Synthetic Aperture Radar (SAR) can observe the Earth without the influence of the weather and sunlight, and Polarimetric SAR (PolSAR) even could acquire four kinds of polarization information at the same time. Therefore, extracting post-earthquake damage information by use of PolSAR has the advantage of timeliness and accuracy. This paper shows a summary of the methods for extracting seismic damage information based on PolSAR data. It firstly review the development of PolSAR and then summarizes the application and comparative analysis of the data types (multi-source data, multi-temporal data and single-temporal data) for extracting seismic damage of buildings in the past 10 years. Next, the methods of building earthquake damage extraction based on polarization decomposition and polarization characteristics and texture features is summarized. Finally, the research work is proposed to supplement the deficiency of PolSAR in earthquake damage extraction accuracy with the combination of geographic information data POI.
The Hydrometeor Classification Algorithm(HCA) is one of the main vegetation research direction to analyze weather characteristics from microphysical perspective. The hydrometeor classification algorithm is of great significance for the observation of hail, rainfall and snowfall. This paper reviewed the advantages of the classical Fuzzy logic Hydrometeor Classification (FHC),including Neuro-Fuzzy Hydrometeor Classification (NF-HC), Support Vector Machine Hydrometeor Classification (SVM-HC) and deep learning methods. We also introduced the details of the application of hydrometeor classification in the hail, rainfall and snowfall and summarized the verification method, including aircraft measurement verification, numerical simulation verification, and ground observation comparison. In addition, the current problems of FHC are proposed, including the setting of membership function parameters and the hydrometeor phase identification of ice phase particles. The current research lacks an effective and convenient method for HCA, which limits the application of dual-polarization weather radar. Finally, directions for future research to HCA were forecasted.
Urban land use/cover changes have an important impact on urban ecosystem services functions and the environmental quality of human settlements. This study mapped urban expansion process using the historical data, remote sensing images and urban planning maps, and acquired the urban impervious surface and green space fraction based on big data platform. We analyzed the process of Tianjin urban expansion rate, intensity and urban land cover change since 1949, and revealed the driving factors in the process of urban expansion with social economic and policy factors. The results showed that the built-up area increased from 49.15 km2 in 1949 to 663.39 km2 in 2015. The expansion has undergone four stages of "acceleration – deceleration – acceleration - deceleration"; the urban expansion mode presents the filled with built-up area and along with the neighboring transportation trunk lines. The proportion of green space in the built-up area is increasing, indicating that the urban ecological greening in the main urban area of Tianjin has been improved.
Rapid urban expansion had a significant impact in land use/cover change along urban-rural gradient, and the increase of impervious construction land and the reduction of vegetation cover had induced and aggravated the urban heat island effect. Studying the impact of urban-rural gradient land cover change on urban heat island effect was significant for urban planning and construction, improving the comfort of human settlements and enhancing the function of urban ecological services. The surface temperature of Xi'an city was retrieved by mono-window algorithm based on Landsat images, and the thermal field intensity map was obtained by calculating the thermal field variation index, and the gradient land cover changes in urban and rural areas were analyzed with land use data. The results showed that: ①The urban heat island effect in Xi'an showed a trend of first increasing and then decreasing from 2000 to 2015. In 2000, the extremely strong heat island effect area accounted for 10.58% of the research area, and gradually increased to 16.14% in 2011, and then decreased to 9.00% in 2015. ②From 2000 to 2015, the area of construction land increased 412.76 km2 and the intensity of extremely strong heat island expanded year by year with the expansion of urban built-up areas. ③About 70% of the non-heat island effect areas were located on farmland and forest land, and the proportion of water area in the non-heat island effect was increasing year by year from 31% to 47%, which showed that the increase of vegetation and water area could effectively alleviate the urban heat island effect.
Urban land cover composition is the key factor affecting the living environment and urban ecosystem service. Based on the Google Earth Engine platform, Landsat 5/8 remote sensing image data were used to adopt the improved "Vegetation-Impervious Surface-Soil" model and linear spectral mixed decomposition method. The variation characteristics of land cover in Nur-Sudan, Almaty, Urumqi cities from 1990 to 2015 were compared and analyzed. The results show that the urban built-up area of Urumqi city expanded the largest area of the three cities from1990 to 2015, with an expansion of 349.81 km2, followed by Nur-Sultan, with a city expansion area of 158.16 km2. As the capital of Kazakhstan was relocated from Almaty to Nur-Sultan, the city of Almaty expanded the slowest during the entire period, with a total expansion of 126.23 km2. In the urban built-up area, the urban surface in Urumqi increased by 7.10% from 1990 to 2015, and the Nur-Sultan and Almaty decreased by 14.9% and 4.49%，respectively. The green space component of the built-up area, Nur-Sultan increased by 6.68% from 1990 to 2015, while Urumqi and Almaty decreased by 6.65% and 2.75%，respectively. The different surface cover patterns of cities are different for different reasons. Urumqi is mainly supported by national policies, and Almaty is known for its historical background and urban planning, while the rapid development of Nur-Sudan was affected by the relocation of Kazakhstan.
This paper improved the quality of time series data sets through three
double star data combination of MODIS, linear interpolation and HANTS smoothing. In this study, we used random forest classification and analyzed the impact of the quality of time series dataset construction on classification accuracy though evaluating the accuracy of classification results. Results showed that the double-star data was beneficial to improve the temporal resolution of time series dataset, accurately depict the coverage change, and provide the basis for subsequent processing; linear interpolation could improve the quality of pixel points and reduce the influence of cloud and rain factors; HANTS smoothing could remove outliers, smooth data, highlight curve features, and reduce classification complexity. After improving the quality of the time series data set, the overall classification accuracy increased from 84.32% to 90.75%, and the Kappa coefficient increased from 79.86% to 88.16%. In a word, when using time series data for land cover classification, the quality of the time series data set should be improved to eliminate the outliers and truly reflect the surface covering phenological features, and the classification accuracy of the results should be improved.
Global land cover datasets play an important role in the fields of ecology, climate and resources. GlobeLand30-2010 (30 m), FROM-GLC-2010 (30 m) and GlobCover-2009 (300 m) are three global high-precision land cover datasets with a wide range of applications. In order to judge whether these data sets are sufficient to describe the real situation of land cover in different seasons, the inter-seasonal accuracy of the aforementioned three global land cover datasets using Pakistan as a representative study area was evaluated. A total of 1 000 land cover sample points were selected from 122 Landsat-5, Landsat-7 multi-spectral remote sensing images during 2009~2011 to generate summer and winter land cover classifications.The results show that the accuracies of summer and winter land cover classifications are different in Pakistan. The overall land cover classification accuracies of GlobeLand30-2010 (65.6% vs. 63.9%) and FROM-GLC-2010 (61.2% vs. 59.0%) in summer are slightly higher than those in winter. The overall accuracy of GlobCover-2009 (59.5% vs. 59.1%) in winter is slightly higher than that in summer. GlobeLand30-2010 performs best in classifying cropland, impervious surface, and water body, FROM-GLC-2010 performs best in classifying vegetation, glaciers, and snow, and GlobCover-2009 performs best in classifying bare land. The classification of cropland, bare land, glaciers, and snow in the three datasets is more in line with the real situation in winter than in summer; the classification of vegetation and water bodies is more in line with the real situation in summer; there is no obvious seasonal difference in impervious surface. There should be at least one sample point per 1 000 square kilometers.
The remote sensing of Unmanned Aerial Vehicle (UAV) and Object-Based Image Analysis（OBIA） technologies have advanced increasingly for environmental monitoring in recent years. However, references to the uses of UAV and OBIA for mapping rural residential environment are still scarce in the field of scientific literature. In this study, an integration framework was developed to extract various ground objects in rural residential environment. First, Estimation of Scale Parameter (ESP) tool and expert judgement were used to identify the optimal Segmentation Scale Parameter (SSP). Then, the expert rule-sets and supervised classification algorithms were applied to extract information of ground objects in rural residential environment, respectively. Finally, the performance accuracy was evaluated by using an area-based method. The results indicated that using ESP tool and expert judgment to determine the optimal SSP is feasible. Furthermore, the overall accuracy (OA) is 75.19%, indicating that the rule-based extraction method is not good at extracting all kinds of ground objects in study area. However, Solar Water Heaters (SWHs) were successfully extracted in rural residential environment by using template matching combined with threshold rules and the extraction accuracy can achieve 92%. Moreover, the influences of training samples and features were analyzed on the classification results of the Random Forest (RF), Support Vector Machines (SVM) and K-Nearest Neighbor (KNN) classifiers, showing that the RF classifier has the best classification result, with its value of OA reaching 91.34%. The results indicated that the integrate framework is a valuable tool in the extraction of ground objects from rural residential environment.
Many current studies focus on urban expansion and its heat island effect, but the impact of different land use intensity on radiant energy needs further analysis. Based on the land use data of 2000 and 2015 in Beijing, this study divided the land use of Beijing into five types according to the influence degree of human activities and vegetation resilience, namely, the old urban areas, urban expansion areas, unchanged cropland areas, mixed pixel areas with changed gridcells, and unchanged pure pixel areas. On this basis, we calculated Radiative Forcing (RF) due to the change of surface albedo and explored the relationship between RF and vegetation cover. The results showed that: (1) In pure pixel areas, natural vegetation had a lower albedo, and the corresponding RF was larger than the other four land use type areas. However, under the influence of human activities, RF in the four land use type areas showed an obvious increasing trend during the research period, and the increment was also larger than RF in PP areas. (2) Comparing with unchanged pure pixel, the EVI within the other four human-affected land type areas (old urban areas, urban expansion areas, mixed pixel, and unchanged cropland) decreased but the LOS extended. The combined effect of LOS and EVI contributed to the decreasing trend of surface albedo, which prompted the increase of RF. Our finding highlights that human activities often enhances RF by affecting the intensity of land use. This study has important reference value for analyzing the climate feedback of land use change from physical mechanism.
In southwestern China, the cultivation conditions are poor, the plots are relatively fragmented, and the types of plots are complex. Therefore, the use of low and medium resolution remote sensing data is not able to satisfy the needs of abandoned farmland extraction. This paper explored the ability of single or multi-phased high resolution remotely sensed images in detecting abandoned farmland in southwest China, using Xiuwen County, Guizhou Province, China as a case study area. Remote sensing based monitoring methods for abandoned farmland were developed, providing a reference for the statistical survey of abandoned farmland in southwest China.The extraction method of abandoned farmland was proposed based on the field survey data, considering different types of abandoned farmland. Sensitive feature sets of different types of abandoned farmland were identified from a series of features including the spectral characteristics, vegetation indices and multi-temporal difference vegetation indices. The CART decision tree classification method was applied on the selected sensitive features to extract abandoned farmland. The results showed that:(1) There was a significant difference in the recognition ability of single-phase image in extracting different types of abandoned farmland, so it was difficult to use only single-phase image to extract abandoned farmland with high accuracy; (2) The vegetation index change characteristics of different time phases had strong recognition ability for abandoned farmland, and the ratio vegetation index was better than the difference vegetation index and normalized vegetation index; (3) The spatial distribution map of abandoned farmland and the statistical analysis of abandoned farmland area were carried out in Xiuwen County, Guizhou Province. The area of abandoned farmland in Xiuwen County was about 6,460 hectares, accounting for 13% of the cultivated land area.（4）Based on multi-temporal high-resolution remote sensing data, the method of detecting abandoned farmland using seasonal variation characteristics can meet the requirements of high-precision extraction of abandoned farmland in southwest China, and the results provided technical reference for remote sensing survey and mapping of abandoned farmland in large-scale.
This research used HUT model, DMRT model and MEMLS model to simulate interactions(absorption and extinction) between snow grainsfor different wave bands (18.7 GHz and 36.5 GHz) of microwave which were used for radiative transfer model. Obtaining the snow grain size is always a difficulty. So this research used Jordan91 snow grain size evolution model to evolve snow grain size which was regarded as input parameter of radiative transfer model, and used measured data to simulate spaceborne brightness temperature for 18.7 GHz horizontal polarization and 36.5 GHz horizontal polarization in a mixed pixel. The results showed that the bias of simulation brightness temperature using extinction coefficient of HUT model, DMRT model and MEMLS model for 18.7 GHz horizontal polarization were -3.6 K、-1.8 K and -0.7 K respectively, and for 36.5 GHz horizontal polarization were 4.0 K、10.4 K and 14.4 K respectively. For 18.7 GHz horizontal polarization and 36.5 GHz horizontal polarization, the bright temperature simulation based on effective snow grain size shows a good linear relationship with the brightness temperature simulation basedon snow grain size evolution process. Therefore, the method based on the snow grain size evolution process is a suitable method for obtaining the snow grain size parameter in the radiative transfer model.
Quantitative simulation of solar radiation is essential for understanding climate change 0n the Loess Plateau, Many machine learning methods were developed to estimate solar radiation well, but different machine learning methods have different simulation accuracy in different regions, In order to achieve optimal simulation of solar radiation on the Loess Plateau, this provides more higher precision solar radiation data for crop models, hydrological models, and climate change models. In this study, three machine learning methods, including Random Forest (RF), Artificial Neural Network (ANN) and Support Vector Machine (SVM), were applied to estimate solar radiation on the Loess Plateau, three machine learning methods were trained using ground measurements at fourteen radiation sites from 2003 to 2009 and ten radiation sites from 2010 to 2016 and corresponding parameter pressure, cloud fraction, cloud optical thickness, ozone, precipitation water vapor and DEM, slope, and aspect to train the three model, The solar radiation estimates based on three machine learning methods were evaluated using ground measurements at four radiation sites from 2010 to 2016. The validation results show that the RF model has the best simulation effect on the Loess Plateau and surrounding areas. The average deviation is -0.17 MJ·m-2, the root mean square error is 1.48 MJ·m-2, and has a good fit of 0.96. The results show that combined RF model and meteorological data and remote sensing data can effectively solve the problem about solar radiation simulation on the non-radiation observation area of the Loess Plateau, which is of great significance to the research of regional solar radiation.
Multi-scale segmentation is the premise and key step of Object-Based Image Analysis (OBIA). The quality of multi-scale segmentation directly affects the accuracy of object-oriented classification. However, scale selection and evaluation remains a challenge in multi-scale segmentation. According to the fact that the optimal segmentation scale of the remote sensing image is closely related to the complexity of the objects of the image, a top-down method to select the optimal scale based on the complexity of segmented objects is proposed. In the top-down segmentation process, image features of each segmented object are extracted to construct the complexity function, and the optimal scale of each object is determined by setting a threshold value and iterating calculation. Then, the segmentation results with the best scale are obtained and applied to the ZY-3 satellite multispectral image and the GF-2 fusion image to obtain segmentation and classification results. Qualitative visual evaluation method, unsupervised evaluation method and supervised classification evaluation method were used to compare them with results obtained by the optimal single-scale segmentation and the unsupervised evaluation method. The experimental results show that the method can accurately obtain the scale matching with the ground targets, and improve segmentation effect and the classification accuracy, it is of practical value.
Recently, Collaborative Representation Classification (CRC) has attracted much attention in hyperspectral image analysis. Due to uses the tangent plane to estimate the local manifold of the test sample. Tangent Collaborative Representation Classification (TCRC) achieve better performance. Furthermore, in order to improve the classification accuracy and reliability of hyperspectral remote sensing images, a novel Boosting-based Tangent Collaborative Representation ensemble method (Boost TCRC) for hyperspectral image classification is proposed. In this algorithm, Boost TCRC algorithm choose TCRC as base classifier and adjust the weight of the training samples adaptively by using the principle of Boosting. Increasing the weight of the misclassified samples so that the classifier concentrates on the training samples that are difficult to classify. Then assigns the weights according to the classification performance of the base classifier based on the residual domain fusion. Finally, the principle of minimum reconstruction error is adopted to classify the test sample. The performance of the proposed algorithm was comprehensively evaluated by hyperspectral remote sensing image data such as HyMap (Hyperspectral Mapper) and AVIRIS (Airbone Visible Infrared Imaging Spectrometer). The Boosting method can effectively improve the classification effect of the TCRC algorithm. For HyMap data, the overall classification accuracy and kappa coefficient of Boost TCRC algorithm are 93.73% and 0.920 8 respectively. Two precision values are higher than TCRC algorithm by 2.82% and 0.032 3, and are higher than the AdaBoost ELM algorithm by 1.81% and 0.022 5. For AVIRIS data, the overall classification accuracy and kappa coefficient of Boost TCRC algorithm are 84.11% and 0.8120 respectively. Two precision values are higher than TCRC algorithm by 3.97% and 0.049 3, and are higher than AdaBoost ELM algorithm by 12.02% and 0.143 6.
When processing multi-component SAR moving target echo data by traditional time-frequency analysis method, there is serious cross-term influence and poor time-frequency clustering. A new time-frequency analysis algorithm named EMD-RSPWVD is proposed. It combines the improved Empirical Mode Decomposition (EMD) algorithm and Reassigned Smoothing Pseudo-Wigner-Ville Distribution (RSPWVD) algorithm. The improved EMD algorithm is used to decompose the multi-component SAR moving target echo signal into independent signal components. Then the time-frequency analysis of independent components which based on RSPWVD algorithm is performed to eliminate cross-terms and obtain high time-frequency resolution. Finally, simulated echo data and real echo data are used to analyze the performance of this algorithm for multi-component SAR motion echo data. The results show that the algorithm has good anti-noise ability, moving target detection ability and high-precision motion parameter estimation performance.
With the increasing demand of synthetic aperture radiometer for high resolution, the scale of signal acquisition channels has risen rapidly, reaching tens or even hundreds of channels. In this paper, a distributed signal sampling and processing system is proposed. At the same time, a high-speed data acquisition and transmission network based on distributed correlation system is designed, with 24 channels and 60 Msps sampling rate. On the hardware, a clock link is designed based on Distributed Multi-level distribution to realize digital synchronous sampling, and the test results show that the phase delay between channels is less than 1.5 degrees. In order to realize the high-speed optical fiber link between the multi-digital sampling array and the back-end correlation units, the Racket I/O transceiver built in the FPGA is used as the physical layer, and the test results show that the bit error rate is lower than 10-7.The system has high stability and reliability, and is suitable for requirements of large-scale synthetic aperture radiometers.
Interferometric Imaging Radar Altimeter (InIRA) is based on interferometric measurement technology with small incident angle, one transmitter and two receivers. Interference Imaging Radar Altimeter can obtain highly coherent sea surface echo and extract interference phase information from the echo, and then achieve sea surface height by inversion. Its prominent advantages are wide swath and high precision. However, because of the huge amount of data, it brings serious challenges to on-board high-capacity data storage and downlink transmission. This paper introduces a method with FPGA to realize Block Adaptive Quantization (BAQ) compression algorithm, which processes the original ocean echo data of a certain satellite borne Interferometric Imaging Radar Altimeter, and analyzes the influence of different compression ratios on the measurement performance. Meanwhile, the traditional BAQ algorithm was improved according to the data characteristics of the new InIRA. It provides reference basis for data compression of future spaceborne Interferometric Imaging Radar Altimeter. The test shows that when the compression ratio is 8/3, the SQNR can reach 15 dB. At 8/6 compression ratio, the measurement error of sea surface height at a resolution of 2 km is about 2.68 cm.
The use of remote sensing technology for information extraction and monitoring of open-pit mining areas has become an important means to solve the natural environment problems of mines. Firstly, this paper improves the fully convolutional neural network with dense block. Then, the opencast mining area sample library is constructed, and the open-pit mining area extraction model for multi-source remote sensing data is trained. Finally, the automatic extraction of the opencast mining area is realized in Tongling. The results show that compared with traditional classification methods and deep learning methods, the proposed method has better accuracy in pixel-based and object-based evaluation. Specifically, the Pixel Accuracy (PA), Intersection over Union (IoU), F1, Kappa Coefficient, Recall, Missing Alarm and False Alarm is 0.977, 0.721, 0.838, 0.825, 0.913, 0.087 and 0.533, respectively. The model also has a great extraction effect for Google-Earth images with poor homogeneity, showing strong generalization and applicability. Therefore, the proposed model of this paper has wide application value in the extraction of opencast mining area by using multi-source remote sensing images.
With the rapid development of satellite remote sensing technology, processing the variety of remotely sensed data has increasingly been complex and difficult. It is also hard to efficiently and intelligently retrieve change information what users need from a massive database of images. In the context of mass remote sensing data, the existing knowledge based on a priori knowledge + the keyword / metadata remote sensing data service model can not meet above-mentioned challenge. Firstly，it is not guaranteed to obtain the totally change information data in the database, as we can not get the all prior knowledge. Second, the keyword / metadata can not accurately describe the different application areas of the user's actual retrieval needs. To deal with this, the Content-Based Image Retrieval (CBIR) is successfully applied on the change detection in this paper. And, Content-Based Remote Sensing Image Change Information Retrieval and Relevance Feedback model is introduced. Firstly, we learn the CBIR theory fully and exclusively. Then, the model structure and framework is built. And, some critical issues, such as data management, multi-features selection and relevance feedback, are considered. Thirdly, an experimental prototype system is set up to demonstrate the validity and practicability of this model. As a new remote sensing image change detection information acquisition mode, the new model can reduce the demands of image pre-processing, overcome synonyms spectrum, seasonal changes and other factors in the change detection, and meet different kinds of needs. Meanwhile, the new model has important implications for improving remote sensing image management skill and autonomic capabilities of information retrieval filed.
Reservoir eutrophication leads serious threat to water supply safety. This paper apples Landsat time series satellite data from 2008 to 2017 to extract the distribution and degree of water bloom in Yuqiao Reservoir based on a threshold method to the correlation analysis results between Normalized Difference Vegetation Index (NDVI) and measured water quality parameters. Through the collaborative analysis of both natural and artificial factors, the water bloom was jointly drive by temperature, precipitation, and human activities. Among them, the ecological restoration project with human intervention could inhibit or slow down the blooms and effectively improve the water quality. Meteorological and spaceborne remote sensing data with higher temporal resolution will be more conducive the analyze the driver force of cyanobacteria blooms on small and medium-sized drinking water surfaces. Meanwhile, remote sensing data based monitoring and early warning technology could be promoted.
Time series is a widely used phenological research method. A new time series vegetation indices which takes full advantage of the red edge information of Sentinel 2 data were used for crop classification to improve the classification accuracy. The NDVI, EVI, and red edge NDVI were combined to construct a time series vegetation index image. Then, four different algorithms (support vector machine, random forest, CART decision tree and maximum likelihood) were used to classify four crops, three forest grasses, bare land, and water bodies. Among the original classification results, the random forest with the highest overall accuracy is 87.92%, and the maximum likelihood with the lowest overall accuracy is 80.07%. In the classification details, the boundaries of random forest and support vector machine are the clearest. Among the four classification results, the classification accuracy of crops is higher than other land types, just smaller than water body. The error mainly comes from the mixture of three forests. It indicates that the time series combined vegetation index is feasible and accurate for crop classification.
Based on Landsat TM/ETM+/OLI remote sensing images，the glacier boundaries in the Chenab basin of western Himalayas in three periods were manually delineated with visual interpretation method, and the characteristics of glacier variation were also analyzed with the temperature and precipitation of the surrounding meteorological stations and CRU reanalysis data．The results show that: ①From 1993 to 2016,the glaciers area in Chenab basin decreased 164.56±161.72 km2，accounting for 5.78% of the total area. The annul average shrinkage rate is 0.25±0.25 %·a-1 and it accelerated shrinking after 2000. ②The glaciers in the Chenab basin have shrinked in all orientations and altitudes. Among them, S orientation glaciers has the maximum shrinkage rate, accounting for 24.35% of the total area of glacial shrinkage. The glaciers areas between 4 600~4 800 m and 4 800~5 000 m is reduced 29.93 km2 and 30.91 km2 near 23 a,accounting for 17.72% and 18.30% of the total shrinkage of the glacier area in the basin respectively. ③From 1993 to 2016, there were 28 different glaciers had advanced in the Chenab basin. ④Analysis of temperature and precipitation changes in the two meteorological stations of Shiquan river and Srinagar and CRU reanalysis data shows that the average annual temperature in the region increased significantly from 1993 to 2016 caused glacier retreat.
Ozone Mapping and Profiler Suites (OMPS) provide vertical distributions of ozone with high vertical resolutions. They can also provide data for studying the temporal and spatial distribution of ozone in atmosphere. To verify the accuracy of OMPS ozone products, the ozone sounding data from 2016 to 2018 in Beijing were selected, comparing the OMPS v2.5 ozone profile with the total amount of v2.1 ozone profile. The results show that the vertical distributions of ozone in OMPS over Beijing are in good agreement with the ozone sounding data in the upper stratosphere, the relative deviation is less than 10%. The relative deviation in the upper stratosphere is in the range of 15%~40% and 80% in some cases; the relative deviation between the total ozone in OMPS stratosphere and the total ozone in the stratosphere integrated by the ozone sounding profile is little and the average deviation is less than 5%, the root mean square error is 18.3 DU, and the correlation coefficient is 0.89. The average deviation of total tropospheric ozone is more than 30%, and the correlation coefficient of total tropospheric column is 0.62.
