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.

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.

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.

The study of brightness temperature (TB) anomalies always postulates a new understanding of the subsurface deposits of the Moon. In this study, the TB performances of the lunar farside highlands were comprehensively studied using TB data derived from the Chang'e (CE)-2 microwave radiometer (MRM), accompanied by Kaguya Multiband Imager data and Lunar Reconnaissance Orbiter Diviner thermal infrared (TIR) data. The main results are as follows: First, we identified two anomaly belts surrounding Giordano Bruno and King Craters, covering an area of about 244,806 km2. Second, through similarity analysis and theoretical simulation, the cause of the anomaly was ascribed to the existence of surface or subsurface rocks. Finally, combining the in-situ observations from the Apollo, CE-3, and CE-4 missions and the sensitivity of the electromagnetic waves to the sizes of the rocks in the TIR and microwave domains, we conclude that the rocks should be in centimeter-meter scale and they are widely distributed in the abnormal belts. These findings provide a new understanding of the surface features in and surrounding the impact craters.

Remote sensing retrieval of sea ice thickness can quickly and accurately obtain the spatiotemporal distribution information of large sea areas. This has important implications for marine resource utilization and global climate change impacts. This study aims to take the Bohai Sea as an example, use Landsat 8 data, and analyze the spectral characteristics of sea ice to establish sensitive indicators for sea ice thickness remote sensing retrieval and determine the functional relationship between these indicators and sea ice thickness. This paper first uses variance analysis to select the sensitive wavebands for sea ice thickness inversion and then uses factor analysis to conduct a comparative sensitivity analysis on various combinations of sensitive wavebands. Subsequently, the relationship between sea ice thickness and sensitive zones was quantitatively analyzed through regression analysis. Finally, the accuracy of the model was verified using measured data of sea ice thickness in the Bohai Sea in 2016 and 2023, compared with the existing albedo model, and the application of the model was demonstrated. The experimental results of variance analysis and factor analysis show that the visible light band shows strong sensitivity to sea ice thickness, among which the red light band is the most sensitive. The linear-weighted combination of visible light bands (B1-coastal, B2-blue, B3-green, and B4-red) shows a significant linear correlation with sea ice thickness, with a linear regression square value as high as 0.9709. Experimental verification shows that there is a significant linear correlation between the sea ice thickness inverted by each model and the measured thickness data. Research conclusions: (1) variance analysis and factor analysis methods can effectively select sensitive bands and evaluate the inversion effect of band combinations. (2) Either a single red light band or a linear combination of visible light bands can be used as a sensitive indicator for remote sensing retrieval of sea ice thickness. (3) There is a significant linear correlation function between sensitive indicators and sea ice thickness. (4) The measured data confirms the accuracy of the model. Compared with the albedo model, the inversion model proposed in this article is more sensitive to sea ice thickness and is less affected by clouds and sediment. It can provide a reference for the inversion simulation of sea ice thickness and also provide a demonstration for the application of the temporal and spatial distribution laws of sea ice.

Accurate early smoke detection is crucial for timely wildfire monitoring. However, the spectral heterogeneity of complex land surfaces often causes background misclassification, leading to significant errors in smoke identification and concentration retrieval. To address this issue, we propose an improved Mahalanobis Distance (MD)–based smoke detection framework that integrates fine-grained background identification with a scattering–absorption physical model. A theoretical relationship between MD and smoke column concentration is derived, elucidating the separability of background clusters under smoke interference. Furthermore, a Normalized Spectral Variation Index (NSVI) is introduced to quantify band-level resistance to smoke effects, identifying the near-infrared bands as optimal for background recognition. Building upon these findings, a comprehensive workflow—comprising Background Identification, Smoke Detection, Concentration Retrieval, and Fire Localization—is established. The framework combines MD, the Maximum Likelihood Method (MLM), and the Spectral Angle Mapper (SAM) for background classification, employs a dual-threshold MD for smoke pixel discrimination, and applies the Laplacian operator to extract smoke concentration peaks as potential fire sources. Cross-platform validation using UAV, Landsat 8/OLI, and MODIS data demonstrates that the proposed method achieves smoke detection accuracies of 94.99%, 84.51%, and 89.24%, respectively, reduces concentration retrieval errors (RMSE by 3.34% and MAE by 23.42%), and localizes fire points within 8 pixels. These results highlight the framework’s capability to significantly enhance smoke detection accuracy and provide a physically interpretable foundation for high-precision background identification and early-stage wildfire monitoring.