The monitoring of vegetation via remote sensing has been widely applied in various fields, such as crop diseases and pests, forest coverage and vegetation growth status, but such monitoring activities were mainly carried out in the daytime, resulting in limitations in sensing the status of vegetation at night. In this article, with the aim of monitoring the health status of outdoor plants at night by remote sensing, a polarized multispectral low-illumination-level imaging system (PMSIS) was established, and a fusion algorithm was proposed to detect vegetation by sensing the spectrum and polarization characteristics of the diffuse and specular reflection of vegetation. The normalized vegetation index (NDVI), degree of linear polarization (DoLP) and angle of polarization (AOP) are all calculated in the fusion algorithm to better detect the health status of plants in the night environment. Based on NDVI, DoLP and AOP fusion images (NDAI), a new index of night plant state detection (NPSDI) was proposed. A correlation analysis was made for the chlorophyll content (SPAD), nitrogen content (NC), NDVI and NPSDI to understand their capabilities to detect plants under stress. The scatter plot of NPSDI shows a good distinction between vegetation with different health levels, which can be seen from the high specificity and sensitivity values. It can be seen that NPSDI has a good correlation with NDVI (coefficient of determination R2 = 0.968), PSAD (R2 = 0.882) and NC (R2 = 0.916), which highlights the potential of NPSDI in the identification of plant health status. The results clearly show that the proposed fusion algorithm can enhance the contrast effect and the generated fusion image will carry richer vegetation information, thereby monitoring the health status of plants at night more effectively. This algorithm has a great potential in using remote sensing platform to monitor the health of vegetation and crops.

Monitoring chlorophyll content changes in the plant via remote sensing is of great significance for understanding plant growth, monitoring vegetation pests and diseases, which is an important method to study the global climate change. However, the monitored information is often interfered by leaf specular reflection, resulting in reduced accuracy of chlorophyll content inversion. In this article, to eliminate the interference of specular reflection in vegetation remote sensing, a polarized multispectral imaging system (PMSIS) used in the different-lightlevel situation to observe vegetation was developed, and a new specular reflection removal vegetation index (NSRVI) was proposed to better detect the vegetation health condition under specular reflection interference. Based on previous studies, several vegetation indices (Simple ratio index (SR), Normalized difference vegetation index (NDVI); mSR, mNDVI (ref. [41]); pSR, pNDVI (ref. [46]); and NSRVI) were established, and the impact of specular reflection on vegetation health detection was evaluated. Correlation analysis was done on Relative Chlorophyll content (SPAD), SR, NDVI, mSR, mNDVI, pSR, pNDVI and NSRVI to understand their potential ability to eliminate specular interference. The results show that SR and NDVI have the highest sensitivity to specular reflection, and the other three methods can alleviate the adverse effects of specular reflection to varying degrees. It was observed that NSRVI was well correlated with SPAD (coefficient of determination (R 2 ) = 0.899, RMSE=6.16), highlighting the potential of NRSVI in eliminating specular reflection interference and identifying vegetation health condition. In summary, this method can effectively eliminate specular interference and improve the detection accuracy of vegetation health condition.