Comparative analysis of GF-1, HJ-1, and Landsat-8 data for estimating the leaf area index of winter wheat

doi:10.1016/S2095-3119(15)61293-X

摘要/Abstract

Abstract: Using simultaneously collected remote sensing data and field measurements, this study firstly assessed the consistency and applicability of China high-resolution earth observation system satellite 1 (GF-1) wide field of view (WFV) camera, environment and disaster monitoring and forecasting satellite (HJ-1) charge coupled device (CCD), and Landsat-8 operational land imager (OLI) data for estimating the leaf area index (LAI) of winter wheat via reflectance and vegetation indices (VIs). The accuracies of these LAI estimates were then assessed through comparison with an empirical model and the PROSAIL radiative transfer model. The effects of radiation calibration, spectral response functions, and spatial resolution on discrepancies in the LAI estimates between the different sensors were also analyzed. The results yielded the following observations: (1) The correlation between reflectance from different sensors is relative good, with the adjusted coefficients of determination (R²) between 0.375 to 0.818. The differences in reflectance are ranging from 0.002 to 0.054. The correlation between VIs from different sensors is high with the R² between 0.729 and 0.933. The differences in the VIs are ranging from 0.07 to 0.156. These results show the three sensors’ images can all be used for cross calibration of the reflectance and VIs. (2) The four VIs from the three sensors are all demonstrated to be highly correlated with LAI (R² between 0.703 and 0.849). The linear models associated with the 2-band enhanced vegetation index (EVI2), which feature the highest R² (higher than 0.746) and the lowest root mean square errors (RMSE) (less than 0.21), were selected to estimate the winter wheat LAI. The accuracy of the estimated LAI from Landsat-8 was the highest, with the relative errors (RE) of 2.18% and an RMSE of 0.13, while the HJ-1 was the lowest, with the RE of 2.43% and the RMSE of 0.15. (3) The inversion errors in the different sensors’ LAI estimates using the PROSAIL model are small. The accuracy of the GF-1 is the highest with the RE of 3.44%, and the RMSE of 0.22, whereas that of the HJ-1 is the lowest with the RE of 4.95%, and the RMSE of 0.26. (4) The effects of the spectral response function and radiation calibration for the different sensors are small and can be ignored, but the effects of spatial resolution are significant and must be taken into consideration in practical applications.

Key words: GF-1 WFV, HJ-1 CCD, Landsat-8 OLI, leaf area index, PROSAIL, vegetation indices

LI He, CHEN Zhong-xin, JIANG Zhi-wei, WU Wen-bin, REN Jian-qiang, LIU Bin, Tuya Hasi. [J]. Journal of Integrative Agriculture, 2017, 16(02): 266-285.

LI He, CHEN Zhong-xin, JIANG Zhi-wei, WU Wen-bin, REN Jian-qiang, LIU Bin, Tuya Hasi. Comparative analysis of GF-1, HJ-1, and Landsat-8 data for estimating the leaf area index of winter wheat[J]. Journal of Integrative Agriculture, 2017, 16(02): 266-285.

参考文献

Allen W A, Gausman H W, Richardson A J. 1969. Interaction of isotropic light with a compact plant leaf. Journal of the Optical Society of America, 10, 1376–1379.
Atzberger C, Richter K. 2012. Spatially constrained inversion of radiative transfer models for improved LAI mapping from future Sentinel-2 imagery. Remote Sensing of Environment, 120, 208–218.
Baret F, Hagolle O, Geiger B, Bicheron P, Miras B, Huc M, Berthelot B, Niño F, Weiss M, Samain O, Roujean J L, Leroy M. 2007. LAI, fAPAR and fCover CYCLOPES global products derived from VEGETATION. Remote Sensing of Environment, 110, 275–286.
Boneau C A. 1960. The effects of violations of assumptions underlying the t test. Psychological Bulletin, 57, 49–64.
Camps-Valls G, Bruzzone L, Rojo-Rojo J L, Melgani F. 2006. Robust support vector regression for biophysical variable estimation from remotely sensed images. IEEE Geoscience and Remote Sensing Letters, 3, 339–343.
Canty M J. 2009. Image Analysis, Classification, and Change Detection in Remote Sensing. 2nd ed., CRC Press, Boca Raton, Florida, USA.
Chander G, Markham B. 2003. Revised landsat-5 TM radiometric calibration procedures and postcalibration dynamic ranges. IEEE Transactions on Geoscience and Remote Sensing, 41, 2674–2677.
Chaurasia S, Dadhwal V K. 2004. Comparison of principal component inversion with VI-empirical approach for LAI estimation using simulated reflectance data. International Journal of Remote Sensing, 25, 2881–2887.
Chen D, Stow D A, Gong P. 2004. Examining the effect of spatial resolution and texture window size on classification accuracy: An urban environment case. International Journal of Remote Sensing, 25, 2177–2192.
Chen J M, Black T A. 1992. Defining leaf area index for non-flat leaves. Plant, Cell and Environment, 15, 421–429.
Chen J M, Liu J, Leblanc S G, Lacaze R, Roujean J L. 2003. Multi-angular optical remote sensing for assessing vegetation structure and carbon absorption. Remote Sensing of Environment, 84, 516–525.
Chen J M, Pavlic G, Brown L, Cihlar J, Leblanc S G, White H P, Hall R J, Peddle D R, King D J, Trofymow J A, Swift E, Van der Sanden J, Pellikka P. 2002. Derivation and validation of Canada-wide coarse-resolution leaf area index maps using high-resolution satellite imagery and ground measurements. Remote Sensing of Environment, 80, 165–184.
Chen X, Vierling L, Rowell E, DeFelice T. 2004. Using lidar and effective LAI data to evaluate IKONOS and Landsat 7 ETM+ vegetation cover estimates in a ponderosa pine forest. Remote Sensing of Environment, 91, 14–26.
Darvishzadeh R, Atzberger C, Skidmore A, Schlerf M. 2011. Mapping grassland leaf area index with airborne hyperspectral imagery: A comparison study of statistical approaches and inversion of radiative transfer models. ISPRS Journal of Photogrammetry and Remote Sensing, 66, 894–906.
Darvishzadeh R, Matkan A A, Ahangar D A. 2012. Inversion of a radiative transfer model for estimation of rice canopy chlorophyll content using a lookup-table approach. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 5, 1222–1230.
Darvishzadeh R, Skidmore A, Schlerf M, Atzberger C. 2008. Inversion of a radiative transfer model for estimating vegetation LAI and chlorophyll in a heterogeneous grassland. Remote Sensing of Environment, 112, 2592–2604.
Deering D W. 1978. Rangeland Reflectance Characteristics Measured by Aircraft and Spacecraft Sensors. Texas A & M University, College Station, TX.
Dong Y, Wang J, Li C, Yang G, Wang Q, Liu F, Zhao J, Wang H, Huang W. 2013. Comparison and analysis of data assimilation algorithms for predicting the leaf area index of crop canopies. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 6, 188–201.
Dorigo W A. 2012. Improving the robustness of cotton status characterisation by radiative transfer model inversion of multi-angular CHRIS/PROBA data. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 5, 18–29.
Dorigo W A, Zurita-Milla R, de Wit A J W, Brazile J, Singh R, Schaepman M E. 2007. A review on reflective remote sensing and data assimilation techniques for enhanced agroecosystem modeling. International Journal of Applied Earth Observation and Geoinformation, 9, 165–193.
Duan S, Li Z, Wu H, Tang B, Ma L, Zhao E, Li C. 2014. Inversion of the PROSAIL model to estimate leaf area index of maize, potato, and sunflower fields from unmanned aerial vehicle hyperspectral data. International Journal of Applied Earth Observation and Geoinformation, 26, 12–20.
Fang H. 2003. Retrieving leaf area index using a genetic algorithm with a canopy radiative transfer model. Remote Sensing of Environment, 85, 257–270.
Fang H L, Liang S L. 2003. Retrieving leaf area index with a neural network method: Simulation and validation. IEEE Transactions on Geoscience and Remote Sensing, 41, 2052–2062.
Fernandes R A, Miller J R, Chen J M, Rubinstein I G. 2004. Evaluating image-based estimates of leaf area index in boreal conifer stands over a range of scales using high-resolution CASI imagery. Remote Sensing of Environment, 89, 200–216.
Gallo K P, Daughtry C S T. 1987. Differences in vegetation indices for simulated Landsat-5 MSS and TM, NOAA-9 AVHRR, and SPOT-1 sensor systems. Remote Sensing of Environment, 23, 439–452.
Garrigues S, Allard D, Baret F, Weiss M. 2006. Influence of landscape spatial heterogeneity on the non-linear estimation of leaf area index from moderate spatial resolution remote sensing data. Remote Sensing of Environment, 105, 286–298.
Goward S N, Davis P E, Fleming D, Miller L, Townshend J R. 2003. Empirical comparison of Landsat 7 and IKONOS multispectral measurements for selected Earth Observation System (EOS) validation sites. Remote Sensing of Environment, 88, 80–99.
Huete A, Didan K, Miura T, Rodriguez E P, Gao X, Ferreira L G. 2003. Overview of the radiometric and biophysical performance of the MODIS vegetation indices. Remote Sensing of Environment, 83, 195–213.
Huete A, Justice C, Liu H. 1994. Development of vegetation and soil indices for MODIS-EOS. Remote Sensing of Environment, 49, 224–234.
Huete A R. 1988. A soil-adjusted vegetation index (SAVI). Remote Sensing of Environment, 25, 295–309.
Jacquemoud S, Baret F. 1990. PROSPECT: A model of leaf optical properties spectra. Remote Sensing of Environment, 34, 75–91.
Jacquemoud S, Baret F, Andrieu B, Danson F M, Jaggard K. 1995. Extraction of vegetation biophysical parameters by inversion of the PROSPECT plus sail models on sugar-beet canopy reflectance data application to TM and AVIRIS sensors. Remote Sensing of Environment, 52, 163–172.
Jacquemoud S, Verhoef W, Baret F, Bacour C, Zarco-Tejada P J, Asner G P, François C, Ustin S L. 2009. PROSPECT+SAIL models: A review of use for vegetation characterization. Remote Sensing of Environment, 113, S56–S66.
Jiang Z, Huete A, Didan K, Miura T. 2008. Development of a two-band enhanced vegetation index without a blue band. Remote Sensing of Environment, 112, 3833–3845.
Jiang Z W, Chen Z X, Chen J, Liu J, Ren J Q, Li Z N, Sun L, Li H. 2014. Application of crop model data assimilation with a particle filter for estimating regional winter wheat yields. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 7, 4422–4431.
Kaufman Y J, Tanre D, Remer L A, Vermote E F, Chu A, Holben B N. 1997. Operational remote sensing of tropospheric aerosol over land from EOS moderate resolution imaging spectroradiometer. Journal of Geophysical Research (Atmospheres), 102, 17051–17067.
Kuusk A. 1991. The hot spot effect in plant canopy reflectance. In: Myneni R B, Ross J, eds., Photon-Vegetation Interactions. Springer-Verlag, New York. pp. 139–159.
Li G, Li X, Li G, Wen W, Wang H, Chen L, Yu J, Deng F. 2013. Comparison of spectral characteristics between China HJ1-CCD and Landsat 5 TM imagery. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 6, 139–148.
Li J, Chen X, Tian L, Yu H, Zhang W. 2015. An evaluation of the temporal stability of HJ-1 CCD data using a desert calibration site and Landsat 7 ETM+. International Journal of Remote Sensing, 36, 3733–3750.
Li P, Jiang L, Feng Z. 2014. Cross-comparison of vegetation indices derived from Landsat-7 Enhanced Thematic Mapper Plus (ETM+) and Landsat-8 Operational Land Imager (OLI) sensors. Remote Sensing, 6, 310–329.
Li X, Zhang Y, Bao Y, Luo J, Jin X, Xu X, Song X, Yang G. 2014. Exploring the best hyperspectral features for LAI estimation using partial least squares regression. Remote Sensing, 6, 6221–6241.
Li Z, Jin X, Wang J, Yang G, Nie C, Xu X, Feng H. 2015. Estimating winter wheat (Triticum aestivum) LAI and leaf chlorophyll content from canopy reflectance data by integrating agronomic prior knowledge with the PROSAIL model. International Journal of Remote Sensing, 36, 2634–2653.
Li Z, Tang H, Xin X, Zhang B, Wang D. 2014. Assessment of the MODIS LAI product using ground measurement data and HJ-1A/1B imagery in the meadow steppe of Hulunber, China. Remote Sensing, 6, 6242–6265.
Liang L, Di L, Zhang L, Deng M, Qin Z, Zhao S, Lin H. 2015. Estimation of crop LAI using hyperspectral vegetation indices and a hybrid inversion method. Remote Sensing of Environment, 165, 123–134.
LI-COR. 2010. LAI-2200 Plant Canopy Analyzer, Introduction Manual. LI-COR, Nebraska, Lincoln.
Matthew M W, Adler-Golden S M, Berk A, Richtsmeier S C, Levine R Y, Bernstein L S, Acharya P K, Anderson G P, Felde G W, Hoke M P, Ratkowski A, Burke H H, Kaiser R D, Miller D P. 2000. Status of atmospheric correction using a MODTRAN4-based algorithm. In: Shen S S, Descour M R, eds., Proceedings of the Society of Photo-Optical Instrumentation Engineers (SPIE). pp. 199–207.
Morfitt R, Barsi J, Levy R, Markham B, Micijevic E, Ong L, Scaramuzza P, Vanderwerff K. 2015. Landsat-8 operational land imager (OLI) radiometric performance on-orbit. Remote Sensing, 7, 2208–2237.
Mousivand A, Menenti M, Gorte B, Verhoef W. 2015. Multi-temporal, multi-sensor retrieval of terrestrial vegetation properties from spectral-directional radiometric data. Remote Sensing of Environment, 158, 311–330.
Nigam R, Bhattacharya B K, Vyas S, Oza M P. 2014. Retrieval of wheat leaf area index from AWiFS multispectral data using canopy radiative transfer simulation. International Journal of Applied Earth Observation and Geoinformation, 32, 173–185.
Pahlevan N, Lee Z, Wei J, Schaaf C B, Schott J R, Berk A. 2014. On-orbit radiometric characterization of OLI (Landsat-8) for applications in aquatic remote sensing. Remote Sensing of Environment, 154, 272–284.
Pasolli L, Asam S, Castelli M, Bruzzone L, Wohlfahrt G, Zebisch M, Notarnicola C. 2015. Retrieval of Leaf Area Index in mountain grasslands in the Alps from MODIS satellite imagery. Remote Sensing of Environment, 165, 159–174.
Perkins T, Adler-Golden S, Matthew M W, Berk A, Bernstein L S, Lee J, Fox M. 2012. Speed and accuracy improvements in FLAASH atmospheric correction of hyperspectral imagery. Optical Enginerring, 51, 1371–1379.
Richter K, Atzberger C, Vuolo F, D’Urso G. 2011. Evaluation of sentinel-2 spectral sampling for radiative transfer model based lai estimation of wheat, sugar beet, and maize. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 4, 458–464.
Richter K, Hank T B, Vuolo F, Mauser W, D Urso G. 2012. Optimal exploitation of the Sentinel-2 spectral capabilities for crop leaf area index mapping. Remote Sensing, 4, 561–582.
Rouse J W, Haas Jr R H, Schell J A, Deering D W. 1974. Monitoring vegetation systems in the Great Plains with ERTS. In: Third ERTS-1 Symposium. NASA, Washington, D.C. pp. 309–317.
Schlerf M, Atzberger C. 2012. Vegetation structure retrieval in beech and spruce forests using spectrodirectional satellite data. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 5, 8–17.
Shang J, Liu J, Huffman T, Qian B, Pattey E, Wang J, Zhao T, Geng X, Kroetsch D, Dong T, Lantz N. 2014. Estimating plant area index for monitoring crop growth dynamics using Landsat-8 and RapidEye images. Journal of Applied Remote Sensing, 8, doi: 10.1117/1.JRS.8.085196
Si Y, Schlerf M, Zurita-Milla R, Skidmore A, Wang T. 2012. Mapping spatio-temporal variation of grassland quantity and quality using MERIS data and the PROSAIL model. Remote Sensing of Environment, 121, 415–425.
Soudani K, François C, le Maire G, Le Dantec V, Dufrêne E. 2006. Comparative analysis of IKONOS, SPOT, and ETM+ data for leaf area index estimation in temperate coniferous and deciduous forest stands. Remote Sensing of Environment, 102, 161–175.
Steven M D, Malthus T J, Baret F, Xu H, Chopping M J. 2003. Intercalibration of vegetation indices from different sensor systems. Remote Sensing of Environment, 88, 412–422.
Teillet P M, Staenz K, Williams D J. 1997. Effects of spectral, spatial, and radiometric characteristics on remote sensing vegetation indices of forested regions. Remote Sensing of Environment, 61, 139–149.
Tillack A, Clasen A, Kleinschmit B, Förster M. 2014. Estimation of the seasonal leaf area index in an alluvial forest using high-resolution satellite-based vegetation indices. Remote Sensing of Environment, 141, 52–63.
Verger A, Baret F, Camacho F. 2011. Optimal modalities for radiative transfer-neural network estimation of canopy biophysical characteristics: Evaluation over an agricultural area with CHRIS/PROBA observations. Remote Sensing of Environment, 115, 415–426.
Verhoef W. 1984. Light scattering by leaf layers with application to canopy reflectance modeling: The SAIL model. Remote Sensing of Environment, 16, 125–141.
Verhoef W. 1985. Earth observation modeling based on layer scattering matrices. Remote Sensing of Environment, 17, 165–178.
Vohland M, Mader S, Dorigo W. 2010. Applying different inversion techniques to retrieve stand variables of summer barley with PROSPECT+SAIL. International Journal of Applied Earth Observation and Geoinformation, 12, 71–80.
Walthall C. 2004. A comparison of empirical and neural network approaches for estimating corn and soybean leaf area index from Landsat ETM+ imagery. Remote Sensing of Environment, 92, 465–474.
Wang L, Dong T, Zhang G, Niu Z. 2013. LAI retrieval using PROSAIL model and optimal angle combination of multi-angular data in wheat. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 6, 1730–1736.
Wang L, Yang R, Tian Q, Yang Y, Zhou Y, Sun Y, Mi X. 2015. Comparative analysis of GF-1 WFV, ZY-3 MUX, and HJ-1 CCD sensor data for grassland monitoring applications. Remote Sensing, 7, 2089–2108.
Wu M, Huang W, Niu Z, Wang C. 2015. Combining HJ CCD, GF-1 WFV and MODIS data to generate daily high spatial resolution synthetic data for environmental process monitoring. International Journal of Environmental Research and Public Health, 12, 9920–9937.
Yang A, Zhong B, Lv W, Wu S, Liu Q. 2015. Cross-calibration of GF-1/WFV over a desert site using Landsat-8/OLI imagery and ZY-3/TLC data. Remote Sensing, 7, 10763–10787.
Yang F, Sun J, Fang H, Yao Z, Zhang J, Zhu Y, Song K, Wang Z, Hu M. 2012. Comparison of different methods for corn LAI estimation over northeastern China. International Journal of Applied Earth Observation and Geoinformation, 18, 462–471.
Yang G, Zhao C, Liu Q, Huang W, Wang J. 2011. Inversion of a radiative transfer model for estimating forest LAI from multisource and multiangular optical remote sensing data. IEEE Transactions on Geoscience and Remote Sensing, 49, 988–1000.
Yao Y, Liu Q, Liu Q, Li X. 2008. LAI retrieval and uncertainty evaluations for typical row-planted crops at different growth stages. Remote Sensing of Environment, 112, 94–106.
Zhang Q, Xiao X, Braswell B, Linder E, Baret F, Mooreiii B. 2005. Estimating light absorption by chlorophyll, leaf and canopy in a deciduous broadleaf forest using MODIS data and a radiative transfer model. Remote Sensing of Environment, 99, 357–371.
Zhao J, Li J, Liu Q, Fan W, Zhong B, Wu S, Yang L, Zeng Y, Xu B, Yin G. 2015. Leaf area index retrieval combining HJ1/CCD and Landsat8/OLI Data in the Heihe river Basin, China. Remote Sensing, 7, 6862–6885.
Zhong B, Zhang Y, Du T, Yang A, Lv W, Liu Q. 2014. Cross-calibration of HJ-1/CCD over a desert site using Landsat ETM plus imagery and ASTER GDEM product. IEEE Transactions on Geoscience and Remote Sensing, 52, 7247–7263.