Study of the Remote Sensing Model of FAPAR over Rugged Terrains

Mountainous areas with rugged terrains are widely distributed around the world. Remotely sensed values of the fraction of absorbed photosynthetically active radiation (FAPAR) suffer from the effect of rugged terrain. In this study, the effect of rugged terrain was incorporated into the FAPAR model based on recollision probability (FAPAR-P), which was improved in two aspects: calculating the sky viewing factor to correct for the fraction of diffuse sky radiation to the total radiation, and correcting the interception probability according to the slope and aspect of each pixel. The newly developed model is called FAPAR-PR (FAPAR-P Model for Rugged Terrain Area). Two study areas were chosen to validate the proposed model: the Dayekou watershed in Gansu Province, and Weichang in Hebei Province, China. The FAPAR values derived from the models were compared with FAPAR values measured in situ using photon flux sensors and the SunScan canopy analysis system (Delta-T Devices Ltd., Cambridge, UK). The validation results show that the FAPAR-PR model is applicable to rugged terrain areas, and it achieves a high level of accuracy. The FAPAR retrieval at different scales was also conducted to estimate the effect of terrain on the FAPAR-P and FAPAR-PR models. In our chosen study area, the effect of rugged terrain was significant in fine resolution pixels, but it was not obvious at larger scales, as the effects of slope and aspect were partly eliminated by the upscaling of the digital elevation model.

[1]  T. Nilson A theoretical analysis of the frequency of gaps in plant stands , 1971 .

[2]  P. Teillet,et al.  On the Slope-Aspect Correction of Multispectral Scanner Data , 1982 .

[3]  Songqiao Zhao,et al.  Physical geography of China , 1986 .

[4]  J. Dozier,et al.  Rapid Calculation Of Terrain Parameters For Radiation Modeling From Digital Elevation Data , 1989, 12th Canadian Symposium on Remote Sensing Geoscience and Remote Sensing Symposium,.

[5]  S. Prince Satellite remote sensing of primary production: comparison of results for Sahelian grasslands 1981-1988 , 1991 .

[6]  S. Prince A model of regional primary production for use with coarse resolution satellite data , 1991 .

[7]  A. Bégué Leaf area index, intercepted photosynthetically active radiation, and spectral vegetation indices: A sensitivity analysis for regular-clumped canopies , 1993 .

[8]  Alan H. Strahler,et al.  Topographic effects on bidirectional and hemispherical reflectances calculated with a geometric-optical canopy model , 1994, IEEE Trans. Geosci. Remote. Sens..

[9]  S. Goward,et al.  Global Primary Production: A Remote Sensing Approach , 1995 .

[10]  C. Justice,et al.  A Revised Land Surface Parameterization (SiB2) for Atmospheric GCMS. Part II: The Generation of Global Fields of Terrestrial Biophysical Parameters from Satellite Data , 1996 .

[11]  H. Mooney,et al.  Modeling the Exchanges of Energy, Water, and Carbon Between Continents and the Atmosphere , 1997, Science.

[12]  Hervé Sinoquet,et al.  Radiation absorption and use by humid savanna grassland: assessment using remote sensing and modelling , 1997 .

[13]  S. Running,et al.  Synergistic algorithm for estimating vegetation canopy leaf area index and fraction of absorbed photosynthetically active , 1998 .

[14]  D. Diner,et al.  Estimation of vegetation canopy leaf area index and fraction of absorbed photosynthetically active radiation from atmosphere‐corrected MISR data , 1998 .

[15]  Rasmus Fensholt,et al.  The spatio-temporal relationship between rainfall and vegetation development in Burkina Faso , 1999 .

[16]  A. Bondeau,et al.  Comparing global models of terrestrial net primary productivity (NPP): analysis of differences in light absorption and light‐use efficiency , 1999 .

[17]  Paul V. Bolstad,et al.  An approach to spatially distributed modeling of net primary production (NPP) at the landscape scale and its application in validation of EOS NPP products , 1999 .

[18]  A. Strahler,et al.  Recent advances in geometrical optical modelling and its applications , 2000 .

[19]  S. Running,et al.  Global products of vegetation leaf area and fraction absorbed PAR from year one of MODIS data , 2002 .

[20]  S. Liang Quantitative Remote Sensing of Land Surfaces , 2003 .

[21]  R. Fensholt,et al.  Evaluation of MODIS LAI, fAPAR and the relation between fAPAR and NDVI in a semi-arid environment using in situ measurements , 2004 .

[22]  Alfredo Huete,et al.  A multi-scale analysis of dynamic optical signals in a Southern California chaparral ecosystem: A comparison of field, AVIRIS and MODIS data , 2004 .

[23]  Scott J. Goetz,et al.  Validation of MODIS F/sub PAR/ products in boreal forests of Alaska , 2006, IEEE Transactions on Geoscience and Remote Sensing.

[24]  Jan Pisek,et al.  Algorithm for global leaf area index retrieval using satellite imagery , 2006, IEEE Transactions on Geoscience and Remote Sensing.

[25]  W. Cohen,et al.  Evaluation of fraction of absorbed photosynthetically active radiation products for different canopy radiation transfer regimes: methodology and results using Joint Research Center products derived from SeaWiFS against ground-based estimations. , 2006 .

[26]  O. Hagolle,et al.  LAI, fAPAR and fCover CYCLOPES global products derived from VEGETATION: Part 1: Principles of the algorithm , 2007 .

[27]  W. Fan,et al.  Accurate LAI retrieval method based on PROBA/CHRIS data. , 2009 .

[28]  Qiang Liu,et al.  Scale effect and scale correction of land-surface albedo in rugged terrain , 2009 .

[29]  Zhang Yanli,et al.  Preparation of High-resolution DEM in Dayekou Basin based on the WorldView-2 and Its Accuracy Analysis , 2013 .

[30]  Wenjie Fan,et al.  A New FAPAR Analytical Model Based on the Law of Energy Conservation: A Case Study in China , 2014, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing.

[31]  Weimin Ju,et al.  GOST: A Geometric-Optical Model for Sloping Terrains , 2014, IEEE Transactions on Geoscience and Remote Sensing.

[32]  Lu Wang,et al.  Scaling Transform Method for Remotely Sensed FAPAR Based on FAPAR-P Model , 2015, IEEE Geoscience and Remote Sensing Letters.

[33]  K. Moffett,et al.  Remote Sens , 2015 .

[34]  Fei Li,et al.  Estimating Forest fAPAR from Multispectral Landsat-8 Data Using the Invertible Forest Reflectance Model INFORM , 2015, Remote. Sens..