The PROSPECT model, one of the most popular leaf radiative transfer models, has long been used for retrieving leaf biochemical traits from leaf directional-hemispherical reflectance factor (DHRF) spectra, while remains underexplored for applications to the leaf bidirectional reflectance factor (BRF) spectra to retrieve leaf biochemical traits. Existence of leaf surface reflectance and anisotropic property could be the main issues that constrain the applicability of the PROSPECT inversion to assess leaf biochemical traits from leaf BRF spectra. This study presents an inversion method by integrating the PROSPECT model with spectral derivatives and similarity metrics (SDM), called PROSDM, to remove the difference between leaf BRF and DHRF spectra and retrieve leaf biochemical traits from leaf BRF spectra. Leaf BRF spectra and the corresponding contents of chlorophyll (C_ab), carotenoid (C_xc), water (C_w), and dry matter (C_m) were obtained from ten datasets across a wide variety of plant species with varied growth stages, nutrition status, and planting regions. The datasets showed that the significant difference existed between leaf BRF and DHRF spectra, which varied with spectral wavelengths and plant species, and affected the accuracies of retrieving C_ab, C_xc, C_w, and C_m. To eliminate the difference between leaf BRF and DHRF spectra independent on wavelengths, spectral derivatives were applied. When the difference between BRF and DHRF spectra varied with wavelengths, spectral derivatives only removed part of the difference, and implementation of the Manhattan distance compensated the limitation of the spectral derivatives to further reduce the difference. As a result, the PROSDM outperformed the PROSPECT and PROCOSINE inversions as well as PROCWT to retrieve C_ab, C_xc, C_w, and C_m from leaf BRF spectra with the root mean square errors (RMSEs) of 7.64 μg/cm², 2.77 μg/cm², 0.0041 g/cm², and 0.0024 g/cm²_, respectively. Significant improvements in the retrievals of C_ab, C_xc, C_w, and C_m were obtained with the RMSEs reduced by 20.33%, 29.34%, 25.45%, and 44.19% compared to the PROSPECT inversion, respectively. It was further found that the model inversions were affected by the spectral saturation, PROSPECT versions, spectral subdomains, and the range of model parameters. The retrieval results by different inversion methods may be improved with the suitable spectral subdomains and range of model parameters. Importantly, the proposed PROSDM showed the great potential to alleviate these negative effects on the retrievals of C_ab, C_xc, C_w, and C_m. The performance assessment of PROSDM demonstrates a promising potential for its applications in remote sensing aiming at retrievals of leaf biochemical traits from leaf BRF spectra.

PROSPECT 模型是最流行的叶片辐射传递模型之一，长期以来一直用于从叶片定向半球反射系数 (DHRF) 光谱中检索叶片生化性状，但在应用于叶片双向反射系数 (BRF) 光谱方面仍未得到充分探索检索叶片生化性状。叶表面反射率和各向异性特性的存在可能是限制 PROSPECT 反演从叶 BRF 光谱评估叶生化性状的适用性的主要问题。本研究提出了一种通过将 PROSPECT 模型与光谱导数和相似性度量 (SDM) 相结合的反演方法，称为 PROSDM，以消除叶片 BRF 和 DHRF 光谱之间的差异，并从叶片 BRF 光谱中检索叶片生化性状。C _ab )、类胡萝卜素 ( C _xc )、水 ( C _w ) 和干物质 ( C _m ) 来自十个数据集，这些数据集涵盖了具有不同生长阶段、营养状况和种植区域的各种植物物种。数据集表明，叶片 BRF 和 DHRF 光谱存在显着差异，该差异随光谱波长和植物种类的不同而不同，并影响检索C _ab、C _xc、C _w和C _m的精度。. 为了消除独立于波长的叶 BRF 和 DHRF 光谱之间的差异，应用了光谱导数。当BRF和DHRF光谱的差异随波长变化时，光谱导数只去除了部分差异，曼哈顿距离的实现弥补了光谱导数的局限性，进一步减小了差异。因此，PROSDM 的性能优于 PROSPECT 和 PROCOSINE 反演以及 PROCWT，以从叶 BRF 光谱中检索C _ab、C _xc、C _w和C _m，均方根误差 (RMSE) 为 7.64 μg/cm ²， 2.77 微克/厘米² , 0.0041 克/厘米²和 0.0024 g/cm ² _，分别。在的检索显著改进Ç _AB，Ç _XC，Ç_瓦特，和Ç_米用由20.33％，29.34％，25.45％，44.19和％降低RMSEs分别获得相对于前景反转。进一步发现模型反演受光谱饱和度、PROSPECT 版本、光谱子域和模型参数范围的影响。通过合适的光谱子域和模型参数范围可以改善不同反演方法的反演结果。重要的是，提议的 PROSDM 显示出减轻这些对检索的负面影响的巨大潜力。Ç _AB，Ç _XC，Ç_瓦特，和Ç_米。PROSDM 的性能评估证明了其在遥感中的应用潜力，旨在从叶片 BRF 光谱中检索叶片生化性状。