水稻的双向反射特性与其冠层结构，各组分光谱性质以及入射光方向和观测方向之间存在着密切的，非线性的相关关系。运用人工神经网络技术，采用水稻田间实测数据，对这种关系进行拟合，所建立的水稻双向反射BP前向和反演模型，都达到了较高的拟合精度。研究表明：采用人工神经网络技术计算水稻双向反射率和成批反演冠层结构参数是可行的。对所建模型做进一步的改进，可模拟水稻双向反射的实际过程，进而监测作物长势。

Therea re nonlinearre lationsa mongth eb i-directionalre flectanceo fri ce,its c anopya rchitecture parameters, the spectral characteristics of the diferent components of rice, and the illumination and viewing geometry. This article explores the use of artificial neural network for both forward and inverse bi-directional reflectance modeling of rice based on the data measured in Zhejiang University(Hangzhou,China) for field experiments from 1999 to 2000. The assumption herei sth atth eb i-directionalre flectanceo fa r icec anopyi sth ef unctiono fth eg eometry ofit sc onstituente lements,th e spatiald istribution,sp ectralfe atureso fth ee lements,a ndth eil uminationa ndv iewingg eometry.T hisim pliesth atth eb idirectional reflectance of the canopy is particular sensitive to the canopy's structural parameters, the spectral characteristico ffo liage,an dth eil luminationa ndv iewingd irection.It al soim pliesth atca nopiesw ithd iferentp arametersw illex hibitd ifferentb i-directionalre flectance.O nt heb asiso fth esea nalysis,we d ecidedt oh ave1 0i nputp arameters:m odelin clinationa ngleo fth ec anopy(0) ,e c centricity(D ),r efl ectanceo ffo liage(R ),t ra nsmittance(T ,), s u nz enitha ngle(B, ),s o il reflectance(sb rf),t he ra tioo fm eanl engtht oc anopyh eight(L ,)a n dt hera tioo fw idtht ole ngth( P ) ,l ea fare ai ndex ([AI),d i fuseto to talin cidentra diation(Q ,). Therea re1 7o utputpa rameters:bi -directionalre flectanceo fth ec anopyin the principal plane,from一60o in the forescattering direction to+60' in the backscattering direction at increments of 7.5oin f orwardB Pm odel.O nt heo therh and,th erea re3 o utputp arameters:le afa reai ndex,th er atioo fm eanl engtht o canopyh eighta ndt hera tioo fw idtht ole ngth,an do ther2 2p arametersm entioneda bovee xceptec centricitya ndm odelin - clinalion angle of the canopy are input parameters in inverse BP model. Aft er m o delde velopment,th en euralne tworkm odelis te steda gainstth ein dependentda tas et.T heR ootm eans quare error between the bi-directional reflectance of rice measured and simulated varies from 4.53 x 10一6to3.67x10一3. The inversion model of artificial neural network is able to inverse the rice canopy structural parameters with 81.8% accuracy. The results of both forward and inverse modehng suggest that the model of artificial neural network is of high precise to simulate the relations of the bi-directional reflectance of rice and its canopy structural parameters. Further research is needed to monitor the rice growth by the neural network model.