最新刊期
卷 24 ， 期 11 ， 2020
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摘要：In this study, a Universal Normalized Vegetation Index (UNVI) composite optimization algorithm was designed on the basis of the data characteristics of MOD09GA and traditional vegetation index composite algorithm. This algorithm was used to generate the UNVI multidimensional dataset of the 1∶1 000 000 16-day composite vegetation index of China’s terrestrial in 2017. It provides convenient vegetation index long-term sequence data products for researchers engaged in global change research.The UNVI composite algorithm mainly consists two steps. The Moderate resolution imaging spectroradiometer data with invalid and negative reflectance values in the 16-day composite cycle are filtered. The number of days of the quality-free band data in the composite cycle is then counted. The corresponding synthesis algorithm for the 16-day composite of UNVI in accordance with the number of days of “cloudless” data is selected.The UNVI time series images with a time resolution of 16 days and a spatial resolution of approximately 0.00286° in China are generated using the proposed algorithm. The UNVI dataset adopts 1∶1 000 000 standard latitude and longitude framing, where the range covered by each scene is 6°×4°. For convenience, the dataset, including a total of 64 1∶10 000 UNVI framing products in the country’s land area, is in multidimensional data format and stored in TSB mode. Researchers can select the corresponding regional vegetation index product download according to their research area.The UNVI dataset used in this study has obvious advantages compared with traditional normalized difference vegetation index and enhanced vegetation index composite datasets in reflecting vegetation density, vegetation coverage, vegetation photosynthesis rate, and inversion of vegetation physical and chemical parameters. Relevant researchers can use this dataset to conduct annual analysis of vegetation phenological changes. In addition, this dataset can be used to generate quantitative physicochemical parameter inversion products based on UNVI datasets and conduct research on phenological changes throughout the year.关键词：remote sensing;UNVI;China terrestrial;MODIS;vegetation index;Multi-Dimensional Dataset- 839
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发布时间：2020-12-01 -
摘要：An aerial hyperspectral remote sensing dataset plays an important role on the research of hyperspectral images, including classification. However, few studies have been conducted on the establishment of a standard hyperspectral dataset. This study introduced a standard hyperspectral dataset that includes a hyperspectral remote sensing image, a land cover map, and sensor parameters. This dataset was acquired by using a newly designed airborne hyperspectral sensor accompanied with synchronous ground survey experiments.An aerial hyperspectral remote sensing image of Xiongan New Area was acquired using the visible and near-infrared imaging spectrometer designed by Shanghai Institute of Technical Physics, Chinese Academy of Sciences on October 2017. The total field of view angle of the spectrometer is 40.6°, the instantaneous field of view is 0.25 mrad, the effective push-scan pixel is 2834, and the maximum speed-to-height ratio is 0.04. The flight height is 2000 m, and the flight areas cover the Xiong County, An County, Rong County, and Baiyangdian Lake. The east-west length is 48 km, the north-south width is 27.5 km, and the total area is 1320 km2. Twenty-one flight lines are found on the east-west direction, and Matiwan Village is located in the 10th and 11th flight lines. The flying weather is clear and cloudless, and the visibility condition is good. Radiation correction, geometric correction, and image mosaic and clipping were conducted before data classification.The spectral range of the aerial hyperspectral remote sensing image of Xiongan New Area (Matiwan Village) is 400—1000 nm, with 250 bands and a spatial resolution of 0.5 m. The image size is 3750 × 1580 pixels. The land cover types labeled here are 19, which are mainly cash crops.The aerial hyperspectral remote sensing image of Matiwan Village in Xiongan New Area was classified using random forest classification. The first three principal components of the spectrum and its corresponding eight spatial texture features and vegetation indices, such as normalized difference water index and normalized difference vegetation index were utilized. The total classification accuracy is 97%, and the kappa coefficient is 0.98. In accordance with the confusion matrix, the confusion of Robinia pseudoacacia, pear tree, and Acer complex is serious. This condition causes the low classification accuracy of Robinia pseudoacacia.An aerial hyperspectral remote sensing dataset of Xiongan New Area (Matiwan Village) with high spatial and spectral resolution was used in this study. The dataset was classified using random forest classification. The total classification accuracy is 97%. This finding shows that the dataset can provide good data support for hyperspectral classification research and can serve as reference for the design and demonstration of hyperspectral imaging spectrometer.关键词：hyperspectral remote sensing;Xiongan New Area;aerial image;image classification;dataset- 2287
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发布时间：2020-12-01
Data Paper
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摘要：The Fraction of absorbed Photosynthetically Active Radiation(FPAR) is a key parameter in various global change process model, which characterizes the optical properties, photosynthesis process and growth state of canopy. The great progress of quantitative remote sensing and various data products make FPAR products widely used in carbon cycle, energy exchange and vegetation research both in global and regional scale. Because of the spatial heterogeneity of landscape, remote sensing is the only to monitor in large scale. Various methods were developed to obtain FPAR based on remote sensing technique. Empirical method based on the relationship between FPAR and vegetation index. High efficiency is the main feature of empirical method and the limit is that the generality of empirical relationship is weak. Physical method based on canopy model such as geometrical optics model and radiative transfer model which can be used in different kinds of land cover and large scale areas. But the input parameter and calculation process of physical method is relatively complex, which can influence the accuracy of result. In order to improve the accuracy of research, high quality and temporal resolution FPAR estimation is needed. In recent years, the improvement of FPAR algorithms, validation of FPAR products, FPAR of leaf and chlorophyll levels, direct and scattered FPAR (direct light and diffused light) and FPAR vertical distribution became new topics in this area. This paper reviewed the theory and methods of FPAR retrieval from remote sensing, and discussed the new progress of remotely sensed FPAR in past 10 years. The conclusion shows that research of FPAR is more and more important in recent years and the concept and scientific problems are gradually clear. New canopy models and algorithms improve the accuracy of products which promote the use of FPAR in various study areas. Especially, neural network becomes a new way of FPAR inversion which can avoid weak point of physic methods and improve the efficiency of the process. But there are also many aspects need to do in future. The accuracy of FPAR products still cannot reach the standard and products based on high spatial resolution data are required. Day average FPAR product is also important work to Net Primary Productivity (NPP) models. Canopy models also need to be improved in order to fit different kinds of vegetation. On the other hand, we need more high quality FPAR observation systems over the world to get enough reliable in-situ data for validation. Progress in photosynthesis mechanism research and sensors make it possible to realization these targets. New sensors were put in use in recent years. Improve the accuracy and diversity of remote sensing FPAR based on new generation satellite instrument will promote the application for FPAR in various fields.关键词：vegetation quantitative remote sensing;FPAR;Canopy Absorption Model;remote sensing algorithm;products and validation- 1157
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发布时间：2020-12-01 -
摘要：Remote sensing image registration using small unmanned aerial vehicles plays a significant role in military and civilian fields, such as natural disaster damage assessment, ground change detection, environmental monitoring, military damage assessment, and ground target identification. This technique aims to align two or more images (i.e., the sensed image and the reference image) of the same scene captured from different viewpoints, from different times or with different sensors. However, the imaging perspective of small unmanned aerial vehicles is vulnerable to wind speed/direction, complex terrain, battery capacity, aircraft posture, flying height, and other human factors. Such issues often cause nonrigid distortions and low overlap ratios within the captured scenes, thereby generating severe outliers during feature point extraction. Two major types of classification, namely, (1) area-based methods and (2) feature-based methods, are found in accordance with the methodological differences in current methods. We mainly focus on developing a feature-based method in this work. We introduce and discuss the current methods during the classification.In this work, the key idea of the proposed method is to maintain a high matching ratio on inliers while using outliers for image registration. The main contributions of the proposed method are: (1) a relatively low initial threshold, which is lower than the default Scale-Invariant Feature Transform (SIFT) threshold, is usually used to extract two large sets of feature points. A putative control point processing strategy is then designed to gradually identify outliers and maximize the number of reliable inlier pairs. Dynamic SIFT threshold helps to build a coarse-to-fine transformation; (2) a local spatial structure similarity preservation is proposed to constrain the local structure of putative inliers during registration while using a global constraint to refine the warping field by coherently moving putative dummy control points. (3) a dynamic Gaussian kernel is developed to control the displacement distances of feature points such that the transformation is gradually changed from rigid to nonrigid for assisting the above coarse-to-fine search strategy.This study considers five groups of multiview small unmanned aerial vehicle images of typical landform in different regions as the study area. The experiments on feature matching and image registration are performed using 50 pairs of small unmanned aerial vehicle images. Compared with five state-of-the-art methods (SIFT, SURF, CPD, GLMATPS, and GL-CATE), our method demonstrates higher registration quality in all scenarios, where the viewpoint change is up to 100°, and the overlap rate is close to 0.5.In this work, we presented a feature-based method for the remote sensing image registration of small unmanned aerial vehicles based on inlier maximization and outlier control. The key idea is to gradually identify the control points and maximize the number of available inlier pairs. The identified control points are simultaneously used to refine the warping grids within the overlap and nonoverlap areas by reasonably and coherently moving them. The image transformation is recovered by the maximized inliers and the reasonably moved control points. Extensive experiments proved that the use of outliers can improve image registration accuracy.关键词：remote sensing;small unmanned aerial vehicles;image registration;outlier registering;dynamic SIFT threshold;dynamic Gaussian kernel- 729
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发布时间：2020-12-01
Doctor's Voice
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An approach to estimate forest LAI with high resolution based on prior knowledge of model parameters
摘要：At present, the high-resolution Leaf Area Index (LAI) is usually estimated by statistical models, which is established by a large quantity of Vegetation Index (VI) data and ground LAI measurements. Compared with a cropland field campaign, the ground LAI measurements of the forest field campaign are less. The accuracy of the high-resolution LAI estimated by statistical models in the forest is low, and it is difficult to meet the application requirements. In this paper, a method was developed to estimate the high-resolution LAI in the forest based on the prior knowledge of modeling parameters for the forest, a small amount of forest ground LAI measurements and the Normalized Differential Vegetation Index (NDVI) data. First, for the power model which contains parameter a and parameter b, the prior knowledge of modeling parameters in the forest was achieved. 20 forest sites with a large amount of ground LAI measurements were collected. LAI and NDVI data were obtained from the 20 forest sites respectively. The LAI-NDVI statistical model which is suitable for each forest site was established with the obtained LAI and NDVI data respectively too. The values of the parameter a were extracted from the 20 statistical models, and the mean value and the standard deviation of the values were calculated to determine the prior distribution of the parameter a. The mean value of the parameter a was chosen as the prior initial value and two times of the standard deviation of parameter a was chosen as the uncertainties of the prior initial value. The same method was used to extract the prior initial value and the uncertainties of the prior initial value for the parameter b. So far, the prior knowledge of the modeling parameters for the forest was extracted. Second, the optimized LAI-NDVI statistical model was constructed for the study area. A new forest site, Concepción, was selected as the study area. The data of this site were divided into two parts: the modeling data and the validation data. The limited modeling data were used to adjust the prior initial value under the limitation of the uncertainties of the prior initial value and obtain an optimization model which is suitable for this new forest site by the optimization method SCE-UA. At last, the high-resolution forest LAIs were estimated and validated in the Concepción site. The high-resolution forest LAIs were estimated using the optimization model and the NDVIs in the validation data. The estimated high-resolution forest LAIs were evaluated by the ground LAI in the validation data. Moreover, the Camerons site, Gnangara site, and Hirsikangas site were selected as the study area to evaluate this method too. Compared the estimated high-resolution forest LAI with the ground LAI, the root mean square errors were 0.6680, 0.4449, 0.2863 and 0.5755 respectively. These results indicated that when only a small amount of ground LAI measurements is available, this method based on the forest prior knowledge could improve the accuracy of the high-resolution LAI estimation in the forest. Therefore, the method based on the forest prior knowledge of modeling parameters provided a reference for high-resolution forest LAI estimation.关键词：remote sensing;prior knowledge;forest model parameters;LAI;high-resolution- 685
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发布时间：2020-12-01 -
摘要：The multiple return phenomenon of laser pulse in the technology of full waveform laser scanning has an inevitable impact on the radiometric signal of LiDAR data as well as their radiometric calibration. Previous studies have been made that characterized this phenomenon as an attenuation of laser pulse energy partially intersected by objects, such as canopy and building edges, through the travel path. In this study, we proposed a novel theoretical explanation of multiple returns by establishing a set of LiDAR equations, one for each sub-pulse that composed the original outgoing pulse. The energy attenuation of LiDAR signals through penetrable targets, such as forest canopy, and its influence on radiometric calibration were particularly analyzed. Comparative experiments were conducted with data from one laser instrument of Riegl LMS-Q680i LiDAR system in two different data collection campaigns. One data collection site was covered by LiDAR flight lines of 600 m and 1200 m Above Ground Level (AGL), and the other site with all 600 m AGL. During the data acquisition, ground earth surface with approximate Lambertian reflectivity behavior were measured with filed spectrometer, and the reflectance of ground reference objects were applied in the radiometric calibration process. The data were processed and radiometrically calibrated on the basis of classical LiDAR equation. In addition, multiple return point cloud of a scene with homogeneous ground surface with planted vegetation were extracted for further quantitative analysis. This process was implemented to reveal and characterize the influence of multiple returns on full waveform LiDAR echoes and subsequent target classification. Through the quantitative comparison of data strips, deviations of overlapping data of different flying altitude were calculated. It was demonstrated from the results that the systematic data deviations of LiDAR strip parameters are successfully eliminated. The overall relative errors of corrected diffuse reflectance of the two regions are less than 10% and 5.5%. The standard deviations of strip difference are 0.044 and 0.077 accordingly. Calibration constants in independent LiDAR surveying campaigns are compared. The constants were found to be with correlation to the LiDAR system and flying configurations. Moreover, it was found that LiDAR returns of different return number were not consistent, despite that they were reflected by the same object surface. Significant weakening was observed in the returns of higher orders. It was concluded that multiple return is the major cause of return intensity weakening on homogeneous surfaces and it has crucial effects on radiometric information based target recognition. This problem cannot be readily solved with the current LiDAR observation mechanism in typical mapping scenarios. Challenges from this phenomenon are inevitable to further target recognition and should be addressed for advanced multiple and hyperspectral LiDAR data in the future.关键词：remote sensing;airborne LiDAR;radiometric calibration;radar equation;uncertainty analysis;classification- 677
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发布时间：2020-12-01 -
摘要：The cloud covers more than 50% of the Earth, which plays a major role in radiation budget of Earth climate system and hydrological cycle through their strong impact on radiation process. Cloud is an important factor to affect the retrieval accuracy of trace gases during the measurement of air pollution based on remote sensing method. The effective cloud fraction and cloud pressure should be used in the process of correcting cloud effects. In this paper, O2-O2 cloud retrieval algorithm based on O2-O2 absorption band at 477 nm will be described.The O2-O2 cloud retrieval algorithm is developed based on the Look-Up Table (LUT) method. Effective cloud fraction and cloud pressure LUTs were generated using VLIDORT radiation transfer model based on the relationships of effective cloud fraction, cloud pressure, continuum reflectance and O2-O2 slant column density. Then, the Differential Optical Absorption Spectroscopy (DOAS) is used to fit the radiance of top-of-atmosphere measured by the satellite payload, to obtain O2-O2 slant column density and the continuum reflectance. Finally, combined with the auxiliary data, the effective cloud fraction and cloud pressure are retrieved by the interpolation based on the LUTs.We have a validation between the O2-O2 cloud retrieval algorithm results based on OMI data and OMCLDO2 products. The spatial distribution of the effective cloud fraction and cloud pressure show great consistency, and the correlation coefficients (R) between them are greater than 0.97. Then the O2-O2 cloud retrieval algorithm was applied to the new generation of atmospheric sounding instrument TROPOMI. The cloud retrieval results also show high correlation compared with the FRESCO+ results, and R of effective cloud fraction and cloud pressure between above two results are greater than 0.97 and 0.95, respectively, when the surface type is ocean. The time series analysis of three months in Beijing from the two algorithms shows the good consistency of the retrieval results. Moreover, we compared with CALIOP Cloud Layer products using all the retrieved cloud pressure results of FRESCO+ and O2-O2 cloud retrieval algorithm. O2-O2 cloud retrieval algorithm performed better than FRESCO+ under low cloud condition. However, the FRESCO+ retrieval results are closer to CALIOP cloud pressure than O2-O2 cloud retrieval algorithm under high cloud condition.The O2-O2 cloud retrieval algorithm was used during the cloud retrieval of OMI and TROPOMI, and it shows high accuracy and feasibility. This kind of algorithm can provide information of effective cloud fraction and cloud pressure in the process of atmospheric trace gases retrieval. The more important is that O2-O2 cloud retrieval algorithm could provide reference for the development of cloud retrieval algorithm applied to the same type of satellite payload of China.关键词：remote sensing;O2-O2;effective cloud fraction;cloud pressure;look-up table;OMI;TROPOMI;CALIPSO- 851
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发布时间：2020-12-01 -
摘要：Polarimetric Synthetic Aperture Radar (PolSAR) has been proven to recognize and classify various ground objects, and multitemporal fully PolSAR can acquire many scattering features to improve the accuracy of recognition and classification. However, the decomposed scattering features with high dimensionality can cause serious problems of “curse of dimensionality.” This paper proposes a multitemporal PolSAR scattering feature dimension reduction method based on Stacked Sparse AutoEncoder (S-SAE) to effectively reduce the dimensionality of high-dimensional scattering features. The proposed method firstly decomposes the PolarSAR data to obtain high-dimensional scattering features and adopts S-SAE to reduce the dimensionality of the acquired multidimensional features. For the S-SAE construction, unsupervised layer-by-layer greedy training is performed to optimize the main parameters. Combined with a sigmoid classifier, the parameters of S-SAE are finely tuned through supervised training to achieve effective dimension reduction of high-dimensional features. The reduced low-dimensional features are taken as the input of Support Vector Machine (SVM) and Convolutional Neural Network (CNN) classifiers to evaluate the performance of feature dimension reduction. The performance of the proposed method is validated on two datasets of multitemporal simulated and real Sentinel-1 data. Experimental results show that the S-SAE method with two hidden layers achieves the best performance of feature dimension reduction. Compared with traditional Locally Linear Embedding (LLE) and Principal Component Analysis (PCA) dimension reduction methods, the overall classification accuracy of S-SAE for the SVM classifier is raised by at least 9% and 14%. The overall classification accuracy of S-SAE for the CNN classifier is at least 7% and 9% higher than that of LLE and PCA, respectively.关键词：dimension reduction;crop classification;Polarimetric Synthetic Aperture Radar (PolSAR);multi-temporal;Stack Sparse AutoEncoder(S-SAE);CNN- 581
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发布时间：2020-12-01 -
摘要：The mass concentration of atmospheric fine particles (PM2.5) is an important indicator of air quality. This study aimed to promote regional PM2.5 mass concentration monitoring and expand the applications of CE318 sun photometer and other optical sensors in the inversion of atmospheric aerosol products. This study used the size distribution data of atmospheric aerosol particles from Beijing’s 2014—2017 atmospheric aerosol product data to extract the volume of PM2.5. These data were combined with the reference values of PM2.5 mass concentrations from 35 air quality measurement stations in Beijing to calculate the conversion coefficient, thereby establishing a conversion model. The conversion coefficients obtained from all CE318 stations and their relative deviations were used to evaluate the spatial distribution of PM2.5 concentration errors in Beijing. Results show that the conversion coefficients that were jointly created using PM2.5 volume from CE318 stations and the PM2.5 mass concentration from nearby air quality stations are closely associated with aerosol physiochemical characteristics. These conversion coefficients can be used for the classification and refinement of the correlation between PM2.5 volume and PM2.5 mass concentration. This correlation is utilized to establish a piecewise conversion function model, so that each segment has high model fitting accuracy. The mean relative errors of the estimated PM2.5 mass concentrations in Beijing based on the conversion coefficient range from 12.9% to 33.8%. The relative deviation of the conversion coefficients significantly affects the relative error of estimated PM2.5 mass concentrations because of the existence of an “r” structure between them. The probability of this deviation appearing is approximately 66.5% when the relative deviation of conversion coefficient ranges from -16.3% to 24.5%. This condition causes the errors of PM2.5 mass concentration estimation to be lower than 20%. Results show that our method is relatively accurate and stable when used to estimate PM2.5 mass concentrations at the corresponding stations. The study results can provide corresponding theoretical support and data reference for the research on ground- and satellite-based optical remote sensing of regional PM2.5 mass concentrations.关键词：remote sensing;atmospheric aerosol;PM2.5;particle size distribution;mass concentration;conversion coefficient;Beijing area- 660
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发布时间：2020-12-01
Technology and Methodology
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摘要：Aboveground biomass is one of the most important indicators of crop growth, and timely and accurate information on biomass is critical for crop growth assessment, yield forecast, and farm management. Crop models based on detailed physiological and biochemical information can provide digital descriptions for key vegetation processes and are ideal choices for crop biomass estimation. Given that crop models are generally designed to simulate crop growth at point scale, applying crop models to the regional scale requires knowledge of a large amount of input parameters with high calibration cost. In recent years, researchers have combined Remote Sensing (RS) and crop models by using assimilation methods to improve the ability of regional biomass estimation. However, considering that RS data provide most spatial information during model execution, any errors in these data will influence the biomass estimation accuracy.Under the influence of reflectance saturation, the crop canopy information capture ability for the RS method in the late crop growth stage is lower and errors can be hardly be avoided. In this study, a new combination method for RS data and crop model was proposed to address the saturation issue during late-stage biomass estimation. In the new method, the whole crop growth season was divided into early stage and late stage. At the early stage, the World Food Studies (WOFOST) crop model was adopted to simulate daily crop growth at point scale. Next, the Leaf Area Index (LAI) based on the time-series multispectral RS was assimilated into the WOFOST model to extend model simulation from point scale to regional scale. The RS-based LAI was calculated through a statistical model using multispectral RS data acquired by an Unmanned Aerial Vehicle (UAV). The statistical model was built according to field LAI and UAV based vegetation indices. At the end of the early stage, the key model status parameters, including dry matter of different organs, soil water, and accumulated temperature, were simulated and output by the WOFOST model.Meanwhile, soil-available nitrogen content was also estimated and transformed to the beginning of the late growth stage. At the late stage, the RS data assimilation was halted to avoid unnecessary errors caused by reflectance saturation. The estimated soil-available nitrogen and model status parameters were input to the WOFOST model, and the aboveground biomass was estimated based on the crop growth simulations. Given that the WOFOST model was only used for a short-term crop growth simulation without RS data, the error accumulation could be effectively avoided. In this study, the spring maize plots in Hongxing Farm, northeast China, were selected to apply the proposed new method and the biomass was estimated in 2015. As a comparison, the RS-based LAI for the late stage was also assimilated into the WOFOST model to estimate the spring maize biomass for the same plots. Using the field campaign data, R2 and RMSE were calculated to analyze the estimation accuracies of the two methods. Analysis results show that the accuracy of the new method (R2 = 0.86, RMSE = 2216.79 kg/ha) was higher than that of the method with RS data assimilation for the whole crop growth season (R2 = 0.45, RMSE = 4254.30 kg/ha). In conclusion, the new method can provide reliable biomass results for spring maize in the late stage by taking advantage of the WOFOST mode and UAV data.关键词：remote sensing;crop model;multispectral data;LAI;data assimilation;vegetation index;crop growth stage- 665
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发布时间：2020-12-01 -
摘要：In the context of global warming, the area and thickness of arctic sea ice is gradually decreasing, which provides the possibility for arctic navigation, promotes the strategic position of Arctic channel, and changes the Arctic geo-environmental pattern. The straits of the Arctic region serve as an important transportation hub for ‘Polar Silk Road’, and their changes in ice conditions directly affect the opening of the Arctic passage. This study takes the sea ice concentration in the northeast passage of the arctic region and the important straits on the northwest passage for nearly 35 years as the research object and adopts the method of the distributed lag nonlinear model. The effects of sea ice surface temperature exposure factors on sea ice concentration change in arctic strait are studied. Studying the threshold and lag effects of sea-ice surface temperature on sea-ice concentration changes in important arctic straits. The research results show that: (1) In addition to the Bering strait, the other 11 straits have a high temperature threshold for the influence of sea ice concentration changes, and their thresholds are concentrated around -10 °C; (2) The effect of high temperature on the change of sea ice concentration has a lag period of 0—3 months, while the lag period of low temperature is 0—4 months. (3) The 14 straits show different lag effects and spatial heterogeneity in the nonlinear lag model. Vilkitsky is the strait with the most severe influence of high temperature in the lag period, and the relative cumulative effect value is -3.34% (-5.6% — -1.1%); (4) On the whole, the lag period of the northeast passage is longer than that of the northwest passage, and the lag effect value of the northeast passage affected by temperature is larger than that of the northwest passage. In addition, high temperature has a more obvious impact on the change of relative sea ice in the high-latitude environment.关键词：arctic geo-environmental;Polar Silk Road;key straits;DLNM;lagged effect- 713
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发布时间：2020-12-01
Remote Sensing Applications
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