Canopy modeling and validation for row planted crops of key growth stages

Row planted crop is the transitional crop type with the discrete structure and continuous structure. There are different canopy structures for different growth stages. The canopy structure will transfer from row structure to continuous structure around the elongth growth stages. Furthermore, the elongth growth stage is key growth stages for the crop. For parameter inversion, it is significant to propose the key growth stages to simplify the model selection and to improve the parameters inversion accuracy. We put the object on one row period for the similar structure of the row planted crop. For each period, four components (sunlit vegetation and soil; viewed vegetation and soil) can be computed based on the bidirectional gap probability model, and structure parameters (W (row width), H (row height), S (row spacing), etc.) and view and solar zenith/azimuth angles. At the same time, the equivalent radiance for vegetation and soil from direct illuminated light and from the diffused and multi-scatted light will be computed. The key growth stages model (KGSM) is the sum of the four component radiance which is the product of each component area and the corresponding equivalent radiance. Through the validation based on the RGM and SAILH model, the canopy bidirectional reflectance can be simulated based the KGSM. The model validation is also done for experiment measurement.

[1]  R D Jackson,et al.  Plant canopy information extraction from composite scene reflectance of row crops. , 1979, Applied optics.

[2]  C. J. Tucker,et al.  Spectral assessment of soybean leaf area and leaf biomass , 1980 .

[3]  Daniel S. Kimes,et al.  Directional radiometric measurements of row-crop temperatures , 1983 .

[4]  Gwynn H. Suits,et al.  Extension of a uniform canopy reflectance model to include row effects , 1983 .

[5]  W. Verhoef.  Light scattering by leaf layers with application to canopy reflectance modeling: The Scattering by Arbitrarily Inclined Leaves (SAIL) model , 1984 .

[6]  D. Jupp,et al.  Interpretation of vegetation structure in Landsat MSS imagery: a case study in disturbed semi-arid eucalypt woodlands. Part 2. Model-based analysis , 1986 .

[7]  Toby Grier,et al.  Estimation of canopy parameters for inhomogeneous vegetation canopies from reflectance data. I: Two-dimensional row canopy , 1986 .

[8]  A. Strahler,et al.  Geometric-Optical Bidirectional Reflectance Modeling of a Conifer Forest Canopy , 1986, IEEE Transactions on Geoscience and Remote Sensing.

[9]  N. Goel.  Models of vegetation canopy reflectance and their use in estimation of biophysical parameters from reflectance data , 1988 .

[10]  S. Running,et al.  Numerical Terradynamic Simulation Group 12-1988 Rapid Estimation of Coniferous Forest Leaf Area Index Using a Portable Integrating Radiometer , 2018 .

[11]  A. Kuusk,et al.  A reflectance model for the homogeneous plant canopy and its inversion , 1989 .

[12]  Richard L. Thompson,et al.  A computer graphics based model for scattering from objects of arbitrary shapes in the optical region , 1991 .

[13]  Gordon B. Bonan,et al.  Importance of leaf area index and forest type when estimating photosynthesis in boreal forests , 1993 .

[14]  A. Kuusk.  A fast, invertible canopy reflectance model , 1995 .

[15]  Jindi Wang,et al.  On utilization ofa priori knowledge in inversion of remote sensing models , 1998 .

[16]  S. Running,et al.  MODIS Leaf Area Index (LAI) And Fraction Of Photosynthetically Active Radiation Absorbed By Vegetation (FPAR) Product , 1999 .

[17]  W. Qin,et al.  3-D Scene Modeling of Semidesert Vegetation Cover and its Radiation Regime , 2000 .

[18]  Wenjie Fan,et al.  A bi-directional gap model for simulating the directional thermal radiance of row crops , 2002 .

[19]  Fang Wen-jie.  The Affecting Factors of Thermal Radiant Directionality of the Row Winter Wheat , 2003 .

[20]  Hongliang Fang,et al.  Retrieving leaf area index with a neural network method: simulation and validation , 2003, IEEE Trans. Geosci. Remote. Sens..

[21]  Wang Jin-di.  The Approach on Leaf Area Index Inversion Using Multiangular and Multispectral Data Sets , 2005 .

[22]  H. Cui.  Thermal bidirectional gap probability model for row crop canopies and validation , 2005 .

[23]  Cao Hong-xin.  Studies on Dynamic Simulation Models of Optimum Leaf Area Index of Wheat under Different Yielding Levels , 2006 .

[24]  Qiang Liu,et al.  LAI retrieval and uncertainty evaluations for typical row-planted crops at different growth stages , 2008 .