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PFG - Journal of Photogrammetry, Remote Sensing and Geoinformation Science (25122819)93(3)pp. 231-250
Snow depth is a critical parameter for meteorological, climatological, and hydrological models, enhancing regional water resource information and weather prediction accuracy. AMSR2 passive microwave data with 10-kilometer spatial resolution results in high error and uncertainty in snow depth estimation. This study attempted to develop machine learning algorithms to downscale AMSR2 snow depth data to 1 kilometer spatial resolution. One of the primary factors inflicting errors in snow depth downscaling is the absence of proper identification of snow-covered area boundaries (mixed pixels), leading to extensional uncertainty. In the present study, a novel method based on the theory of random sets has been developed to resolve the issue of mixed pixels in the boundaries of snow-covered areas for the European Alps. In addition to snow cover data acquired from random sets, auxiliary data for developing and training machine learning algorithms comprises of the Digital Elevation Model (DEM), Land Surface Temperature (LST), and Land Cover Type data were processed. The machine learning algorithms employed includes Support Vector Regression (SVR), Multi-Layer Perceptron (MLP), and Random Forest (RF). The RF algorithm, when considering snow cover data derived from random sets method, confirmed the excellent performance among the SVR and MLP algorithms. Using RF, the RMSE decreased from 31.67–16.34 cm, the correlation coefficient increased from 0.312–0.81, and the relative error decreased from 9.85–0.52% (reduced error and increased accuracy). The results based on evaluation metrics indicates an outstanding performance of the proposed innovative method for downscaling of AMSR2 snow depth. © Deutsche Gesellschaft für Photogrammetrie, Fernerkundung und Geoinformation (DGPF) e.V. 2025.
Iranian Journal of Remote Sensing and GIS (25886185)17(1)pp. 61-78
Atmospheric water vapor is a key parameter in modeling the energy balance on the earth's surface and plays a major role in keeping the temperature of the earth's atmosphere balanced. Retrieving of this parameter, as the most influential atmospheric parameter on the sensors received radiance, is of great importance. Since the atmospheric water vapor content in the near of surface is more and its temporal and spatial changes are more intense, the measurements of ground meteorological stations, despite their high accuracy, are not generalizable due to temporal and spatial limitations and point measurements. Therefore, it seems necessary to provide practical satellite-based methods to accurate and continuous retrieval of this parameter with appropriate spatial distribution. The aim of this research is to present four innovative and accurate methods to estimate the near surface atmospheric water vapor of Isfahan province in 2020 with a resolution of 1 km, through the integration of meteorological station data, sensor data and finally validating and comparing their performance. For this purpose, correcting the bias error of water vapor sensor data during the co-scaling stage and correcting the interpolation error of ground station observations was put on the agenda. Material and Methods: Different sensors measure water vapor with different sensitivities and spatial resolution. Therefore, it is necessary to provide methods based on the simultaneous use of diffferent sensor data and their integration to ground station observations, in order to simultaneously improve the accuracy and spatial resolution (1 km) of retrieved near surface water vapor. In the first method used in this research, the near surface water vapor is retrieved using the water vapor absorbing and non-absorbing bands of the MODIS, through the band ratio method and using ground observations. In the second method, first, observations of near surface water vapor of ground stations are converted to 1 km grid using the inverse distance interpolation (IDW) method. Then, during the steps of the proposed method and using the water vapor values estimated by the first method, the interpolation error in each pixel is removed. In the third method, the resolution of AIRS-derieved water vapor product is reduced to 1 km by combining MODIS data during an operation similar to the steps of the second method, with the difference that the AIRS sensor product is used instead of ground station observations. It is necessary to eliminate the bias error of near surface water vapor product of the AIRS during the co-scaling stage by first. Estimation of near surface water vapor using MODIS column water vapor product is the fourth method. Of course, due to the difference in content, it is necessary to unite the two sets and equate them with an approprite method. Results and Discussion: In order to model and validate the estimation of atmospheric near surface water vapor at a spatial resolution of 1 km using the different mentioned methods, 66.6% of the data were randomly used for training and the remaining 33.3% were used to evaluate the accuracy and validation. Finally, the implementation results of the methods have been compared with each other. The validation results of proposed methods show that the second method, which is based on the generalization of accurate observations of ground stations and removing their interpolation error, during integration with the water vapor values retrieved from first method, has the best performance (R2=0.55, RMSE=1.05 Gr/Kr). Conclusion: Considering the better performance of the second method in retrieving the mixing ratio of near surface water vapor with high accuracy and resolution of 1 km, and with the aim of using the capabilities of satellite-based products and data, it is recommended to combine them with each other and also with ground observations. © 2025, Shahid Beheshti University. All rights reserved.
Iranian Journal of Remote Sensing and GIS (25886185)16(2)pp. 85-104
Introduction: Air pollution represents one of the most important challenges currently facing the majority of countries, largely as a consequence of the advancement of industry and technology. . It is evident that the country of Iran, and in particular the city of Tehran, is not exempt from this phenomenon. The impact of urban air pollution on the environment and human health has raised increasing concerns among researchers, policy makers, and citizens. In order to minimize the adverse effects on human health, it is of paramount importance to monitor air pollution at high temporal and spatial resolution. On the other hand, air pollution measurement stations in the urban areas, despite their high accuracy in pollutant measurement, are not generalisable due to temporal and spatial limitations and point measurement. An alternative solution is the use of remote sensing and satellite data, which is a suitable method for monitoring air pollution due to the optimal cost and wide coverage. Nitrogen dioxide (NO2) and ozone (O3) pollutants are among the most important indicators of air pollution. Therefore, the objective of this research, is to develop a for the concentration distribution of these pollutants inTehran with an equal spatial resolution (approximately one kilometer) and a higher level of accuracy than satellite data. Material and methods: In order to model the concentration distribution of two pollutants, NO2 and O3, with appropriate accuracy and resolution, an innovative method based on the kriging interpolation method has been employed. This modeling method has been developed by simultaneously utilizing the advantages of both pollution measurement station data and high resolution Sentinel-5P satellite data. The former comprises 21 active air pollution measurement stations that have been identified as offering the highest accuracy in measuring parameters in different parts of Tehran. The Google Earth Engine system, has been employed to generate concentration distribution maps of the two pollutants in all 22 districts of Tehran on a monthly basis. Additionally, the system has been used to generate point satellite data of the two pollutants in the spatial coordinates of the ground stations on an hourly, daily and monthly basis. The data was prepared and collected in the Google Earth system over the course of one year, from 1 April 1400 to 1 April 1401. Following the correlation between the satellite data and the ground measurement station data and removal of the bias from the satellite data, different stages of innovative kriging interpolation modeling were employed to model the concentration distribution of the two parameters. Results and discussion: In order to validate the output data from pollutant distribution modeling, 70% of the stations were selected as training data (Train) and 30% of the stations were selected as test data (Test). The points were randomly selected for each month of the year. The final modeling of pollutant distribution was conducted using the training data with the model subsequently validated using the test data. Validation was conducted using both the average error between the predicted data by the model and the station data extracted from the Tehran Air Quality Control Company (in ppb units) and also calculating the RMSE index. The results demonstarte that the average monthly error of the proposed model has decreased from 16.8 to 1.73% for NO2 pollutant and from 21.9 to 2.53% for O3 pollutant compared to the data of the Steinel 5P satellite. Additionally, the root mean square error (RMSE) of this model is equal to 2.79 ppb and 0.86 ppb for NO2 and O3 pollutant, respectively. In a comparable scenario, the RMSE index of the Sentinel 5P satellite output map in relation to the pollution measurement station data for NO2 and O3 pollutants is 10.083 ppb and 6.238 ppb, respectively. Conclusion: Considering that the proposed integrated model has performed very well in modeling the concentration distribution of the two pollutants throughout the year with an accuracy and spatial resolution of almost one kilometer, it is recommended that the simultaneous use of satellite and ground data be employed in the estimation of pollutants. © 2020, Shahid Beheshti University. All rights reserved.
Iranian Journal of Remote Sensing and GIS (25886185)15(4)pp. 17-30
Ground surface ozone is one of the most dangerous pollutants that has significant harmful effects on the residents of urban areas. The purpose of this study is to identify the factors affecting ozone concentration and modeling its changes using satellite data and different machine learning methods in Tehran. For this purpose, pollutant concentration and meteorological data were used along with the satellite product of land surface temperature (LST) in the period from 2015 to 2021. After calculating the correlation between ozone concentration and independent parameters, ozone concentration modeling was done in five different modes in terms of input parameters and learning method and applying data refinement. In the first and second mode, modeling was done using pollutant concentration and meteorological data through multivariate linear regression method. The only difference between these two modes is the filtering of the input data using the WTEST method in the second mode. In the third mode, the LST product was added to the input data, and in the fourth and fifth mode, ozone modeling was done using multilayer neural network and recurrent neural network, respectively. The comparison of the five modes showed that the modeling of the first to fifth stages with adjusted coefficient of determination of 0.5, 0.64, 0.69, 0.74 and 0.8 were able to recover the ozone concentration, respectively. It was also found that among different pollutants, nitrogen monoxide, nitrogen dioxide and nitrox have the greatest impact on ozone concentration, just as temperature, humidity and wind speed are the most influential among meteorological data. Although the use of WTEST statistics led to the identification and elimination of inconsistencies and errors in the observations of pollution measurement stations, the neural network learning method showed better performance in modeling than multivariate regression due to its less sensitivity to noise. As a notable result, adding the LST product to the input data brought a 5% increase in accuracy in estimating ozone concentration. © 2023 by the authors.
PFG - Journal of Photogrammetry, Remote Sensing and Geoinformation Science (25122819)91(5)pp. 391-404
Snow cover is an informative indicator of climate change and surface hydrological cycles. Despite its essential accurate dynamic measurement (i.e., accumulation, erosion, and runoff), it is poorly known, particularly in mountainous regions. Since passive microwave sensors can contribute to obtaining information about snowpack volume, microwave brightness temperatures (BT) have long been used to assess spatiotemporal variations in snow water equivalent (SWE). However, SWE is greatly influenced by geographic location, terrain parameters/covers, and BT differences, and thus, the low spatial resolution of existing SWE products (i.e., the coarse resolution of AMSR-based products) leads to less satisfactory results, especially in regions with complex terrain conditions, strong seasonal transitions and, great spatiotemporal heterogeneity. A novel multifactor SWE downscaling algorithm based on the support vector regression (SVR) technique has been developed in this study for the Zayandehroud River basin. Thereby, passive microwave BT, location (latitude and longitude), terrain parameters (i.e., elevation, slope, and aspect), and vegetation cover serve as model input data. Evaluation of downscaled SWE estimates against ground-based observations demonstrated that when moving into higher spatial resolution, not only was there no significant decrease in accuracy, but a 4% increase was observed. In addition, this study suggests that integrating passive microwave remote sensing data with other auxiliary data can lead to a more efficient and effective algorithm for retrieving SWE with appropriate spatial resolution over various scales. © 2023, Deutsche Gesellschaft für Photogrammetrie, Fernerkundung und Geoinformation (DGPF) e.V.
Water Resources Management (09204741)36(6)pp. 1813-1832
Accurate soil moisture (SM) data with continuous spatiotemporal distribution has greatly contributed to various analyses in the fields of agricultural dryness and irrigation, regional water cycle, soil erosion, and energy exchange. While, spatial and temporal resolutions are practically in conflict with each other, data fusion is considered to be efficient for accessing spatiotemporally high resolution data. In the present research to obtain daily surface SM at a spatial resolution of 100 m, an Enhanced Spatial and Temporal Adaptive Reflectance Fusion Model (ESTARFM) was used by combining Landsat8 and Moderate resolution Imaging Spectroradiometer (MODIS) data. Furthermore, to improve the accuracy of SM retrieval, a novel scheme land surface temperature (LST)-vegetation index (VI) universal triangle was introduced to increase the LST retrieval accuracy using the TOPSIS method. This algorithm was also examined within two regions in Fars province in Iran. Simultaneously with the satellite passing through the study areas, SM of several points was measured by time-domain reflectometry (TDR). To evaluate the performance of the proposed method, the error metrics including the coefficient of determination (R2) and Root Mean Square Error (RMSE) were calculated between the in-situ SM measurements and those estimated. The resulted fusion SM was compared with the Landsat-derived and in-situ SM which reported lower (R2 = 0.73 and RMSE = 0.005cm3/cm3) and higher (R2 = 0.38 and RMSE = 0.048cm3/cm3) error values, respectively. The outcomes of the study indicated the high ability of the proposed fusion approach for achieving accurate and consistent SM monitoring by using the specified ESTARFM model, especially when the LST was obtained using the weighted average of several LST determination methods with TOPSIS method. © 2022, The Author(s), under exclusive licence to Springer Nature B.V.
Vegetation cover and soil surface roughness are vital parameters in the soil moisture retrieval algorithms. Due to the high sensitivity of passive microwave and optical observations to Vegetation Water Content (VWC), this study assesses the integration of these two types of data to approximate the effect of vegetation on passive microwave Brightness Temperature (BT) to obtain the vegetation transmissivity parameter. For this purpose, a newly introduced index named Passive microwave and Optical Vegetation Index (POVI) was developed to improve the representative-ness of VWC and converted into vegetation transmissivity through linear and nonlinear modelling approaches. The modified vegetation transmissivity is then applied in the Simultaneous Land Parameters Retrieval Model (SLPRM), which is an error minimization method for better retrieval of BT. Afterwards, the Volumetric Soil Moisture (VSM), Land Surface Temperature (LST) as well as canopy temperature (TC) were retrieved through this method in a central region of Iran (300 × 130 km2 ) from November 2015 to August 2016. The algorithm validation returned promising results, with a 20% improvement in soil moisture retrieval. © 2022 by the authors. Licensee MDPI, Basel, Switzerland.
Computers and Electronics in Agriculture (01681699)186
This study evaluates the potential of AMSR2 (Advance Microwave Scanning Radiometer2) data for the estimation of Volumetric Soil Moisture (VSM) for bare and agricultural areas. At the first step, the sensitivity of the Microwave Polarization Difference Index (MPDI) to variations in soil and vegetation characteristics were examined at different frequencies. At lower frequencies, the signal attenuation due to vegetation is minimal and thus, denser vegetation usually depolarizes the soil emission. Interestingly, the results also reveal that at higher frequencies, the sensitivity of V and H polarizations over relatively dense vegetation covers is not the same at all. Therefore, MPDI at both low and high frequencies can be a good indicator of the soil moisture and Vegetation Water Content (VWC), respectively. After evaluation of AMSR2 datasets, a model called Multi-channel/MPDI-based Land Parameters Retrieval Model (MMLPRM) is proposed. The MMLPRM optimizes optical depth of vegetation and soil dielectric constant, with simultaneous retrieval of soil moisture and surface temperature by using the AMSR2 brightness temperature data. This algorithm also includes the surface roughness parameters to increase the soil moisture retrieval efficiency. In this way, calibration and validation have been done, using in situ observations of 50 monitoring stations obtained from the International Soil Moisture Network (ISMN) over the United States. Consequently, the analysis on the MMLPRM retrieval model demonstrates its potential and usefulness for soil moisture retrieval. The outcome of this study will help in estimating the accurate soil moisture to optimize the irrigation management strategies and help in water conservation. © 2021 Elsevier B.V.
Iranian Journal of Remote Sensing and GIS (25886185)12(3)pp. 37-46
Atmospheric column water vapor, which is the total atmospheric precipitable water vapor contained in a vertical air column, is one of the most important factors in all surface-atmosphere interactions (such as energy fluxes between the earth and the atmosphere) and plays a key role in wide variety of environmental studies, ecological and agricultural applications. However, measuring this parameter at meteorological stations requires the use of radiosonde instruments, which being pointwise and costly are limitations of these observations. Therefore, remote sensing is used as an alternative to estimate this important atmospheric parameter. Compared to other atmospheric parameters, atmospheric water vapor which attenuates remotely sensed radiance is of great importance. Although this atmospheric parameter is measured by AIRS (Atmospheric Infrared Sounder) sensor, its low resolution (about 40 km) is not acceptable for many applications. Therefore, developing an algorithm to downscale the AIRS-derived column water vapor is the main goal of this study, so that its spatial resolution can be improved. To do this, using the ratio method, the AIRS-derived column water vapor is fused with the MODIS (Moderate Resolution Imaging spectroradiometer) data. Then, due to the major influence of this parameter on Land Surface Temperature (LST) estimation, the role of improved resolution atmospheric column water vapor in the estimation of LST is investigated as a secondary goal. In order to validate the estimated parameters and evaluate their accuracy, independent datasets were used. Results of the implementation indicate that proposed downscaling method has high potential to enhance the spatial resolution of AIRS-derived atmospheric column water vapor, without significant degradation of the RMSE. It was also found that the atmospheric column water vapor when moving into higher spatial resolution can dramatically increase the accuracy of the LST estimation. © 2020, Shahid Beheshti University. All rights reserved.
Journal of Earth System Science (23474327)127(2)
Microwave remote sensing provides a unique capability for soil parameter retrievals. Therefore, various soil parameters estimation models have been developed using brightness temperature (BT) measured by passive microwave sensors. Due to the low resolution of satellite microwave radiometer data, the main goal of this study is to develop a downscaling approach to improve the spatial resolution of soil moisture estimates with the use of higher resolution visible/infrared sensor data. Accordingly, after the soil parameters have been obtained using Simultaneous Land Parameters Retrieval Model algorithm, the downscaling method has been applied to the soil moisture estimations that have been validated against in situ soil moisture data. Advance Microwave Scanning Radiometer-EOS BT data in Soil Moisture Experiment 2003 region in the south and north of Oklahoma have been used to this end. Results illustrated that the soil moisture variability is effectively captured at 5 km spatial scales without a significant degradation of the accuracy. © 2018, Indian Academy of Sciences.
Physics and Chemistry of the Earth (14747065)94pp. 127-135
Using remotely-sensed data, various soil moisture estimation models have been developed for bare soil areas. Previous studies have shown that the brightness temperature (BT) measured by passive microwave sensors were affected by characteristics of the land surface parameters including soil moisture, vegetation cover and soil roughness. Therefore knowledge of vegetation cover and soil roughness is important for obtaining frequent and global estimations of land surface parameters especially soil moisture. In this study, a model called Simultaneous Land Parameters Retrieval Model (SLPRM) that is an iterative least-squares minimization method is proposed. The algorithm estimates surface soil moisture, land surface temperature and canopy temperature simultaneously in vegetated areas using AMSR-E (Advance Microwave Scanning Radiometer-EOS) brightness temperature data. The simultaneous estimations of the three parameters are based on a multi-parameter inversion algorithm which includes model construction, calibration and validation using observations carried out for the SMEX03 (Soil Moisture Experiment, 2003) region in the South and North of Oklahoma. Roughness parameter has also been included in the algorithm to increase the soil parameters retrieval accuracy. Unlike other methods, the SLPRM method works efficiently in all land covers types. The study focuses on soil parameters estimation by comparing three different scenarios with the inclusion of roughness data and selects the most appropriate one. The difference between the resulted accuracies of scenarios is due to the roughness calculation approach. The analysis on the retrieval model shows a meaningful and acceptable accuracy on soil moisture estimation according to the three scenarios. The SLPRM method has shown better performance when the SAR (Synthetic Aperture RADAR) data are used for roughness calculation. © 2016 Elsevier Ltd
Photogrammetric Engineering and Remote Sensing (00991112)82(10)pp. 803-810
Brightness temperature (BT) measured by passive microwave sensors is usually affected by soil moisture, vegetation cover, and soil roughness. Soil moisture estimates have been limited to regions that had either bare soil or low to moderate amounts of vegetation cover. In this study, Simultaneous Land Parameters Retrieval Model (SLPRM) as an iterative least-squares minimization method has been used. This algorithm retrieves surface soil moisture, land surface temperature, and canopy temperature simultaneously using brightness temperature data in bare soil, low to moderate and higher amounts of vegetation cover. Furthermore, a new index called MSVI (Multi Sensor Vegetation Index) has been introduced to approximate vegetation effects on properly observed brightness temperatures. The algorithm includes model construction, calibration, and validation using observations carried out for the SMEX03 (Soil Moisture Experiment 2003) region in the South and North of Oklahoma. The results indicated about 0.9 percent improvement on soil moisture estimation accuracy using the MSVI. © 2016 American Society for Photogrammetry and Remote Sensing.
Arabian Journal of Geosciences (discontinued) (18667538)7(5)pp. 1891-1897
Atmospheric water vapor validation needs simultaneous, well-defined, and independent information which are not easily available causing limitations in the development of remote sensing water vapor retrieval algorithms. This study is concerned with the retrieval of total atmospheric water vapor content and its validation. A band ratio method has been used to estimate the water vapor content based on Moderate Resolution Imaging Spectroradiometer (MODIS) Near InfraRed (NIR) data. The method uses MODIS bands 17, 18, and 19 as NIR bands and band 2 to remove the land cover reflectance. Furthermore, the Atmospheric Infrared Sounder (AIRS) has been used for both algorithm development and analysis of the results. The method has been modified to take into account the dry condition of the central parts of Iran. Using some various datasets, the method is implemented and evaluated quantitatively. The validation of the water vapor estimates has been undertaken by an analysis of AIRS data. The validation results shows error as low as 9 % for the estimated water vapor using the MODIS NIR band ratio method. © 2013 Saudi Society for Geosciences.
CTIT workshop proceedings series (16821750)37pp. 523-528
One of the most important parameters in all surface-atmosphere interactions (e.g. energy fluxes between the ground and the atmosphere) is atmospheric water vapor. It is also an indicator among others to modeling the energy balance at the Earth's surface. Total atmospheric water vapor content is an important parameter in some remote sensing applications especially land surface temperature (LST) estimation. As such, total atmospheric water vapor content and LST are used as key parameters for a variety of environmental studies and agricultural ecological applications. Estimation of an accurate LST requires the atmospheric water vapor content estimation. This study is concerned with retrieving total atmospheric water vapor content (W) using Moderate Resolution Imaging Spectrometer (MODIS). We have used a ratio technique to estimate the column water vapor based on MODIS data. However Atmospheric Infrared Sounder (AIRS) column water vapor and AIRS MMR near surface water vapor have been taken into account to calculate coefficients of the equation in the ratio technique. Then the accuracy of the results was examined using independent data set. It is concluded in this study that MODIS data is appropriate in mapping water vapor content as a suitable alternative to meteorological stations measurement data. © 2008 International Society for Photogrammetry and Remote Sensing. All rights reserved.
