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Fraction of absorbed photosynthetically active radiation inversion algorithm of GF-1 data combining radiative transfer model simulation and deep learning

  • role： First author第一作者
  • Affiliation：

    State Key Laboratory of Remote Sensing Science, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100101, China

  • Email：lilifs@aircas.ac.cn
  • Introduction：李丽，研究方向为光合有效辐射、光合有效辐射吸收比例等定量遥感参数反演及生态环境遥感监测。E-mail： lilifs@aircas.ac.cn
LI Li1,  
  • role： Corresponding author通信作者
  • Affiliation：

    State Key Laboratory of Remote Sensing Science, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100101, China

  • Email：xinxz@aircas.ac.cn
  • Introduction：辛晓洲，研究方向为地表辐射与能量平衡遥感估算理论与方法。E-mail： xinxz@aircas.ac.cn
XIN Xiaozhou1*,  
  • Affiliation：

    State Key Laboratory of Remote Sensing Science, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100101, China

TANG Yong1,  
  • Affiliation：

    State Key Laboratory of Remote Sensing Science, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100101, China

BAI Junhua1,  
  • Affiliation：

    State Key Laboratory of Remote Sensing Science, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100101, China

DU Yongming1,  
  • Affiliation：

    College of Geodesy and Geomatics, Shandong University of Science and Technology, Qingdao 266590, China

SUN Lin2,  
  • Affiliation：

    State Key Laboratory of Remote Sensing Science, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100101, China

    College of Resources and Environment, University of Chinese Academy of Sciences, Beijing 100049, China

WEN Jianguang13,  
  • Affiliation：

    State Key Laboratory of Remote Sensing Science, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100101, China

ZHONG Bo1,  
  • Affiliation：

    State Key Laboratory of Remote Sensing Science, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100101, China

WU Shanlong1,  
  • Affiliation：

    State Key Laboratory of Remote Sensing Science, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100101, China

ZHANG Hailong1,  
  • Affiliation：

    State Key Laboratory of Remote Sensing Science, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100101, China

YU Shanshan1,  
  • Affiliation：

    State Key Laboratory of Remote Sensing Science, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100101, China

    College of Resources and Environment, University of Chinese Academy of Sciences, Beijing 100049, China

LIU Qinhuo13

Resümee

The Fraction of absorbed Photosynthetically Active Radiation (FPAR) is one of the key parameters in the light use efficiency model of the carbon cycle. High-spatiotemporal-resolution data have been provided for the inversion of quantitative remote sensing products since the launch of GF satellites. The FPAR products derived from GF satellite data provide precise and accurate input parameters for the analysis and evaluation of the ecosystem’s carbon cycle. In this study, a deep learning algorithm was developed to retrieve FPAR over China based on the simulated data of the radiative transfer model. The inputs are surface reflectance, cloud detection, and land cover products of GF-1 satellite data, whereas the output is FPAR. The FPAR product has a spatial resolution of 16 m and a temporal resolution of 10 days. This method uses the SAIL model to simulate output canopy FPAR and reflectance under various input variables, such as solar and observing angles and atmospheric conditions. The FPAR inversion model of GF-1 satellite data was obtained using a deep belief network. The long-term crop and grassland FPAR observation data in Huailai and Heihe were used to compare and validate the FPAR products, with a root mean square error of 0.15 and 0.17, respectively. The inversed FPAR is in good agreement with the measured FPAR in the low values, but lower than the measured FPAR in the high values. The radiative transfer model, the representativeness of the simulated data, and the preprocessing (calibration and geometric and atmospheric correction) of the GF-1 satellite data inevitably introduce some biases in the inversion process. This method uses the multidimensional atmospheric and surface variables as the input and the simulated vegetation canopy by the radiative transfer model parameters as the output. The simulated dataset, used as the training samples for deep learning, makes up for the errors in the deep learning training process caused by the insufficient number of training samples and incomplete observation data. The input of the inversion is only the surface reflectance product with the information of the sun angle and the observation angle. It lessens the difficulty of obtaining input parameters, reduces the influence of the error transmission of the input parameters, and is conducive to the realization of the commercial production of the product. The high FPAR is mainly distributed in the northeast, north, central, east, southwest, and south parts of China. The interannual variation of the FPAR time series, combined with the vegetation growing cycle and phenology, is high in spring and summer and low in autumn and winter.

Schlüsselwort

quantitative remote sensing inversion;GF-1;FPAR;SAIL model;deep learning

References

  1. 1.
    Baret F, Hagolle O, Geiger B, Bicheron P, Miras B, Huc M, Berthelot B, Niño F, Weiss M, Samain O, Roujean J L and Leroy M. 2007. LAI, fAPAR and fCover CYCLOPES global products derived from VEGETATION: Part 1: principles of the algorithm. Remote Sensing of Environment, 110(3): 275-286
  2. 2.
    Chen J M. 1996. Canopy architecture and remote sensing of the fraction of photosynthetically active radiation absorbed by boreal conifer forests. IEEE Transactions on Geoscience and Remote Sensing, 34(6): 1353-1368
  3. 3.
    Chen L F, Gao Y H, Li L, Liu Q H and Gu X F. 2008. Forest NPP estimation based on MODIS data under cloudless condition. Science in China Series D: Earth Sciences, 51(3): 331-338
  4. 4.
    Dawson T P, North P R J, Plummer S E and Curran P J. 2003. Forest ecosystem chlorophyll content: implications for remotely sensed estimates of net primary productivity. International Journal of Remote Sensing, 24(3): 611-617
  5. 5.
    Fan W J, Liu Y, Xu X R, Chen G X and Zhang B T. 2014. A new FAPAR analytical model based on the law of energy conservation: a case study in China. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 7(9): 3945-3955
  6. 6.
    Gobron N, Pinty B, Mélin F, Taberner M, Verstraete M M, Robustelli M and Widlowski J L. 2007. Evaluation of the MERIS/ENVISAT FAPAR product. Advances in Space Research, 39(1): 105-115
  7. 7.
    Goward S N and Huemmrich K F. 1992. Vegetation canopy PAR absorptance and the normalized difference vegetation index: an assessment using the SAIL model. Remote Sensing of Environment, 39(2): 119-140
  8. 8.
    Huete A R, Liu H Q, Batchily K and van Leeuwen W. 1997. A comparison of vegetation indices over a global set of TM images for EOS-MODIS. Remote Sensing of Environment, 59(3): 440-451
  9. 9.
    Knyazikhin Y, Martonchik J V, Myneni R B, Diner D J and Running S W. 1998. Synergistic algorithm for estimating vegetation canopy leaf area index and fraction of absorbed photosynthetically active radiation from MODIS and MISR data. Journal of Geophysical Research: Atmospheres, 103(D24): 32257-32275
  10. 10.
    Li L, Du Y M, Tang Y, Xin X Z, Zhang H L, Wen J G and Liu Q H. 2015a. A new algorithm of the FPAR product in the Heihe river basin considering the contributions of direct and diffuse solar radiation separately. Remote Sensing, 7(5): 6414-6432
  11. 11.
    Li L, Fan W J, Du Y M, Tang Y, Xin X Z, Zhang H L and Liu Q H. 2015b. A characteristic study of crop canopy direct and diffuse fraction of absorbed photosynthetically active radiation based on SAIL model simulation. Acta Scientiarum Naturalium Universitatis Pekinensis, 51(1): 99-108
  12. 12.
    Li W J and Fang H L. 2015. Estimation of direct, diffuse, and total FPARs from Landsat surface reflectance data and ground-based estimates over six FLUXNET sites. Journal of Geophysical Research: Biogeosciences, 120(1): 96-112
  13. 13.
    Li W J, Fang H L, Wei S S, Weiss M and Baret F. 2021. Critical analysis of methods to estimate the fraction of absorbed or intercepted photosynthetically active radiation from ground measurements: application to rice crops. Agricultural and Forest Meteorology, 297: 108273
  14. 14.
    Liu L Y, Zhang X, Xie S, Liu X J, Song B W, Chen S Y and Peng D L. 2019. Global white-sky and black-sky FAPAR retrieval using the energy balance residual method: algorithm and validation. Remote Sensing, 11(9): 1004
  15. 15.
    Liu Q H，Wen J G，Zhou X，Zhao J，Li Z Y，Li X，Ma M G，Wang W Z，Liao X H，Liu S M，Fan W J，Xiao Q，ZhongB，Li J，Xin X Z，Li L，Jia L，Gao Z H，Jin J D，Liang S，Xin J，Liao C J and Wu Y R. 2023. Technique system ofremote sensing product generation and validation of GF common products. National Remote Sensing Bulletin， 27(3)：544-562
  16. 16.
    Liu S M, Li X, Xu Z W, Che T, Xiao Q, Ma M G, Liu Q H, Jin R, Guo J W, Wang L X, Wang W Z, Qi Y, Li H Y, Xu T R, Ran Y H, Hu X L, Shi S J, Zhu Z L, Tan J L, Zhang Y and Ren Z G. 2018. The Heihe integrated observatory network: a basin-scale land surface processes observatory in China. Vadose Zone Journal, 17(1): 180072
  17. 17.
    Liu S M, Xu Z W, Wang W Z, Jia Z Z, Zhu M J, Bai J and Wang J M. 2011. A comparison of eddy-covariance and large aperture scintillometer measurements with respect to the energy balance closure problem. Hydrology and Earth System Sciences, 15(4): 1291-1306
  18. 18.
    Lu C L, Wang R and Yin H. 2014. GF-1 satellite remote sensing characters. Spacecraft Recovery and Remote Sensing, 35(4): 67-73
  19. 19.
    Myneni R B, Hoffman S, Knyazikhin Y, Privette J L, Glassy J, Tian Y, Wang Y, Song X, Zhang Y, Smith G R, Lotsch A, Friedl M, Morisette J T, Votava P, Nemani R R and Running S W. 2002. Global products of vegetation leaf area and fraction absorbed PAR from year one of MODIS data. Remote Sensing of Environment, 83(1/2): 214-231
  20. 20.
    Pinty B, Lavergne T, Widlowski J L, Gobron N and Verstraete M M.2009. On the need to observe vegetation canopies in the near-infrared to estimate visible light absorption. Remote Sensing of Environment, 113(1): 10-23
  21. 21.
    Rahman M M, Lamb D W and Stanley J N. 2015. The impact of solar illumination angle when using active optical sensing of NDVI to infer FAPAR in a pasture canopy. Agricultural and Forest Meteorology, 202: 39-43
  22. 22.
    Roujean J L and Breon F M. 1995. Estimating PAR absorbed by vegetation from bidirectional reflectance measurements. Remote Sensing of Environment, 51(3): 375-384
  23. 23.
    Sellers P J. 1985. Canopy reflectance, photosynthesis and transpiration. International Journal of Remote Sensing, 6(8): 1335-1372
  24. 24.
    Sellers P J, Tucker C J, Collatz G J, Los S O, Justice C O, Dazlich D A and Randall D A. 1994. A global 1°×1° NDVI data set for climate studies. Part 2: the generation of global fields of terrestrial biophysical parameters from the NDVI. International Journal of Remote Sensing, 15(17): 3519-3545
  25. 25.
    Steven M D, Malthus T J, Baret F, Xu H and Chopping M J. 2003. Intercalibration of vegetation indices from different sensor systems. Remote Sensing of Environment, 88(4): 412-422
  26. 26.
    Tao X, Liang S L and He T. 2013. Estimation of fraction of Absorbed Photosynthetically Active Radiation from multiple satellite data//Proceedings of 2013 IEEE International Geoscience and Remote Sensing Symposium. Melbourne: IEEE: 3072-3075
  27. 27.
    Tian D F, Fan W J and Ren H Z. 2020. Progress of fraction of absorbed photosynthetically active radiation retrieval from remote sensing data. Journal of Remote Sensing, 24(11): 1307-1324
  28. 28.
    Tian Y, Dickinson R E, Zhou L, Zeng X, Dai Y, Myneni R B, Knyazikhin Y, Zhang X, Friedl M, Yu H, Wu W and Shaikh M. 2004. Comparison of seasonal and spatial variations of leaf area index and fraction of absorbed photosynthetically active radiation from Moderate Resolution Imaging Spectroradiometer (MODIS) and Common Land Model. Journal of Geophysical Research: Atmospheres, 109(D1): D01103
  29. 29.
    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
  30. 30.
    Xiao Z Q, Liang S L, Sun R, Wang J D and Jiang B. 2015. Estimating the fraction of absorbed photosynthetically active radiation from the MODIS data based GLASS leaf area index product. Remote Sensing of Environment, 171: 105-117

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