esamultimedia.esa.int/docs/EarthObservation/Copernicus_CHIME_MRD_v3.0_Issued_21_01_2021.pdf (accessed on 28 February 2019).
  • Schlemmer, M., Gitelson, A., Schepers, J., Ferguson, R., Peng, Y., Shanahan, J., & Rundquist, D. (2013). Remote estimation of nitrogen and chlorophyll contents in maize at leaf and canopy levels. International Journal of Applied Earth Observation and Geoinformation, 25, 47–54. https://doi.org/10.1016/j.jag.2013.04.003
  • THE STATE OF FOOD AND AGRICULTURE. CLIMATE CHANGE, AGRICULTURE AND FOOD SECURITY. FAO 2016 ISBN 978-92-5-109374-0, ISSN 0081-4539. Available online: https://www.fao.org/3/i6030e/i6030e.pdf (accessed on 10 April 2021)
  • Stroppiana, D., Boschetti, M., Brivio, P. A., & Bocchi, S. (2009). Plant nitrogen concentration in paddy rice from field canopy hyperspectral radiometry. Field Crops Research, 111(1–2), 119–129. https://doi.org/10.1016/j.fcr.2008.11.004
  • Tagliabue, G., Boschetti, M., Bramati, G., Candiani, G., Colombo, R., Nutini, F., Pompilio, L., Rivera-Caicedo, J. P., Rossi, M., Rossini, M., Verrelst, J., & Panigada, C. (2022). Hybrid retrieval of crop traits from multi-temporal PRISMA hyperspectral imagery. ISPRS Journal of Photogrammetry and Remote Sensing, 187, 362–377. https://doi.org/10.1016/j.isprsjprs.2022.03.014
  • Thompson, D. R., Schimel, D. S., Poulter, B., Brosnan, I., Hook, S. J., Green, R. O., Glenn, N., Guild, L., Henn, C., Cawse-Nicholson, K., Kokaly, R., Lee, C., Luvall, J., Miller, C. E., Nastal, J., Pavlick, R., Phillips, B., Schneider, F., Uz, S. S., … Wang, W. (2020). NASA’s surface biology and geology concept study: status and next steps. International Geoscience and Remote Sensing Symposium, 3269–3271. https://doi.org/10.1109/IGARSS39084.2020.9323295
  • Upreti, D., Huang, W., Kong, W., Pascucci, S., Pignatti, S., Zhou, X., Ye, H., & Casa, R. (2019). A comparison of hybrid machine learning algorithms for the retrieval of wheat biophysical variables from sentinel-2. Remote Sensing, 11(5), 481. https://doi.org/10.3390/rs11050481
  • Verhoef, W. (1984). Light scattering by leaf layers with application to canopy reflectance modeling: The SAIL model. Remote Sensing of Environment, 16(2), 125–141. https://doi.org/10.1016/0034-4257(84)90057-9
  • Verrelst, J., Berger, K., & Rivera-Caicedo, J. P. (2020). Intelligent sampling for vegetation nitrogen mapping based on hybrid machine learning algorithms. IEEE Geoscience and Remote Sensing Letters, 18(12), 2038–2042. https://doi.org/10.1109/LGRS.2020.3014676
  • Verrelst, J., Malenovský, Z., Van der Tol, C. et al. Quantifying Vegetation Biophysical Variables from Imaging Spectroscopy Data: A Review on Retrieval Methods. Surv Geophys, 40(3), 589–629 (2019). https://doi.org/10.1007/s10712-018-9478-y
  • Verrelst, J., Rivera, J. P., Alonso, L., & Moreno, J. (2011, April). ARTMO: An Automated Radiative Transfer Models Operator toolbox for automated retrieval of biophysical parameters through model inversion. In Proc. EARSeL 7th SIG-Imag. Spectrosc. Workshop (pp. 11–13).
  • Verrelst, J., Rivera-Caicedo, J. P., Reyes-Muñoz, P., Morata, M., Amin, E., Tagliabue, G., Panigada, C., Hank, T., & Berger, K. (2021a). Mapping landscape canopy nitrogen content from space using PRISMA data. ISPRS Journal of Photogrammetry and Remote Sensing, 178, 382–395. https://doi.org/10.1016/j.isprsjprs.2021.06.017
  • Verrelst, J., Rivera-Caicedo, J. P., Reyes-Muñoz, P., Morata, M., Amin, E., Tagliabue, G., Panigada, C., Hank, T., & Berger, K. (2021b). Mapping landscape canopy nitrogen content from space using PRISMA data. ISPRS Journal of Photogrammetry and Remote Sensing, 178, 382–395. https://doi.org/10.1016/J.ISPRSJPRS.2021.06.017
  • Wang, S., Guan, K., Wang, Z., Ainsworth, E. A., Zheng, T., Townsend, P. A., Liu, N., Nafziger, E., Masters, M. D., Li, K., Wu, G., & Jiang, C. (2021). Airborne hyperspectral imaging of nitrogen deficiency on crop traits and yield of maize by machine learning and radiative transfer modeling. International Journal of Applied Earth Observation and Geoinformation, 105, 102617. https://doi.org/10.1016/J.JAG.2021.102617
  • Weiss, M.; Baret, F. S2ToolBox Level 2 Products: LAI, FAPAR, FCOVER. Available online: https://step.esa.int/docs/extra/ATBD_S2ToolBox_L2B_V1.1.pdf (accessed on 21 January 2020).
  • Weiss, M., Jacob, F., & Duveiller, G. (2020). Remote sensing for agricultural applications: A meta-review. Remote Sensing of Environment, 236, 111402. https://doi.org/10.1016/j.rse.2019.111402
  • Wutzler, T., Migliavacca, M., & Julitta, T. (2016). FieldSpectroscopycc: R package for Characterization and Calibration of spectrometers. R Package Version 0.5.227. Available online: https://rdrr.io/github/tommasojulitta/FieldSpectroscopyCC/ (accessed on 22 May)
  • Zarco-Tejada, P. J., Hubbard, N., & Loudjani, P. (2014). Precision agriculture: an opportunity for EU farmers—potential support with the CAP 2014–2020. Joint Research Centre (JRC) of the European Commission. Available online: https://policycommons.net/artifacts/1339069/precision-agriculture/1948411/ (accessed on 12 July 2021)
  • Zhang, K., Yuan, Z., Yang, T., Lu, Z., Cao, Q., Tian, Y., Zhu, Y., Cao, W., & Liu, X. (2020). Chlorophyll meter–based nitrogen fertilizer optimization algorithm and nitrogen nutrition index for in-season fertilization of paddy rice. Agronomy Journal, 112(1), 288–300. https://doi.org/10.1002/agj2.20036