scholarly journals Water vapor mapping by fusing InSAR and GNSS remote sensing data and atmospheric simulations

2015 ◽  
Vol 19 (12) ◽  
pp. 4747-4764 ◽  
Author(s):  
F. Alshawaf ◽  
B. Fersch ◽  
S. Hinz ◽  
H. Kunstmann ◽  
M. Mayer ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Water Vapor ◽  
Data Fusion ◽  
Spatial Resolution ◽  
Heterogeneous Data ◽  
Spatial Density ◽  
Data Sets ◽  
Data Set ◽  
Fixed Rank Kriging ◽  
Fixed Rank

Abstract. Data fusion aims at integrating multiple data sources that can be redundant or complementary to produce complete, accurate information of the parameter of interest. In this work, data fusion of precipitable water vapor (PWV) estimated from remote sensing observations and data from the Weather Research and Forecasting (WRF) modeling system are applied to provide complete grids of PWV with high quality. Our goal is to correctly infer PWV at spatially continuous, highly resolved grids from heterogeneous data sets. This is done by a geostatistical data fusion approach based on the method of fixed-rank kriging. The first data set contains absolute maps of atmospheric PWV produced by combining observations from the Global Navigation Satellite Systems (GNSS) and Interferometric Synthetic Aperture Radar (InSAR). These PWV maps have a high spatial density and a millimeter accuracy; however, the data are missing in regions of low coherence (e.g., forests and vegetated areas). The PWV maps simulated by the WRF model represent the second data set. The model maps are available for wide areas, but they have a coarse spatial resolution and a still limited accuracy. The PWV maps inferred by the data fusion at any spatial resolution show better qualities than those inferred from single data sets. In addition, by using the fixed-rank kriging method, the computational burden is significantly lower than that for ordinary kriging.

scholarly journals Water vapor mapping by fusing InSAR and GNSS remote sensing data and atmospheric simulations

2015 ◽  
Vol 12 (1) ◽  
pp. 363-404
Author(s):  
F. Alshawaf ◽  
B. Fersch ◽  
S. Hinz ◽  
H. Kunstmann ◽  
M. Mayer ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Water Vapor ◽  
Data Fusion ◽  
Spatial Resolution ◽  
Heterogeneous Data ◽  
Spatial Density ◽  
Data Sets ◽  
Data Set ◽  
Fixed Rank Kriging ◽  
Fixed Rank

Abstract. Data fusion aims at integrating multiple data sources that can be redundant or complementary to produce complete, accurate information of the parameter of interest. In this work, data fusion of precipitable water vapor (PWV) estimated from remote sensing observations and data from the Weather Research and Forecasting (WRF) modeling system is applied to provide complete, accurate grids of PWV. Our goal is to infer spatially continuous, precise grids of PWV from heterogeneous data sets. This is done by a geostatistical data fusion approach based on the method of fixed-rank kriging. The first data set contains absolute maps of atmospheric water vapor produced by combining observations from Global Navigation Satellite Systems (GNSS) and Interferometric Synthetic Aperture Radar (InSAR). These PWV maps have a high spatial density and an accuracy of submillimeter; however, data are missing in regions of low coherence (e.g., forests and vegetated areas). The PWV maps simulated by the WRF model represent the second data set. The model maps are available for wide areas, but they have a coarse spatial resolution and a yet limited accuracy. The PWV maps inferred by the data fusion at any spatial resolution are more accurate than those inferred from single data sets. In addition, using the fixed-rank kriging method, the computational burden is significantly lower than that for ordinary kriging.

scholarly journals Land cover change analysis in Mexico using 30m Landsat and 250m MODIS data

2015 ◽  
Vol XL-7/W3 ◽  
pp. 367-374 ◽  
Author(s):  
R. R. Colditz ◽  
R. M. Llamas ◽  
R. A. Ressl
Keyword(s):  
Remote Sensing ◽  
Change Detection ◽  
Spatial Resolution ◽  
Spatial Scales ◽  
Time Interval ◽  
Data Sets ◽  
Change Analysis ◽  
Dimensional Error ◽  
Analysis Technique ◽  
Coarse Spatial Resolution

Change detection is one of the most important and widely requested applications of terrestrial remote sensing. Despite a wealth of techniques and successful studies, there is still a need for research in remote sensing science. This paper addresses two important issues: the temporal and spatial scales of change maps. Temporal scales relate to the time interval between observations for successful change detection. We compare annual change detection maps accumulated over five years against direct change detection over that period. Spatial scales relate to the spatial resolution of remote sensing products. We compare fractions from 30m Landsat change maps to 250m grid cells that match MODIS change products. Results suggest that change detection at annual scales better detect abrupt changes, in particular those that do not persist over a longer period. The analysis across spatial scales strongly recommends the use of an appropriate analysis technique, such as change fractions from fine spatial resolution data for comparison with coarse spatial resolution maps. Plotting those results in bi-dimensional error space and analyzing various criteria, the “lowest cost”, according to a user defined (here hyperbolic) cost function, was found most useful. In general, we found a poor match between Landsat and MODIS-based change maps which, besides obvious differences in the capabilities to detect change, is likely related to change detection errors in both data sets.

scholarly journals Precipitation Trends over the Indus Basin

Climate ◽  
2019 ◽  
Vol 7 (10) ◽  
pp. 116 ◽  
Author(s):  
Nir Y. Krakauer ◽  
Tarendra Lakhankar ◽  
Ghulam H. Dars
Keyword(s):  
Remote Sensing ◽  
Large Population ◽  
Tropical Rainfall Measuring Mission ◽  
Indus Basin ◽  
Data Sets ◽  
Precipitation Trend ◽  
Precipitation Data ◽  
Data Set ◽  
Station Data ◽  
Precipitation Trends

A large population relies on water input to the Indus basin, yet basinwide precipitation amounts and trends are not well quantified. Gridded precipitation data sets covering different time periods and based on either station observations, satellite remote sensing, or reanalysis were compared with available station observations and analyzed for basinwide precipitation trends. Compared to observations, some data sets tended to greatly underestimate precipitation, while others overestimate it. Additionally, the discrepancies between data set and station precipitation showed significant time trends in such cases, suggesting that the precipitation trends of those data sets were not consistent with station data. Among the data sets considered, the station-based Global Precipitation Climatology Centre (GPCC) gridded data set showed good agreement with observations in terms of mean amount, trend, and spatial and temporal pattern. GPCC had average precipitation of about 500 mm per year over the basin and an increase in mean precipitation of about 15% between 1891 and 2016. For the more recent past, since 1958 or 1979, no significant precipitation trend was seen. Among the remote sensing based data sets, the Tropical Rainfall Measuring Mission Multi-Satellite Precipitation Analysis (TMPA) compared best to station observations and, though available for a shorter time period than station-based data sets such as GPCC, may be especially valuable for parts of the basin without station data. The reanalyses tended to have substantial biases in precipitation mean amount or trend relative to the station data. This assessment of precipitation data set quality and precipitation trends over the Indus basin may be helpful for water planning and management.

scholarly journals Harmonizing Multi-Source Remote Sensing Images for Summer Corn Growth Monitoring

2019 ◽  
Vol 11 (11) ◽  
pp. 1266 ◽  
Author(s):  
Mingzheng Zhang ◽  
Dehai Zhu ◽  
Wei Su ◽  
Jianxi Huang ◽  
Xiaodong Zhang ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Time Series ◽  
Kalman Filter ◽  
Spatial Resolution ◽  
Temporal Resolution ◽  
Meteorological Data ◽  
High Temporal Resolution ◽  
Growth Monitoring ◽  
Landsat 8 ◽  
Data Set

Continuous monitoring of crop growth status using time-series remote sensing image is essential for crop management and yield prediction. The growing season of summer corn in the North China Plain with the period of rain and hot, which makes the acquisition of cloud-free satellite imagery very difficult. Therefore, we focused on developing image datasets with both a high temporal resolution and medium spatial resolution by harmonizing the time-series of MOD09GA Normalized Difference Vegetation Index (NDVI) images and 30-m-resolution GF-1 WFV images using the improved Kalman filter model. The harmonized images, GF-1 images, and Landsat 8 images were then combined and used to monitor the summer corn growth from 5th June to 6th October, 2014, in three counties of Hebei Province, China, in conjunction with meteorological data and MODIS Evapotranspiration Data Set. The prediction residuals ( Δ P R K ) in NDVI between the GF-1 observations and the harmonized images was in the range of −0.2 to 0.2 with Gauss distribution. Moreover, the obtained phenological curves manifested distinctive growth features for summer corn at field scales. Changes in NDVI over time were more effectively evaluated and represented corn growth trends, when considered in conjunction with meteorological data and MODIS Evapotranspiration Data Set. We observed that the NDVI of summer corn showed a process of first decreasing and then rising in the early growing stage and discuss how the temperature and moisture of the environment changed with the growth stage. The study demonstrated that the synthesized dataset constructed using this methodology was highly accurate, with high temporal resolution and medium spatial resolution and it was possible to harmonize multi-source remote sensing imagery by the improved Kalman filter for long-term field monitoring.

scholarly journals Geo-Object-Based Land Cover Map Update for High-Spatial-Resolution Remote Sensing Images via Change Detection and Label Transfer

2020 ◽  
Vol 12 (1) ◽  
pp. 174
Author(s):  
Tianjun Wu ◽  
Jiancheng Luo ◽  
Ya’nan Zhou ◽  
Changpeng Wang ◽  
Jiangbo Xi ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Land Cover ◽  
Change Detection ◽  
Spatial Resolution ◽  
High Spatial Resolution ◽  
Data Set ◽  
Label Transfer ◽  
Object Based ◽  
Training Samples ◽  
Map Updating

Land cover (LC) information plays an important role in different geoscience applications such as land resources and ecological environment monitoring. Enhancing the automation degree of LC classification and updating at a fine scale by remote sensing has become a key problem, as the capability of remote sensing data acquisition is constantly being improved in terms of spatial and temporal resolution. However, the present methods of generating LC information are relatively inefficient, in terms of manually selecting training samples among multitemporal observations, which is becoming the bottleneck of application-oriented LC mapping. Thus, the objectives of this study are to speed up the efficiency of LC information acquisition and update. This study proposes a rapid LC map updating approach at a geo-object scale for high-spatial-resolution (HSR) remote sensing. The challenge is to develop methodologies for quickly sampling. Hence, the core step of our proposed methodology is an automatic method of collecting samples from historical LC maps through combining change detection and label transfer. A data set with Chinese Gaofen-2 (GF-2) HSR satellite images is utilized to evaluate the effectiveness of our method for multitemporal updating of LC maps. Prior labels in a historical LC map are certified to be effective in a LC updating task, which contributes to improve the effectiveness of the LC map update by automatically generating a number of training samples for supervised classification. The experimental outcomes demonstrate that the proposed method enhances the automation degree of LC map updating and allows for geo-object-based up-to-date LC mapping with high accuracy. The results indicate that the proposed method boosts the ability of automatic update of LC map, and greatly reduces the complexity of visual sample acquisition. Furthermore, the accuracy of LC type and the fineness of polygon boundaries in the updated LC maps effectively reflect the characteristics of geo-object changes on the ground surface, which makes the proposed method suitable for many applications requiring refined LC maps.

scholarly journals Agricultural Expansion in Mato Grosso from 1986–2000: A Bayesian Time Series Approach to Tracking Past Land Cover Change

2020 ◽  
Vol 12 (4) ◽  
pp. 688 ◽  
Author(s):  
Jacky Lee ◽  
Jeffrey A. Cardille ◽  
Michael T. Coe
Keyword(s):  
Remote Sensing ◽  
Time Series ◽  
Land Cover ◽  
Spatial Resolution ◽  
Processing Speed ◽  
Remote Sensing Data ◽  
Google Earth ◽  
Wide Audience ◽  
Data Set ◽  
Mato Grosso

Landsat 5 has produced imagery for decades that can now be viewed and manipulated in Google Earth Engine, but a general, automated way of producing a coherent time series from these images—particularly over cloudy areas in the distant past—is elusive. Here, we create a land use and land cover (LULC) time series for part of tropical Mato Grosso, Brazil, using the Bayesian Updating of Land Cover: Unsupervised (BULC-U) technique. The algorithm built backward in time from the GlobCover 2009 data set, a multi-category global LULC data set at 300 m resolution for the year 2009, combining it with Landsat time series imagery to create a land cover time series for the period 1986–2000. Despite the substantial LULC differences between the 1990s and 2009 in this area, much of the landscape remained the same: we asked whether we could harness those similarities and differences to recreate an accurate version of the earlier LULC. The GlobCover basis and the Landsat-5 images shared neither a common spatial resolution nor time frame, But BULC-U successfully combined the labels from the coarser classification with the spatial detail of Landsat. The result was an accurate fine-scale time series that quantified the expansion of deforestation in the study area, which more than doubled in size during this time. Earth Engine directly enabled the fusion of these different data sets held in its catalog: its flexible treatment of spatial resolution, rapid prototyping, and overall processing speed permitted the development and testing of this study. Many would-be users of remote sensing data are currently limited by the need to have highly specialized knowledge to create classifications of older data. The approach shown here presents fewer obstacles to participation and allows a wide audience to create their own time series of past decades. By leveraging both the varied data catalog and the processing speed of Earth Engine, this research can contribute to the rapid advances underway in multi-temporal image classification techniques. Given Earth Engine’s power and deep catalog, this research further opens up remote sensing to a rapidly growing community of researchers and managers who need to understand the long-term dynamics of terrestrial systems.

Data Fusion of Different Spatial Resolution Remote Sensing Images Applied to Forest-Type Mapping

2011 ◽  
Vol 49 (12) ◽  
pp. 4977-4986 ◽  
Author(s):  
P. Kempeneers ◽  
F. Sedano ◽  
L. Seebach ◽  
P. Strobl ◽  
J. San-Miguel-Ayanz
Keyword(s):  
Remote Sensing ◽  
Data Fusion ◽  
Spatial Resolution ◽  
Forest Type ◽  
Remote Sensing Images ◽  
Type Mapping

3D ray+Born migration/inversion—Part 2: Application to the SEG/EAGE overthrust experiment

Geophysics ◽  
2003 ◽  
Vol 68 (4) ◽  
pp. 1357-1370 ◽  
Author(s):  
Stéphane Operto ◽  
Gilles Lambaré ◽  
Pascal Podvin ◽  
Philippe Thierry
Keyword(s):  
Spatial Resolution ◽  
Seismic Data ◽  
Velocity Model ◽  
Nonuniform Distribution ◽  
Data Sets ◽  
Data Set ◽  
Quantitative Estimates ◽  
3D Processing ◽  
Type Data ◽  
New Formula

The SEG/EAGE overthrust model is a synthetic onshore velocity model that was used to generate several large synthetic seismic data sets using acoustic finite‐difference modeling. From this database, several realistic subdata sets were extracted and made available for testing 3D processing methods. For example, classic onshore‐type data‐acquisition geometries are available such as a swath acquisition, which is characterized by a nonuniform distribution of long offsets with azimuth and midpoints. In this paper, we present an application of 2.5D and 3D ray‐Born migration/inversion to several classical data sets from the SEG/EAGE overthrust experiment. The method is formulated as a linearized inversion of the scattered wavefield. The method allows quantitative estimates of short wavelength components of the velocity model. First, we apply a 3D migration/inversion formula formerly developed for marine acquisitions to the swath data set. The migrated sections exhibit significant amplitude artifacts and acquisition footprints, also revealed by the shape of the local spatial resolution filters. From the analysis of these spatial resolution filters, we propose a new formula significantly improving the migrated dip section. We also present 3D migrated results for the strike section and a small 3D target containing a channel. Finally, the applications demonstrate, that the ray+Born migration formula must be adapted to the acquisition geometry to obtain reliable estimates of the true amplitude of the model perturbations. This adaptation is relatively straightforward in the frame of the ray+Born formalism and can be guided by the analysis of the resolution operator.

scholarly journals Impact of climate, vegetation, soil and crop management variables on multi-year ISBA-A-gs simulations of evapotranspiration over a Mediterranean crop site

2015 ◽  
Vol 8 (10) ◽  
pp. 3033-3053 ◽  
Author(s):  
S. Garrigues ◽  
A. Olioso ◽  
D. Carrer ◽  
B. Decharme ◽  
J.-C. Calvet ◽  
...  
Keyword(s):  
Spatial Resolution ◽  
Land Surface ◽  
Large Scale ◽  
Surface Model ◽  
Data Sets ◽  
Data Set ◽  
Area Index ◽  
Hydrodynamic Parameters ◽  
Land Surface Parameter ◽  
Crop Succession

Abstract. Generic land surface models are generally driven by large-scale data sets to describe the climate, the soil properties, the vegetation dynamic and the cropland management (irrigation). This paper investigates the uncertainties in these drivers and their impacts on the evapotranspiration (ET) simulated from the Interactions between Soil, Biosphere, and Atmosphere (ISBA-A-gs) land surface model over a 12-year Mediterranean crop succession. We evaluate the forcing data sets used in the standard implementation of ISBA over France where the model is driven by the SAFRAN (Système d'Analyse Fournissant des Renseignements Adaptés à la Nivologie) high spatial resolution atmospheric reanalysis, the leaf area index (LAI) time courses derived from the ECOCLIMAP-II land surface parameter database and the soil texture derived from the French soil database. For climate, we focus on the radiations and rainfall variables and we test additional data sets which include the ERA-Interim (ERA-I) low spatial resolution reanalysis, the Global Precipitation Climatology Centre data set (GPCC) and the MeteoSat Second Generation (MSG) satellite estimate of downwelling shortwave radiations. The evaluation of the drivers indicates very low bias in daily downwelling shortwave radiation for ERA-I (2.5 W m−2) compared to the negative biases found for SAFRAN (−10 W m−2) and the MSG satellite (−12 W m−2). Both SAFRAN and ERA-I underestimate downwelling longwave radiations by −12 and −16 W m−2, respectively. The SAFRAN and ERA-I/GPCC rainfall are slightly biased at daily and longer timescales (1 and 0.5 % of the mean rainfall measurement). The SAFRAN rainfall is more precise than the ERA-I/GPCC estimate which shows larger inter-annual variability in yearly rainfall error (up to 100 mm). The ECOCLIMAP-II LAI climatology does not properly resolve Mediterranean crop phenology and underestimates the bare soil period which leads to an overall overestimation of LAI over the crop succession. The simulation of irrigation by the model provides an accurate irrigation amount over the crop cycle but the timing of irrigation occurrences is frequently unrealistic. Errors in the soil hydrodynamic parameters and the lack of irrigation in the simulation have the largest influence on ET compared to uncertainties in the large-scale climate reanalysis and the LAI climatology. Among climate variables, the errors in yearly ET are mainly related to the errors in yearly rainfall. The underestimation of the available water capacity and the soil hydraulic diffusivity induce a large underestimation of ET over 12 years. The underestimation of radiations by the reanalyses and the absence of irrigation in the simulation lead to the underestimation of ET while the overall overestimation of LAI by the ECOCLIMAP-II climatology induces an overestimation of ET over 12 years. This work shows that the key challenges to monitor the water balance of cropland at regional scale concern the representation of the spatial distribution of the soil hydrodynamic parameters, the variability of the irrigation practices, the seasonal and inter-annual dynamics of vegetation and the spatiotemporal heterogeneity of rainfall.

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