Measuring the microscopic and macroscopic optical properties of smoke aerosols is important in column concentration retrieval by remote sensing for environmental monitoring. Based on the radiation transfer theory, we constructed a physical model to correlate microscopic and macroscopic optical properties of smoke aerosols. According to the experiment, the mass extinction coefficient and mass angular scattering coefficient of smoke aerosols in the visible-near infrared (400 to 2400 nm) show the strongest extinction and scattering effects on the visible light, which are negatively correlated with wavelength. They were applied to smoke column concentration retrieval based on Landsat8/operational land imager and MODIS images by the established model. Then the optimal bands for smoke detection were analyzed. It indicates that the surface underlying the smoke has impact on the optimal band of smoke detection, and red-light band is optimal for vegetation background.

The currently used energy methods in spectral emissivity measurement are susceptible to the difference in temperature between the target and the reference blackbody. It is also limited by the state of the observation target and observation. This paper introduces the irradiance condition, while using the correlation between the information of emission energy and reflected energy of the high-temperature target. Based on the principle of radiative transmission and energy conservation, the relationship between the emissivity and bidirectional reflectance factor (BRF) was used to perform the retrieval of emissivity and temperature. An experimental device was designed, and graphite and rock were considered to verify the feasibility of the experimental scheme. The error of emissivity and temperature of both targets were, respectively, less than 5% and 0.5%, due to the Lambertian assumption, and the systematic errors had negligible impact on the retrieval. This verifies that the experimental observation method and scheme is reasonable.

In remote sensing monitoring of forest fires, smoke and clouds exhibit similar spectral characteristics in satellite imagery, which can easily lead to clouds being misjudged as smoke. This incorrect discrimination may result in missed detections or false alarms of fire points. The precise differentiation of smoke and clouds has become increasingly challenging, significantly limiting the ability to accurately identify fires in their early stages. Additionally, electromagnetic waves penetrating the smoke and clouds interact with the underlying surface, which interferes with the effective separation of smoke and clouds. In response to the aforementioned issues, this paper systematically studies the impact mechanism of different underlying surfaces on the spectral response of smoke and clouds. We constructed a dataset using sample collection and gradation methods. It contains smoke at varying concentrations and clouds of different thicknesses over three typical underlying surfaces: vegetation, soil, and water. Based on the analysis of spectral characteristics, analysis of variance (ANOVA) was applied to screen sensitive bands suitable for the separation of smoke and clouds. Furthermore, considering the distribution characteristics of smoke and cloud samples in spectral space, single-band threshold models, visible-band index (VBI) models, ratio index models, and Fisher smoke and cloud recognition index (FSCRI) models were developed for three typical underlying surfaces. The validation results demonstrate that the FSCRI models significantly outperform other models in terms of both robustness and accuracy. Their recognition accuracy rates for smoke and clouds in the underlying surfaces of vegetation, soil and water reached 95.5%, 93.5% and 99%, respectively. The proposed method effectively suppresses cloud interference to improve smoke and cloud separation. This capability enables more accurate early detection of forest fires and localization of their sources.

In order to solve the problem of inaccurate emissivity obtained by empirical or experimental measurements in traditional volcanic inversion methods, which leads to the discrepancy between the temperature inversion results and the actual temperature, this paper constructs a joint inversion model of volcanic temperature and emissivity, and solves the volcanic temperature and emissivity simultaneously from the image element scale.In order to obtain the two unknown parameters of volcanic temperature and emissivity simultaneously, the study image element is assumed to be a pure image element and Lambertian body, and based on the blackbody radiation law and energy conservation law, the volcanic radiation energy transfer equation under different irradiance conditions is established and solved jointly. In this paper, the study area is Halemaʻumaʻu crater in Hawaii, and the short-wave infrared band of Landsat 8 satellite day and night remote sensing images with an imaging interval of 36 hours are used as the study data, and the temperature and emissivity are calculated using the golden mean and parabolic interpolation method with the same emissivity of the same image element in both scenes and the temperature approximation as the constraints. The results are as follows: the temperature inversion of the study area has a banding phenomenon, the temperature of the lava lake is 800–1050 K and the emissivity is 0.5–0.7; the temperature of the crater surface outside the lava lake is 500–800 K and the emissivity is 0.7–1.0. The inversion results are consistent with the actual situation. The results show that the method can realize the simultaneous inversion of volcanic temperature and emissivity parameters on the image element scale, which provides a new method for the inversion of volcanic temperature and emissivity.