Studies have shown that camera auto-exposure underestimates the LAI (leaf area index) measured by DHP (digital hemispheric photography) to varying degrees. To address this problem, this paper proposes the use of multi-exposure fusion to reconstruct information from canopy images to compensate for the loss of information caused by overexposure or underexposure of canopy images acquired by the camera in auto-exposure mode. By fusing a series of canopy images with different exposure times from the same canopy layer, the LAI is then calculated using DHP on the fused image. Experimental results show that the method improves the R2from 0.698 to 0.837 and reduces the RMSE from 0.87 to 0.37 compared with the automatic exposure mode of the camera, with LAI-2200 measurements as a reference. This method contributes to resolving the problem of underestimating LAI in DHP caused by the automatic exposure mode, thereby improving the accuracy of DHP.

Hemispherical photography is an important method of leaf area index (LAI) measurement, but the intermediate processes such as image segmentation and clumping index estimation will introduce errors. In this paper, an end-to-end model was proposed to directly estimate LAI from a hemispheric image based on GhostNet, which avoids errors introduced by the intermediate processing. Hemispherical images of crops and forest vegetation obtained from Shihezi, Xinjiang and Xiong'an, Hebei, and the measured values using LAI-2200 were utilized as the dataset. Compared with the LAI-2200 measurement results, the analysis results show that the correlation between them is extremely significant, with$\mathrm{R}^{2}=0.80,\ \text{RMSE}=0.65,\ \text{MAE}=0.46$. The experimental results show that the estimation model based on GhostNet can accurately estimate the LAI of the hemispheric image and is suitable for timely and accurate estimation of the LAI value of various types of vegetation through edge devices.

Optimal exposure time is essential for accurate measurement of LAI (leaf area index) by DHP (digital hemispheric photography) method, The results showed that LAI was underestimated in different degrees under automatic exposure mode. To address this problem, in this paper we constructed a model of light intensity and optimal exposure time by studying the quantitative relationship between light intensity under the canopy and exposure time. The result shows that using the exposure based on this model rather than the automatic exposure, the comparison of LAI from the LAI-2200 and digital photographs is greatly improved, with R2 increasing from 0.398 to 0.845, and RMSE decreasing from 1.298 to 0.293. The method helps to solve the uncertainty of optimal exposure time in DHP method and improve the accuracy of DHP method in LAI measurement.

Operational aerosol optical depth (AOD) products are limited to coarse resolution (kilometers or hundreds of meters). In this study, the Sentinel-2 images were used to generate high-resolution (60 m) AODs over urban surfaces. Compared with traditional aerosol retrieval algorithm, the proposed algorithm has three major improvements including: 1) taking advantage of the aerosol-sensitive coastal band in aerosol retrieval; 2) no estimation of surface reflectance; and 3) not using of the shortwave infrared (SWIR) band. For validation, measurements from four Aerosol Robotic Network (AERONET) sites located in Beijing covering 2018 to 2021 were collected. The validation results show that the retrieved Sentinel-2 AODs highly correlate with AERONET measurements, with overall correlation coefficient for all four sites of 0.927, expected error (EE) of 68.75%, mean absolute error (MAE) of 0.082, and root-mean-square error (RMSE) of 0.108. The proposed algorithm can provide reliable AODs at 60 m resolution over urban surfaces.

Road extraction from remote sensing images based on deep learning has always been a hot research topic. However, due to the complexity of road conditions, many deep-learning models cannot obtain satisfactory results of road extraction. To improve the accuracy of road extraction, this paper proposes an improved deep-learning model based on the structure of Deeplabv3+. The proposed model uses four blocks with ResNeSt and ASPP to extract the feature maps, which can improve the integrity of the extracted roads. And each of the extracted low-level features is transmitted to the decoder. The decoder focuses on the effective fusion of low-level and high-level feature maps and gradually restores this information layer by layer, which can produce a more accurate result of segmentation. And because the road results extracted by neural networks often have broken road lines, this paper also proposes a data post-processing method to effectively solve this problem. The final experimental results show that the proposed model has further improvements in accuracy, mean Intersection over Union, and Fl-score compared to some other state-of-the-art(SOTA) models.

With the further improvement of industrialization, the problem of atmospheric pollution has become a major issue closely related to our human existence that is of common concern to scientists worldwide. The rapid development of remote sensing technology makes it possible to observe the atmosphere over a long period of time and on a large scale. In this study, we compared the Moderate-resolution Imaging Spectroradiometer (MODIS) aerosol product MCD19A2 with the NO2 retrieved by Sentinel-5 Precursor (Sentinel-5P). It is found that the data based on Sentinel-5P can clearly detect the local steel smelting enterprises while MODIS cannot, and the column density distribution trend has a better correlation with the steel output of the current month. If the spatial resolution of related products is improved in the future, this method of atmospheric observation can be better used for pollution monitoring.