Simulating canopy reflectance time series for typical subtropical forest by coupling PROSPECT5 and 4SAIL models

doi:10.13287/j.1001-9332.201708.024

Abstract

Abstract: Canopy reflectance has important meanings in the aspects of accurate interpretation of forest vegetation type and key parameter (leaf area index, chlorophyll content, etc.) retrieval of forest carbon assimilation. In this study, the canopy reflectance time series of three typical subtropical forests (moso bamboo forest, MBF; lei bamboo forest, LBF; evergreen and deciduous broadleaf forest, EDBF) was simulated by coupling PROSPECT5 and 4SAIL models. First, the sensitivity of parameters in PROSPECT5 and 4SAIL model was analyzed to discuss the effects of parameters on the canopy reflectance simulation. Second, unsensitive parameters were optimized using the observed reflectance. Finally, the canopy reflectances of three typical subtropical forests were simulated using PROSPECT5 and 4SAIL models, and compared with MODIS reflectance dataset. The results showed that leaf area index was most sensitive to bands 1, 2, 3, 5 and 7, with the total-order sensitivity indexes of 0.80, 0.83, 0.49, 0.66 and 0.47, respectively. Chlorophyll content was most sensitive to band 4, with the total-order sensitivity index of 0.59. Leaf water content was most sensitive to band 6, with the total-order sensitivity index of 0.54. Leaf structure, carotenoids content, dry matter content, hot parameter and soil factors were not/low sensitive to these bands. Canopy reflectance simulated using the optimized PROSPECT5 and 4SAIL models had the capability in reflecting the real seasonal changes of three typical subtropical forests. The simulated canopy reflectance significantly correlated with MODIS reflectance, with high R² of 0.86, 0.90, 0.93, and low root mean square of 0.09, 0.07, 0.05 for MBF, LBF, and EDBF, respectively. The simulated canopy reflectance solved to some extent the problems of influence on MODIS reflectance products by precipitation and snow in winter, and mixed pixels.

ZHANG Lu-ying, LI Xue-jian, DU Hua-qiang, CUI Lu, MAO Fang-jie, LIU Yu-li, LI Yang-guang, ZHU Di-en. Simulating canopy reflectance time series for typical subtropical forest by coupling PROSPECT5 and 4SAIL models[J]. Chinese Journal of Applied Ecology, 2017, 28(8): 2461-2469.

References

[1] Wang C, Du HQ, Xu XJ, et al. Multi-scale crown closure retrieval for moso bamboo forest using multi-source remotely sensed imagery based on geometric-optical and Erf-BP neural network models. International Journal of Remote Sensing, 2015, 36: 1-19
[2] Gu CY, Du HQ, Mao FJ, et al. Global sensitivity analysis of PROSAIL model parameters when simulating Moso bamboo forest canopy reflectance. International Journal of Remote Sensing, 2016, 37: 5270-5286
[3] Niu Z (牛铮), Wang C-Y (王长耀), et al. Remote Sensing Applications for Carbon Cycle. Beijing: Science Press, 2008 (in Chinese)
[4] Du HQ, Fan WL, Zhou GM, et al. Retrieval of canopy closure and lai of moso bamboo forest using spectral mixture analysis based on real scenario simulation. IEEE Transactions on Geoscience and Remote Sensing, 2011, 49: 4328-4340
[5] Li X-J (李雪建), Mao F-J (毛方杰), Du H-Q (杜华强), et al. Simulating of carbon fluxes in bamboo forest ecosystem using beps model based on the LAI assimilated with dual ensemble Kalman filter. Chinese Journal of Applied Ecology (应用生态学报), 2016, 27(12): 1-13 (in Chinese)
[6] Li H-Y (李海洋), Fan W-Y (范文义), Yu Y (于颖), et al. Leaf area index retrieval based on prospect, liberty and geosail models. Scientia Silvae Sinicae (林业科学), 2011, 47(9): 75-81 (in Chinese)
[7] Gu C-Y (谷成燕), Du H-Q (杜华强), Zhou G-M (周国模), et al. Retrieval of leaf area index of Moso bamboo forest with Landsat Thematic Mapper image based on PROSAIL canopy radiative transfer model. Chinese Journal of Applied Ecology (应用生态学报), 2013, 24(8): 2248-2256 (in Chinese)
[8] Jacquemoud S, Baret F. PROSPECT: A model of leaf optical properties spectra. Remote Sensing of Environment, 1990, 34: 75-91
[9] Feret JB, François C, Asner GP, et al. PROSPECT-4 and 5: Advances in the leaf optical properties model separating photosynthetic pigments. Remote Sensing of Environment, 2008, 112: 3030-3043
[10] Verhoef W. Light scattering by leaf layers with application to canopy reflectance modeling: The SAIL model. Remote Sensing of Environment, 1984, 16: 125-141
[11] Nilson T, Kuusk A. A reflectance model for the homogeneous plant canopy and its inversion. Remote Sensing of Environment, 1989, 27: 157-167
[12] Verhoef W, Bach H. Coupled soil-leaf-canopy and atmosphere radiative transfer modeling to simulate hyperspectral multi-angular surface reflectance and TOA radiance data. Remote Sensing of Environment, 2007, 109: 166-182
[13] Xiao Y-F (肖艳芳), Zhou D-M (周德民), Gong H-L (宫辉力), et al. Sensitivity of canopy reflectance to biochemical and biophysical variables. Journal of Remote Sensing (遥感学报), 2015, 19(3): 368-374 (in Chinese)
[14] Ma J-W (马建威), Huang S-F (黄诗峰), Li J-R (李纪人), et al. Global sensitivity analysis of parameters in the PROSAIL model based on modified Sobol’s method. Bulletin of Surveying and Mapping (测绘通报), 2016(3): 33-35 (in Chinese)
[15] Liu B-J (刘博杰), Lu F (逯非), Wang X-K (王效科), et al. Greenhouse gas emissions, carbon leakage and net carbon sequestration from afforestation and forest management: A review. Chinese Journal of Applied Ecolo-gy (应用生态学报), 2017, 28(2): 673-688 (in Chinese)
[16] Zhou GM, Meng CF, Jiang PK, et al. Review of carbon fixation in bamboo forests in China. Botanical Review, 2011, 77: 262-270
[17] Chen XG, Zhang XQ, Zhang YP, et al. Changes of carbon stocks in bamboo stands in China during 100 years. Forest Ecology and Management, 2009, 258: 1489-1496
[18] Tang M-P (汤孟平), Zhou G-M (周国模), Shi Y-J (施拥军), et al. Study of dominant plant populations and their spatial patterns in evergreen broadleaved forest in TianMu mountain, China. Chinese Journal of Plant Ecology (植物生态学报), 2006, 30(5): 743-752 (in Chinese)
[19] Sun S-B (孙少波), Du H-Q (杜华强), Li P-H (李平衡), et al. Retrieval of leaf net photosynthetic rate of moso bamboo forests using hyperspectral remote sensing based on wavelet transform. Chinese Journal of Applied Ecology (应用生态学报), 2016, 27(1): 49-58 (in Chinese)
[20] Lu G-F (陆国富), Du H-Q (杜华强), Zhou G-M (周国模), et al. Dynamic change of Phyllostachys edulis forest canopy parameters and their relationships with photosynthetic active radiation in the bamboo shooting growth phase. Journal of Zhejiang A & F University (浙江农林大学), 2012, 29(6): 844-850 (in Chinese)
[21] Saltelli A, Tarantola S, Chen KPS. A quantitative model-independent method for global sensitivity analysis of model output. Technometrics, 1999, 41: 39-56
[22] Zhang J-X (张静潇), Su W (苏伟). Sensitivity analysis of CERES-Wheat model parameters based on EFAST method. Journal of China Agricultural University (中国农业大学学报), 2012, 17(5): 149-154 (in Chinese)
[23] Mei A-X (梅安新). Remote Sensing Introduction. Beijing: Higher Education Press, 2001 (in Chinese)
[24] Jacquemoud S, Verhoef W, Baret F, et al. PROSPECT+SAIL models: A review of use for vegetation characteri-zation. Remote Sensing of Environment, 2009, 113: S56-S66
[25] Quaife T, Lewis P, Dekauwe M, et al. Assimilating canopy reflectance data into an ecosystem model with an Ensemble Kalman Filter. Remote Sensing of Environment, 2008, 112: 1347-1364
[26] Liu YB, Ju WM, Chen JM, et al. Spatial and temporal variations of forest LAI in China during 2000-2010. Chinese Science Bulletin, 2012, 57: 2846-2856
[27] Fan W-L (范渭亮), Du H-Q (杜华强), Zhou G-M (周国模), et al. Spectral mixture analysis method based on the simulation of real scenario. Journal of Remote Sensing (遥感学报), 2010, 14(6): 1241-1258 (in Chinese)
[28] Zhao Y-G (赵永光), Ma L-L (马灵玲), Li C-R (李传荣), et al. A method to reconstruct surface reflectance spectrum from multispectral image based on canopy radiation transfer model. Spectroscopy and Spectral Analysis (光谱学与光谱分析), 2015, 35(7): 1763-1769 (in Chinese)