The indirect estimation of leaf area index (LAI) is important to agricultural science and ecological science. In this paper, we reconstructed the three-dimensional (3D) structure of the plant based on 3D reconstruct technology, such as the Structure from Motion (SFM) and the multiview stereo (MVS). A LAI measurement method was also proposed based on the ratio of the leaf point cloud area to the ground area. The experiment results showed that the R2between the true value of the leaf area (LI-3000) and the measured value is 0.9657; compared with the true LAI value calculated based on the leaf area, the relative error of the LAI value extracted from the 3D model is about 44.4%. After removing the stems and smoothing the leaves, the R2between the true value of the leaf area and the measured value reached 0.9741; the relative error of the measured value of LAI is 9.13%. The experimental results also implied that the deviation of the LAI measurement value mainly comes from the incomplete reconstruction of the covered part of the plant, the influence of the stem, and the smoothness of the leaf surfaces after reconstruction.

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.

Leaf area index (LAI) is very important for crop growth monitoring, biomass estimation, and many plant growth simulation models. Direct methods are the most accurate, but they have the disadvantage of being extremely time-consuming and labor-intensive as a consequence making them not be the preferred solutions. Indirect methods mainly include the radiometric method, inclined point quadrats, and hemispherical canopy photography. This paper focuses on the analysis of the principles and characteristics of the three indirect methods and affirms the advantages and development value of hemispheric canopy photography. The probability model is used to verify the feasibility of LAI inversion which is based on hemispheric canopy photography. In this paper, the canopy images of four sample plots on the campus were collected for LAI estimation, and the results were compared with LAI-2200C. Finally, the improvement of hemispherical canopy photography has prospected.

Monitoring aerosol distributions at the city level is of great importance. However, because the differences in spectral response functions and spectral channels among different sensors, the widely used second-generation operational aerosol retrieval (V5.2) algorithm cannot be directly applied to high-resolution Landsat 8 OLI images. In this study, we proposed a new high-resolution aerosol retrieval algorithm for Landsat 8 OLI images over urban areas. This new algorithm retrieved aerosol optical depth (AOD) directly from the red-blue surface reflectance relationship, without estimation surface reflectance. Therefore, satellite measurements even without V5.2-required$1.24\mu \mathrm{m}$or$2.1\mu \mathrm{m}$channel are able to retrieve AODs using this algorithm. For validation, the AERONET measurements located in Beijing were collected. The validation results show that our 30m retrievals agree well with the AERONET measurements, with correlation coefficient of 0.944, EE of 85.19%, MAE of 0.051, and RMSE of 0.065, and are able to provide detailed aerosol distributions over urban areas.

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.

Aerosol distributions may change at fine spatial scales in urban areas due to building and transport infrastructure and human population density variations. The recent availability of Sentinel-2 satellite data provides the opportunity for aerosol optical depth (AOD) estimation at higher spatial resolution than provided by other satellites. In this study, a novel high-spatial-resolution AOD retrieval algorithm for bright unban surfaces was developed based on the Sentinel-2 images. The surface reflectance for AOD retrieval was estimated from the image that has the minimal aerosol contamination in a temporal window. Validation of the Sentinel-2 AOD retrievals was conducted against four Aerosol Robotic Network (AERONET) sites located in Beijing. The results show that the Sentinel-2 AOD retrievals are highly consistent with the AERONET AOD measurements ( R=0.9424), with 85.56% of them falling within the Expected Error (EE). The mean absolute error (MAE) and the root mean square error (RMSE) are 0.0688 and 0.0882, respectively. These results suggest that our high-resolution AOD retrieval algorithm is robust and useful to retrieve high-resolution AOD over bright urban surfaces based on Sentinel-2 images.

For the measurement of LAI (Leaf Area Index) by DHP (Digital Hemispherical Photography) method, imprecise segmentation is the key error source. In this paper, to our knowledge, a deep learning algorithm is used for the first time to segment upward hemispherical image of vegetation. Pix2pix, a general mapping learning model, was improved in our study to make it more suitable for processing segmentation problem. Thousands of images collected in the field were labeled to train the model, and the conventional methods based on pattern recognition, such as the Otsu and HSV, were compared. The result shows that the improved pix2pix algorithm significantly improved the accuracy of the segmentation, which reached to 0.9834. Furthermore, this model has a good performance in processing pictures of complex environments, and the segmentation of edge details has also been optimized. Those results show that the method has great potential to improve the LAI measurement accuracy.

With the continuous development of optical lens and imaging chip technology, the fisheye camera method has been widely studied in the world because of its characteristics of TRAC instrument and LAI-2200C coronal analyzer. To obtain the critical ecological parameter of the leaf area index at low cost, a synchronous LAI-2200C leaf area index hemispheric image acquisition system was proposed in this paper. The system consists of an embedded platform, a low-cost image sensor, and a fisheye lens, fixed to the LAI-2200C optical sensor detector, triggered by the LAI-2200C synchronous of hemispheric vegetation image. This study used the system to measure the coronary photos of the tall shrubs in the Chengdu area at different times. It used an image processing algorithm to analyze and obtain LAI. The results show that there is a significant linear correlation between LAI and LAI-2200C measurements obtained by the Fisheye Camera Method (DH-P) (R2= 0.814), the average square root error is 0.278.The test results show that the system can effectively collect the image of the vegetation canopy can be low-cost, and obtain a leaf area index results with minor error.

The popular enhanced deep blue (DB) algorithm, though performs well in aerosol retrieval over the entire land surfaces, limited by the low temporal resolution and insufficient satellite-derived products of most high resolution satellite sensors, is hardly to be applied to urban areas using high spatial resolution satellite imageries. In this paper, we developed a simplified deep blue algorithm to retrieve 30m spatial resolution aerosol optical thickness (AOT) over urban surfaces using Landsat 8 OLI measurements. With a few atmospheric correction surface reflectance-apparent reflectance pairs, robust relationships between visible (0.65μm and 0.48μm) and 2.2μm reflectance can be constructed by this algorithm. Difficulties described above are overcame, and high consistency with ground-based AERONET measurements are achieved over Beijing, with correlation coefficient (R2) ~0.951, root mean square error (RMSE) ~0.005, mean absolute error (MAE) ~0.05 and 82.61% retrievals fall within the expected error (EE) envelop. This study demonstrates that the simplified deep blue algorithm has advantages in retrieval high spatial resolution AOTs over urban areas.

Leaf area index (LAI) is the basic factor to understand canopy productivity, soil water evaporation, total transpiration loss, and soil temperature. On the basis of analyzing the merits and demerits of various LAI measurement methods, this paper affirms the development prospect and application value of the hemispherical image method. The paper focuses on the inversion theory of LAI and the extraction of canopy porosity. The essence of the hemispherical image method is further elaborated: after the canopy porosity is obtained by image processing, LAI is retrieved based on Lambert-Beer law. In this paper, four tall arbor forests of Chengdu city are selected as research objects to explore the method of obtaining LAI by hemispherical images and compare with LAI-2200C Plant Canopy Analyzer. The results show that LAI measurement based on the hemispherical image is feasible and credible.

The panchromatic and multispectral (PMS) sensor is a high spatial resolution sensor aboard the GF-1 satellite launched on April 26, 2013. This paper focuses on the cross-calibration of the PMS sensor using Terra/Moderate-Resolution Imaging Spectroradiometer (MODIS) and Landsat 8/Operational Land Imager (OLI). Two matched-image adjustment factors (MIAFs) are used in the cross-calibration which are the radiance MIAF and reflectance MIAF. Two test sites are chosen as the regions of interest. One is the Dunhuang test site, which has been used for the vicarious calibration of Chinese satellites since later 1990s. The other is the Golmud test site, which is a new site with no ground measured data available. The results show that both the Dunhuang and Golmud test sites can be used for cross-calibration. This paper reveals that the cross-calibration of the PMS sensor using OLI is better than using MODIS, as the calibration coefficient difference between the two test sites with OLI is smaller than that with MODIS. The uncertainty analysis results show that the uncertainty of cross-calibration using OLI is 5%-7% when the ground data are not available.

In summer of 2008, an outbreak of Enteromorpha Prolifra (EP), a kind of green algae, occurred in the Yellow Sea of china and posed a serious threat to the 29th Olympic Sailing Games. A color array camera was installed on the aircraft and used to monitoring the spatial distribution of EP. In this paper, we measured the spectral properties of EP and analyzed the R, G, B three band aerial remote sensing images which contains EP, sea water and sunglint. After preprocessing of array images, a decision tree was constituted considering the analysis which can retrieval EP and eliminate the sunglint from images. The retrieval results were validated by field survey.

In this paper, a model for light interaction has been developed to compute bidirectional reflectance from realistic 3D canopies approximated by an arbitrary configuration of plants. It can well simulate multi-spectral reflectance of semi-arid nature grassland. There are two important parts of the simulation model. The first part is the generation of the 3D realistic grassland scene. In this model, the Clumped Architecture Model of Plants (CLAMP) is used to. The second part is the determining the visibility and brightness of grass canopy scene using Geometric Optics Model. The simulating model is validated by comparing the simulation result with the HJ satellite data at synchronous time. As a result, it can describe directional reflectance properties of semi-grassland canopies in terms of canopy architecture parameters and optical scattering properties of discrete phytoelements.