Analysis of designs used in monitoring crop growth based on remote sensing methods

Analysis of designs used in monitoring crop growth based on remote sensing methods

Choosing appropriate designs and methods for monitoring crop growth is a challenging process of major importance. Remote sensing from space and manned or unmanned airborne operations are used to measure crop reflectance and a wide variety of other agricultural parameters. While some experiments use only a few, specific methods and designs and organize the results in lists of evidence, other experiments use a wider range of techniques to create a more credible and comprehensive assessment of crop yield. Particular situations related to the available resources in terms of data collection and expertise in addition to the intended use of the results may require specific designs or a combination of methods and design. This review intended to explore the challenges and document a range of possible approaches for remote evaluation of crop growth. The scope of the analysis was designed to provide information on methods that work under different circumstances and why and how effective a particular method is an approach to monitoring crop growth. Considering the agricultural ecosystems as complex systems and the working methodology as having a high degree of complexity, we propose an approach suitable for complex systems. New sets of models and methods need to be developed for approaching complex systems, which are characterized by self-organization, nonlinearity, ongoing adaptation, and networking.

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  • Atherton J, MacArthur A, Hakala T, Maseyk K, Robinson I et al. (2018). Drone measurements of solar-induced chlorophyll fluorescence acquired with a low-weight DFOV spectrometer system. In: Proceedings of the International Geoscience and Remote Sensing Symposium (IGARSS); Valencia, Spain; pp. 8834-8836.
  • Ac A, Malenovský Z, Olejnícková J, Gallé A, Rascher U, Mohammed G (2015). Meta-analysis assessing potential of steady-state chlorophyll fluorescence for remote sensing detection of plant water, temperature and nitrogen stress. Remote Sensing of Environment 168: 420-436.
  • Bacour C, Maignan F, Peylin P, MacBean N, Bastrikov V et al. (2019). Differences between OCO-2 and GOME-2 SIF products from a model-data fusion perspective. Journal of Geophysical Research: Biogeoscience 124: 3143–3157.
  • Bandopadhyay S, Rastogi A, Juszczak R (2020). Review of top-ofcanopy Sun-Induced Fluorescence (SIF) studies from ground, UAV, airborne to spaceborne observations. Sensors 20: 1144.
  • Bandopadhyay S, Rastogi A, Rascher U, Rademske P, Schickling A et al. (2019). Hyplant-derived Sun-Induced Fluorescence-a new opportunity to disentangle complex vegetation signals from diverse vegetation types. Remote Sensing 11: 1691.
  • 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: 869-880.
  • Blackstone A (2020). Principles of Sociological Inquiry – Qualitative and Quantitative Methods. Book reference?
  • Brutus S, Aguinis H, Wassmer U (2013). Self-reported limitations and future directions in scholarly reports: Analysis and recommendations. Journal of Management 39 (1): 48-75. doi: 10.1177/0149206312455245
  • Calderón R, Navas-Cortés JA, Lucena C, Zarco-Tejada PJ (2013). High-resolution airborne hyperspectral and thermal imagery for early detection of Verticillium wilt of olive using fluorescence, temperature and narrow-band spectral indices. Remote Sensing of Environment 139: 231-245.
  • Cao Y, Jiang K, Wu J, Yu F, Du W et al. (2020). Inversion modeling of japonica rice canopy chlorophyll content with UAV hyperspectral remote sensing. PLoS ONE 15 (9): e0238530. doi: 10.1371/journal.pone.0238530
  • Chen JM (1996). Evaluation of vegetation indices and a modified simple ratio for boreal applications. Canadian Journal of Remote Sensing 22: 229-242.
  • Cendrero-Mateo MP, Wieneke S, Damm A, Alonso L, Pinto F et al. (2019). Sun-induced chlorophyll fluorescence III: Benchmarking retrieval methods and sensor characteristics for proximal sensing. Remote Sensing 11: 962. doi: 10.3390/ rs11080962
  • Cogliati S, Rossini M, Julitta T, Meroni M, Schickling A et al. (2015). Continuous and long-term measurements of reflectance and sun-induced chlorophyll fluorescence by using novel automated field spectroscopy systems. Remote Sensing of Environment 164: 270-281.
  • Dabrowska-Zielinska K, Malinska A, Bochenek Z, Bartold M, Gurdak R et al. (2020). Drought model DISS based on the fusion of satellite and meteorological data under variable climatic conditions. Remote Sensing 12: 2944. doi: 10.3390/ rs12182944
  • Damm A, Elber J, Erler A, Gioli B, Hamdi K, Hutjes R, Kosvancova M et al. (2010). Remote sensing of sun-induced fluorescence to improve modeling of diurnal courses of gross primary production (GPP). Global Change Biology 16: 171-186.
  • Daumard F, Goulas Y, Champagne S, Fournier A, Ounis A et al. (2012). Continuous monitoring of canopy level sun-induced chlorophyll fluorescence during the growth of a Sorghum field. IEEE Transactions on Geoscience and Remote Sensing 50 (11): 4292-4300. doi: 10.1109/TGRS.2012.2193131
  • Davidson EA, Janssens IA, Lou Y (2006). On the variability of respiration in terrestrial ecosystems: Moving beyond Q10. Global Change Biology 12: 154-164.
  • Dobrota CT, Lazăr L, Baciu C (2015). Assessment of physiological state of Betula pendula and Carpinus betulus through leaf reflectance measurements. Flora 216: 26-34.
  • Dobrota CT, Butiuc-Keul A, Carpa R (2020). Adaptive Strategies of Betula Species to Environmental Stress, In: Bertelsen CT, (editors). Betula Ecology and Uses. New York, USA: Nove Science Publishers. pp. 37-64.
  • Dorigo WA (2012). Improving the robustness of cotton status characterisation by radiative transfer model inversion of multiangular CHRIS/PROBA data. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 5: 18-29.
  • Drusch M, Moreno J, Del Bello U, Franco R, Goulas Y et al. (2017). The Fluorescence Explorer Mission Concept-ESA’s Earth Explorer 8. IEEE Transactions on Geoscience and Remote Sensing 55: 1273- 1284.
  • Du S, Liu L, Liu X, Guo J, Hu J et al. (2019). SIFSpec: Measuring solar-induced chlorophyll fluorescence observations for remote sensing of photosynthesis. Sensors 19 (13): 3009. doi: 10.3390/ s19133009
  • Evain S, Ounis A, Baret F, Goulas Y, Louis J et al. (2002). Passive vegetation fluorosensing using atmospheric oxygen absorption bands. Recent Advances in Quantitative Remote Sensing 509- 513.
  • Frankenberg C, Berry J, Guanter L, Joiner J (2013). Remote sensing of terrestrial chlorophyll fluorescence from space. SPIE Newsroom.
  • Frankenberg C, O’Dell C, Berry J, Guanter L, Joiner J et al. (2014). Prospects for chlorophyll fluorescence remote sensing from the Orbiting Carbon Observatory-2. Remote Sensing of Environment 147: 1-12.
  • Frankenberg C, Köhler P, Magney TS, Geier S, Lawson P et al. (2018). The chlorophyll fluorescence imaging spectrometer (CFIS), mapping far red fluorescence from aircraft. Remote Sensing of Environment 217: 523-536.
  • Fu Z, Jiang J, Gao Y, Krienke B, Wang M et al. (2020). Wheat growth monitoring and yield estimation based on multi-rotor unmanned aerial vehicle. Remote Sensing 12(3): 508. doi: 10.3390/rs12030508
  • Garzonio R, di Mauro B, Colombo R, Cogliati S (2017). Surface reflectance and sun-induced fluorescence spectroscopy measurements using a small hyperspectral UAS. Remote Sensing 9 (5): 472. doi: 10.3390/rs9050472
  • Gerhards M, Schlerf M, Rascher U, Udelhoven T, Juszczak R et al. (2018). Analysis of airborne optical and thermal imagery for detection of water stress symptoms. Remote Sensing 10: 1139.
  • Guan K, Berry JA, Zhang Y, Joiner J, Guanter L et al. (2016). Improving the monitoring of crop productivity using spaceborne solarinduced fluorescence. Global Change Biology 22: 716-726.
  • Guanter L, Alonso L, Gómez-Chova L, Amorós-López J, Vila J, Moreno J (2007). Estimation of solar-induced vegetation fluorescence from space measurements. Geophysical Research Letters 34 (8): L08401.
  • Guanter L, Zhanga Y, Jung M, Joiner J, Voigt M et al. (2014). Global and time-resolved monitoring of crop photosynthesis with chlorophyll fluorescence. doi: 10.1073/pnas.1320008111
  • Joiner J, Guanter L, Lindstrot R, Voigt M, Vasilkov AP et al. (2013). Global monitoring of terrestrial chlorophyll fluorescence from moderate-spectral-resolution near-infrared satellite measurements: Methodology, simulations, and application to GOME-2. Atmospheric Measurement Techniques 6: 2803-2823.
  • Joiner J, Yoshida Y, Vasilkov, AP, Schaefer K, Jung M et al. (2014). The seasonal cycle of satellite chlorophyll fluorescence observations and its relationship to vegetation phenology and ecosystem atmosphere carbon exchange. Remote Sensing of Environment 152: 375-391.
  • Joiner J, Yoshida Y, Guanter L, Middleton EM (2016). New methods for the retrieval of chlorophyll red fluorescence from hyperspectral satellite instruments: Simulations and application to GOME-2 and SCIAMACHY. Atmospheric Measurement Techniques 9: 3939-3967.
  • Haboudane D, Tremblay N, Miller JR, Vigneault P (2008). Remote estimation of crop chlorophyll content using spectral indices derived from hyperspectral data IEEE Transactions on Geoscience and Remote Sensing 46 (2): 423.
  • Haboudane D, Miller JR, Tremblay N, Zarco-Tejada PJ, Dextraze L (2002). Integrated narrow-band vegetation indices for prediction of crop chlorophyll content for application to precision agriculture. Remote Sensing of Environment 81: 416-426.
  • He L, Chen JM, Liu J, Mo G, Joiner J (2017). Angular normalization of GOME-2 sun-induced chlorophyll fluorescence observation as a better proxy of vegetation productivity. Geophysical Research Letter 44: 5691-5699.
  • Herrick JE, Van Zee JW, McCord SE, Courtright EM, Karl JV et al. (2017). Monitoring Manual for Grassland, Shrubland, and Savanna Ecosystems, Volume I: Core Methods. In Monitoring Manual for Grassland, Shrubland, and Savanna Ecosystems 2nd Edition Date: 06/14/17Publisher: USDA-ARS Jornada Experimental Range P.O. Box 30003, MSC 3JER, NMSU Las Cruces, New Mexico 88003-8003.
  • Huete A, Didan K, Miura T, Rodriguez EP, Gao X et al. (2002). Overview of the radiometric and biophysical performance of the MODIS vegetation indices. Remote Sensing of Environment 83: 195-213.
  • Ingram KT, Hilton HW (1986). Nitrogen potassium fertilization and SOIL-MOISTURE effects on growth and development of DRIP-IRRIGATED Sugarcane. Crop Science 26: 1034-1039.
  • Khumairoh U, Groot JCJ, Lantinga EA (2012). Complex agroecosystems for food security in a changing climate. Ecology and Evolution 2 (7): 1696-1704.
  • Keating BA, Robertson MJ, Muchow RC, Huth NI (1999). Modelling sugarcane production systems I. Development and performance of the sugarcane module. Field Crops Research 61: 253-271.
  • Kumhalova J, Novak P, Madaras M (2018). Monitoring oats and winter wheat within field spatial variability by satellite images. Scientia Agriculturae Bohemica 49 (2): 127-135.
  • Kebabian PL, Theisen AF, Kallelis S, Freedman A (1999). A passive two-band sensor of sunlight-excited plant fluorescence. Review of Scientific Instruments 70: 4386-4393.
  • Köhler P, Frankenberg C, Magney TS, Guanter L, Joiner J et al. (2018). Global retrievals of Solar-Induced Chlorophyll Fluorescence with TROPOMI: first results and intersensor comparison to OCO-2. Geophysical Ressearch Letters 45: 10456-10463.
  • Itakura K, Kamakura I, Hosoi F (2019). Three-dimensional monitoring of plant structural parameters and chlorophyll distribution. Sensors 19 (2): 413. doi: 10.3390/s19020413
  • Lang S (2008). Object-based image analysis for remote sensing applications: modeling reality – dealing with complexity in object based image analysis. In: Blaschke T, Lang S, Hay G (editors). Object-Based Image Analysis - Spatial concepts for knowledge-driven remote sensing applications. Berlin, Germany: Springer-Verlag, pp. 3-28.
  • Lew TTS, Sarojam R, Jang IC, Park BS, Naqvi N et al. (2020). Species-independent analytical tools for next-generation agriculture. Nature Plants 6: 1408-1417. doi: 10.1038/s41477- 020-00808-7
  • Li X, Xiao J (2019). Mapping photosynthesis solely from solarinduced chlorophyll fluorescence: A global, fine-resolution dataset of gross primary production derived from OCO-2. Remote Sensing 25: 2563.
  • Li C, Zhu X, Wei Y, Cao S, Guo X et al. (2018). Estimating apple tree canopy chlorophyll content based on Sentinel-2A remote sensing imaging. Scientific Reports 8: 3756. doi:10.1038/ s41598-018-21963-0 1
  • Li Z, Zhanga Q, Lia J, Yang X, Wua Y et al. (2020). Solar-induced chlorophyll fluorescence and its link to canopy photosynthesis in maize from continuous ground measurements. Remote Sensing of Environment 236: 111420.
  • Liu L, Liu X, Hu J (2015). Effects of spectral resolution and SNR on the vegetation solar-induced fluorescence retrieval using FLDbased methods at canopy level. European Journal of Remote Sensing 48: 743-762.
  • Liu HQ, Huete AR (1995). A feedback based modification of the NDVI to minimize canopy background and atmospheric noise. IEEE Transactions on Geoscience and Remote Sensing 33: 457-465.
  • Liu L, Xi Y, Zhou H, Liu S, Zhou L et al. (2018) Evaluating the utility of solar-induced chlorophyll fluorescence for drought monitoring by comparison with NDVI derived from wheat canopy. Science of the Total Environment 625: 1208-1217.
  • Lu S, Lu F, You W, Wang Z, Liu Y, Omasa K (2018). A robust vegetation index for remotely assessing chlorophyll content of dorsiventral leaves across several species in different seasons. Plant Methods 14: 15. doi: 10.1186/s13007-018-0281-z
  • Lu S, Lu X, Zhao W, Liu Y, Wang Z et al. (2015). Comparing vegetation indices for remote chlorophyll measurement of white poplar and Chinese elm leaves with different adaxial and abaxial surfaces. Journal of Experimental Botany 66 (18): 5625-5637.
  • Ludbrook J (1997). Comparing methods of measurements. Clinical and Experimental Pharmacology and Physiology 24 (2): 193- 203.
  • Mac Arthur A, Rossini M, Robinson I, Davies N, Mcdonald K (2014). A dual-field-of-view spectrometer system for reflectance and fluorescence measurement. In: Proceedings of the 5th International Workshop on Remote Sensing of Vegetation Fluorescence, Paris, France; pp. 22-24.
  • Maes WH, Steppe K (2018). Perspectives for remote sensing with unmanned aerial vehicles in precision agriculture. Trends in Plant Science 24 (2). doi: 10.1016/j.tplants.2018.11.007
  • Magney TS, Frankenberg C, Fisher JB, Sun Y, North GB et al. (2017). Connecting active to passive fluorescence with photosynthesis: A method for evaluating remote sensing measurements of Chl fluorescence. New Phytologist 215: 1594-1608.
  • Magney TS, Frankenberg C, Köhler P, North G, Davis TS et al. (2019). Disentangling changes in the spectral shape ofchlorophyll fluorescence: Implicationsfor remote sensing of photosynthesis. Journal of Geophysical Research: Biogeosciences 124: 1491- 1507. doi: 10.1029/2019JG005029
  • Mahlein AK (2016). Plant disease detection by imaging sensors – parallels and specific demands for precision agriculture and plant phenotyping. Plant Disease 100: 241-254.
  • Martini D, Pacheco-Labrador J, Perez-Priego O, Van Der Tol C, ElMadany TS et al. (2019). Nitrogen and phosphorus effect on sun-induced fluorescence and gross primary productivity in mediterranean grassland. Remote Sensing 11: 2562.
  • Maimaitijiang M, Sagan V, Erkbol H, Adrian J, Newcomb M et al. (2020). UAV-based sorghum growth monitoring: a comparative analysis of lidar and photogrammetry. ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences V-3-2020, XXIV ISPRS Congress.
  • Meroni M, Picchi V, Rossini M, Cogliati S, Panigada C et al. (2008). Leaf level early assessment of ozone injuries by passive fluorescence and photochemical reflectance index. International Journal of Remote Sensing 29: 5409-5422.
  • Meroni M, Rossini M, Guanter L, Alonso L, Rascher U et al. (2009). Remote sensing of solar-induced chlorophyll fluorescence: Review of methods and applications. Remote Sensing of Environment 113: 2037-2051.
  • Miao Y, Mulla DJ, Randall GW, Vetsch JA, Vintila R (2009). Combining chlorophyll meter readings and high spatial resolution remote sensing images for in-season site-specific nitrogen management of corn. Precision Agriculture 10: 45-62.
  • Migliavacca M, Perez-Priego O, Rossini M, El-Madany TS, Moreno G et al. (2017). Plant functional traits and canopy structure control the relationship between photosynthetic CO2 uptake and far-red sun-induced fluorescence in a Mediterranean grassland under different nutrient availability. New Phytologist 214: 1078-1091.
  • Mohammed GH, Colombo R, Middleton EM, Rascher U, Van Der Tol C et al. (2019). Remote sensing of solar-induced chlorophyll fluorescence (SIF) in vegetation: 50 years of progress. Remote Sensing of Environment 231: 111177.
  • Molijn RA, Iannini L, Rocha JV, Hanssen RF (2018). Ground reference data for sugarcane biomass estimation in Sao Paulo state. Brazil Science Data 5: 18.
  • Montogomery D, (2017). Design and analysis of experiments. New York, USA: John Wiley.
  • Moya I, Camenen L, Evain S, Goulas Y, Cerovic ZG et al. (2004). A new instrument for passive remote sensing: 1. Measurements of sunlight-induced chlorophyll fluorescence. Remote Sensing of Environment 91: 186-197.
  • Moya I, Daumard F, Moise N, Ounis A, Goulas Y (2006). First airborne multiwavelength passive chlorophyll fluorescence measurements over La Mancha (Spain) fields. In Proceedings of the Recent Advances in Quantitative Remote Sensing; Torrent, Spain. pp. 820-825.
  • Ni Z, Lu Q, Huo H, Zhang H (2019). Estimation of chlorophyll fluorescence at different scales: A review. Sensors 19: 3000.
  • Pacheco-Labrador J, Hueni A, Mihai L, Sakowska K, Julitta T et al. (2019). Sun-induced chlorophyll fluorescence I: Instrumental considerations for proximal spectroradiometers. Remote Sensing 2019 11: 960.
  • Penuelas J, Filella I, Biel C, Serrano L, Save R (1993). The reflectance at the 950–970 nm region as an indicator of plant water status. International Journal of Remote Sensing 14: 1887-1905.
  • Piegari E, Gossn JI, Grings F, Barraza Bernadas V, Juárez AB et al. (2021). Estimation of leaf area index and leaf chlorophyll content in Sporobolus densiflorus using hyperspectral measurements and PROSAIL model simulations. International Journal of Remote Sensing 42 (4): 1181-1200. doi:10.1080/01431161.2020.1826058
  • Rascher U, Alonso L, Burkart A, Cilia C, Cogliati S et al. (2015). Suninduced fluorescence - A new probe of photosynthesis: First maps from the imaging spectrometer HyPlant. Global Change Biology 21: 4673-4684.
  • Rossini M, Panigada C, Cilia C, Meroni M, Busetto L et al. (2015). Discriminating irrigated and rainfed maize with diurnal fluorescence and canopy temperature airborne maps. ISPRS International Journal of Geo-Information 4: 626-646.
  • Schickling A, Matveeva M, Damm A, Schween JH, Wahner A et al. (2016). Combining sun-induced chlorophyll fluorescence and photochemical reflectance index improves diurnal modeling of gross primary productivity. Remote Sensing 8: 574.
  • Shafian S, Rajan N, Schnell R, Bagavathiannan M, Valasek J et al. (2018) Unmanned aerial systems-based remote sensing for monitoring sorghum growth and development. PLoS ONE 13 (5): e0196605.
  • Sofonia J, Shendryk Y, Phinna S, Roelfsema C, Kendoul F et al. (2019). Monitoring sugarcane growth response to varying nitrogen application rates: A comparison of UAV SLAM LiDAR and photogrammetry. International Journal of Applied Earth Observation and Geoinformation 82: 101878.
  • Shafian S, Rajan N, Schnell R, Bagavathiannan M, Valasek J et al. (2018). Unmanned aerial systems-based remote sensing for monitoring sorghum growth and development, PloSONE 13(5): e0196605. doi: 10.1371/journal.pone.0196605
  • Song L, Guanter L, Guan K, You L, Huete A et al. (2018). Satellite sun-induced chlorophyll fluorescence detects early response of winter wheat to heat stress in the Indian Indo-Gangetic Plains. Global Change Biology 24: 4023-4037.
  • Sonobe R, Yamashita H, Mihara H, Morita A, Ikka T (2021). Hyperspectral reflectance sensing for quantifying leaf chlorophyll content in wasabi leaves using spectral pre-processing techniques and machine learning algorithms. International Journal of Remote Sensing 42 (4): 1311-1329. doi: 10.1080/01431161.2020.1826065
  • Sun Y, Fu R, Dickinson R, Joiner J, Frankenberg C et al. (2015). Drought onset mechanisms revealed by satellite solar-induced chlorophyll fluorescence: Insights from two contrasting extreme events. Journal of Geophysical Research: Biogeosciences 120: 2427-2440.
  • Sun Y, Frankenberg C, Wood JD, Schimel DS, Jung M et al. (2017). OCO-2 advances photosynthesis observation from space via solar-induced chlorophyll fluorescence. Science 358 (6360): eaam5747.
  • Terrill WR (1994). A FAQ on Vegetation in Remote Sensing Division of Geological and Planetary Sciences California Institute of Technology.
  • Tao H, Feng H, Xu L, Miao M, Long H et al. (2020). Estimation of crop growth parameters using UAV-based hyperspectral remote sensing data. Sensors 20 (5): 1296. doi: 10.3390/s20051296
  • Urschel MR, Pocock T (2018). Remote detection of growth dynamics in red lettuce using a novel chlorophyll a fluorometer. Agronomy 8 (10): 227.
  • Vanbrabant Y, Tits L, Delalieux S, Pauly K, Verjans W et al. (2019). Multitemporal chlorophyll mapping in pome fruit orchards from remotely piloted aircraft systems. Remote Sensing 11 (12): 1468. doi: 10.3390/rs11121468
  • Van Rensen JJS (1989). Herbicides interacting with photosystem II. Herbicides and plant metabolism. Cambridge, UK: Cambridge University Press, pp. 21-36.
  • Wagle P, Zhang Y, Jin C, Xiao X (2016). Comparison of solar-induced chlorophyll fluorescence, light-use efficiency and process-based GPP models in maize. Ecological Applications 26: 1211-1222.
  • Wohlfahrt G, Gerdel K, Migliavacca M, Rotenberg E, Tatarinov F et al. (2018). Sun-induced fluorescence and gross primary productivity during a heat wave. Scientific Reports 8: 1-9.
  • Yang J, Cheng Y, Du L, Gong W, Shi S et al (2019). Analyzing the effect of the incidence angle on chlorophyll fluorescence intensity based on laser-induced fluorescence lidar. Optics Express 27 (9): 12541-12550.
  • Yang X, Tang J, Mustard JF, Lee JE, Rossini M et al. (2015). Solarinduced chlorophyll fluorescence that correlates with canopy photosynthesis on diurnal and seasonal scales in a temperate deciduous forest. Geophysical Research Letter 42: 2977-2987.
  • Yu,K, Lenz-Wiedemann V, Chen X, Bareth G (2014). Estimating leaf chlorophyll of barley at different growth stages using spectral indices to reduce soil background and canopy structure effects ISPRS Journal of Photogrammetry and Remote Sensing 97: 58- 77.
  • Yue J, Feng H, Tian Q, Zhou C (2020). A robust spectral angle index for remotely assessing soybean canopy chlorophyll content in different growing stages. Plant Methods 16: 104. doi: 10.1186/ s13007-020-00643-z
  • Zarco-Tejada PJ, Miller JR, Haboudane D, Tremblay N, Apostol S (2003). Detection of chlorophyll fluorescence in vegetation from airborne hyperspectral CASI imagery in the red edge spectral region. In: Proceedings of the International Geoscience and Remote Sensing Symposium (IGARSS); Toulouse, France. pp. 598-600.
  • Zarco-Tejada PJ, Suarez L, Gonzalez-Dugo V (2013). Spatial resolution effects on chlorophyll fluorescence retrieval in a heterogeneous canopy using hyperspectral imagery and radiative transfer simulation. IEEE Geoscience and Remote Sensing Letters 10: 937-941.
  • Zarco-Tejada PJ, González-Dugo MV, Fereres E (2016). Seasonal stability of chlorophyll fluorescence quantified from airborne hyperspectral imagery as an indicator of net photosynthesis in the context of precision agriculture. Remote Sensing of Environment 179: 89-103.
  • Zhang Y, Guanter L, Berry JA, Joiner J, Van Der Tol C et al. (2014). Estimation of vegetation photosynthetic capacity from spacebased measurements of chlorophyll fluorescence for terrestrial biosphere models. Global Change Biology 20: 3727-3742.
  • Zhou X, Zhang J, Chen D, Huang Y, Kong W et al. (2020). Assessment of leaf chlorophyll content models for winter wheat using Landsat-8 multispectral remote sensing data. Remote Sensing 12: 2574. doi: 10.3390/rs12162574
  • Zhang Z, Xu W, Qin Q, Long Z (2020). Downscaling solar-induced chlorophyll fluorescence based on convolutional neural network method to monitor agricultural drought. IEEE Transactions on Geoscience and Remote Sensing (99): 1-17.
  • FAO (2011). The State of the World’s Land and Water Resources For Food and Agriculture (SOLAW) – Managing Systems At Risk. London, UK: Food and Agriculture Organization of the United Nations, Rome and Earthscan.