{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,17]],"date-time":"2026-04-17T15:10:54Z","timestamp":1776438654647,"version":"3.51.2"},"reference-count":44,"publisher":"MDPI AG","issue":"5","license":[{"start":{"date-parts":[[2017,4,29]],"date-time":"2017-04-29T00:00:00Z","timestamp":1493424000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["41371407"],"award-info":[{"award-number":["41371407"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Timely assessment of crop growth conditions under heavy metal pollution is of great significance for agricultural decision-making and estimation of crop productivity. The object of this study is to assess the effects of heavy metal stress on physiological functions of rice through the spatial-temporal analysis of the fraction of absorbed photosynthetically active radiation (FAPAR). The calculation of daily FAPAR is conducted based on a coupled model consisting of the leaf-canopy radiative transfer model and World Food Study Model (WOFOST). These two models are connected by leaf area index (LAI) and a fraction of diffused incoming solar radiation (SKYL) in the rice growth period. The input parameters of the coupled model are obtained from measured data and GF-1 images. Meanwhile, in order to improve accuracy of FAPAR, the crop growth model is optimized by data assimilation. The validation result shows that the correlation between the simulated FAPAR and the measured data is strong in the rice growth period, with the correlation coefficients being above 7.5 for two areas. The discrepancy of FAPAR between two areas of different stress levels is visualized by spatial-temporal analysis. FAPAR discrepancy starts to appear in the jointing-booting period and experiences a gradual rise, reaching its maximum in the heading-flowering stage. This study suggests that the coupled model, consisting of the leaf-canopy radiative transfer model and the WOFOST model, is able to accurately simulate daily FAPAR during crop growth period and FAPAR can be used as a potential indicator to reflect the impact of heavy metal stress on crop growth.<\/jats:p>","DOI":"10.3390\/rs9050424","type":"journal-article","created":{"date-parts":[[2017,5,2]],"date-time":"2017-05-02T11:37:20Z","timestamp":1493725040000},"page":"424","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":29,"title":["Estimating FAPAR of Rice Growth Period Using Radiation Transfer Model Coupled with the WOFOST Model for Analyzing Heavy Metal Stress"],"prefix":"10.3390","volume":"9","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-8795-1850","authenticated-orcid":false,"given":"Gaoxiang","family":"Zhou","sequence":"first","affiliation":[{"name":"School of Information Engineering, China University of Geoscience, Beijing 100083, China"}]},{"given":"Xiangnan","family":"Liu","sequence":"additional","affiliation":[{"name":"School of Information Engineering, China University of Geoscience, Beijing 100083, China"}]},{"given":"Shuang","family":"Zhao","sequence":"additional","affiliation":[{"name":"School of Information Engineering, China University of Geoscience, Beijing 100083, China"}]},{"given":"Ming","family":"Liu","sequence":"additional","affiliation":[{"name":"School of Information Engineering, China University of Geoscience, Beijing 100083, China"}]},{"given":"Ling","family":"Wu","sequence":"additional","affiliation":[{"name":"School of Information Engineering, China University of Geoscience, Beijing 100083, China"}]}],"member":"1968","published-online":{"date-parts":[[2017,4,29]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Gitelson, A.A., Vina, A., Arkebauer, T.J., Rundquist, D.C., Keydan, G., and Leavitt, B. (2003). Remote estimation of leaf area index and green leaf biomass in maize canopies. Geophys. Res. Lett., 30.","DOI":"10.1029\/2002GL016450"},{"key":"ref_2","first-page":"293","article-title":"Soil heavy metal pollution of cultivated land in China","volume":"20","author":"Song","year":"2013","journal-title":"Res. Soil Water Conserv."},{"key":"ref_3","first-page":"66","article-title":"The assimilation of spectral sensing and the wofost model for the dynamic simulation of cadmium accumulation in rice tissues","volume":"25","author":"Wu","year":"2013","journal-title":"Int. J. Appl. Earth Obs. Geoinfor."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"2587","DOI":"10.1016\/j.wasman.2014.08.012","article-title":"Environmental effects of heavy metals derived from the e-waste recycling activities in China: A systematic review","volume":"34","author":"Song","year":"2014","journal-title":"Waste Manag."},{"key":"ref_5","first-page":"118","article-title":"An improved assimilation method with stress factors incorporated in the wofost model for the efficient assessment of heavy metal stress levels in rice","volume":"41","author":"Jin","year":"2015","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"75","DOI":"10.1080\/01904169809365384","article-title":"Effect of excess lead on sunflower growth and photosynthesis","volume":"21","author":"Kastori","year":"1998","journal-title":"J. Plant Nutr."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"1072","DOI":"10.1016\/j.chemosphere.2006.11.061","article-title":"Effect of arsenic on photosynthesis, growth and yield of five widely cultivated rice (Oryza sativa L.) varieties in Bangladesh","volume":"67","author":"Rahman","year":"2007","journal-title":"Chemosphere"},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"256","DOI":"10.1065\/espr2002.11.141.2","article-title":"Effects of heavy metals on plants and resistance mechanisms","volume":"10","author":"Cheng","year":"2003","journal-title":"Environ. Sci. Pollut. Res."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"572","DOI":"10.1016\/S0034-4257(02)00181-5","article-title":"Comparison of two hyperspectral imaging and two laser-induced fluorescence instruments for the detection of zinc stress and chlorophyll concentration in bahia grass (paspalum notatum flugge)","volume":"84","author":"Schuerger","year":"2003","journal-title":"Remote Sens. Environ."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"241","DOI":"10.1016\/j.envpol.2005.02.025","article-title":"Reflectance properties and physiological responses of salicornia virginica to heavy metal and petroleum contamination","volume":"137","author":"Rosso","year":"2005","journal-title":"Environ. Pollut."},{"key":"ref_11","first-page":"676","article-title":"Remote sensing of soybean stress as an indicator of chemical concentration of biosolid amended surface soils","volume":"13","author":"Sridhar","year":"2011","journal-title":"Int. J. Appl. Earth Obs. Geoinfor."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"1353","DOI":"10.1109\/36.544559","article-title":"Canopy architecture and remote sensing of the fraction of photosynthetically active radiation absorbed by boreal conifer forests","volume":"34","author":"Chen","year":"1996","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"81","DOI":"10.1016\/S0304-3800(97)00080-X","article-title":"Scaling par absorption from the leaf to landscape level in spatially heterogeneous ecosystems","volume":"103","author":"Asner","year":"1997","journal-title":"Ecol. Model."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"927","DOI":"10.3390\/rs5020927","article-title":"Global data sets of vegetation leaf area index (lai)3g and Fraction of Photosynthetically Active Radiation (FPAR)3G derived from Global Inventory Modeling and Mapping Studies (GIMMS) Normalized Difference Vegetation Index (NDVI3G) for the period 1981 to 2011","volume":"5","author":"Zhu","year":"2013","journal-title":"Remote Sens."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"1167","DOI":"10.1016\/j.rse.2010.01.004","article-title":"Estimating crop stresses, aboveground dry biomass and yield of corn using multi-temporal optical data combined with a radiation use efficiency model","volume":"114","author":"Liu","year":"2010","journal-title":"Remote Sens. Environ."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.chnaes.2015.12.003","article-title":"Sensitivity analysis of retrieving Fraction of Absorbed Photosynthetically Active Radiation (FPAR) using remote sensing data","volume":"36","author":"Dong","year":"2016","journal-title":"Acta Ecol. Sin."},{"key":"ref_17","first-page":"91","article-title":"Regional heavy metal pollution in crops by integrating physiological function variability with spatio-temporal stability using multi-temporal thermal remote sensing","volume":"51","author":"Liu","year":"2016","journal-title":"Int. J. Appl. Earth Obs. Geoinfor."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"501","DOI":"10.1177\/0309133307084626","article-title":"Recent developments in estimating land surface biogeophysical variables from optical remote sensing","volume":"31","author":"Liang","year":"2007","journal-title":"Prog. Phys. Geogr."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"7425","DOI":"10.3390\/rs70607425","article-title":"Estimating forest fapar from multispectral landsat-8 data using the invertible forest reflectance model inform","volume":"7","author":"Yuan","year":"2015","journal-title":"Remote Sens."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"539","DOI":"10.1016\/j.rse.2016.07.036","article-title":"Estimation of fraction of absorbed photosynthetically active radiation from multiple satellite data: Model development and validation","volume":"184","author":"Tao","year":"2016","journal-title":"Remote Sens. Environ."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"1818","DOI":"10.1109\/TGRS.2005.862266","article-title":"Validation of modis f-par products in boreal forests of Alaska","volume":"44","author":"Steinberg","year":"2006","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"415","DOI":"10.1016\/j.rse.2010.09.012","article-title":"Optimal modalities for radiative transfer-neural network estimation of canopy biophysical characteristics: Evaluation over an agricultural area with chris\/proba observations","volume":"115","author":"Verger","year":"2011","journal-title":"Remote Sens. Environ."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"324","DOI":"10.1016\/j.rse.2005.09.021","article-title":"Local-scale heterogeneity of Photosynthetically Active Radiation (PAR), absorbed par and net radiation as a function of topography, sky conditions and leaf area index","volume":"103","author":"Oliphant","year":"2006","journal-title":"Remote Sens. Environ."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"880","DOI":"10.1016\/j.rse.2009.01.002","article-title":"Can a satellite-derived estimate of the Fraction of PAR Absorbed by Chlorophyll (FAPAR(CHL)) improve predictions of light-use efficiency and ecosystem photosynthesis for a boreal aspen forest?","volume":"113","author":"Zhang","year":"2009","journal-title":"Remote Sens. Environ."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"98","DOI":"10.1016\/j.agrformet.2014.11.002","article-title":"Variations in the influence of diffuse light on gross primary productivity in temperate ecosystems","volume":"201","author":"Cheng","year":"2015","journal-title":"Agric. For. Meteorol."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"96","DOI":"10.1002\/2014JG002754","article-title":"Estimation of direct, diffuse, and total fpars from landsat surface reflectance data and ground-based estimates over six fluxnet sites","volume":"120","author":"Li","year":"2015","journal-title":"J. Geophys. Res. Biogeosci."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.agrformet.2016.03.018","article-title":"Nutritional and developmental influences on components of rice crop light use efficiency","volume":"223","author":"Xue","year":"2016","journal-title":"Agric. For. Meteorol."},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Li, L., Du, Y., Tang, Y., and Liu, Q. (2012). A study of fraction of absorbed photosynthetically active radiation characteristics based on SAIL model simulation. Proc. SPIE.","DOI":"10.1117\/12.977769"},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"1685","DOI":"10.1109\/JSTARS.2015.2499258","article-title":"Optimizing the temporal scale in the assimilation of remote sensing and wofost model for dynamically monitoring heavy metal stress in rice","volume":"9","author":"Liu","year":"2016","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"75","DOI":"10.1109\/JSTARS.2016.2529647","article-title":"Distinguishing heavy-metal stress levels in rice using synthetic spectral index responses to physiological function variations","volume":"10","author":"Jin","year":"2017","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"3030","DOI":"10.1016\/j.rse.2008.02.012","article-title":"Prospect-4 and 5: Advances in the leaf optical properties model separating photosynthetic pigments","volume":"112","author":"Feret","year":"2008","journal-title":"Remote Sens. Environ."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"S56","DOI":"10.1016\/j.rse.2008.01.026","article-title":"Prospect plus sail models: A review of use for vegetation characterization","volume":"113","author":"Jacquemoud","year":"2009","journal-title":"Remote Sens. Environ."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"1003","DOI":"10.1890\/1051-0761(1998)008[1003:SDOAOP]2.0.CO;2","article-title":"Scale dependence of absorption of photosynthetically active radiation in terrestrial ecosystems","volume":"8","author":"Asner","year":"1998","journal-title":"Ecol. Appl."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1023\/A:1010653913530","article-title":"Physical and biogeochemical controls over terrestrial ecosystem responses to nitrogen deposition","volume":"54","author":"Asner","year":"2001","journal-title":"Biogeochemistry"},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"205","DOI":"10.1016\/S0167-8809(02)00021-X","article-title":"Remote sensing of regional crop production in the yaqui valley, mexico: Estimates and uncertainties","volume":"94","author":"Lobell","year":"2003","journal-title":"Agric. Ecosyst. Environ."},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"7190","DOI":"10.5846\/stxb201110211562","article-title":"Overview on methods of deriving fraction of absorbed Photosynthetically Active Radiation (FPAR) using remote sensing","volume":"32","author":"Dong","year":"2012","journal-title":"Acta Ecol. Sin."},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"166","DOI":"10.1016\/j.rse.2006.12.013","article-title":"Coupled soil\u2013leaf-canopy and atmosphere radiative transfer modeling to simulate hyperspectral multi-angular surface reflectance and toa radiance data","volume":"109","author":"Verhoef","year":"2007","journal-title":"Remote Sens. Environ."},{"key":"ref_38","doi-asserted-by":"crossref","unstructured":"Huang, Z., Liu, X.N., Jin, M., Ding, C., Jiang, J.L., and Wu, L. (2016). Deriving the characteristic scale for effectively monitoring heavy metal stress in rice by assimilation of gf-1 data with the wofost model. Sensors.","DOI":"10.3390\/s16030340"},{"key":"ref_39","first-page":"3","article-title":"Heavy metal pollution of soils and vegetables from midstream and downstream of xiangjiang river","volume":"63","author":"Zhaohui","year":"2008","journal-title":"Acta Geogr. Sin."},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"1330","DOI":"10.1109\/JSTARS.2014.2371058","article-title":"The dynamic assessment model for monitoring cadmium stress levels in rice based on the assimilation of remote sensing and the wofost model","volume":"8","author":"Liu","year":"2015","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"026038","DOI":"10.1117\/1.JRS.10.026038","article-title":"Root mass ratio: Index derived by assimilation of synthetic aperture radar and the improved world food study model for heavy metal stress monitoring in rice","volume":"10","author":"Liu","year":"2016","journal-title":"J. Appl. Remote Sens."},{"key":"ref_42","unstructured":"Hosgood, B., Jacquemoud, S., Andreoli, G., Verdebout, J., Pedrini, G., and Schmuck, G. (2017, March 10). Leaf optical properties experiment 93 (lopex93). Available online: http:\/\/data.ecosis.org\/dataset\/13aef0ce-dd6f-4b35-91d9-28932e506c41\/resource\/4029b5d3-2b84-46e3-8fd8-c801d86cf6f1\/download\/leaf-optical-properties-experiment-93-lopex93.pdf."},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"22","DOI":"10.1016\/S0034-4257(01)00191-2","article-title":"Detecting vegetation leaf water content using reflectance in the optical domain","volume":"77","author":"Ceccato","year":"2001","journal-title":"Remote Sens. Environ."},{"key":"ref_44","first-page":"167","article-title":"A inversion model for remote sensing of leaf water content based on the leaf optical property","volume":"35","author":"Fang","year":"2015","journal-title":"Spectrosc. Spectr. Anal."}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/9\/5\/424\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T18:34:14Z","timestamp":1760207654000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/9\/5\/424"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2017,4,29]]},"references-count":44,"journal-issue":{"issue":"5","published-online":{"date-parts":[[2017,5]]}},"alternative-id":["rs9050424"],"URL":"https:\/\/doi.org\/10.3390\/rs9050424","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2017,4,29]]}}}