Special Issue "Uncertainties in Remote Sensing"

A special issue of Remote Sensing (ISSN 2072-4292).

Deadline for manuscript submissions: closed (31 October 2016).

Special Issue Editors

Dr. Yudong Tian
E-Mail Website
Guest Editor
NASA Goddard Space Flight Center and University of Maryland Greenbelt, United States
Interests: remote sensing; uncertainty and prediction; Bayesian statistical modeling; weather and climate; hydrology
Dr. Ken Harrison
E-Mail Website
Co-Guest Editor
NASA Goddard Space Flight Center and University of Maryland Greenbelt, United States
Interests: decision-making under uncertainty; Bayesian statistical modeling; remote sensing; environmental and water resources systems analysis

Special Issue Information

Dear Colleagues,

Uncertainty is one of the most common challenges among the diverse areas of remote sensing. Voluminous data are being produced by various remote-sensing applications. However, one needs quantitative uncertainty information associated with the data to extract information and distill knowledge from the data. Uncertainty quantification is also a critical scientific effort for both data producers and end users, as the process will provide revealing error characteristics to guide further improvements in data production and rational use of the data.

Recent years have seen considerable progress in uncertainty-related research in remote sensing. This progress covers theoretical foundations, methodologies, models, techniques, algorithms, tools, and many specific applications. This Special Issue is designed to collect articles from these areas, to reflect the state of the science in quantifying and utilizing uncertainty information in remote sensing. Submissions to address the following, by no means comprehensive, list of topics are welcome:

  • Theory of uncertainty in remote sensing
  • Bayesian probability theory and its application in uncertainty quantification
  • Error modeling and parameter estimation
  • Characterization of systematic and random errors and separation of signal and noise
  • Uncertainty and decision
  • Uncertainty and calibration and validation procedures
  • Uncertainty in the remote sensing of extreme events
  • Representation of error and uncertainty information in data
  • Communication of uncertainty information to users and decision makers
  • Assimilating remote sensing data with uncertainty

Yudong Tian
Guest Editor

Manuscript Submission Information

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Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 1800 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.


Keywords

  • uncertainties
  • systematic errors
  • random errors
  • remote sensing
  • error modeling
  • data assimilation
  • Bayesian statistics
  • uncertainty quantification
  • signal and noise
  • algorithms

Published Papers (20 papers)

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Research

Open AccessArticle
An Assessment of Satellite-Derived Rainfall Products Relative to Ground Observations over East Africa
Remote Sens. 2017, 9(5), 430; https://doi.org/10.3390/rs9050430 - 02 May 2017
Cited by 26
Abstract
Accurate and consistent rainfall observations are vital for climatological studies in support of better agricultural and water management decision-making and planning. In East Africa, accurate rainfall estimation with an adequate spatial distribution is limited due to sparse rain gauge networks. Satellite rainfall products [...] Read more.
Accurate and consistent rainfall observations are vital for climatological studies in support of better agricultural and water management decision-making and planning. In East Africa, accurate rainfall estimation with an adequate spatial distribution is limited due to sparse rain gauge networks. Satellite rainfall products can potentially play a role in increasing the spatial coverage of rainfall estimates; however, their performance needs to be understood across space–time scales and factors relating to their errors. This study assesses the performance of seven satellite products: Tropical Applications of Meteorology using Satellite and ground-based observations (TAMSAT), African Rainfall Climatology And Time series (TARCAT), Climate Hazards Group InfraRed Precipitation with Station data (CHIRPS), Tropical Rainfall Measuring Mission (TRMM-3B43), Climate Prediction Centre (CPC) Morphing technique (CMORPH), Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks Climate Data Record (PERSIANN-CDR), CPC Merged Analysis of Precipitation (CMAP), and Global Precipitation Climatology Project (GPCP), using locally developed gridded (0.05°) rainfall data for 15 years (1998–2012) over East Africa. The products’ assessments were done at monthly and yearly timescales and were remapped to the gridded rain gauge data spatial scale during the March to May (MAM) and October to December (OND) rainy seasons. A grid-based statistical comparison between the two datasets was used, but only pixel values located at the rainfall stations were considered for validation. Additionally, the impact of topography on the performance of the products was assessed by analyzing the pixels in areas of highest negative bias. All the products could substantially replicate rainfall patterns, but their differences are mainly based on retrieving high rainfall amounts, especially of localized orographic types. The products exhibited systematic errors, which decreased with an increase in temporal resolution from a monthly to yearly scale. Challenges in retrieving orographic rainfall, especially during the OND season, were identified as the main cause of high underestimations. Underestimation was observed when elevation was <2500 m and above this threshold; overestimation was evident in mountainous areas. CMORPH, CHIRPS, and TRMM showed consistently high performance during both seasons, and this was attributed to their ability to retrieve rainfall of different rainfall regimes. Full article
(This article belongs to the Special Issue Uncertainties in Remote Sensing)
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Open AccessArticle
Uncertainty in Terrestrial Laser Scanner Surveys of Landslides
Remote Sens. 2017, 9(2), 113; https://doi.org/10.3390/rs9020113 - 29 Jan 2017
Cited by 7
Abstract
Terrestrial laser scanning (TLS) is a relatively new, versatile, and efficient technology for landslide monitoring. The evaluation of uncertainty of the surveyed data is not trivial because the final accuracy of the point position is unknown. An a priori evaluation of the accuracy [...] Read more.
Terrestrial laser scanning (TLS) is a relatively new, versatile, and efficient technology for landslide monitoring. The evaluation of uncertainty of the surveyed data is not trivial because the final accuracy of the point position is unknown. An a priori evaluation of the accuracy of the observed points can be made based on both the footprint size and of the resolution, as well as in terms of effective instantaneous field of view (EIFOV). Such evaluations are surely helpful for a good survey design, but the further operations, such as cloud co-registration, georeferencing and editing, digital elevation model (DEM) creation, and so on, cause uncertainty which is difficult to evaluate. An assessment of the quality of the survey can be made evaluating the goodness of fit between the georeferenced point cloud and the terrain model built using it. In this article, we have considered a typical survey of a landsliding slope. We have presented an a priori quantitative assessment and we eventually analyzed how good the comparison is of the computed point cloud to the actual ground points. We have used the method of cross-validation to eventually suggest the use of a robust parameter for estimating the reliability of the fitting procedure. This statistic can be considered for comparing methods and parameters used to interpolate the DEM. Using kriging allows one to account for the spatial distribution of the data (including the typical anisotropy of the survey of a slope) and to obtain a map of the uncertainties over the height of the grid nodes. This map can be used to compute the estimated error over the DEM-derived quantities, and also represents an “objective” definition of the area of the survey that can be trusted for further use. Full article
(This article belongs to the Special Issue Uncertainties in Remote Sensing)
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Open AccessArticle
An Algorithm for In-Flight Spectral Calibration of Imaging Spectrometers
Remote Sens. 2016, 8(12), 1017; https://doi.org/10.3390/rs8121017 - 11 Dec 2016
Cited by 9
Abstract
Accurate spectral calibration of satellite and airborne spectrometers is essential for remote sensing applications that rely on accurate knowledge of center wavelength (CW) positions and slit function parameters (SFP). We present a new in-flight spectral calibration algorithm that retrieves CWs and SFPs across [...] Read more.
Accurate spectral calibration of satellite and airborne spectrometers is essential for remote sensing applications that rely on accurate knowledge of center wavelength (CW) positions and slit function parameters (SFP). We present a new in-flight spectral calibration algorithm that retrieves CWs and SFPs across a wide spectral range by fitting a high-resolution solar spectrum and atmospheric absorbers to in-flight radiance spectra. Using a maximum a posteriori optimal estimation approach, the quality of the fit can be improved with a priori information. The algorithm was tested with synthetic spectra and applied to data from the APEX imaging spectrometer over the spectral range of 385–870 nm. CWs were retrieved with high accuracy (uncertainty <0.05 spectral pixels) from Fraunhofer lines below 550 nm and atmospheric absorbers above 650 nm. This enabled a detailed characterization of APEX’s across-track spectral smile and a previously unknown along-track drift. The FWHMs of the slit function were also retrieved with good accuracy (<10% uncertainty) for synthetic spectra, while some obvious misfits appear for the APEX spectra that are likely related to radiometric calibration issues. In conclusion, our algorithm significantly improves the in-flight spectral calibration of APEX and similar spectrometers, making them better suited for the retrieval of atmospheric and surface variables relying on accurate calibration. Full article
(This article belongs to the Special Issue Uncertainties in Remote Sensing)
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Open AccessArticle
Using a Kalman Filter to Assimilate TRMM-Based Real-Time Satellite Precipitation Estimates over Jinghe Basin, China
Remote Sens. 2016, 8(11), 899; https://doi.org/10.3390/rs8110899 - 02 Nov 2016
Cited by 4
Abstract
In this study, efforts are focused on the comparison and validation of standard Tropical Rainfall Measuring Mission (TRMM) Multisatellite Precipitation Analysis (TMPA) products—Version-7 3B42RT estimates before and after assimilation by using a Kalman filter with independent rain gauge networks located within the Jinghe [...] Read more.
In this study, efforts are focused on the comparison and validation of standard Tropical Rainfall Measuring Mission (TRMM) Multisatellite Precipitation Analysis (TMPA) products—Version-7 3B42RT estimates before and after assimilation by using a Kalman filter with independent rain gauge networks located within the Jinghe basin of China. Generally, the direct comparison of TMPA precipitation estimates to 200 collocated rain gauges from 2006 to 2008 demonstrate that the spatial and temporal rainfall characteristics over the region are well captured by the assimilation estimates. Especially, results also show that using Kalman filter to assimilate TRMM-based multi-satellite real-time precipitation estimates tends to perform well over regions, where gauge network is rather sparse. Last, this study highlights that accurate detection and estimation of precipitation in the summer season by Kalman filter, particularly for nonlinear convective precipitation events, is still a challenging task for the future development of assimilation technique for improving the satellite-based precipitation accuracy. Full article
(This article belongs to the Special Issue Uncertainties in Remote Sensing)
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Open AccessArticle
Evaluation and Uncertainty Estimation of the Latest Radar and Satellite Snowfall Products Using SNOTEL Measurements over Mountainous Regions in Western United States
Remote Sens. 2016, 8(11), 904; https://doi.org/10.3390/rs8110904 - 01 Nov 2016
Cited by 8
Abstract
Snow contributes to regional and global water budgets, and is of critical importance to water resources management and our society. Along with advancement in remote sensing tools and techniques to retrieve snowfall, verification and refinement of these estimates need to be performed using [...] Read more.
Snow contributes to regional and global water budgets, and is of critical importance to water resources management and our society. Along with advancement in remote sensing tools and techniques to retrieve snowfall, verification and refinement of these estimates need to be performed using ground-validation datasets. A comprehensive evaluation of the Multi-Radar/Multi-Sensor (MRMS) snowfall products and Integrated Multi-satellitE Retrievals for GPM (Global Precipitation Measurement) (IMERG) precipitation products is conducted using the Snow Telemetry (SNOTEL) daily precipitation and Snow Water Equivalent (SWE) datasets. Severe underestimations are found in both radar and satellite products. Comparisons are conducted as functions of air temperature, snowfall intensity, and radar beam height, in hopes of resolving the discrepancies between measurements by remote sensing and gauge, and finally developing better snowfall retrieval algorithms in the future. Full article
(This article belongs to the Special Issue Uncertainties in Remote Sensing)
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Open AccessArticle
Optimal Use of Space-Borne Advanced Infrared and Microwave Soundings for Regional Numerical Weather Prediction
Remote Sens. 2016, 8(10), 816; https://doi.org/10.3390/rs8100816 - 30 Sep 2016
Cited by 3
Abstract
Satellite observations can either be assimilated as radiances or as retrieved physical parameters to reduce error in the initial conditions used by the Numerical Weather Prediction (NWP) model. Assimilation of radiances requires a radiative transfer model to convert atmospheric state in model space [...] Read more.
Satellite observations can either be assimilated as radiances or as retrieved physical parameters to reduce error in the initial conditions used by the Numerical Weather Prediction (NWP) model. Assimilation of radiances requires a radiative transfer model to convert atmospheric state in model space to that in radiance space, thus requiring a lot of computational resources especially for hyperspectral instruments with thousands of channels. On the other hand, assimilating the retrieved physical parameters is computationally more efficient as they are already in thermodynamic states, which can be compared with NWP model outputs through the objective analysis scheme. A microwave (MW) sounder and an infrared (IR) sounder have their respective observational limitation due to the characteristics of adopted spectra. The MW sounder observes at much larger field-of-view (FOV) compared to an IR sounder. On the other hand, MW has the capability to reveal the atmospheric sounding when the clouds are presented, but IR observations are highly sensitive to clouds, The advanced IR sounder is able to reduce uncertainties in the retrieved atmospheric temperature and moisture profiles due to its higher spectral-resolution than the MW sounder which has much broader spectra bands. This study tries to quantify the optimal use of soundings retrieved from the microwave sounder AMSU and infrared sounder AIRS onboard the AQUA satellite in the regional Weather and Research Forecasting (WRF) model through three-dimensional variational (3D-var) data assimilation scheme. Four experiments are conducted by assimilating soundings from: (1) clear AIRS single field-of-view (SFOV); (2) retrieved from using clear AMSU and AIRS observations at AMSU field-of-view (SUP); (3) all SFOV soundings within AMSU FOVs must be clear; and (4) SUP soundings which must have all clear SFOV soundings within the AMSU FOV. A baseline experiment assimilating only conventional data is generated for comparison. Various atmospheric state variables at different pressure levels are used to assess the impact from assimilating these different data by comparing them with European Centre for Medium Range Weather Forecast (ECMWF) reanalysis data. Results indicate assimilation of SUP soundings improve the mid and upper troposphere, whereas assimilation of SFOV soundings has positive impact on the lower troposphere. Two additional assimilation experiments are carried out to determine the combination of SUP and SFOV soundings that will provide the best performance throughout the troposphere. The results indicate that optimal combination is to assimilate clear-sky matched IR retrievals with non-matched MW soundings. Full article
(This article belongs to the Special Issue Uncertainties in Remote Sensing)
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Open AccessArticle
The Variations and Trends of MODIS C5 & C6 Products’ Errors in the Recent Decade over the Background and Urban Areas of North China
Remote Sens. 2016, 8(9), 754; https://doi.org/10.3390/rs8090754 - 13 Sep 2016
Cited by 21
Abstract
With ten-year (2004–2013) ground-based observations of Beijing Forest (BJF) and Beijing City (BJC) sites in North China, we validated the high-quality MODerate resolution Imaging Spectroradiometer (MODIS) Collection 5 (C5) and Collection 6 (C6) Aerosol Optical Depth (AOD) products’ precision and discussed the sensors [...] Read more.
With ten-year (2004–2013) ground-based observations of Beijing Forest (BJF) and Beijing City (BJC) sites in North China, we validated the high-quality MODerate resolution Imaging Spectroradiometer (MODIS) Collection 5 (C5) and Collection 6 (C6) Aerosol Optical Depth (AOD) products’ precision and discussed the sensors degradation issues. The annual mean AOD and Angstrom exponent (α) were 0.20 ± 0.02 and 0.83 ± 0.15 in the background over the past ten years, and they were 0.59 ± 0.07 and 1.13 ± 0.08 in the urban, respectively. Ground-based AOD had both slightly declining trends, with variations of 0.023 and 0.057 over the past decade in the background and urban, respectively. There were large differences among the eight kinds of MODIS AOD products (Terra vs. Aqua, C5 vs. C6, DT (Deep Target) vs. DB (Deep Blue), and DTDB in the background and urban areas), but all the products’ monthly errors had larger variations in the spring and summer, and smaller ones in the autumn and winter. In the background, more than 62% of DT matchups for C5 and C6 products were within NASA’s expected error (EE) envelope. In the urban, 69%~72% of C6 DB retrievals were falling within EE envelope. The new dataset named C6 DTDB had better performance in the background, whereas it overestimated by 37%~41% in the urban caused by surface reflectivity estimation error. The range of monthly average error varied from −0.21 to 0.28 in the background and from −0.63 to 0.48 in the urban. From the background to the urban areas, the retrieval errors of Terra and Aqua had slightly increased by 0.0023~0.0158 and 0.0011~0.0124 per year, respectively, which implied that the two MODIS instruments had degraded slowly. Full article
(This article belongs to the Special Issue Uncertainties in Remote Sensing)
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Open AccessArticle
A Scale-Driven Change Detection Method Incorporating Uncertainty Analysis for Remote Sensing Images
Remote Sens. 2016, 8(9), 745; https://doi.org/10.3390/rs8090745 - 12 Sep 2016
Cited by 8
Abstract
Change detection (CD) based on remote sensing images plays an important role in Earth observation. However, the CD accuracy is usually affected by sunlight and atmospheric conditions and sensor calibration. In this study, a scale-driven CD method incorporating uncertainty analysis is proposed to [...] Read more.
Change detection (CD) based on remote sensing images plays an important role in Earth observation. However, the CD accuracy is usually affected by sunlight and atmospheric conditions and sensor calibration. In this study, a scale-driven CD method incorporating uncertainty analysis is proposed to increase CD accuracy. First, two temporal images are stacked and segmented into multiscale segmentation maps. Then, a pixel-based change map with memberships belonging to changed and unchanged parts is obtained by fuzzy c-means clustering. Finally, based on the Dempster-Shafer evidence theory, the proposed scale-driven CD method incorporating uncertainty analysis is performed on the multiscale segmentation maps and the pixel-based change map. Two experiments were carried out on Landsat-7 Enhanced Thematic Mapper Plus (ETM+) and SPOT 5 data sets. The ratio of total errors can be reduced to 4.0% and 7.5% for the ETM+ and SPOT 5 data sets in this study, respectively. Moreover, the proposed approach outperforms some state-of-the-art CD methods and provides an effective solution for CD. Full article
(This article belongs to the Special Issue Uncertainties in Remote Sensing)
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Open AccessArticle
Assessing Uncertainty in LULC Classification Accuracy by Using Bootstrap Resampling
Remote Sens. 2016, 8(9), 705; https://doi.org/10.3390/rs8090705 - 26 Aug 2016
Cited by 6
Abstract
Supervised land-use/land-cover (LULC) classifications are typically conducted using class assignment rules derived from a set of multiclass training samples. Consequently, classification accuracy varies with the training data set and is thus associated with uncertainty. In this study, we propose a bootstrap resampling and [...] Read more.
Supervised land-use/land-cover (LULC) classifications are typically conducted using class assignment rules derived from a set of multiclass training samples. Consequently, classification accuracy varies with the training data set and is thus associated with uncertainty. In this study, we propose a bootstrap resampling and reclassification approach that can be applied for assessing not only the uncertainty in classification results of the bootstrap-training data sets, but also the classification uncertainty of individual pixels in the study area. Two measures of pixel-specific classification uncertainty, namely the maximum class probability and Shannon entropy, were derived from the class probability vector of individual pixels and used for the identification of unclassified pixels. Unclassified pixels that are identified using the traditional chi-square threshold technique represent outliers of individual LULC classes, but they are not necessarily associated with higher classification uncertainty. By contrast, unclassified pixels identified using the equal-likelihood technique are associated with higher classification uncertainty and they mostly occur on or near the borders of different land-cover. Full article
(This article belongs to the Special Issue Uncertainties in Remote Sensing)
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Open AccessArticle
Flood Damage Analysis: First Floor Elevation Uncertainty Resulting from LiDAR-Derived Digital Surface Models
Remote Sens. 2016, 8(7), 604; https://doi.org/10.3390/rs8070604 - 19 Jul 2016
Cited by 9
Abstract
The use of high resolution ground-based light detection and ranging (LiDAR) datasets provides spatial density and vertical precision for obtaining highly accurate Digital Surface Models (DSMs). As a result, the reliability of flood damage analysis has improved significantly, owing to the increased accuracy [...] Read more.
The use of high resolution ground-based light detection and ranging (LiDAR) datasets provides spatial density and vertical precision for obtaining highly accurate Digital Surface Models (DSMs). As a result, the reliability of flood damage analysis has improved significantly, owing to the increased accuracy of hydrodynamic models. In addition, considerable error reduction has been achieved in the estimation of first floor elevation, which is a critical parameter for determining structural and content damages in buildings. However, as with any discrete measurement technique, LiDAR data contain object space ambiguities, especially in urban areas where the presence of buildings and the floodplain gives rise to a highly complex landscape that is largely corrected by using ancillary information based on the addition of breaklines to a triangulated irregular network (TIN). The present study provides a methodological approach for assessing uncertainty regarding first floor elevation. This is based on: (i) generation an urban TIN from LiDAR data with a density of 0.5 points·m−2, complemented with the river bathymetry obtained from a field survey with a density of 0.3 points·m−2. The TIN was subsequently improved by adding breaklines and was finally transformed to a raster with a spatial resolution of 2 m; (ii) implementation of a two-dimensional (2D) hydrodynamic model based on the 500-year flood return period. The high resolution DSM obtained in the previous step, facilitated addressing the modelling, since it represented suitable urban features influencing hydraulics (e.g., streets and buildings); and (iii) determination of first floor elevation uncertainty within the 500-year flood zone by performing Monte Carlo simulations based on geostatistics and 1997 control elevation points in order to assess error. Deviations in first floor elevation (average: 0.56 m and standard deviation: 0.33 m) show that this parameter has to be neatly characterized in order to obtain reliable assessments of flood damage assessments and implement realistic risk management. Full article
(This article belongs to the Special Issue Uncertainties in Remote Sensing)
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Open AccessArticle
Diagnosing Horizontal and Inter-Channel Observation Error Correlations for SEVIRI Observations Using Observation-Minus-Background and Observation-Minus-Analysis Statistics
Remote Sens. 2016, 8(7), 581; https://doi.org/10.3390/rs8070581 - 08 Jul 2016
Cited by 24
Abstract
It has been common practice in data assimilation to treat observation errors as uncorrelated; however, meteorological centres are beginning to use correlated inter-channel observation errors in their operational assimilation systems. In this work, we are the first to characterise inter-channel and spatial error [...] Read more.
It has been common practice in data assimilation to treat observation errors as uncorrelated; however, meteorological centres are beginning to use correlated inter-channel observation errors in their operational assimilation systems. In this work, we are the first to characterise inter-channel and spatial error correlations for Spinning Enhanced Visible and Infrared Imager (SEVIRI) observations that are assimilated into the Met Office high-resolution model. The errors are calculated using a diagnostic that calculates statistical averages of observation-minus-background and observation-minus-analysis residuals. This diagnostic is sensitive to the background and observation error statistics used in the assimilation, although, with careful interpretation of the results, it can still provide useful information. We find that the diagnosed SEVIRI error variances are as low as one-tenth of those currently used in the operational system. The water vapour channels have significantly correlated inter-channel errors, as do the surface channels. The surface channels have larger observation error variances and inter-channel correlations in coastal areas of the domain; this is the result of assimilating mixed pixel (land-sea) observations. The horizontal observation error correlations range between 30 km and 80 km, which is larger than the operational thinning distance of 24 km. We also find that estimates from the diagnostics are unaffected by biased observations, provided that the observation-minus-background and observation-minus-analysis residual means are subtracted. Full article
(This article belongs to the Special Issue Uncertainties in Remote Sensing)
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Open AccessArticle
Similarity and Error Intercomparison of the GPM and Its Predecessor-TRMM Multisatellite Precipitation Analysis Using the Best Available Hourly Gauge Network over the Tibetan Plateau
Remote Sens. 2016, 8(7), 569; https://doi.org/10.3390/rs8070569 - 07 Jul 2016
Cited by 43
Abstract
The performance of Day-1 Integrated Multi-satellitE Retrievals for the Global Precipitation Measurement (GPM) mission (IMERG) and its predecessor, the Tropical Rainfall Measuring Mission (TRMM) Multisatellite Precipitation Analysis 3B42 Version 7 (3B42V7), was cross-evaluated using data from the best-available hourly gauge network over the [...] Read more.
The performance of Day-1 Integrated Multi-satellitE Retrievals for the Global Precipitation Measurement (GPM) mission (IMERG) and its predecessor, the Tropical Rainfall Measuring Mission (TRMM) Multisatellite Precipitation Analysis 3B42 Version 7 (3B42V7), was cross-evaluated using data from the best-available hourly gauge network over the Tibetan Plateau (TP). Analyses of three-hourly rainfall estimates in the warm season of 2014 reveal that IMERG shows appreciably better correlations and lower errors than 3B42V7, though with very similar spatial patterns for all assessment indicators. IMERG also appears to detect light rainfall better than 3B42V7. However, IMERG shows slightly lower POD than 3B42V7 for elevations above 4200 m. Both IMERG and 3B42V7 successfully capture the northward dynamic life cycle of the Indian monsoon reasonably well over the TP. In particular, the relatively light rain from early and end Indian monsoon moisture surge events often fails to be captured by the sparsely-distributed gauges. In spite of limited snowfall field observations, IMERG shows the potential of detecting solid precipitation, which cannot be retrieved from the 3B42V7 products. Full article
(This article belongs to the Special Issue Uncertainties in Remote Sensing)
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Open AccessArticle
Characteristics and Diurnal Cycle of GPM Rainfall Estimates over the Central Amazon Region
Remote Sens. 2016, 8(7), 544; https://doi.org/10.3390/rs8070544 - 25 Jun 2016
Cited by 18
Abstract
Studies that investigate and evaluate the quality, limitations and uncertainties of satellite rainfall estimates are fundamental to assure the correct and successful use of these products in applications, such as climate studies, hydrological modeling and natural hazard monitoring. Over regions of the globe [...] Read more.
Studies that investigate and evaluate the quality, limitations and uncertainties of satellite rainfall estimates are fundamental to assure the correct and successful use of these products in applications, such as climate studies, hydrological modeling and natural hazard monitoring. Over regions of the globe that lack in situ observations, such studies are only possible through intensive field measurement campaigns, which provide a range of high quality ground measurements, e.g., CHUVA (Cloud processes of tHe main precipitation systems in Brazil: A contribUtion to cloud resolVing modeling and to the GlobAl Precipitation Measurement) and GoAmazon (Observations and Modeling of the Green Ocean Amazon) over the Brazilian Amazon during 2014/2015. This study aims to assess the characteristics of Global Precipitation Measurement (GPM) satellite-based precipitation estimates in representing the diurnal cycle over the Brazilian Amazon. The Integrated Multi-satellitE Retrievals for Global Precipitation Measurement (IMERG) and the Goddard Profiling Algorithm—Version 2014 (GPROF2014) algorithms are evaluated against ground-based radar observations. Specifically, the S-band weather radar from the Amazon Protection National System (SIPAM), is first validated against the X-band CHUVA radar and then used as a reference to evaluate GPM precipitation. Results showed satisfactory agreement between S-band SIPAM radar and both IMERG and GPROF2014 algorithms. However, during the wet season, IMERG, which uses the GPROF2014 rainfall retrieval from the GPM Microwave Imager (GMI) sensor, significantly overestimates the frequency of heavy rainfall volumes around 00:00–04:00 UTC and 15:00–18:00 UTC. This overestimation is particularly evident over the Negro, Solimões and Amazon rivers due to the poorly-calibrated algorithm over water surfaces. On the other hand, during the dry season, the IMERG product underestimates mean precipitation in comparison to the S-band SIPAM radar, mainly due to the fact that isolated convective rain cells in the afternoon are not detected by the satellite precipitation algorithm. Full article
(This article belongs to the Special Issue Uncertainties in Remote Sensing)
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Open AccessArticle
Spatial Autocorrelation and Uncertainty Associated with Remotely-Sensed Data
Remote Sens. 2016, 8(7), 535; https://doi.org/10.3390/rs8070535 - 23 Jun 2016
Cited by 16
Abstract
Virtually all remotely sensed data contain spatial autocorrelation, which impacts upon their statistical features of uncertainty through variance inflation, and the compounding of duplicate information. Estimating the nature and degree of this spatial autocorrelation, which is usually positive and very strong, has been [...] Read more.
Virtually all remotely sensed data contain spatial autocorrelation, which impacts upon their statistical features of uncertainty through variance inflation, and the compounding of duplicate information. Estimating the nature and degree of this spatial autocorrelation, which is usually positive and very strong, has been hindered by computational intensity associated with the massive number of pixels in realistically-sized remotely-sensed images, a situation that more recently has changed. Recent advances in spatial statistical estimation theory support the extraction of information and the distilling of knowledge from remotely-sensed images in a way that accounts for latent spatial autocorrelation. This paper summarizes an effective methodological approach to achieve this end, illustrating results with a 2002 remotely sensed-image of the Florida Everglades, and simulation experiments. Specifically, uncertainty of spatial autocorrelation parameter in a spatial autoregressive model is modeled with a beta-beta mixture approach and is further investigated with three different sampling strategies: coterminous sampling, random sub-region sampling, and increasing domain sub-regions. The results suggest that uncertainty associated with remotely-sensed data should be cast in consideration of spatial autocorrelation. It emphasizes that one remaining challenge is to better quantify the spatial variability of spatial autocorrelation estimates across geographic landscapes. Full article
(This article belongs to the Special Issue Uncertainties in Remote Sensing)
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Open AccessArticle
Distinguishing Land Change from Natural Variability and Uncertainty in Central Mexico with MODIS EVI, TRMM Precipitation, and MODIS LST Data
Remote Sens. 2016, 8(6), 478; https://doi.org/10.3390/rs8060478 - 07 Jun 2016
Cited by 1
Abstract
Precipitation and temperature enact variable influences on vegetation, impacting the type and condition of land cover, as well as the assessment of change over broad landscapes. Separating the influence of vegetative variability independent and discrete land cover change remains a major challenge to [...] Read more.
Precipitation and temperature enact variable influences on vegetation, impacting the type and condition of land cover, as well as the assessment of change over broad landscapes. Separating the influence of vegetative variability independent and discrete land cover change remains a major challenge to landscape change assessments. The heterogeneous Lerma-Chapala-Santiago watershed of central Mexico exemplifies both natural and anthropogenic forces enacting variability and change on the landscape. This study employed a time series of Enhanced Vegetation Index (EVI) composites from the Moderate Resolution Imaging Spectoradiometer (MODIS) for 2001–2007 and per-pixel multiple linear regressions in order to model changes in EVI as a function of precipitation, temperature, and elevation. Over the seven-year period, 59.1% of the variability in EVI was explained by variability in the independent variables, with highest model performance among changing and heterogeneous land cover types, while intact forest cover demonstrated the greatest resistance to changes in temperature and precipitation. Model results were compared to an independent change uncertainty assessment, and selected regional samples of change confusion and natural variability give insight to common problems afflicting land change analyses. Full article
(This article belongs to the Special Issue Uncertainties in Remote Sensing)
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Open AccessArticle
Error-Component Analysis of TRMM-Based Multi-Satellite Precipitation Estimates over Mainland China
Remote Sens. 2016, 8(5), 440; https://doi.org/10.3390/rs8050440 - 23 May 2016
Cited by 17
Abstract
The Tropical Rainfall Measuring Mission (TRMM) Multi-Satellite Precipitation Analysis (TMPA) products have been widely used, but their error and uncertainty characteristics over diverse climate regimes still need to be quantified. In this study, we focused on a systematic evaluation of TMPA’s error characteristics [...] Read more.
The Tropical Rainfall Measuring Mission (TRMM) Multi-Satellite Precipitation Analysis (TMPA) products have been widely used, but their error and uncertainty characteristics over diverse climate regimes still need to be quantified. In this study, we focused on a systematic evaluation of TMPA’s error characteristics over mainland China, with an improved error-component analysis procedure. We performed the analysis for both the TMPA real-time and research product suite at a daily scale and 0.25° × 0.25° resolution. Our results show that, in general, the error components in TMPA exhibit rather strong regional and seasonal differences. For humid regions, hit bias and missed precipitation are the two leading error sources in summer, whereas missed precipitation dominates the total errors in winter. For semi-humid and semi-arid regions, the error components of two real-time TMPA products show an evident topographic dependency. Furthermore, the missed and false precipitation components have the similar seasonal variation but they counter each other, which result in a smaller total error than the individual components. For arid regions, false precipitation is the main problem in retrievals, especially during winter. On the other hand, we examined the two gauge-correction schemes, i.e., climatological calibration algorithm (CCA) for real-time TMPA and gauge-based adjustment (GA) for post-real-time TMPA. Overall, our results indicate that the upward adjustments of CCA alleviate the TMPA’s systematic underestimation over humid region but, meanwhile, unfavorably increased the original positive biases over the Tibetan plateau and Tianshan Mountains. In contrast, the GA technique could substantially improve the error components for local areas. Additionally, our improved error-component analysis found that both CCA and GA actually also affect the hit bias at lower rain rates (particularly for non-humid regions), as well as at higher ones. Finally, this study recommends that future efforts should focus on improving hit bias of humid regions, false error of arid regions, and missed snow events in winter. Full article
(This article belongs to the Special Issue Uncertainties in Remote Sensing)
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Open AccessArticle
Simulation of the Impact of a Sensor’s PSF on Mixed Pixel Decomposition: 1. Nonuniformity Effect
Remote Sens. 2016, 8(5), 437; https://doi.org/10.3390/rs8050437 - 21 May 2016
Cited by 1
Abstract
The nonuniformity of the spatial response to surface radiation is a fundamental characteristic of all airborne and spaceborne sensors that inevitably introduces uncertainty into the estimation of object proportions by the spectral unmixing of mixed pixels. Simulated data of the surface radiation distribution [...] Read more.
The nonuniformity of the spatial response to surface radiation is a fundamental characteristic of all airborne and spaceborne sensors that inevitably introduces uncertainty into the estimation of object proportions by the spectral unmixing of mixed pixels. Simulated data of the surface radiation distribution and a TM (thematic mapper) response matrix were developed and utilized to imitate the generation of mixed pixels and the extraction of the object proportion via a Monte Carlo simulation, and then, the nonuniformity effect of a sensor’s PSF (point spread function) was explored. The following conclusions were drawn: (1) given a nonuniform spatial response of a sensor to a surface scene with a constant object proportion and various object distribution patterns, the mixed pixel DN (digital number) of a remotely-sensed image becomes a random variable, which causes a PSF nonuniform effect on the object proportion extraction; (2) for the estimated object proportion, the corresponding true object proportion appears with a random variation; its upper and lower bounds take on an asymmetrical spindle shape; and models of these bound curves at any probability level were established; (3) there exists a negative linear relationship between the bias of the spectral unmixing and the estimated proportion; the bias is zero at an estimated proportion of 50%, and when the estimated proportions are approximately 100% and 0%, the object proportion is overestimated by 0.78% and underestimated by 0.78%, respectively; (4) the relationship between the standard deviation of the spectral unmixing and the estimated proportion follows a symmetrical polynomial function opening downward; the standard deviation reaches a maximum of 4.4% at the estimated proportion of 50%, and when the estimated proportion is approximately 100% or 0%, the standard deviation is a minimum, 1.05%. The above findings contribute to a comprehensive understanding of the PSF nonuniformity effect, have the potential to compensate for the bias of proportion estimation and present its confidence interval at any probability level. Full article
(This article belongs to the Special Issue Uncertainties in Remote Sensing)
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Open AccessArticle
Study of the Effect of Temporal Sampling Frequency on DSCOVR Observations Using the GEOS-5 Nature Run Results (Part II): Cloud Coverage
Remote Sens. 2016, 8(5), 431; https://doi.org/10.3390/rs8050431 - 20 May 2016
Cited by 3
Abstract
This is the second part of a study on how temporal sampling frequency affects satellite retrievals in support of the Deep Space Climate Observatory (DSCOVR) mission. Continuing from Part 1, which looked at Earth’s radiation budget, this paper presents the effect of sampling [...] Read more.
This is the second part of a study on how temporal sampling frequency affects satellite retrievals in support of the Deep Space Climate Observatory (DSCOVR) mission. Continuing from Part 1, which looked at Earth’s radiation budget, this paper presents the effect of sampling frequency on DSCOVR-derived cloud fraction. The output from NASA’s Goddard Earth Observing System version 5 (GEOS-5) Nature Run is used as the “truth”. The effect of temporal resolution on potential DSCOVR observations is assessed by subsampling the full Nature Run data. A set of metrics, including uncertainty and absolute error in the subsampled time series, correlation between the original and the subsamples, and Fourier analysis have been used for this study. Results show that, for a given sampling frequency, the uncertainties in the annual mean cloud fraction of the sunlit half of the Earth are larger over land than over ocean. Analysis of correlation coefficients between the subsamples and the original time series demonstrates that even though sampling at certain longer time intervals may not increase the uncertainty in the mean, the subsampled time series is further and further away from the “truth” as the sampling interval becomes larger and larger. Fourier analysis shows that the simulated DSCOVR cloud fraction has underlying periodical features at certain time intervals, such as 8, 12, and 24 h. If the data is subsampled at these frequencies, the uncertainties in the mean cloud fraction are higher. These results provide helpful insights for the DSCOVR temporal sampling strategy. Full article
(This article belongs to the Special Issue Uncertainties in Remote Sensing)
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Open AccessArticle
Uncertainties in Tidally Adjusted Estimates of Sea Level Rise Flooding (Bathtub Model) for the Greater London
Remote Sens. 2016, 8(5), 366; https://doi.org/10.3390/rs8050366 - 28 Apr 2016
Cited by 6
Abstract
Sea-level rise (SLR) from global warming may have severe consequences for coastal cities, particularly when combined with predicted increases in the strength of tidal surges. Predicting the regional impact of SLR flooding is strongly dependent on the modelling approach and accuracy of topographic [...] Read more.
Sea-level rise (SLR) from global warming may have severe consequences for coastal cities, particularly when combined with predicted increases in the strength of tidal surges. Predicting the regional impact of SLR flooding is strongly dependent on the modelling approach and accuracy of topographic data. Here, the areas under risk of sea water flooding for London boroughs were quantified based on the projected SLR scenarios reported in Intergovernmental Panel on Climate Change (IPCC) fifth assessment report (AR5) and UK climatic projections 2009 (UKCP09) using a tidally-adjusted bathtub modelling approach. Medium- to very high-resolution digital elevation models (DEMs) are used to evaluate inundation extents as well as uncertainties. Depending on the SLR scenario and DEMs used, it is estimated that 3%–8% of the area of Greater London could be inundated by 2100. The boroughs with the largest areas at risk of flooding are Newham, Southwark, and Greenwich. The differences in inundation areas estimated from a digital terrain model and a digital surface model are much greater than the root mean square error differences observed between the two data types, which may be attributed to processing levels. Flood models from SRTM data underestimate the inundation extent, so their results may not be reliable for constructing flood risk maps. This analysis provides a broad-scale estimate of the potential consequences of SLR and uncertainties in the DEM-based bathtub type flood inundation modelling for London boroughs. Full article
(This article belongs to the Special Issue Uncertainties in Remote Sensing)
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Open AccessArticle
Study of the Effect of Temporal Sampling Frequency on DSCOVR Observations Using the GEOS-5 Nature Run Results (Part I): Earth’s Radiation Budget
Remote Sens. 2016, 8(2), 98; https://doi.org/10.3390/rs8020098 - 27 Jan 2016
Cited by 3
Abstract
Satellites always sample the Earth-atmosphere system in a finite temporal resolution. This study investigates the effect of sampling frequency on the satellite-derived Earth radiation budget, with the Deep Space Climate Observatory (DSCOVR) as an example. The output from NASA’s Goddard Earth Observing System [...] Read more.
Satellites always sample the Earth-atmosphere system in a finite temporal resolution. This study investigates the effect of sampling frequency on the satellite-derived Earth radiation budget, with the Deep Space Climate Observatory (DSCOVR) as an example. The output from NASA’s Goddard Earth Observing System Version 5 (GEOS-5) Nature Run is used as the truth. The Nature Run is a high spatial and temporal resolution atmospheric simulation spanning a two-year period. The effect of temporal resolution on potential DSCOVR observations is assessed by sampling the full Nature Run data with 1-h to 24-h frequencies. The uncertainty associated with a given sampling frequency is measured by computing means over daily, monthly, seasonal and annual intervals and determining the spread across different possible starting points. The skill with which a particular sampling frequency captures the structure of the full time series is measured using correlations and normalized errors. Results show that higher sampling frequency gives more information and less uncertainty in the derived radiation budget. A sampling frequency coarser than every 4 h results in significant error. Correlations between true and sampled time series also decrease more rapidly for a sampling frequency less than 4 h. Full article
(This article belongs to the Special Issue Uncertainties in Remote Sensing)
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