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Monitoring Cold-Region Water Cycles Using Remote Sensing Big Data

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A special issue of Remote Sensing (ISSN 2072-4292). This special issue belongs to the section "Environmental Remote Sensing".

Deadline for manuscript submissions: closed (20 February 2024) | Viewed by 14511

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Special Issue Editors

Prof. Dr. Hongyi Li

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Northwest Institute of Eco-Environment and Resources, Chinese Academy of Sciences, Lanzhou 730000, China
Interests: cold-region hydrology; hydrological modelling; water cycle; remote sensing; satellite image analysis
Special Issues, Collections and Topics in MDPI journals

Prof. Dr. Xiaohua Hao

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Guest Editor

Northwest Institute of Eco-Environment and Resources, Chinese Academy of Sciences, Lanzhou 730000, China
Interests: remote sensing on snow cover and glaciers; snow ecology; climate change
Special Issues, Collections and Topics in MDPI journals

Prof. Dr. Youhua Ran

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Guest Editor

Northwest Institute of Eco-Environment and Resources, Chinese Academy of Sciences, Lanzhou 730000, China
Interests: remote sensing and modeling of the frozen ground and environment; climate change
Special Issues, Collections and Topics in MDPI journals

Prof. Dr. Pengfeng Xiao

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Guest Editor

School of Geography and Ocean Science, Nanjing University, Nanjing 210023, China
Interests: remote sensing; glacier change; snow ecology; machine learning; image analysis

Special Issue Information

Dear Colleagues,

Water resources in cold regions, such as glaciers, snowpacks, frozen ground, lake/river ice, and discharge, have been jeopardized by the highly uncertain effects of climate change. Clarifying the water resources in cold regions is a scientific frontier for the sustainable development of cold regions. Due to the limitations of conventional remote sensing techniques, the water resources in cold regions cannot be well monitored and evaluated using big data. Currently, machine learning techniques have made significant advances in the reconstruction of missing hydrological information from remote sensing big data. The combination of machine learning and hydrological models is a promising direction for future water resource assessment in cold regions.

For a better understanding of the water cycle in cold regions, this Special Issue aims to publish research based on how remote sensing big data helps to monitor the water resources in cold regions. Articles may address, but are not limited, to the following topics:

Remote sensing in monitoring cryosphere elements such as glaciers, snow, frozen ground, and lake/river ice. Machine learning techniques and data-driven methods are encouraged.
The application of remote sensing in retrieving water cycle processes such as precipitation, evapotranspiration, discharge, and groundwater in cold regions.
Methods fusing remote sensing data and hydrological models, such as parameter calibration, validation, and data assimilation.
Evaluations of water resources and environmental effects in cold regions using remote sensing data or a combination with a hydrological model.

Prof. Dr. Hongyi Li
Prof. Dr. Xiaohua Hao
Prof. Dr. Youhua Ran
Prof. Dr. Pengfeng Xiao
Guest Editors

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Keywords

glacier
snow
frozen soil
river ice
lake ice
discharge
water resources
hydrological models
climate change
cold region

Published Papers (11 papers)

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20 pages, 6281 KiB

Open AccessArticle

Comparison of Machine Learning Models in Simulating Glacier Mass Balance: Insights from Maritime and Continental Glaciers in High Mountain Asia

by Weiwei Ren, Zhongzheng Zhu, Yingzheng Wang, Jianbin Su, Ruijie Zeng, Donghai Zheng and Xin Li

Remote Sens. 2024, 16(6), 956; https://doi.org/10.3390/rs16060956 - 08 Mar 2024

Viewed by 678

Abstract

Accurately simulating glacier mass balance (GMB) data is crucial for assessing the impacts of climate change on glacier dynamics. Since physical models often face challenges in comprehensively accounting for factors influencing glacial melt and uncertainties in inputs, machine learning (ML) offers a viable alternative due to its robust flexibility and nonlinear fitting capability. However, the effectiveness of ML in modeling GMB data across diverse glacier types within High Mountain Asia has not yet been thoroughly explored. This study addresses this research gap by evaluating ML models used for the simulation of annual glacier-wide GMB data, with a specific focus on comparing maritime glaciers in the Niyang River basin and continental glaciers in the Manas River basin. For this purpose, meteorological predictive factors derived from monthly ERA5-Land datasets, and topographical predictive factors obtained from the Randolph Glacier Inventory, along with target GMB data rooted in geodetic mass balance observations, were employed to drive four selective ML models: the random forest model, the gradient boosting decision tree (GBDT) model, the deep neural network model, and the ordinary least-square linear regression model. The results highlighted that ML models generally exhibit superior performance in the simulation of GMB data for continental glaciers compared to maritime ones. Moreover, among the four ML models, the GBDT model was found to consistently exhibit superior performance with coefficient of determination (

R^{2}

) values of 0.72 and 0.67 and root mean squared error (

R M S E

) values of 0.21 m w.e. and 0.30 m w.e. for glaciers within Manas and Niyang river basins, respectively. Furthermore, this study reveals that topographical and climatic factors differentially influence GMB simulations in maritime and continental glaciers, providing key insights into glacier dynamics in response to climate change. In summary, ML, particularly the GBDT model, demonstrates significant potential in GMB simulation. Moreover, the application of ML can enhance the accuracy of GMB modeling, providing a promising approach to assess the impacts of climate change on glacier dynamics. Full article

(This article belongs to the Special Issue Monitoring Cold-Region Water Cycles Using Remote Sensing Big Data)

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23 pages, 79143 KiB

Open AccessArticle

Remote Sensing-Based Simulation of Snow Grain Size and Spatial–Temporal Variation Characteristics of Northeast China from 2001 to 2019

by Fan Zhang, Lijuan Zhang, Yanjiao Zheng, Shiwen Wang and Yutao Huang

Remote Sens. 2023, 15(20), 4970; https://doi.org/10.3390/rs15204970 - 15 Oct 2023

Viewed by 949

Abstract

The size of snow grains is an important parameter in cryosphere studies. It is the main parameter affecting snow albedo and can have a feedback effect on regional climate change, the water cycle and ecological security. Larger snow grains increase the likelihood of light absorption and are important for passive microwave remote sensing, snow physics and hydrological modelling. Snow models would benefit from more observations of surface grain size. This paper uses an asymptotic radiative transfer model (ART model) based on MOD09GA ground reflectance data. A simulation of snow grain size (SGS) in northeast China from 2001 to 2019 was carried out using a two-channel algorithm. We verified the accuracy of the inversion results by using ground-based observations to obtain stratified snow grain sizes at 48 collection sites in northeastern China. Furthermore, we analysed the spatial and temporal trends of snow grain size in Northeastern China. The results show that the ART model has good accuracy in inverting snow grain size, with an RMSD of 65 μm, which showed a non-significant increasing trend from 2001 to 2019 in northeast China. The annual average SGS distribution ranged from 430.83 to 452.38 μm in northeast China, 2001–2019. The mean value was 441.78 μm, with an annual increase of 0.26 μm/a, showing a non-significant increasing trend and a coefficient of variation of 0.014. The simulations show that there is also intermonth variation in SGS, with December having the largest snow grain size with a mean value of 453.92 μm, followed by January and February with 450.77 μm and 417.78 μm, respectively. The overall spatial distribution of SGS in the northeastern region shows the characteristics of being high in the north and low in the south, with values ranging from 380.248 μm to 497.141 μm. Overall, we clarified the size and distribution of snow grains over a long time series in the northeast. The results are key to an accurate evaluation of their effect on snow–ice albedo and their radiative forcing effect. Full article

(This article belongs to the Special Issue Monitoring Cold-Region Water Cycles Using Remote Sensing Big Data)

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16 pages, 2973 KiB

Open AccessArticle

Unraveling Effect of Snow Cover on Spring Vegetation Phenology across Different Vegetation Types in Northeast China

by Chong Ren, Lijuan Zhang and Bin Fu

Remote Sens. 2023, 15(19), 4783; https://doi.org/10.3390/rs15194783 - 30 Sep 2023

Cited by 1 | Viewed by 754

Abstract

Snow cover has significantly changed due to global warming in recent decades, causing large changes in the vegetation ecosystem. However, the impact of snow cover changes on the spring phenology of different vegetation types in Northeast China remains unclear. In this study, we investigated the response of the start of the growing season (SOS) to different snow cover indicators using partial correlation analysis and stepwise regression analysis in Northeast China from 1982 to 2015 based on multiple remote sensing datasets. Furthermore, we revealed the underlying mechanisms using a structural equation model. The results show that decreased snow cover days (SCD) and an advanced snow cover end date (SCED) led to an advanced SOS in forests. Conversely, an increased SCD and a delayed SCED led to an advanced SOS in grasslands. The trends of SCD and SCED did not exhibit significant changes in rainfed cropland. The maximum snow water equivalent (SWE_max) increased in most areas. However, the proportion of the correlation between SWE_max and SOS was small. The impact of snow cover changes on the SOS varied across different vegetation types. Snow cover indicators mainly exhibited positive correlations with the SOS of forests, including deciduous broadleaf forests and deciduous coniferous forests, with positive and negative correlations of 18.61% and 2.58%, respectively. However, snow cover indicators mainly exhibited negative correlations in the SOS of grasslands and rainfed croplands, exhibiting positive and negative correlations of 4.87% and 13.06%, respectively. Snow cover impacted the SOS through the “temperature effect” in deciduous broadleaf forests, deciduous coniferous forests, and rainfed croplands, while it affected SOS through the “moisture effect” in grasslands. These results provide an enhanced understanding of the differences in snow cover changes affecting SOS in different vegetation types under climate change in Northeast China. Full article

(This article belongs to the Special Issue Monitoring Cold-Region Water Cycles Using Remote Sensing Big Data)

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17 pages, 6484 KiB

Open AccessArticle

DSRSS-Net: Improved-Resolution Snow Cover Mapping from FY-4A Satellite Images Using the Dual-Branch Super-Resolution Semantic Segmentation Network

by Xi Kan, Zhengsong Lu, Yonghong Zhang, Linglong Zhu, Kenny Thiam Choy Lim Kam Sian, Jiangeng Wang, Xu Liu, Zhou Zhou and Haixiao Cao

Remote Sens. 2023, 15(18), 4431; https://doi.org/10.3390/rs15184431 - 08 Sep 2023

Viewed by 834

Abstract

The Qinghai–Tibet Plateau is one of the regions with the highest snow accumulation in China. Although the Fengyun-4A (FY4A) satellite is capable of monitoring snow-covered areas in real time and on a wide scale at high temporal resolution, its spatial resolution is low. In this study, the Qinghai–Tibet Plateau, which has a harsh climate with few meteorological stations, was selected as the study area. We propose a deep learning model called the Dual-Branch Super-Resolution Semantic Segmentation Network (DSRSS-Net), in which one branch focuses with super resolution to obtain high-resolution snow distributions and the other branch carries out semantic segmentation to achieve accurate snow recognition. An edge enhancement module and coordinated attention mechanism were introduced into the network to improve the classification performance and edge segmentation effect for cloud versus snow. Multi-task loss is also used for optimization, including feature affinity loss and edge loss, to obtain fine structural information and improve edge segmentation. The 1 km resolution image obtained by coupling bands 1, 2, and 3; the 2 km resolution image obtained by coupling bands 4, 5, and 6; and the 500 m resolution image for a single channel, band 2, were inputted into the model for training. The accuracy of this model was verified using ground-based meteorological station data. Snow classification accuracy, false detection rate, and total classification accuracy were compared with the MOD10A1 snow product. The results show that, compared with MOD10A1, the snow classification accuracy and the average total accuracy of DSRSS-Net improved by 4.45% and 5.1%, respectively. The proposed method effectively reduces the misidentification of clouds and snow, has higher classification accuracy, and effectively improves the spatial resolution of FY-4A satellite snow cover products. Full article

(This article belongs to the Special Issue Monitoring Cold-Region Water Cycles Using Remote Sensing Big Data)

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21 pages, 8753 KiB

Open AccessArticle

Monitoring Thermokarst Lake Drainage Dynamics in Northeast Siberian Coastal Tundra

by Aobo Liu, Yating Chen and Xiao Cheng

Remote Sens. 2023, 15(18), 4396; https://doi.org/10.3390/rs15184396 - 07 Sep 2023

Cited by 1 | Viewed by 1043

Abstract

Thermokarst lakes in permafrost regions are highly dynamic due to drainage events triggered by climate warming. This study focused on mapping lake drainage events across the Northeast Siberian coastal tundra from 2000 to 2020 and identifying influential factors. An object-based lake analysis method was developed to detect 238 drained lakes using a well-established surface water dynamics product. The LandTrendr change detection algorithm, combined with continuous Landsat satellite imagery, precisely dated lake drainage years with 83.2% accuracy validated against manual interpretation. Spatial analysis revealed the clustering of drained lakes along rivers and in subsidence-prone Yedoma regions. The statistical analysis showed significant warming aligned with broader trends but no evident temporal pattern in lake drainage events. Our machine learning model identified lake area, soil temperature, summer evaporation, and summer precipitation as the top predictors of lake drainage. As these climatic parameters increase or surpass specific thresholds, the likelihood of lake drainage notably increases. Overall, this study enhanced the understanding of thermokarst lake drainage patterns and environmental controls in vulnerable permafrost regions. Spatial and temporal dynamics of lake drainage events were governed by complex climatic, topographic, and permafrost interactions. Integrating remote sensing with field studies and modeling will help project lake stability and greenhouse gas emissions under climate change. Full article

(This article belongs to the Special Issue Monitoring Cold-Region Water Cycles Using Remote Sensing Big Data)

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19 pages, 7069 KiB

Open AccessArticle

Landsat Satellites Observed Dynamics of Snowline Altitude at the End of the Melting Season, Himalayas, 1991–2022

by Jingwen Wang, Zhiguang Tang, Gang Deng, Guojie Hu, Yuanhong You and Yancheng Zhao

Remote Sens. 2023, 15(10), 2534; https://doi.org/10.3390/rs15102534 - 11 May 2023

Cited by 4 | Viewed by 1568

Abstract

Studying the dynamics of snowline altitude at the end of the melting season (SLA-EMS) is beneficial in predicting future trends of glaciers and non-seasonal snow cover and in comprehending regional and global climate change. This study investigates the spatiotemporal variation characteristics of SLA-EMS in nine glacier areas of the Himalayas, utilizing Landsat images from 1991 to 2022. The potential correlations between SLA-EMS, alterations in temperature, and variations in precipitation across the Himalayas region glacier are also being analyzed. The results obtained are summarized below: (1) the Landsat-extracted SLA-EMS exhibits a strong agreement with the minimum snow coverage at the end of the melting season derived from Sentinel-2, achieving an overall accuracy (OA) of 92.6% and a kappa coefficient of 0.85. The SLA-EMS can be accurately obtained by using this model. (2) In the last 30 years, the SLA-EMS in the study areas showed an upward trend, with the rising rate ranging from 0.4 m·a⁻¹ to 9.4 m·a⁻¹. Among them, the SLA-EMS of Longbasaba rose fastest, and that of Namunani rose slowest. (3) The SLA-EMS in different regions of the Himalayas in a W-E direction have different sensitivity to precipitation and temperature. However, almost all of them show a positive correlation with temperature and a negative correlation with precipitation. Full article

(This article belongs to the Special Issue Monitoring Cold-Region Water Cycles Using Remote Sensing Big Data)

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21 pages, 6681 KiB

Open AccessArticle

Machine Learning-Based Bias Correction of Precipitation Measurements at High Altitude

by Hongyi Li, Yang Zhang, Huajin Lei and Xiaohua Hao

Remote Sens. 2023, 15(8), 2180; https://doi.org/10.3390/rs15082180 - 20 Apr 2023

Cited by 4 | Viewed by 2052

Abstract

Accurate precipitation measurements are essential for understanding hydrological processes in high-altitude regions. Conventional gauge measurements often yield large underestimations of actual precipitation, prompting the development of statistical methods to correct the measurement bias. However, the complex conditions at high altitudes pose additional challenges to the statistical methods. To improve the correction of precipitation measurements in high-altitude areas, we selected the Yakou station, situated at an altitude of 4147 m on the Tibetan plateau, as the study site. In this study, we employed the machine learning method XGBoost regression to correct precipitation measurements using meteorological variables and remote sensing data, including Global Satellite Mapping of Precipitation (GSMaP), Integrated Multi-satellitE Retrievals for GPM (IMERG) and Climate Hazards Group InfraRed Precipitation with Station data (CHIRPS). Additionally, we examined the transferability of this method between different stations in our study site, Norway, and the United States. Our results show that the Yakou station experiences a large underestimation of precipitation, with a magnitude of 51.4%. This is significantly higher than similar measurements taken in the Arctic or lower altitudes. Furthermore, the remote sensing precipitation datasets underestimated precipitation when compared to the Double Fence Intercomparison Reference (DFIR) precipitation observation. Our findings suggest that the machine learning method outperformed the traditional statistical method in accuracy metrics and frequency distribution. Introducing remote sensing data, especially the GSMaP precipitation, could potentially replace the role of in situ wind speed in precipitation correction, highlighting the potential of remote sensing data for correcting precipitation rather than in situ meteorological observation. Moreover, our results indicate that the machine learning method with remote sensing data demonstrated better transferability than the traditional statistical method when we cross-validated the method with sites located in different countries. This study offers a promising strategy for obtaining more accurate precipitation measurements in high-altitude regions. Full article

(This article belongs to the Special Issue Monitoring Cold-Region Water Cycles Using Remote Sensing Big Data)

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16 pages, 3734 KiB

Open AccessArticle

Simulations of Snowmelt Runoff in a High-Altitude Mountainous Area Based on Big Data and Machine Learning Models: Taking the Xiying River Basin as an Example

by Guoyu Wang, Xiaohua Hao, Xiaojun Yao, Jian Wang, Hongyi Li, Rensheng Chen and Zhangwen Liu

Remote Sens. 2023, 15(4), 1118; https://doi.org/10.3390/rs15041118 - 18 Feb 2023

Cited by 6 | Viewed by 2055

Abstract

As an essential data-driven model, machine learning can simulate runoff based on meteorological data at the watershed level. It has been widely used in the simulation of hydrological runoff. Considering the impact of snow cover on runoff in high-altitude mountainous areas, based on remote sensing data and atmospheric reanalysis data, in this paper we established a runoff simulation model with a random forest model and ANN (artificial neural network) model for the Xiying River Basin in the western Qilian region The verification of the measured data showed that the NSE (Nash–Sutcliffe efficiency), RMSE (root mean square error), and PBIAS (percent bias) values of the random forest model and ANN model were 0.701 and 0.748, 6.228 m³/s and 4.554 m³/s, and 4.903% and 8.329%, respectively. Considering the influence of ice and snow on runoff, the simulation accuracy of both the random forest model and ANN model was improved during the period of significant decreases in the annual snow and ice water equivalent in the Xiying River Basin from April to May, after the snow remote sensing data were introduced into the model. Specifically, for the random forest model, the NSE increased by 0.099, the RMSE decreased by 0.369 m³/s, and the PBIAS decreased by 1.689%. For the ANN model, the NSE increased by 0.207, the RMSE decreased by 0.700 m³/s, and the PBIAS decreased by 1.103%. In this study, based on remote sensing data and atmospheric reanalysis data, the random forest model and ANN model were used to effectively simulate hydrological runoff processes in high-altitude mountainous areas without observational data. In particular, the accuracy of the machine learning simulations of snowmelt runoff (especially during the snowmelt period) was effectively improved by introducing the snow remote sensing data, which can provide a methodological reference for the simulation and prediction of snowmelt runoff in alpine mountains. Full article

(This article belongs to the Special Issue Monitoring Cold-Region Water Cycles Using Remote Sensing Big Data)

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19 pages, 15997 KiB

Open AccessArticle

Estimating Fractional Snow Cover in the Pan-Arctic Region Using Added Vegetation Extraction Algorithm

by Yuan Ma, Donghang Shao, Jian Wang, Haojie Li, Hongyu Zhao and Wenzheng Ji

Remote Sens. 2023, 15(3), 775; https://doi.org/10.3390/rs15030775 - 29 Jan 2023

Viewed by 1477

Abstract

Snow cover is an essential indicator of global climate change. The composition of the underlying surface in the Pan-Arctic region is complex; forest and other areas with high vegetation coverage have a significant influence on the retrieval accuracy of fractional snow cover (FSC). Therefore, to explore the impact of vegetation on the extraction of the FSC algorithm, this study developed the normalized difference vegetation index (NDVI)-based Bivariate Linear Regression Model (BV-BLRM) to calculate the FSC. Then, the overall accuracy of the model and its changes under different classification conditions were evaluated and the relationship between the accuracy improvement and different underlying surfaces and elevations was analyzed. The results show that the BV-BLRM model is more accurate than MODIS’s traditional univariate linear algorithm for FSC (MOD-FSC) in each underlying surface. Overall, regarding the accuracy of the BV-BLRM model, the RMSE is 0.2, MAE is 0.15, and accuracy is 28.6% higher than the MOD-FSC model. The newly developed BV-BLRM model has the most significant improvement in the accuracy of FSC retrieval when the underlying surface has high vegetation coverage. Under different classification accuracies, the accuracy of BV-BLRM model was higher than that of MOD-FSC model, with an average of 30.5%. The improvement of FSC extraction accuracy by the model is smaller when the underlying surface is perpetual snow zone, with an average of 12.2%. This study is applicable to the scale mapping of FSC in large areas and is helpful to improve the FSC accuracy in areas with high vegetation coverage. Full article

(This article belongs to the Special Issue Monitoring Cold-Region Water Cycles Using Remote Sensing Big Data)

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23 pages, 12965 KiB

Open AccessArticle

Quantifying the Reliability and Uncertainty of Satellite, Reanalysis, and Merged Precipitation Products in Hydrological Simulations over the Topographically Diverse Basin in Southwest China

by Huajin Lei, Hongyu Zhao, Tianqi Ao and Wanpin Hu

Remote Sens. 2023, 15(1), 213; https://doi.org/10.3390/rs15010213 - 30 Dec 2022

Cited by 2 | Viewed by 1265

Abstract

With the continuous emergence of remote sensing technologies and atmospheric models, multi-source precipitation products (MSPs) are increasingly applied in hydrometeorological research, especially in ungauged or data-scarce regions. This study comprehensively evaluates the reliability of MSPs and quantifies the uncertainty of sources in streamflow simulation. Firstly, the performance of seven state-of-the-art MSPs is assessed using rain gauges and the Block-wise use of the TOPMODEL (BTOP) hydrological model under two calibration schemes over Jialing River Basin, China. Then, a variance decomposition approach (Analysis of variance, ANOVA) is employed to quantify the uncertainty contribution of precipitation products, model parameters, and their interaction in streamflow simulation. The MSPs include five satellite-based (GSMaP, IMERG, PERCDR, CHIRPS, CMORPH), one reanalysis (ERA5L), and one ensembled product (PXGB2). The results of precipitation evaluation show that the MSPs have temporal and spatial variability and PXGB2 has the best performance. The hydrologic utility of MSPs is different under different calibration methods. When using gauge-based calibration parameters, the PXGB2-based simulation performs best, whereas CHIRPS, PERCDR, and ERA5L show relatively poor performance. In comparison, the model recalibrated by individual MSPs significantly improves the simulation accuracy of most MSPs, with GSMaP having the best performance. The ANOVA results reveal that the contribution of precipitation products to the streamflow uncertainty is larger than model parameters and their interaction. The impact of interaction suggests that a better simulation attributes to an optimal combination of precipitation products and model parameters rather than solely relying on the best MSPs. These new findings are valuable for improving the suitability of MSPs in hydrologic applications. Full article

(This article belongs to the Special Issue Monitoring Cold-Region Water Cycles Using Remote Sensing Big Data)

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15 pages, 6130 KiB

Open AccessTechnical Note

An On-Orbit Relative Sensor Normalization for Unbalance Images from the Ice Pathfinder Satellite (BNU-1)

by Sishi Zhang, Xinyi Shang, Lanjing Li, Ying Zhang, Xiaoxu Wu, Fengming Hui, Huabing Huang and Xiao Cheng

Remote Sens. 2023, 15(23), 5439; https://doi.org/10.3390/rs15235439 - 21 Nov 2023

Viewed by 627

Abstract

The Ice Pathfinder satellite (code: BNU-1) is the first Chinese microsatellite, designed for monitoring polar climate and environmental changes. The major payload of BNU-1 is the wide-field camera which provides multispectral satellite images with a 73.69 m spatial resolution and a 739 km swath width. However, the color misrepresentation issue can be observed as the BUN-1 image appears yellowish as it gets farther towards the center field of view (FOV). The blue band of the image appears to be higher near the center FOV and declines generously towards both the edge areas of the image, which may cause the color misrepresentation issue. In this study, we develop a relative sensor normalization method to reduce the radiance errors of the blue band of BNU-1 images. This method uses the radiometric probability density distribution of the BNU-1 panchromatic band as a reference, correcting the probability density distribution of the blue band radiance first. Then, the mean adjustment is used to correct the mean of the blue band radiance after probability density function (PDF) correction, obtaining the corrected radiance in the blue band. Comparisons with the ground measurements and the Landsat8 image reveal the following: (1) The radiances of snow surfaces also have good consistency with ground observations and Landsat-8 images in the red, green, and blue bands. (2) The radiance errors of the uncorrected BNU-1 images are eliminated. The RMSE decreases from 80.30 to 32.54 W/m²/μm/sr. All these results indicate that the on-orbit relative correction method proposed in this study can effectively reduce the radiance errors of the BNU-1 images. Full article

(This article belongs to the Special Issue Monitoring Cold-Region Water Cycles Using Remote Sensing Big Data)

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