Identifying Crop Leaf Angle Distribution Based on Two-Temporal and Bidirectional Canopy Reflectance

The effect of crop leaf angle on the canopy-reflected spectrum cannot be ignored in the inversion of leaf area index (LAI) and the monitoring of the crop-growth condition using remote-sensing technology. In this paper, experiments on winter wheat (Triticum aestivum L.) were conducted to identify the crop leaf angle distribution (LAD) by two-temporal (erecting and elongation stages) and bidirectional in situ reflected spectrum and the Airborne Multiangle Thermal Infrared (TIR) Visible Near-Infrared (VNIR) Imaging System (AMTIS) images. The distribution characters of the leaf angle for different LAD varieties were expressed using the beta-distribution function and the SAILTH radiative transfer models. The proportion of the leaf angle in 5deg angle classes (from 5deg to 90deg) for erectophile, planophile, and horizontal varieties was dominated by 75deg, 55deg, and 35deg. The different LAD varieties had a similar canopy reflectance in 680 nm (red) and 800 nm (near-infrared band) at the erecting stage, while they had significant differences at the elongation stage. The ratio of the canopy reflectance of 800 nm at the erecting stage [R800(B)] to the canopy reflectance of 800 nm at the elongation stage [R800(A)] was used to identify the different LAD varieties through the selected two-temporal canopy reflectance. A method based on the semiempirical model of the bidirectional reflectance distribution function (BRDF) was also introduced in this paper. The structural parameter-sensitive index (SPEI) was used in this paper for crop LAD identification. SPEI is proved to be more sensitive to identify erectophile, planophile, and horizontal LAD varieties than the structural scattering index and the normalized difference f-index. We found that it is feasible to identify horizontal, planophile, and erectophile LAD varieties of wheat by studying two-temporal and bidirectional canopy-reflected spectrum

[1]  F. Gao,et al.  Detecting vegetation structure using a kernel-based BRDF model , 2003 .

[2]  P. Sellers Canopy reflectance, photosynthesis and transpiration , 1985 .

[3]  J. Privette,et al.  Inversion methods for physically‐based models , 2000 .

[4]  J. Roujean,et al.  A bidirectional reflectance model of the Earth's surface for the correction of remote sensing data , 1992 .

[5]  Jinheng Zhang,et al.  Predicting Nitrogen Status of Rice Using Multispectral Data at Canopy Scale , 2006 .

[6]  S. Kaeppler,et al.  Quantitative trait loci controlling leaf and tassel traits in a B73 × Mo17 population of maize , 2002 .

[7]  Grégoire Mercier,et al.  Estimation and monitoring of bare soil/vegetation ratio with SPOT VEGETATION and HRVIR , 2005, IEEE Transactions on Geoscience and Remote Sensing.

[8]  Wout Verhoef,et al.  A new forest light interaction model in support of forest monitoring , 1992 .

[9]  Tadaki Hirose,et al.  Leaf angle as a strategy for light competition: Optimal and evolutionarily stable light-extinction coefficient within a leaf canopy , 1997 .

[10]  Jindi Wang,et al.  Thermal bidirectional gap probability model for row crop canopies and validation , 2003 .

[11]  D. Roberts,et al.  Spectral and Structural Measures of Northwest Forest Vegetation at Leaf to Landscape Scales , 2004, Ecosystems.

[12]  S. Sandmeier,et al.  Physical Mechanisms in Hyperspectral BRDF Data of Grass and Watercress , 1998 .

[13]  Alan H. Strahler,et al.  Retrieval of red spectral albedo and bidirectional reflectance using AVHRR HRPT and GOES satellite observations of the New England region , 1999 .

[14]  Grégoire Mercier,et al.  Estimation and monitoring of bare soil/vegetation ratio with SPOT VEGETATION and HRVIR , 2003, IGARSS 2003. 2003 IEEE International Geoscience and Remote Sensing Symposium. Proceedings (IEEE Cat. No.03CH37477).

[15]  Daniel S. Falster,et al.  Leaf size and angle vary widely across species: what consequences for light interception? , 2003, The New phytologist.

[16]  Karl Fred Huemmrich,et al.  Remote Sensing of Forest Biophysical Structure Using Mixture Decomposition and Geometric Reflectance Models , 1995 .

[17]  N. Goel,et al.  Simple Beta Distribution Representation of Leaf Orientation in Vegetation Canopies1 , 1984 .

[18]  Yan Guang Spectral Prior Knowledge and Its Use in the Remote Sensing Based Inversion of Vegetation Structure , 2002 .

[19]  A. Walkley,et al.  AN EXAMINATION OF THE DEGTJAREFF METHOD FOR DETERMINING SOIL ORGANIC MATTER, AND A PROPOSED MODIFICATION OF THE CHROMIC ACID TITRATION METHOD , 1934 .

[20]  T. N. Hajare,et al.  Gram yield estimation through SVI under variable soil and management conditions , 1998 .

[21]  Wenjiang Huang,et al.  Predicting winter wheat condition, grain yield and protein content using multi‐temporal EnviSat‐ASAR and Landsat TM satellite images , 2006 .

[22]  A. Strahler,et al.  Geometric-Optical Modeling of a Conifer Forest Canopy , 1985, IEEE Transactions on Geoscience and Remote Sensing.

[23]  M. S. Moran,et al.  Opportunities and limitations for image-based remote sensing in precision crop management , 1997 .

[24]  Hajime Utsugi,et al.  Angle distribution of foliage in individual Chamaecyparis obtusa canopies and effect of angle on diffuse light penetration , 1999, Trees.

[25]  S. R. Olsen,et al.  Estimation of available phosphorus in soils by extraction with sodium bicarbonate , 1954 .

[26]  R. H. Bray,et al.  SOIL‐PLANT RELATIONS: I. THE QUANTITATIVE RELATION OF EXCHANGEABLE POTASSIUM TO CROP YIELDS AND TO CROP RESPONSE TO POTASH ADDITIONS , 1944 .

[27]  G. E. Pepper,et al.  Leaf Orientation and Yield of Maize 1 , 1977 .