Remote Sensing

1782 KiB

Open AccessArticle

Analysis of the Land Surface Temperature Scaling Problem: A Case Study of Airborne and Satellite Data over the Heihe Basin

by Tian Hu, Qinhuo Liu, Yongming Du, Hua Li, Heshun Wang and Biao Cao

Remote Sens. 2015, 7(5), 6489-6509; https://doi.org/10.3390/rs70506489 - 22 May 2015

Cited by 10 | Viewed by 6324

Abstract

This study analyzed the scaling problem of land surface temperature (LST) data retrieved with the Temperature Emissivity Separation (TES) algorithm. We compiled a remotely sensed dataset that included Thermal Airborne Hyperspectral Imager (TASI) and satellite-based Advanced Spaceborne Thermal Emission Reflection (ASTER) data, which [...] Read more.

This study analyzed the scaling problem of land surface temperature (LST) data retrieved with the Temperature Emissivity Separation (TES) algorithm. We compiled a remotely sensed dataset that included Thermal Airborne Hyperspectral Imager (TASI) and satellite-based Advanced Spaceborne Thermal Emission Reflection (ASTER) data, which were acquired simultaneously. This dataset provided the range of spatial heterogeneities of land surface necessary for the study, which was quantified by the dispersion variance. The LST scaling problem was studied by comparing the remotely sensed LST products in two ways. First, the LST products calculated in the distributed method and the lumped method were compared. Second, the airborne and satellite-based LST products derived from the TES algorithm were compared. Four upscaling methods of LST were used in the process. A scaling correction methodology was developed based on the comparisons. The results showed that the scaling effect could be as large as 0.8 when the spatial resolution of the TASI LST data was coarse. The scaling effect increases quickly with the spatial resolution until it reaches the characteristic scale of the landscape and is positively correlated with the spatial heterogeneity. The first two upscaling methods denoted as Methods 1–2 can upscale the LST more effectively when compared with the other two scaling methods (Methods 3–4). The scaling effect for the ASTER data is not notable. The comparison between the TASI and ASTER data showed that they were highly consistent, with a root mean square error (RMSE) of approximately 0.88 K, when the pixels were relatively homogeneous. When the spatial heterogeneity was significant, the RMSE was as large as 2.68 K The scaling correction methodology provided resolution-invariant results with scaling effects of less than 0.5 K. Full article

(This article belongs to the Special Issue The Development and Validation of Remote Sensing Products for Terrestrial, Hydrological, and Ecological Applications at the Regional Scale)

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35371 KiB

Open AccessArticle

High-Resolution Precipitation Datasets in South America and West Africa based on Satellite-Derived Rainfall, Enhanced Vegetation Index and Digital Elevation Model

by Guido Ceccherini, Iban Ameztoy, Claudia Patricia Romero Hernández and Cesar Carmona Moreno

Remote Sens. 2015, 7(5), 6454-6488; https://doi.org/10.3390/rs70506454 - 22 May 2015

Cited by 44 | Viewed by 20737

Abstract

Mean Annual Precipitation is one of the most important variables used in water resource management. However, quantifying Mean Annual Precipitation at high spatial resolution, needed for advanced hydrological analysis, is challenging in developing countries which often present a sparse gauge network and a [...] Read more.

Mean Annual Precipitation is one of the most important variables used in water resource management. However, quantifying Mean Annual Precipitation at high spatial resolution, needed for advanced hydrological analysis, is challenging in developing countries which often present a sparse gauge network and a highly variable climate. In this work, we present a methodology to quantify Mean Annual Precipitation at 1 km spatial resolution using different precipitation products from satellite estimates and gauge observations at coarse spatial resolution (i.e., ranging from 4 km to 25 km). Examples of this methodology are given for South America and West Africa. We develop a downscaling method that exploits the relationship among satellite-derived rainfall, Digital Elevation Model and Enhanced Vegetation Index. Finally, we validate its performance using rain gauge measurements: comparable annual precipitation estimates for both South America and West Africa are retrieved. Validation indicates that high resolution Mean Annual Precipitation downscaled from CHIRP (Climate Hazards Group Infrared Precipitation) and GPCC (Global Precipitation Climatology Centre) datasets present the best ensemble of performance statistics for both South America and West Africa. Results also highlight the potential of the presented technique to downscale satellite-derived rainfall worldwide. Full article

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8031 KiB

Open AccessArticle

A Revised Temporal Scaling Method to Yield Better ET Estimates at a Regional Scale

by Yi Song, Mingguo Ma, Long Jin and Xufeng Wang

Remote Sens. 2015, 7(5), 6433-6453; https://doi.org/10.3390/rs70506433 - 21 May 2015

Cited by 1 | Viewed by 5344

Abstract

This study presents a revised temporal scaling method based on a detection algorithm for the temporal stability of the evaporative fraction (EF) to estimate total daytime evapotranspiration (ET) at a regional scale. The study area is located in the Heihe River Basin, which [...] Read more.

This study presents a revised temporal scaling method based on a detection algorithm for the temporal stability of the evaporative fraction (EF) to estimate total daytime evapotranspiration (ET) at a regional scale. The study area is located in the Heihe River Basin, which is the second largest inland river basin in China. The remote sensing data and field observations used in this study were obtained from the Heihe Watershed Allied Telemetry Experimental Research (HiWATER) project. The half-hourly EF values (EF^EC) calculated using meteorological observations from an eddy covariance (EC) system and an automatic meteorological station (AMS) represented the diurnal pattern of the EF across the majority of the study area. The remotely sensed instantaneous midday EF (EF_ASTER), which indicates the spatial distribution of the midday EF over the entire study area, was calculated from an Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) image. The temporal stability of EF^EC was examined using a detection algorithm. Intervals with inconsistent EF^EC values were distinguished from those with consistent EF^EC values; the total daytime ET (from 9:00 to 19:00) within these interval types was integrated separately. Validation of the total daytime ET at the satellite pixel scale was conducted using measurements from17 EC towers. Using the detection algorithm for the temporal stability of the EF and dynamic adjustment, the revised temporal scaling method resulted in a root-mean-square error (RMSE) of 0.54 (mm·d⁻¹), a mean relative error (MRE) of 7.26% and a correlation coefficient (Corr.) of 0.81; all of these values were superior to those of the two other methods (i.e., the constant EF and variable EF methods). The revised method easily extends to other areas and exhibits a superior performance in flat and regularly-irrigated farmlands at the regional scale. Full article

(This article belongs to the Special Issue The Development and Validation of Remote Sensing Products for Terrestrial, Hydrological, and Ecological Applications at the Regional Scale)

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27755 KiB

Open AccessArticle

A New Algorithm of the FPAR Product in the Heihe River Basin Considering the Contributions of Direct and Diffuse Solar Radiation Separately

by Li Li, Yongming Du, Yong Tang, Xiaozhou Xin, Hailong Zhang, Jianguang Wen and Qinhuo Liu

Remote Sens. 2015, 7(5), 6414-6432; https://doi.org/10.3390/rs70506414 - 21 May 2015

Cited by 11 | Viewed by 6906

Abstract

It remains a challenging issue to accurately estimate the fraction of absorbed photosynthetically-active radiation (FPAR) using remote sensing data, as the direct and diffuse radiation reaching the vegetation canopy have different effects for FPAR. In this research, a FPAR inversion model was developed [...] Read more.

It remains a challenging issue to accurately estimate the fraction of absorbed photosynthetically-active radiation (FPAR) using remote sensing data, as the direct and diffuse radiation reaching the vegetation canopy have different effects for FPAR. In this research, a FPAR inversion model was developed that may distinguish direct and diffuse radiation (the DnD model) based on the energy budget balance principle. Taking different solar zenith angles and diffuse PAR proportions as inputs, the instantaneous FPAR could be calculated. As the leaf area index (LAI) and surface albedo do not vary in a short periods, the FPAR not only on a clear day, but also on a cloudy day may be calculated. This new method was used to produce the FPAR products in the Heihe River Basin with the Moderate-Resolution Imaging Spectroradiometer (MODIS) LAI and surface albedo products as the input data source. The instantaneous FPAR was validated by using field-measured data (RMSE is 0.03, R² is 0.85). The daily average FPAR was compared with the MODIS FPAR product. The inversion results and the MODIS FPAR product are highly correlated, but the MODIS FPAR product is slightly high in forest areas, which is in agreement with other studies for MODIS FPAR products. Full article

(This article belongs to the Special Issue The Development and Validation of Remote Sensing Products for Terrestrial, Hydrological, and Ecological Applications at the Regional Scale)

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10860 KiB

Open AccessReview

Object-Based Image Analysis in Wetland Research: A Review

by Iryna Dronova

Remote Sens. 2015, 7(5), 6380-6413; https://doi.org/10.3390/rs70506380 - 21 May 2015

Cited by 223 | Viewed by 15153

Abstract

The applications of object-based image analysis (OBIA) in remote sensing studies of wetlands have been growing over recent decades, addressing tasks from detection and delineation of wetland bodies to comprehensive analyses of within-wetland cover types and their change. Compared to pixel-based approaches, OBIA [...] Read more.

The applications of object-based image analysis (OBIA) in remote sensing studies of wetlands have been growing over recent decades, addressing tasks from detection and delineation of wetland bodies to comprehensive analyses of within-wetland cover types and their change. Compared to pixel-based approaches, OBIA offers several important benefits to wetland analyses related to smoothing of the local noise, incorporating meaningful non-spectral features for class separation and accounting for landscape hierarchy of wetland ecosystem organization and structure. However, there has been little discussion on whether unique challenges of wetland environments can be uniformly addressed by OBIA across different types of data, spatial scales and research objectives, and to what extent technical and conceptual aspects of this framework may themselves present challenges in a complex wetland setting. This review presents a synthesis of 73 studies that applied OBIA to different types of remote sensing data, spatial scale and research objectives. It summarizes the progress and scope of OBIA uses in wetlands, key benefits of this approach, factors related to accuracy and uncertainty in its applications and the main research needs and directions to expand the OBIA capacity in the future wetland studies. Growing demands for higher-accuracy wetland characterization at both regional and local scales together with advances in very high resolution remote sensing and novel tasks in wetland restoration monitoring will likely continue active exploration of the OBIA potential in these diverse and complex environments. Full article

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961 KiB

Open AccessArticle

An Improvement of the Radiative Transfer Model Component of a Land Data Assimilation System and Its Validation on Different Land Characteristics

by Hui Lu, Kun Yang, Toshio Koike, Long Zhao and Jun Qin

Remote Sens. 2015, 7(5), 6358-6379; https://doi.org/10.3390/rs70506358 - 21 May 2015

Cited by 15 | Viewed by 5988

Abstract

The paper reports the recent progress in the radiative transfer model (RTM) development, which serves as the observation operator of a Land Data Assimilation System (LDAS), and its validation at two Planetary Boundary Layer (PBL) stations with different weather and land cover conditions: [...] Read more.

The paper reports the recent progress in the radiative transfer model (RTM) development, which serves as the observation operator of a Land Data Assimilation System (LDAS), and its validation at two Planetary Boundary Layer (PBL) stations with different weather and land cover conditions: Wenjiang station of humid and cropped field and Gaize station of arid and bare soil field. In situ observed micrometeorological data were used as the driven data of LDAS, in which AMSR-E brightness temperatures (TB) were assimilated into a land surface model (LSM). Near surface soil moisture content output from LDAS, together with the one simulated by a LSM with default parameters, were compared to the in-situ soil moisture observation. The comparison results successfully validated the capability of LDAS with new RTM to simulate near surface soil moisture at various environments, supporting that LDAS can generally simulate soil moisture with a reasonable accuracy for both humid vegetated fields and arid bare soil fields while the LSM overestimates near surface soil moisture for humid vegetated fields and underestimates soil moisture for arid bare soil fields. Full article

(This article belongs to the Special Issue Earth Observations for the Sustainable Development)

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4708 KiB

Open AccessArticle

Empirical Radiometric Normalization of Road Points from Terrestrial Mobile Lidar System

by Tee-Ann Teo and Hui-Lin Yu

Remote Sens. 2015, 7(5), 6336-6357; https://doi.org/10.3390/rs70506336 - 21 May 2015

Cited by 21 | Viewed by 6541

Abstract

Lidar data provide both geometric and radiometric information. Radiometric information is influenced by sensor and target factors and should be calibrated to obtain consistent energy responses. The radiometric correction of airborne lidar system (ALS) converts the amplitude into a backscatter cross-section with physical [...] Read more.

Lidar data provide both geometric and radiometric information. Radiometric information is influenced by sensor and target factors and should be calibrated to obtain consistent energy responses. The radiometric correction of airborne lidar system (ALS) converts the amplitude into a backscatter cross-section with physical meaning value by applying a model-driven approach. The radiometric correction of terrestrial mobile lidar system (MLS) is a challenging task because it does not completely follow the inverse square range function at near-range. This study proposed a radiometric normalization workflow for MLS using a data-driven approach. The scope of this study is to normalize amplitude of road points for road surface classification, assuming that road points from different scanners or strips should have similar responses in overlapped areas. The normalization parameters for range effect were obtained from crossroads. The experiment showed that the amplitude difference between scanners and strips decreased after radiometric normalization and improved the accuracy of road surface classification. Full article

(This article belongs to the Special Issue Lidar/Laser Scanning in Urban Environments)

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632 KiB

Open AccessArticle

Application of the Ultraviolet Scanning Elastic Backscatter LiDAR for the Investigation of Aerosol Variability

by Fei Gao, Samo Stanič, Klemen Bergant, Ying Li, Songhui Li, Dengxin Hua and Longlong Wang

Remote Sens. 2015, 7(5), 6320-6335; https://doi.org/10.3390/rs70506320 - 20 May 2015

Cited by 8 | Viewed by 6797

Abstract

In order to investigate the aerosol variability over the southwest region of Slovenia, an ultraviolet scanning elastic backscatter LiDAR was utilized to make the vertical scan for atmospheric probing. With the assumption of horizontal atmospheric homogeneity, aerosol optical variables were retrieved from the [...] Read more.

In order to investigate the aerosol variability over the southwest region of Slovenia, an ultraviolet scanning elastic backscatter LiDAR was utilized to make the vertical scan for atmospheric probing. With the assumption of horizontal atmospheric homogeneity, aerosol optical variables were retrieved from the horizontal pixel data points of two-dimensional range-height-indicator (RHI) diagrams by using a multiangle retrieval method, in which optical depth is defined as the slope of the resulting linear function when height is kept constant. To make the data retrieval feasible and precise, a series of key procedures complemented the data processing, including construction of the RHI diagram, correction of Rayleigh scattering, assessment of horizontal atmospheric homogeneity and retrieval of aerosol optical variables. The measurement example demonstrated the feasibility of the ultraviolet scanning elastic backscatter LiDAR in the applications of the retrieval of aerosol extinction and determination of the atmospheric boundary layer height. Three months’ data combined with the modeling of air flow trajectories using Hybrid Single Particle Lagrangian Integrated Trajectory Model were analyzed to investigate aerosol variability. The average value of aerosol extinction with the presence of land-based air masses from the European continent was found to be two-times larger than that influenced by marine aerosols from the Mediterranean or Adriatic Sea. Full article

(This article belongs to the Special Issue Aerosol and Cloud Remote Sensing)

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28315 KiB

Open AccessArticle

An Improved Physics-Based Model for Topographic Correction of Landsat TM Images

by Ainong Li, Qingfang Wang, Jinhu Bian and Guangbin Lei

Remote Sens. 2015, 7(5), 6296-6319; https://doi.org/10.3390/rs70506296 - 20 May 2015

Cited by 36 | Viewed by 7298

Abstract

Optical remotely sensed images in mountainous areas are subject to radiometric distortions induced by topographic effects, which need to be corrected before quantitative applications. Based on Li model and Sandmeier model, this paper proposed an improved physics-based model for the topographic correction of [...] Read more.

Optical remotely sensed images in mountainous areas are subject to radiometric distortions induced by topographic effects, which need to be corrected before quantitative applications. Based on Li model and Sandmeier model, this paper proposed an improved physics-based model for the topographic correction of Landsat Thematic Mapper (TM) images. The model employed Normalized Difference Vegetation Index (NDVI) thresholds to approximately divide land targets into eleven groups, due to NDVI’s lower sensitivity to topography and its significant role in indicating land cover type. Within each group of terrestrial targets, corresponding MODIS BRDF (Bidirectional Reflectance Distribution Function) products were used to account for land surface’s BRDF effect, and topographic effects are corrected without Lambertian assumption. The methodology was tested with two TM scenes of severely rugged mountain areas acquired under different sun elevation angles. Results demonstrated that reflectance of sun-averted slopes was evidently enhanced, and the overall quality of images was improved with topographic effect being effectively suppressed. Correlation coefficients between Near Infra-Red band reflectance and illumination condition reduced almost to zero, and coefficients of variance also showed some reduction. By comparison with the other two physics-based models (Sandmeier model and Li model), the proposed model showed favorable results on two tested Landsat scenes. With the almost half-century accumulation of Landsat data and the successive launch and operation of Landsat 8, the improved model in this paper can be potentially helpful for the topographic correction of Landsat and Landsat-like data. Full article

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4191 KiB

Open AccessLetter

An Improved Endmember Selection Method Based on Vector Length for MODIS Reflectance Channels

by Yuanliu Xu, Jiancheng Shi and Jinyang Du

Remote Sens. 2015, 7(5), 6280-6295; https://doi.org/10.3390/rs70506280 - 20 May 2015

Cited by 9 | Viewed by 4437

Abstract

Endmember selection is the basis for sub-pixel land cover classifications using multiple endmember spectral mixture analysis (MESMA) that adopts variant endmember matrices for each pixel to mitigate errors caused by endmember variability in SMA. A spectral library covering a large number of endmembers [...] Read more.

Endmember selection is the basis for sub-pixel land cover classifications using multiple endmember spectral mixture analysis (MESMA) that adopts variant endmember matrices for each pixel to mitigate errors caused by endmember variability in SMA. A spectral library covering a large number of endmembers can account for endmember variability, but it also lowers the computational efficiency. Therefore, an efficient endmember selection scheme to optimize the library is crucial to implement MESMA. In this study, we present an endmember selection method based on vector length. The spectra of a land cover class were divided into subsets using vector length intervals of the spectra, and the representative endmembers were derived from these subsets. Compared with the available endmember average RMSE (EAR) method, our approach improved the computational efficiency in endmember selection. The method accuracy was further evaluated using spectral libraries derived from the ground reference polygon and Moderate Resolution Imaging Spectroradiometer (MODIS) imagery respectively. Results using the different spectral libraries indicated that MESMA combined with the new approach performed slightly better than EAR method, with Kappa coefficient improved from 0.75 to 0.78. A MODIS image was used to test the mapping fraction, and the representative spectra based on vector length successfully modeled more than 90% spectra of the MODIS pixels by 2-endmember models. Full article

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15279 KiB

Open AccessArticle

Remote Sensing Based Spatial Statistics to Document Tropical Rainforest Transition Pathways

by Abduwasit Ghulam, Oghlan Ghulam, Maitiniyazi Maimaitijiang, Karen Freeman, Ingrid Porton and Matthew Maimaitiyiming

Remote Sens. 2015, 7(5), 6257-6279; https://doi.org/10.3390/rs70506257 - 20 May 2015

Cited by 36 | Viewed by 7267

Abstract

In this paper, grid cell based spatial statistics were used to quantify the drivers of land-cover and land-use change (LCLUC) and habitat degradation in a tropical rainforest in Madagascar. First, a spectral database of various land-cover and land-use information was compiled using multi-year [...] Read more.

In this paper, grid cell based spatial statistics were used to quantify the drivers of land-cover and land-use change (LCLUC) and habitat degradation in a tropical rainforest in Madagascar. First, a spectral database of various land-cover and land-use information was compiled using multi-year field campaign data and photointerpretation of satellite images. Next, residential areas were extracted from IKONOS-2 and GeoEye-1 images using object oriented feature extraction (OBIA). Then, Landsat Thematic Mapper (TM) and Enhanced Thematic Mapper Plus (ETM+) data were used to generate land-cover and land-use maps from 1990 to 2011, and LCLUC maps were developed with decadal intervals and converted to 100 m vector grid cells. Finally, the causal associations between LCLUC were quantified using ordinary least square regression analysis and Moran’s I, and a forest disturbance index derived from the time series Landsat data were used to further confirm LCLUC drivers. The results showed that (1) local spatial statistical approaches were most effective at quantifying the drivers of LCLUC, and (2) the combined threats of habitat degradation in and around the reserve and increasing encroachment of invasive plant species lead to the expansion of shrubland and mixed forest within the former primary forest, which was echoed by the forest disturbance index derived from the Landsat data. Full article

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Open AccessArticle

Improving Remote Sensing of Aerosol Optical Depth over Land by Polarimetric Measurements at 1640 nm: Airborne Test in North China

by Lili Qie, Zhengqiang Li, Xiaobing Sun, Bin Sun, Donghui Li, Zhao Liu, Wei Huang, Han Wang, Xingfeng Chen, Weizhen Hou and Yanli Qiao

Remote Sens. 2015, 7(5), 6240-6256; https://doi.org/10.3390/rs70506240 - 19 May 2015

Cited by 25 | Viewed by 6211

Abstract

An improved aerosol retrieval algorithm based on the Advanced Multi-angular Polarized Radiometer (AMPR) is presented to illustrate the utility of additional 1640-nm observations for measuring aerosol optical depth (AOD) over land using look-up table approaches. Spectral neutrality of the polarized surface reflectance over [...] Read more.

An improved aerosol retrieval algorithm based on the Advanced Multi-angular Polarized Radiometer (AMPR) is presented to illustrate the utility of additional 1640-nm observations for measuring aerosol optical depth (AOD) over land using look-up table approaches. Spectral neutrality of the polarized surface reflectance over visible to short-wavelength infrared bands is verified, and the 1640-nm measurements corrected for atmospheric effects are used to estimate the polarized surface reflectance at shorter wavelengths. The AMPR measurements over the Beijing-Tianjin-Hebei region in north China reveal that the polarized surface reflectance of 670, 865 and 1640 nm are highly correlated with correlation slopes close to one (0.985 and 1.03) when the scattering angle is less than 145°. The 1640-nm measurements are then employed to estimate polarized surface reflectance at shorter wavelengths for each single viewing direction, which are then used to improve the retrieval of AOD over land. The comparison between AMPR retrievals and ground-based Sun-sky radiometer measurements during three experimental flights illustrates that this approach retrieves AOD at 865 nm with uncertainties ranging from 0.01 to 0.06, while AOD varies from 0.05 to 0.17. Full article

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Open AccessArticle

Surface Shortwave Net Radiation Estimation from FengYun-3 MERSI Data

by Dongdong Wang, Shunlin Liang, Tao He, Yunfeng Cao and Bo Jiang

Remote Sens. 2015, 7(5), 6224-6239; https://doi.org/10.3390/rs70506224 - 19 May 2015

Cited by 19 | Viewed by 5606

Abstract

The Medium-Resolution Spectral Imager (MERSI) is one of the major payloads of China’s second-generation polar-orbiting meteorological satellite, FengYun-3 (FY-3), and it is similar to the Moderate-Resolution Imaging Spectroradiometer (MODIS). The MERSI data are suitable for mapping terrestrial, atmospheric and oceanographic variables at continental [...] Read more.

The Medium-Resolution Spectral Imager (MERSI) is one of the major payloads of China’s second-generation polar-orbiting meteorological satellite, FengYun-3 (FY-3), and it is similar to the Moderate-Resolution Imaging Spectroradiometer (MODIS). The MERSI data are suitable for mapping terrestrial, atmospheric and oceanographic variables at continental to global scales. This study presents a direct-estimation method to retrieve surface shortwave net radiation (SSNR) data from MERSI top-of-atmosphere (TOA) reflectance and cloud mask products. This study is the first attempt to use the MERSI to retrieve SSNR data. Several critical issues concerning remote sensing of SSNR were investigated, including scale effects in validating SSNR data, impacts of the MERSI calibration update on the estimation of SSNR and the dependency of the retrieval accuracy of SSNR data on view geometry. We also incorporated data from twin MODIS sensors to assess how time and the number of satellite overpasses affect the retrieval of SSNR data. Validation against one-year data over seven Surface Radiation Budget Network (SURFRAD) stations showed that the presented algorithm estimated daily SSNR at the original resolution of the MERSI with a root mean square error (RMSE) of 41.9 W/m² and a bias of −1.6 W/m². Aggregated to a spatial resolution of 161 km, the RMSE of MERSI retrievals can be reduced by approximately 10 W/m². Combined with MODIS data, the RMSE of daily SSNR estimation can be further reduced to 22.2 W/m². Compared with that of daily SSNR, estimation of monthly SSNR is less affected by the number of satellite overpasses per day. The RMSE of monthly SSNR from a single MERSI sensor is as small as 13.5 W/m². Full article

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Open AccessArticle

Supervised Vicarious Calibration (SVC) of Multi-Source Hyperspectral Remote-Sensing Data

by Anna Brook and Eyal Ben-Dor

Remote Sens. 2015, 7(5), 6196-6223; https://doi.org/10.3390/rs70506196 - 19 May 2015

Cited by 21 | Viewed by 7138

Abstract

Introduced in 2011, the supervised vicarious calibration (SVC) approach is a promising approach to radiometric calibration and atmospheric correction of airborne hyperspectral (HRS) data. This paper presents a comprehensive study by which the SVC method has been systematically examined and a complete protocol [...] Read more.

Introduced in 2011, the supervised vicarious calibration (SVC) approach is a promising approach to radiometric calibration and atmospheric correction of airborne hyperspectral (HRS) data. This paper presents a comprehensive study by which the SVC method has been systematically examined and a complete protocol for its practical execution has been established—along with possible limitations encountered during the campaign. The technique was applied to multi-sourced HRS data in order to: (1) verify the at-sensor radiometric calibration and (2) obtain radiometric and atmospheric correction coefficients. Spanning two select study sites along the southeast coast of France, data were collected simultaneously by three airborne sensors (AisaDUAL, AHS and CASI-1500i) aboard two aircrafts (CASA of National Institute for Aerospace Technology INTA ES and DORNIER 228 of NERC-ARSF Centre UK). The SVC ground calibration site was assembled along sand dunes near Montpellier and the thematic data were acquired from other areas in the south of France (Salon-de-Provence, Marseille, Avignon and Montpellier) on 28 October 2010 between 12:00 and 16:00 UTC. The results of this study confirm that the SVC method enables reliable inspection and, if necessary, in-situ fine radiometric recalibration of airborne hyperspectral data. Independent of sensor or platform quality, the SVC approach allows users to improve at-sensor data to obtain more accurate physical units and subsequently improved reflectance information. Flight direction was found to be important, whereas the flight altitude posed very low impact. The numerous rules and major outcomes of this experiment enable a new standard of atmospherically corrected data based on better radiometric output. Future research should examine the potential of SVC to be applied to super-and-hyperspectral data obtained from on-orbit sensors. Full article

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Open AccessArticle

Topo-Bathymetric LiDAR for Monitoring River Morphodynamics and Instream Habitats—A Case Study at the Pielach River

by Gottfried Mandlburger, Christoph Hauer, Martin Wieser and Norbert Pfeifer

Remote Sens. 2015, 7(5), 6160-6195; https://doi.org/10.3390/rs70506160 - 19 May 2015

Cited by 118 | Viewed by 17735

Abstract

Airborne LiDAR Bathymetry (ALB) has been rapidly evolving in recent years and now allows fluvial topography to be mapped in high resolution (>20 points/m²) and height accuracy (<10 cm) for both the aquatic and the riparian area. This article presents methods [...] Read more.

Airborne LiDAR Bathymetry (ALB) has been rapidly evolving in recent years and now allows fluvial topography to be mapped in high resolution (>20 points/m²) and height accuracy (<10 cm) for both the aquatic and the riparian area. This article presents methods for enhanced modeling and monitoring of instream meso- and microhabitats based on multitemporal data acquisition. This is demonstrated for a near natural reach of the Pielach River, with data acquired from April 2013 to October 2014, covering two flood events. In comparison with topographic laser scanning, ALB requires a number of specific processing steps. We present, firstly, a novel approach for modeling the water surface in the case of sparse water surface echoes and, secondly, a strategy for improved filtering and modeling of the Digital Terrain Model of the Watercourse (DTM-W). Based on the multitemporal DTM-W we discuss the massive changes of the fluvial topography exhibiting deposition/erosion of 10³ m³ caused by the 30-years flood event in May 2014. Furthermore, for the first time, such a high-resolution data source is used for monitoring of hydro-morphological units (mesohabitat scale) including the consequences for the target fish species nase (Chondrostoma nasus, microhabitat scale). The flood events caused a spatial displacement of the hydro-morphological units but did not effect their overall frequency distribution, which is considered an important habitat feature as it documents resilience against disturbances. Full article

(This article belongs to the Special Issue Remote Sensing and GIS for Habitat Quality Monitoring)

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Open AccessArticle

Ice Freeze-up and Break-up Detection of Shallow Lakes in Northern Alaska with Spaceborne SAR

by Cristina M. Surdu, Claude R. Duguay, Homa Kheyrollah Pour and Laura C. Brown

Remote Sens. 2015, 7(5), 6133-6159; https://doi.org/10.3390/rs70506133 - 18 May 2015

Cited by 38 | Viewed by 8510

Abstract

Shallow lakes, with depths less than ca. 3.5–4 m, are a ubiquitous feature of the Arctic Alaskan Coastal Plain, covering up to 40% of the land surface. With such an extended areal coverage, lakes and their ice regimes represent an important component [...] Read more.

Shallow lakes, with depths less than ca. 3.5–4 m, are a ubiquitous feature of the Arctic Alaskan Coastal Plain, covering up to 40% of the land surface. With such an extended areal coverage, lakes and their ice regimes represent an important component of the cryosphere. The duration of the ice season has major implications for the regional and local climate, as well as for the physical and biogeochemical processes of the lakes. With day and night observations in all weather conditions, synthetic aperture radar (SAR) sensors provide year-round acquisitions. Monitoring the evolution of radar backscatter (σ°) is useful for detecting the timing of the beginning and end of the ice season. Analysis of the temporal evolution of C-band σ° from Advanced Synthetic Aperture Radar (ASAR) Wide Swath and RADARSAT-2 ScanSAR, with a combined frequency of acquisitions from two to five days, was employed to evaluate the potential of SAR to detect the timing of key lake-ice events. SAR observations from 2005 to 2011 were compared to outputs of the Canadian Lake Ice Model (CLIMo). Model simulations fall within similar ranges with those of the SAR observations, with a mean difference between SAR observations and model simulations of only one day for water-clear-of-ice (WCI) from 2006 to 2010. For freeze onset (FO), larger mean differences were observed. SAR analysis shows that the mean FO date for these shallow coastal lakes is 30 September and the mean WCI date is 5 July. Results reveal that greater variability existed in the mean FO date (up to 26 days) than in that of melt onset (MO) (up to 12 days) and in that of WCI (6 days). Additionally, this study also identifies limitations and provides recommendations for future work using C-band SAR for monitoring the lake- ice phenology of shallow Arctic lakes. Full article

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Open AccessArticle

Towards Detection of Cutting in Hay Meadows by Using of NDVI and EVI Time Series

by Andrej Halabuk, Matej Mojses, Marek Halabuk and Stanislav David

Remote Sens. 2015, 7(5), 6107-6132; https://doi.org/10.3390/rs70506107 - 15 May 2015

Cited by 21 | Viewed by 8223

Abstract

The main requirement for preserving European hay meadows in good condition is through prerequisite cut management. However, monitoring these practices on a larger scale is very difficult. Our study analyses the use of MODIS vegetation indices products, namely EVI and NDVI, to discriminate [...] Read more.

The main requirement for preserving European hay meadows in good condition is through prerequisite cut management. However, monitoring these practices on a larger scale is very difficult. Our study analyses the use of MODIS vegetation indices products, namely EVI and NDVI, to discriminate cut and uncut meadows in Slovakia. We tested the added value of simple transformations of raw data series (seasonal statistics, first difference series), compared EVI and NDVI, and analyzed optimal periods, the number of scenes and the effect of smoothing on classification performance. The first difference series transformation saw substantial improvement in classification results. The best case NDVI series classification yielded overall accuracy of 85% with balanced rates of producer’s and user’s accuracies for both classes. EVI yielded slightly lower values, though not significantly different, although user accuracy of cut meadows achieved only 67%. Optimal periods for discriminating cut and uncut meadows lay between 16 May and 4 August, meaning only seven consecutive images are enough to accurately detect cutting in hay meadows. More importantly, the 16-day compositing period seemed to be enough for detection of cutting, which would be the time span that might be hopefully achieved by upcoming on-board HR sensors (e.g., Sentinel-2). Full article

(This article belongs to the Special Issue Remote Sensing and GIS for Habitat Quality Monitoring)

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5272 KiB

Open AccessArticle

Segmentation-Based PolSAR Image Classification Using Visual Features: RHLBP and Color Features

by Jian Cheng, Yaqi Ji and Haijun Liu

Remote Sens. 2015, 7(5), 6079-6106; https://doi.org/10.3390/rs70506079 - 15 May 2015

Cited by 25 | Viewed by 5962

Abstract

A segmentation-based fully-polarimetric synthetic aperture radar (PolSAR) image classification method that incorporates texture features and color features is designed and implemented. This method is based on the framework that conjunctively uses statistical region merging (SRM) for segmentation and support vector machine (SVM) for [...] Read more.

A segmentation-based fully-polarimetric synthetic aperture radar (PolSAR) image classification method that incorporates texture features and color features is designed and implemented. This method is based on the framework that conjunctively uses statistical region merging (SRM) for segmentation and support vector machine (SVM) for classification. In the segmentation step, we propose an improved local binary pattern (LBP) operator named the regional homogeneity local binary pattern (RHLBP) to guarantee the regional homogeneity in PolSAR images. In the classification step, the color features extracted from false color images are applied to improve the classification accuracy. The RHLBP operator and color features can provide discriminative information to separate those pixels and regions with similar polarimetric features, which are from different classes. Extensive experimental comparison results with conventional methods on L-band PolSAR data demonstrate the effectiveness of our proposed method for PolSAR image classification. Full article

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Open AccessArticle

Soil Clay Content Mapping Using a Time Series of Landsat TM Data in Semi-Arid Lands

by Marouen Shabou, Bernard Mougenot, Zohra Lili Chabaane, Christian Walter, Gilles Boulet, Nadhira Ben Aissa and Mehrez Zribi

Remote Sens. 2015, 7(5), 6059-6078; https://doi.org/10.3390/rs70506059 - 15 May 2015

Cited by 71 | Viewed by 13194

Abstract

Clay content (fraction < 2 µm) is one of the most important soil properties. It controls soil hydraulic properties like wilting point, field capacity and saturated hydraulic conductivity, which in turn control the various fluxes of water in the unsaturated zone. In our [...] Read more.

Clay content (fraction < 2 µm) is one of the most important soil properties. It controls soil hydraulic properties like wilting point, field capacity and saturated hydraulic conductivity, which in turn control the various fluxes of water in the unsaturated zone. In our study site, the Kairouan plain in central Tunisia, existing soil maps are neither exhaustive nor sufficiently precise for water balance modeling or thematic mapping. The aim of this work was to produce a clay-content map at fine spatial resolution over the Kairouan plain using a time series of Landsat Thematic Mapper images and to validate the produced map using independent soil samples, existing soil map and clay content produced by TerraSAR-X radar data. Our study was based on 100 soil samples and on a dataset of four Landsat TM data acquired during the summer season. Relationships between textural indices (MID-Infrared) and topsoil clay content were studied for each selected image and were used to produce clay content maps at a spatial resolution of 30 m. Cokriging was used to fill in the gaps created by green vegetation and crop residues masks and to predict clay content of each pixel of the image at 100 m grid spatial resolution. Results showed that mapping clay content using a time series of Landsat TM data is possible and that the produced clay content map presents a reasonable accuracy (R² = 0.65, RMSE = 100 g/kg). The produced clay content map is consistent with existing soil map of the studied region. Comparison with clay content map generated from TerraSAR-X radar data on a small area with no calibration point revealed similarities in topsoil clay content over the largest part of this extract, but significant differences for several areas. In-situ observations at those locations showed that the Landsat TM mapping was more consistent with observations than the TerraSAR-X mapping. Full article

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Open AccessArticle

Phenology-Based Vegetation Index Differencing for Mapping of Rubber Plantations Using Landsat OLI Data

by Hui Fan, Xiaohua Fu, Zheng Zhang and Qiong Wu

Remote Sens. 2015, 7(5), 6041-6058; https://doi.org/10.3390/rs70506041 - 15 May 2015

Cited by 95 | Viewed by 9176

Abstract

Accurate and up-to-date mapping and monitoring of rubber plantations is challenging. In this study, we presented a simple method for rapidly and accurately mapping rubber plantations in the Xishuangbanna region of southwest China using phenology-based vegetation index differencing. Temporal profiles of the Normalized [...] Read more.

Accurate and up-to-date mapping and monitoring of rubber plantations is challenging. In this study, we presented a simple method for rapidly and accurately mapping rubber plantations in the Xishuangbanna region of southwest China using phenology-based vegetation index differencing. Temporal profiles of the Normalized Difference Vegetation Index (NDVI), Enhanced Vegetation Index (EVI), Atmospherically Resistant Vegetation Index (ARVI), Normalized Difference Moisture Index (NDMI), and Tasselled Cap Greenness (TCG) for rubber trees, natural forests and croplands were constructed using 11 Landsat 8 OLI images acquired within one year. These vegetation index time series accurately demonstrated the unique seasonal phenological dynamics of rubber trees. Two distinct phenological phases (i.e., defoliation and foliation) of rubber trees were clearly distinguishable from natural forests and croplands. Rubber trees undergo a brief defoliation-foliation process between late December and mid-March. Therefore, vegetation index differencing between the nearly complete defoliation (leaf-off) and full foliation (leaf flushing) phases was used to delineate rubber plantations within fragmented tropical mountainous landscapes. The method presented herein greatly improved rubber plantation classification accuracy. Overall classification accuracies derived from the differences of the five vegetation indices varied from 92% to 96% with corresponding kappa coefficients of 0.84–0.92. These results demonstrate the promising potential of phenology-based vegetation index differencing for mapping and monitoring rubber expansion at the regional scale. Full article

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Open AccessArticle

MODIS EVI and LST Temporal Response for Discrimination of Tropical Land Covers

by Chittana Phompila, Megan Lewis, Bertram Ostendorf and Kenneth Clarke

Remote Sens. 2015, 7(5), 6026-6040; https://doi.org/10.3390/rs70506026 - 13 May 2015

Cited by 25 | Viewed by 7011

Abstract

MODIS enhanced vegetation index (EVI) and land surface temperature (LST) are key indicators for monitoring vegetation cover changes in broad ecosystems. However, there has been little evaluation of these indices for detecting changes in a range of land covers in tropical regions. In [...] Read more.

MODIS enhanced vegetation index (EVI) and land surface temperature (LST) are key indicators for monitoring vegetation cover changes in broad ecosystems. However, there has been little evaluation of these indices for detecting changes in a range of land covers in tropical regions. In this study, we investigated the characteristics and seasonal responses of LST and EVI for four different land covers in Lao tropical forests: native forest, rubber plantation, mixed wooded/cleared areas and agriculture. We calculated long-term averages of MODIS LST and EVI 16-day time series and compared their monthly transitions over the seven-year period from 2006 to 2012. We also tested whether these indices can be used to classify these four land covers. The findings demonstrate the complex interrelationship of LST and EVI and their monthly transitions for different land covers: they each showed distinctly different intra-annual LST and EVI variations. Native forests have the highest EVI, and the lowest LST throughout the year. In contrast, agricultural areas with little or no vegetation cover have the highest LST. The transition of LST/EVI for the land covers other than native forests showed marked seasonality. Linear discriminant analysis (LDA) showed that there was high overall accuracy of separation of land covers by these indices (86%). The encouraging results indicate that the combined use of MODIS LST and EVI holds promise for improving monitoring of changes in a Lao tropical forest. Full article

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Open AccessArticle

A Remote Sensing Method for Estimating Surface Air Temperature and Surface Vapor Pressure on a Regional Scale

by Renhua Zhang, Yuan Rong, Jing Tian, Hongbo Su, Zhao-Liang Li and Suhua Liu

Remote Sens. 2015, 7(5), 6005-6025; https://doi.org/10.3390/rs70506005 - 13 May 2015

Cited by 36 | Viewed by 6065

Abstract

This paper presents a method of estimating regional distributions of surface air temperature (T_a) and surface vapor pressure (e_a), which uses remotely-sensed data and meteorological data as its inputs. The method takes into account the effects of both local [...] Read more.

This paper presents a method of estimating regional distributions of surface air temperature (T_a) and surface vapor pressure (e_a), which uses remotely-sensed data and meteorological data as its inputs. The method takes into account the effects of both local driving force and horizontal advection on T_a and e_a. Good correlation coefficients (R²) and root mean square error (RMSE) between the measurements of T_a/e_a at weather stations and T_a/e_a estimates were obtained; with R² of 0.77, 0.82 and 0.80 and RMSE of 0.42K, 0.35K and 0.20K for T_a and with R² of 0.85, 0.88, 0.88 and RMSE of 0.24hpa, 0.35hpa and 0.16hpa for e_a, respectively, for the three-day results. This result is much better than that estimated from the inverse distance weighted method (IDW). The performance of T_a/e_a estimates at Dongping Lake illustrated that the method proposed in the paper also has good accuracy for a heterogeneous surface. The absolute biases of T_a and e_a estimates at Dongping Lake from the proposed method are less than 0.5Kand 0.7hpa, respectively, while the absolute biases of them from the IDW method are more than 2K and 3hpa, respectively. Sensitivity analysis suggests that the T_a estimation method presented in the paper is most sensitive to surface temperature and that the e_a estimation method is most sensitive to available energy. Full article

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Open AccessArticle

Image Segmentation Based on Constrained Spectral Variance Difference and Edge Penalty

by Bo Chen, Fang Qiu, Bingfang Wu and Hongyue Du

Remote Sens. 2015, 7(5), 5980-6004; https://doi.org/10.3390/rs70505980 - 13 May 2015

Cited by 44 | Viewed by 8216

Abstract

Segmentation, which is usually the first step in object-based image analysis (OBIA), greatly influences the quality of final OBIA results. In many existing multi-scale segmentation algorithms, a common problem is that under-segmentation and over-segmentation always coexist at any scale. To address this issue, [...] Read more.

Segmentation, which is usually the first step in object-based image analysis (OBIA), greatly influences the quality of final OBIA results. In many existing multi-scale segmentation algorithms, a common problem is that under-segmentation and over-segmentation always coexist at any scale. To address this issue, we propose a new method that integrates the newly developed constrained spectral variance difference (CSVD) and the edge penalty (EP). First, initial segments are produced by a fast scan. Second, the generated segments are merged via a global mutual best-fitting strategy using the CSVD and EP as merging criteria. Finally, very small objects are merged with their nearest neighbors to eliminate the remaining noise. A series of experiments based on three sets of remote sensing images, each with different spatial resolutions, were conducted to evaluate the effectiveness of the proposed method. Both visual and quantitative assessments were performed, and the results show that large objects were better preserved as integral entities while small objects were also still effectively delineated. The results were also found to be superior to those from eCongnition’s multi-scale segmentation. Full article

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Open AccessArticle

Impact of Sowing Date on Yield and Water Use Efficiency of Wheat Analyzed through Spatial Modeling and FORMOSAT-2 Images

by Benoit Duchemin, Rémy Fieuzal, Miguel Augustin Rivera, Jamal Ezzahar, Lionel Jarlan, Julio César Rodriguez, Olivier Hagolle and Christopher Watts

Remote Sens. 2015, 7(5), 5951-5979; https://doi.org/10.3390/rs70505951 - 13 May 2015

Cited by 55 | Viewed by 8393

Abstract

Regional analysis of water use efficiency (WUE) is a relevant method for diagnosing the performance of irrigation systems in water-limited environments. In this study, we investigated the potential of FORMOSAT-2 images to provide spatial estimates of WUE over irrigated wheat crops cultivated within [...] Read more.

Regional analysis of water use efficiency (WUE) is a relevant method for diagnosing the performance of irrigation systems in water-limited environments. In this study, we investigated the potential of FORMOSAT-2 images to provide spatial estimates of WUE over irrigated wheat crops cultivated within the semi-arid Yaqui Valley, in the northwest of Mexico. FORMOSAT-2 provided us with a unique dataset of 36 images at a high resolution (8 m) encompassing the wheat growing season from November 2007 to May 2008. Time series of green leaf area index were derived from these satellite images and used to calibrate a simple crop/water balance model. The method was applied over an 8 × 8 km² irrigated area on up to 530 wheat fields. It allowed us to accurately reproduce the time courses of Leaf Area Index and dry aboveground biomass, as well as evapotranspiration and soil moisture. In a second step, we analyzed the variations of WUE as the ratio of accumulated dry aboveground biomass to seasonal evapotranspiration. Despite the study area being rather small and homogeneous (soil, climate), we observed a large range in wheat biomass production, from 5 to 15 t·ha⁻¹, which was primarily related to the timing of plant emergence. In contrast, the seasonal evapotranspiration only varied from 350 to 450 mm, with no evident link with sowing practices. A significant gain in crop water productivity was found for the fields sown the earliest (maximal WUE around 3.5 kg·m⁻³) compared to those sown the latest (minimal WUE around 1.5 kg·m⁻³). These results demonstrated the value of the FORMOSAT-2 images to provide spatial estimates of crop production and water consumption. The detailed information provided by such high space and time resolution imaging systems is highly valuable to identify agricultural practices that could enlarge crop water productivity. Full article

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Urban Growth Simulation of Atakum (Samsun, Turkey) Using Cellular Automata-Markov Chain and Multi-Layer Perceptron-Markov Chain Models

by Derya Ozturk

Remote Sens. 2015, 7(5), 5918-5950; https://doi.org/10.3390/rs70505918 - 13 May 2015

Cited by 141 | Viewed by 11985

Abstract

In this study, urban growth of the Atakum District in Samsun, Turkey, was simulated by Cellular Automata-Markov Chain (CA-MC) and Multi-layer Perceptron-Markov Chain (MLP-MC) hybrid models in a geographical information system (GIS) environment. Historical land use/land cover (LU/LC) data were extracted from 1989, [...] Read more.

In this study, urban growth of the Atakum District in Samsun, Turkey, was simulated by Cellular Automata-Markov Chain (CA-MC) and Multi-layer Perceptron-Markov Chain (MLP-MC) hybrid models in a geographical information system (GIS) environment. Historical land use/land cover (LU/LC) data were extracted from 1989, 2000 and 2013 Landsat TM/ETM+/OLI images. Using the LU/LC data for the years 1989 and 2000, the urban growth for 2013 was simulated using the CA-MC and MLP-MC models. The simulation results were compared with the 2013 LU/LC data to assess the validity of the simulation. The MLP-MC method provided the best results according to the validation based on the kappa index of agreement. Based on this result, the urban growth for the year 2025 was simulated using MLP-MC. The simulation estimated an urban growth rate of 35.2% between 2013 and 2025, an increase in the area of artificial surfaces from 1681.9 ha to 2274.3 ha and the destruction of 511.7 ha of agricultural land and 4.4 ha of forest. The results of this study demonstrate that the urban growth models provide a better understanding of the current patterns and temporal dynamics and can predict future changes according to past and current dynamics. The results also show that simulations are most accurate when using a model that best conforms to the changes in the given study area. Full article

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Open AccessArticle

Evaluating Different Methods for Grass Nutrient Estimation from Canopy Hyperspectral Reflectance

by Junjie Wang, Tiejun Wang, Andrew K. Skidmore, Tiezhu Shi and Guofeng Wu

Remote Sens. 2015, 7(5), 5901-5917; https://doi.org/10.3390/rs70505901 - 11 May 2015

Cited by 33 | Viewed by 6776

Abstract

The characterization of plant nutrients is important to understand the process of plant growth in natural ecosystems. This study attempted to evaluate the performances of univariate linear regression with various vegetation indices (VIs) and multivariate regression methods in estimating grass nutrients (i.e. [...] Read more.

The characterization of plant nutrients is important to understand the process of plant growth in natural ecosystems. This study attempted to evaluate the performances of univariate linear regression with various vegetation indices (VIs) and multivariate regression methods in estimating grass nutrients (i.e., nitrogen (N) and phosphorus (P)) with canopy hyperspectral reflectance. Synthetically considering predictive accuracy, simplicity, robustness and interpretation, the successive projections algorithm coupled with multiple linear regression (SPA-MLR) method was considered optimal for grass nutrient estimation at the canopy level, when compared with the performances of 12 statistical modeling methods, i.e., univariate linear regression with nine published VIs and three classical multivariate regression methods (stepwise multiple linear regression (SMLR), partial least squares regression (PLSR) and support vector regression (SVR)). The simple ratio index ( , is derivative reflectance) model had comparable performance to SPA-MLR model for P estimation. SPA-MLR provided comparable prediction accuracies with only three first derivative spectral bands for N (715, 731 and 2283 nm) and P (714, 729 and 1319 nm) estimations, compared with PLSR and SVR models, which used the full spectrum. Moreover, SPA-MLR provided robust prediction with the lowest bias values for N (−0.007%) and P (0.001%) estimations, and the fitting line between predicted and measured values was closer to the 1:1 line than the other models. Finally, most of the bands selected by SPA-MLR indirectly relate to foliar chlorophyll content, which suggests good physical interpretation. Full article

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Open AccessArticle

Geostationary Satellite Observation of Precipitable Water Vapor Using an Empirical Orthogonal Function (EOF) based Reconstruction Technique over Eastern China

by Man Sing Wong, Xiaomeng Jin, Zhizhao Liu, Janet Elizabeth Nichol, Shirong Ye, Peng Jiang and Pak Wai Chan

Remote Sens. 2015, 7(5), 5879-5900; https://doi.org/10.3390/rs70505879 - 8 May 2015

Cited by 14 | Viewed by 6833

Abstract

Water vapor, as one of the most important greenhouse gases, is crucial for both climate and atmospheric studies. Considering the high spatial and temporal variations of water vapor, a timely and accurate retrieval of precipitable water vapor (PWV) is urgently needed, but has [...] Read more.

Water vapor, as one of the most important greenhouse gases, is crucial for both climate and atmospheric studies. Considering the high spatial and temporal variations of water vapor, a timely and accurate retrieval of precipitable water vapor (PWV) is urgently needed, but has long been constrained by data availability. Our study derived the vertically integrated precipitable water vapor over eastern China using Multi-functional Transport Satellite (MTSAT) data, which is in geostationary orbit with high temporal resolution. The missing pixels caused by cloud contamination were reconstructed using an Empirical Orthogonal Function (EOF) decomposition method over both spatial and temporal dimensions. GPS meteorology data were used to validate the retrieval and the reconstructed results. The diurnal variation of PWV over eastern China was analyzed using harmonic analysis, which indicates that the reconstructed PWV data can depict the diurnal cycle of PWV caused by evapotranspiration and local thermal circulation. Full article

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Open AccessArticle

Mapping Annual Precipitation across Mainland China in the Period 2001–2010 from TRMM3B43 Product Using Spatial Downscaling Approach

by Yuli Shi, Lei Song, Zhen Xia, Yurong Lin, Ranga B. Myneni, Sungho Choi, Lin Wang, Xiliang Ni, Cailian Lao and Fengkai Yang

Remote Sens. 2015, 7(5), 5849-5878; https://doi.org/10.3390/rs70505849 - 8 May 2015

Cited by 67 | Viewed by 10001

Abstract

Spatially explicit precipitation data is often responsible for the prediction accuracy of hydrological and ecological models. Several statistical downscaling approaches have been developed to map precipitation at a high spatial resolution, which are mainly based on the valid conjugations between satellite-driven precipitation data [...] Read more.

Spatially explicit precipitation data is often responsible for the prediction accuracy of hydrological and ecological models. Several statistical downscaling approaches have been developed to map precipitation at a high spatial resolution, which are mainly based on the valid conjugations between satellite-driven precipitation data and geospatial predictors. Performance of the existing approaches should be first evaluated before applying them to larger spatial extents with a complex terrain across different climate zones. In this paper, we investigate the statistical downscaling algorithms to derive the high spatial resolution maps of precipitation over continental China using satellite datasets, including the Normalized Distribution Vegetation Index (NDVI) from the Moderate Resolution Imaging Spectroradiometer (MODIS), the Global Digital Elevation Model (GDEM) from the Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER), and the rainfall product from the Tropical Rainfall Monitoring Mission (TRMM). We compare three statistical techniques (multiple linear regression, exponential regression, and Random Forest regression trees) for modeling precipitation to better understand how the selected model types affect the prediction accuracy. Then, those models are implemented to downscale the original TRMM product (3B43; 0.25° resolution) onto the finer grids (1 × 1 km²) of precipitation. Finally we validate the downscaled annual precipitation (a wet year 2001 and a dry year 2010) against the ground rainfall observations from 596 rain gauge stations over continental China. The result indicates that the downscaling algorithm based on the Random Forest regression outperforms, when compared to the linear regression and the exponential regression. It also shows that the addition of the residual terms does not significantly improve the accuracy of results for the RF model. The analysis of the variable importance reveals the NDVI related predictors, latitude, and longitude, elevation are key elements for statistical downscaling, and their weights vary across different climate zones. In particular, the NDVI, which is generally considered as a powerful geospatial predictor for precipitation, correlates weakly with precipitation in humid regions. Full article

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Open AccessArticle

Using the Surface Temperature-Albedo Space to Separate Regional Soil and Vegetation Temperatures from ASTER Data

by Lisheng Song, Shaomin Liu, William P. Kustas, Ji Zhou and Yanfei Ma

Remote Sens. 2015, 7(5), 5828-5848; https://doi.org/10.3390/rs70505828 - 8 May 2015

Cited by 14 | Viewed by 7411

Abstract

Soil and vegetation component temperatures in non-isothermal pixels encapsulate more physical meaning and are more applicable than composite temperatures. The component temperatures however are difficult to be obtained from thermal infrared (TIR) remote sensing data provided by single view angle observations. Here, we [...] Read more.

Soil and vegetation component temperatures in non-isothermal pixels encapsulate more physical meaning and are more applicable than composite temperatures. The component temperatures however are difficult to be obtained from thermal infrared (TIR) remote sensing data provided by single view angle observations. Here, we present a land surface temperature and albedo (T-α) space approach combined with the mono-surface energy balance (SEB-1S) model to derive soil and vegetation component temperatures. The T-α space can be established from visible and near infrared (VNIR) and TIR data provided by single view angle observations. This approach separates the soil and vegetation component temperatures from the remotely sensed composite temperatures by incorporating soil wetness iso-lines for defining equivalent soil temperatures; this allows vegetation temperatures to be extracted from the T-α space. This temperature separation methodology was applied to advanced scanning thermal emission and reflection radiometer (ASTER) VNIR and high spatial resolution TIR image data in an artificial oasis area during the entire growing season. Comparisons with ground measurements showed that the T-α space approach produced reliable soil and vegetation component temperatures in the study area. Low root mean square error (RMSE) values of 0.83 K for soil temperatures and 1.64 K for vegetation temperatures, respectively, were obtained, compared to component temperatures measurements from a ground-based thermal camera. These results support the use of soil wetness iso-lines to derive soil surface temperatures. It was also found that the estimated vegetation temperatures were extremely close to the near surface air temperature observations when the landscape is well watered under full vegetation cover. More robust soil and vegetation temperature estimates will improve estimates of soil evaporation and vegetation transpiration, leading to more reliable the monitoring of crop water stress and drought. Full article

(This article belongs to the Special Issue The Development and Validation of Remote Sensing Products for Terrestrial, Hydrological, and Ecological Applications at the Regional Scale)

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7173 KiB

Open AccessArticle

Active Collection of Land Cover Sample Data from Geo-Tagged Web Texts

by Dongyang Hou, Jun Chen, Hao Wu, Songnian Li, Fei Chen and Weiwei Zhang

Remote Sens. 2015, 7(5), 5805-5827; https://doi.org/10.3390/rs70505805 - 7 May 2015

Cited by 17 | Viewed by 8226

Abstract

Sample data plays an important role in land cover (LC) map validation. Traditionally, they are collected through field survey or image interpretation, either of which is costly, labor-intensive and time-consuming. In recent years, massive geo-tagged texts are emerging on the web and they [...] Read more.

Sample data plays an important role in land cover (LC) map validation. Traditionally, they are collected through field survey or image interpretation, either of which is costly, labor-intensive and time-consuming. In recent years, massive geo-tagged texts are emerging on the web and they contain valuable information for LC map validation. However, this kind of special textual data has seldom been analyzed and used for supporting LC map validation. This paper examines the potential of geo-tagged web texts as a new cost-free sample data source to assist LC map validation and proposes an active data collection approach. The proposed approach uses a customized deep web crawler to search for geo-tagged web texts based on land cover-related keywords and string-based rules matching. A data transformation based on buffer analysis is then performed to convert the collected web texts into LC sample data. Using three provinces and three municipalities directly under the Central Government in China as study areas, geo-tagged web texts were collected to validate artificial surface class of China’s 30-meter global land cover datasets (GlobeLand30-2010). A total of 6283 geo-tagged web texts were collected at a speed of 0.58 texts per second. The collected texts about built-up areas were transformed into sample data. User’s accuracy of 82.2% was achieved, which is close to that derived from formal expert validation. The preliminary results show that geo-tagged web texts are valuable ancillary data for LC map validation and the proposed approach can improve the efficiency of sample data collection. Full article

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Remote Sens., Volume 7, Issue 5 (May 2015) – 67 articles , Pages 4973-6509

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