{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,1]],"date-time":"2026-04-01T06:12:26Z","timestamp":1775023946808,"version":"3.50.1"},"reference-count":80,"publisher":"MDPI AG","issue":"6","license":[{"start":{"date-parts":[[2022,3,20]],"date-time":"2022-03-20T00:00:00Z","timestamp":1647734400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Gross Primary Productivity (GPP) for cropland is often estimated using a fixed value for maximum light use efficiency (LUEmax) which is reduced to light use efficiency (LUE) by environmental stress scalars. This may not reflect variation in LUE within a crop season, and environmental stress scalars developed for ecosystem scale modelling may not apply linearly to croplands. We predicted LUE on several vegetation indices, crop type, and agroclimatic predictors using supervised random forest regression with training data from flux towers. Using a fixed LUEmax and environmental stress scalars produced an overestimation of GPP with a root mean square error (RMSE) of 6.26 gC\/m2\/day, while using predicted LUE from random forest regression produced RMSEs of 0.099 and 0.404 gC\/m2\/day for models with and without crop type as a predictor, respectively. Prediction uncertainty was greater for the model without crop type. These results show that LUE varies between crop type, is dynamic within a crop season, and LUE models that reflect this are able to produce much more accurate estimates of GPP over cropland than using fixed LUEmax with stress scalars. Therefore, we suggest a paradigm shift from setting the LUE variable in cropland productivity models based on environmental stress to focusing more on the variation of LUE within a crop season.<\/jats:p>","DOI":"10.3390\/rs14061495","type":"journal-article","created":{"date-parts":[[2022,3,20]],"date-time":"2022-03-20T21:37:17Z","timestamp":1647812237000},"page":"1495","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":11,"title":["Modelling Within-Season Variation in Light Use Efficiency Enhances Productivity Estimates for Cropland"],"prefix":"10.3390","volume":"14","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-6585-6120","authenticated-orcid":false,"given":"Michael J.","family":"Wellington","sequence":"first","affiliation":[{"name":"Fenner School of Environment and Society, Australian National University, Canberra, ACT 2601, Australia"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9070-0091","authenticated-orcid":false,"given":"Petra","family":"Kuhnert","sequence":"additional","affiliation":[{"name":"CSIRO Data61, Dutton Park, QLD 4102, Australia"}]},{"given":"Luigi J.","family":"Renzullo","sequence":"additional","affiliation":[{"name":"Fenner School of Environment and Society, Australian National University, Canberra, ACT 2601, Australia"}]},{"given":"Roger","family":"Lawes","sequence":"additional","affiliation":[{"name":"CSIRO Agriculture and Food, Floreat, WA 6014, Australia"}]}],"member":"1968","published-online":{"date-parts":[[2022,3,20]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Jaafar, H., and Mourad, R. (2021). GYMEE: A Global Field-Scale Crop Yield and ET Mapper in Google Earth Engine Based on Landsat, Weather, and Soil Data. Remote Sens., 13.","DOI":"10.3390\/rs13040773"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"324","DOI":"10.1016\/j.rse.2015.04.021","article-title":"A scalable satellite-based crop yield mapper","volume":"164","author":"Lobell","year":"2015","journal-title":"Remote Sens. Environ."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"3871","DOI":"10.5194\/bg-11-3871-2014","article-title":"Global cropland monthly gross primary production in the year 2000","volume":"11","author":"Chen","year":"2014","journal-title":"Biogeosciences"},{"key":"ref_4","doi-asserted-by":"crossref","unstructured":"Yan, J., Ma, Y., Zhang, D., Li, Z., Zhang, W., Wu, Z., Wang, H., and Wen, L. (2021). High-Resolution Monitoring and Assessment of Evapotranspiration and Gross Primary Production Using Remote Sensing in a Typical Arid Region. Land, 10.","DOI":"10.3390\/land10040396"},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"747","DOI":"10.2307\/2401901","article-title":"Solar Radiation and Productivity in Tropical Ecosystems","volume":"9","author":"Monteith","year":"1972","journal-title":"J. Appl. Ecol."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"277","DOI":"10.1098\/rstb.1977.0140","article-title":"Climate and the efficiency of crop production in Britain","volume":"281","author":"Monteith","year":"1977","journal-title":"Philos. Trans. R. Soc. Lond. B Biol. Sci."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"L14707","DOI":"10.1029\/2011GL047533","article-title":"Evaluation of cropland maximum light use efficiency using eddy flux measurements in North America and Europe","volume":"38","author":"Chen","year":"2011","journal-title":"Geophys. Res. Lett."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"722","DOI":"10.1046\/j.1365-2486.2002.00503.x","article-title":"Satellite estimates of productivity and light use efficiency in United States agriculture, 1982\u20131998","volume":"8","author":"Lobell","year":"2002","journal-title":"Glob. Chang. Biol."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"2248","DOI":"10.1016\/j.rse.2010.05.001","article-title":"Deriving maximal light use efficiency from coordinated flux measurements and satellite data for regional gross primary production modeling","volume":"114","author":"Wang","year":"2010","journal-title":"Remote Sens. Environ."},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Wang, M., Sun, R., Zhu, A., and Xiao, Z. (2020). Evaluation and Comparison of Light Use Efficiency and Gross Primary Productivity Using Three Different Approaches. Remote Sens., 12.","DOI":"10.3390\/rs12061003"},{"key":"ref_11","unstructured":"Mul, M., Karimi, P., Coerver, H., Pareeth, S., and Rebelo, L. (2020). Water Productivity and Water Accounting Methodology Manual, IHE Delft Institute for Water Education, International Water Management Institute. Report."},{"key":"ref_12","unstructured":"Pareeth, S. (2020). PySEBAL Documentation, IHE Delft Institute for Water Education. Available online: https:\/\/pysebal-doc.readthedocs.io\/en\/version3.7.3\/."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"477","DOI":"10.1016\/j.agrformet.2008.09.014","article-title":"Reviewing SEBAL input parameters for assessing evapotranspiration and water productivity for the Low-Middle S\u00e3o Francisco River Basin, Brazil: Part B: Application to the regional scale","volume":"149","author":"Teixeira","year":"2009","journal-title":"Agric. For. Meteorol."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"349","DOI":"10.1016\/j.rse.2014.09.007","article-title":"Evaluation of the remote-sensing-based DIFFUSE model for estimating photosynthesis of vegetation","volume":"155","author":"Donohue","year":"2014","journal-title":"Remote Sens. Environ."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"196","DOI":"10.1016\/j.rse.2014.09.017","article-title":"The need for a common basis for defining light-use efficiency: Implications for productivity estimation","volume":"156","author":"Gitelson","year":"2015","journal-title":"Remote Sens. Environ."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"100","DOI":"10.1016\/j.jplph.2014.12.015","article-title":"Productivity, absorbed photosynthetically active radiation, and light use efficiency in crops: Implications for remote sensing of crop primary production","volume":"177","author":"Gitelson","year":"2015","journal-title":"J. Plant Physiol."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"74","DOI":"10.1016\/0034-4257(94)00066-V","article-title":"Global net primary production: Combining ecology and remote sensing","volume":"51","author":"Field","year":"1995","journal-title":"Remote Sens. Environ."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"811","DOI":"10.1029\/93GB02725","article-title":"Terrestrial ecosystem production: A process model based on global satellite and surface data","volume":"7","author":"Potter","year":"1993","journal-title":"Glob. Biogeochem. Cycles"},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"492","DOI":"10.1007\/s004420050337","article-title":"The photochemical reflectance index: An optical indicator of photosynthetic radiation use efficiency across species, functional types, and nutrient levels","volume":"112","author":"Gamon","year":"1997","journal-title":"Oecologia"},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"291","DOI":"10.1111\/j.1469-8137.1995.tb03064.x","article-title":"Assessment of photosynthetic radiation-use efficiency with spectral reflectance","volume":"131","author":"Penuelas","year":"1995","journal-title":"New Phytol."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"2189","DOI":"10.1073\/pnas.1616919114","article-title":"Satellite-based assessment of yield variation and its determinants in smallholder African systems","volume":"114","author":"Burke","year":"2017","journal-title":"Proc. Natl. Acad. Sci. USA"},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"104","DOI":"10.1109\/JSTARS.2016.2605303","article-title":"Deriving Maximum Light Use Efficiency From Crop Growth Model and Satellite Data to Improve Crop Biomass Estimation","volume":"10","author":"Dong","year":"2017","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_23","unstructured":"Pareeth, S. (2020). PySEBAL Script, IHE Delft Institute for Water Education. Available online: https:\/\/github.com\/spareeth\/PySEBAL_dev."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"111","DOI":"10.1016\/j.agrformet.2014.11.004","article-title":"Multi-scale evaluation of light use efficiency in MODIS gross primary productivity for croplands in the Midwestern United States","volume":"201","author":"Xin","year":"2015","journal-title":"Agric. For. Meteorol."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"187","DOI":"10.1016\/j.agrformet.2014.01.006","article-title":"Impacts of light use efficiency and fPAR parameterization on gross primary production modeling","volume":"189-190","author":"Cheng","year":"2014","journal-title":"Agric. For. Meteorol."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"91","DOI":"10.1016\/S1161-0301(02)00019-9","article-title":"Change with time in potential radiation-use efficiency in field pea","volume":"19","author":"Lecoeur","year":"2003","journal-title":"Eur. J. Agron."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"978","DOI":"10.1016\/j.rse.2010.12.001","article-title":"Remote estimation of gross primary production in maize and support for a new paradigm based on total crop chlorophyll content","volume":"115","author":"Peng","year":"2011","journal-title":"Remote Sens. Environ."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"1267","DOI":"10.1016\/j.agrformet.2011.05.005","article-title":"Application of chlorophyll-related vegetation indices for remote estimation of maize productivity","volume":"151","author":"Peng","year":"2011","journal-title":"Agric. For. Meteorol."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"e01724","DOI":"10.1002\/ecs2.1724","article-title":"A global study of GPP focusing on light-use efficiency in a random forest regression model","volume":"8","author":"Wei","year":"2017","journal-title":"Ecosphere"},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"2415","DOI":"10.1175\/1520-0477(2001)082<2415:FANTTS>2.3.CO;2","article-title":"FLUXNET: A new tool to study the temporal and spatial variability of ecosystem-scale carbon dioxide, water vapor, and energy flux densities","volume":"82","author":"Baldocchi","year":"2001","journal-title":"Bull. Am. Meteorol. Soc."},{"key":"ref_31","unstructured":"Ryu, Y., Kang, M., and Kim, J. (2018). FLUXNET-CH4 KR-CRK Cheorwon Rice Paddy 2015\u20132018, Seoul National University."},{"key":"ref_32","unstructured":"Alberto, M., and Wassmann, R. (2014). FLUXNET-CH4 PH-RiF Philippines Rice Institute Flooded, International Rice Research Institute."},{"key":"ref_33","unstructured":"Reba, M., Runkle, B., and Suvocarev, K. (2017). FLUXNET-CH4 US-HRC Humnoke Farm Rice Field\u2014Field A, Delta Water Management Research."},{"key":"ref_34","unstructured":"Reba, M., Runkle, B., and Suvocarev, K. (2017). FLUXNET-CH4 US-HRC Humnoke Farm Rice Field\u2014Field C, Delta Water Management Research."},{"key":"ref_35","unstructured":"FLUXNET (2013). FLUXNET2015 US-Ne1 Mead\u2014Irrigated Continuous Maize Site, University of Nebraska."},{"key":"ref_36","unstructured":"FLUXNET (2013). FLUXNET 2015 US-Ne2 Mead\u2014Irrigated Maize-Soybean Rotation Site, University of Nebraska."},{"key":"ref_37","unstructured":"FLUXNET (2013). FLUXNET 2015 US-Ne3 Mead\u2014Rainfed Maize-Soybean Rotation Site, University of Nebraska."},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"111034","DOI":"10.1016\/j.rse.2018.12.033","article-title":"Time series trends of Landsat-based ET using automated calibration in METRIC and SEBAL: The Bekaa Valley, Lebanon","volume":"238","author":"Jaafar","year":"2020","journal-title":"Remote Sens. Environ."},{"key":"ref_39","unstructured":"Hijmans, R.J. (2021). Raster: Geographic Data Analysis and Modeling, R Foundation for Statistical Computing. Version 3.4-13."},{"key":"ref_40","unstructured":"R Foundation for Statistical Computing (2021). R: A Language and Environment for Statistical Computing, R Foundation for Statistical Computing."},{"key":"ref_41","unstructured":"Fang, H., Beaudoing, H.K., Rodell, M., Teng, W.L., and Vollmer, B.E. (2009, January 9\u201313). Global Land data assimilation system (GLDAS) products, services and application from NASA hydrology data and information services center (HDISC). Proceedings of the ASPRS 2009 Annual Conference, Baltimore, MD, USA."},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"583","DOI":"10.1029\/EO081i048p00583","article-title":"Shuttle Radar Topography Mission produces a wealth of data","volume":"81","author":"Farr","year":"2000","journal-title":"Eos Trans. Am. Geophys. Union"},{"key":"ref_43","unstructured":"De Boer, F. (2016). HiHydroSoil: A High Resolution Soil Map of Hydraulic Properties, FutureWater."},{"key":"ref_44","doi-asserted-by":"crossref","unstructured":"Levitan, N., Kang, Y., \u00d6zdo\u011fan, M., Magliulo, V., Castillo, P., Moshary, F., and Gross, B. (2019). Evaluation of the Uncertainty in Satellite-Based Crop State Variable Retrievals Due to Site and Growth Stage Specific Factors and Their Potential in Coupling with Crop Growth Models. Remote Sens., 11.","DOI":"10.3390\/rs11161928"},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"65","DOI":"10.1016\/j.rse.2014.01.004","article-title":"Relationships between gross primary production, green LAI, and canopy chlorophyll content in maize: Implications for remote sensing of primary production","volume":"144","author":"Gitelson","year":"2014","journal-title":"Remote Sens. Environ."},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"3695","DOI":"10.5194\/gmd-8-3695-2015","article-title":"A simple two-dimensional parameterisation for Flux Footprint Prediction (FFP)","volume":"8","author":"Kljun","year":"2015","journal-title":"Geosci. Model Dev."},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"108350","DOI":"10.1016\/j.agrformet.2021.108350","article-title":"Representativeness of Eddy-Covariance flux footprints for areas surrounding AmeriFlux sites","volume":"301\u2013302","author":"Chu","year":"2021","journal-title":"Agric. For. Meteorol."},{"key":"ref_48","doi-asserted-by":"crossref","first-page":"321","DOI":"10.1016\/S0167-8809(02)00034-8","article-title":"A new crop yield forecasting model based on satellite measurements applied across the Indus Basin, Pakistan","volume":"94","author":"Bastiaanssen","year":"2003","journal-title":"Agric. Ecosyst. Environ."},{"key":"ref_49","doi-asserted-by":"crossref","first-page":"141","DOI":"10.1016\/0034-4257(92)90132-4","article-title":"Spectral estimates of absorbed radiation and phytomass production in corn and soybean canopies","volume":"39","author":"Daughtry","year":"1992","journal-title":"Remote Sens. Environ."},{"key":"ref_50","unstructured":"Stewart, J. (1987, January 9\u201322). On the use of the Penrnan-Monteith equation for determining areal evapotranspiration. Proceedings of the Estimation of Areal Evapotranspiration, Vancouver, BC, Canada."},{"key":"ref_51","doi-asserted-by":"crossref","first-page":"19","DOI":"10.1016\/0168-1923(88)90003-2","article-title":"Modelling surface conductance of pine forest","volume":"43","author":"Stewart","year":"1988","journal-title":"Agric. For. Meteorol."},{"key":"ref_52","doi-asserted-by":"crossref","first-page":"593","DOI":"10.1098\/rstb.1976.0035","article-title":"The interpretation of the variations in leaf water potential and stomatal conductance found in canopies in the field","volume":"273","author":"Jarvis","year":"1976","journal-title":"Philos. Trans. R. Soc. Lond. B Biol. Sci."},{"key":"ref_53","first-page":"5","article-title":"Temperature and crop development","volume":"31","author":"Ritchie","year":"1991","journal-title":"Model. Plant Soil Syst."},{"key":"ref_54","unstructured":"Maidment, D.R. (1993). Handbook of Hydrology, McGraw-Hill. Number 631.587."},{"key":"ref_55","doi-asserted-by":"crossref","first-page":"1515","DOI":"10.1046\/j.1365-3040.1999.00513.x","article-title":"Survey and synthesis of intra-and interspecific variation in stomatal sensitivity to vapour pressure deficit","volume":"22","author":"Oren","year":"1999","journal-title":"Plant Cell Environ."},{"key":"ref_56","doi-asserted-by":"crossref","first-page":"1211","DOI":"10.1007\/s00484-018-1524-4","article-title":"The functional dependence of canopy conductance on water vapor pressure deficit revisited","volume":"62","author":"Fuchs","year":"2018","journal-title":"Int. J. Biometeorol."},{"key":"ref_57","first-page":"17","article-title":"Evapotranspiration and evaporation\/transpiration partitioning with dual source energy balance models in agricultural lands","volume":"380","author":"Boulet","year":"2018","journal-title":"Proc. Int. Assoc. Hydrol. Sci."},{"key":"ref_58","unstructured":"USGS (2022, January 20). What Are the Band Designations for the Landsat Satellites?, Available online: https:\/\/www.usgs.gov\/faqs\/what-are-band-designations-landsat-satellites."},{"key":"ref_59","first-page":"309","article-title":"Monitoring vegetation systems in the Great Plains with ERTS","volume":"1","author":"Rouse","year":"1973","journal-title":"ERTS"},{"key":"ref_60","doi-asserted-by":"crossref","first-page":"289","DOI":"10.1016\/S0034-4257(96)00072-7","article-title":"Use of a green channel in remote sensing of global vegetation from EOS-MODIS","volume":"58","author":"Gitelson","year":"1996","journal-title":"Remote Sens. Environ."},{"key":"ref_61","doi-asserted-by":"crossref","first-page":"295","DOI":"10.1016\/0034-4257(88)90106-X","article-title":"A soil-adjusted vegetation index (SAVI)","volume":"25","author":"Huete","year":"1988","journal-title":"Remote Sens. Environ."},{"key":"ref_62","unstructured":"Hijmans, R.J. (2021). Geosphere: Spherical Trigonometry, R Foundation for Statistical Computing. Version 1.5-14."},{"key":"ref_63","unstructured":"Lee, C., Herbek, J., Murdock, L., Schwab, G., Green, J., Martin, J., Bessin, R., Johnson, D., Hershman, D., and Vincelli, P. (2007). Corn and Soybean Production Calendar, University of Kentucky Cooperative Extension Service."},{"key":"ref_64","unstructured":"FAO (2021, November 29). GIEWS\u2014Global Information and Early Warning System\u2014Philippines. Available online: https:\/\/www.fao.org\/giews\/countrybrief\/country.jsp?code=PHL&lang=en."},{"key":"ref_65","unstructured":"FAO (2021, November 29). GIEWS\u2014Global Information and Early Warning System\u2014Korea. Available online: https:\/\/www.fao.org\/giews\/countrybrief\/country.jsp?code=KOR&lang=en."},{"key":"ref_66","doi-asserted-by":"crossref","first-page":"5","DOI":"10.1023\/A:1010933404324","article-title":"Random forests","volume":"45","author":"Breiman","year":"2001","journal-title":"Mach. Learn."},{"key":"ref_67","first-page":"18","article-title":"Classification and regression by randomForest","volume":"2","author":"Liaw","year":"2002","journal-title":"R News"},{"key":"ref_68","unstructured":"Wager, S. (2021). randomForestCI: Confidence Intervals for Random Forests, R Foundation for Statistical Computing. Version 1.0.0."},{"key":"ref_69","doi-asserted-by":"crossref","first-page":"440","DOI":"10.1016\/j.rse.2011.10.021","article-title":"Remote estimation of gross primary productivity in soybean and maize based on total crop chlorophyll content","volume":"117","author":"Peng","year":"2012","journal-title":"Remote Sens. Environ."},{"key":"ref_70","doi-asserted-by":"crossref","first-page":"239","DOI":"10.1016\/S0378-4290(99)00011-8","article-title":"Occam\u2019s Razor, radiation-use efficiency, and vapor pressure deficit","volume":"62","author":"Sinclair","year":"1999","journal-title":"Field Crops Res."},{"key":"ref_71","doi-asserted-by":"crossref","first-page":"90","DOI":"10.2135\/cropsci1989.0011183X002900010023x","article-title":"Leaf nitrogen, photosynthesis, and crop radiation use efficiency: A review","volume":"29","author":"Sinclair","year":"1989","journal-title":"Crop Sci."},{"key":"ref_72","doi-asserted-by":"crossref","first-page":"53","DOI":"10.1016\/S0167-8809(99)00146-2","article-title":"Leaf nitrogen concentration of wheat subjected to elevated [CO2] and either water or N deficits","volume":"79","author":"Sinclair","year":"2000","journal-title":"Agric. Ecosyst. Environ."},{"key":"ref_73","doi-asserted-by":"crossref","first-page":"37","DOI":"10.1016\/0378-4290(93)90093-3","article-title":"Irrigated spring wheat and timing and amount of nitrogen fertilizer. I. Grain yield and protein content","volume":"33","author":"Fischer","year":"1993","journal-title":"Field Crops Res."},{"key":"ref_74","doi-asserted-by":"crossref","first-page":"297","DOI":"10.1104\/pp.72.2.297","article-title":"Nitrogen and photosynthesis in the flag leaf of wheat (Triticumaestivum L.)","volume":"72","author":"Evans","year":"1983","journal-title":"Plant Physiol."},{"key":"ref_75","doi-asserted-by":"crossref","first-page":"72","DOI":"10.2134\/agronj2005.0072","article-title":"Maize radiation use efficiency under optimal growth conditions","volume":"97","author":"Lindquist","year":"2005","journal-title":"Agron. J."},{"key":"ref_76","doi-asserted-by":"crossref","first-page":"721","DOI":"10.2135\/cropsci1994.0011183X003400030022x","article-title":"Nitrogen response of leaf photosynthesis and canopy radiation use efficiency in field-grown maize and sorghum","volume":"34","author":"Muchow","year":"1994","journal-title":"Crop Sci."},{"key":"ref_77","doi-asserted-by":"crossref","first-page":"2903","DOI":"10.5194\/bg-14-2903-2017","article-title":"OzFlux Data: Network integration from collection to curation","volume":"14","author":"Isaac","year":"2017","journal-title":"Biogeosciences"},{"key":"ref_78","doi-asserted-by":"crossref","first-page":"1457","DOI":"10.5194\/bg-14-1457-2017","article-title":"Dynamic INtegrated Gap-filling and partitioning for OzFlux (DINGO)","volume":"14","author":"Beringer","year":"2017","journal-title":"Biogeosciences"},{"key":"ref_79","doi-asserted-by":"crossref","first-page":"281","DOI":"10.1016\/j.rse.2010.08.023","article-title":"The photochemical reflectance index (PRI) and the remote sensing of leaf, canopy and ecosystem radiation use efficiencies: A review and meta-analysis","volume":"115","author":"Garbulsky","year":"2011","journal-title":"Remote Sens. Environ."},{"key":"ref_80","doi-asserted-by":"crossref","first-page":"264","DOI":"10.1016\/S0034-4257(01)00224-3","article-title":"Remote sensing of canopy light use efficiency using the photochemical reflectance index: Model and sensitivity analysis","volume":"78","author":"Barton","year":"2001","journal-title":"Remote Sens. Environ."}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/14\/6\/1495\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T22:39:50Z","timestamp":1760135990000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/14\/6\/1495"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,3,20]]},"references-count":80,"journal-issue":{"issue":"6","published-online":{"date-parts":[[2022,3]]}},"alternative-id":["rs14061495"],"URL":"https:\/\/doi.org\/10.3390\/rs14061495","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,3,20]]}}}