Stability evaluation of the PROSPECT model for leaf chlorophyll content retrieval

The radiative transfer model, PROSPECT, has been widely applied for retrieving leaf biochemical traits. However, little work has been conducted to evaluate the stability of the PROSPECT model with consideration of multiple factors (i.e., spectral resolution, signal-to-noise ratio, plant growth stages, and treatments). This study aims to investigate the stability of the PROSPECT model for retrieving leaf chlorophyll (Chl) content (Cab). Leaf hemispherical reflectance and transmittance of oilseed rape were acquired at different spectral resolutions, noise levels, growth stages, and nitrogen treatments. The Chl content was also measured destructively by using a microplate spectrophotometer. The performance of the PROSPECT model was compared with a commonly used random forest (RF) model. The results showed that the prediction accuracy of PROSPECT and RF models for Cab did not produce significant differences under varied spectral resolutions ranging from 1 to 20 nm. The ranges of the relative root mean square errors (rRMSE) of the PROSPECT and RF models were 12%-13% and 11.70%-12.86%, respectively. However, the performance of both models for leaf Chl retrieval was strongly influenced by the noise level with the rRMSE of 13-15.37% and 12.04%-15.80% for PROSPECT and RF, respectively. For different growth stages, the PROSPECT model had similar prediction accuracies (rRMSE = 9.26%-12.41%) to the RF model (rRMSE = 9.17%-12.70%). Furthermore, the superiority of the PROSPECT model (rRMSE = 10.10%-12.82%) over the RF model (rRMSE = 11.81%-15.47%) was prominently observed when tested with plants growth at different nitrogen treatment levels. The results demonstrated that the PROSPECT model has a more stable performance than the RF model for all datasets in this study.