Fusion Of Satellite Data Research Articles

Retrieval of High Spatiotemporal Resolution Leaf Area Index with Gaussian Processes, Wireless Sensor Network, and Satellite Data Fusion

Many applications, including crop growth and yield monitoring, require accurate long-term time series of leaf area index (LAI) at high spatiotemporal resolution with a quantification of the associated uncertainties. We propose an LAI retrieval approach based on a combination of the LAINet observation system, the Consistent Adjustment of the Climatology to Actual Observations (CACAO) method, and Gaussian process regression (GPR). First, the LAINet wireless sensor network provides temporally continuous field measurements of LAI. Then, the CACAO approach generates synchronous reflectance data at high spatiotemporal resolution (30-m and 8-day) from the fusion of multitemporal MODIS and high spatial resolution Landsat satellite imagery. Finally, the GPR machine learning regression algorithm retrieves the LAI maps and their associated uncertainties. A case study in a cropland site in China showed that the accuracy of LAI retrievals is 0.36 (12.7%) in terms of root mean square error and R2 = 0.88 correlation with ground measurements as evaluated over the entire growing season. This paper demonstrates the potential of the joint use of newly developed software and hardware technologies in deriving concomitant LAI and uncertainty maps with high spatiotemporal resolution. It will contribute to precision agriculture, as well as to the retrieval and validation of LAI products.

COSMO-SkyMed high resolution fully polarimetric synthetic aperture radar exploitation - the ‘strip-spot’ approach

ABSTRACTThis focused study aimed to generate a fully polarimetric synthetic aperture radar (SAR) data set of 1 m resolution based on the spotlight and stripmap COSMO-SkyMed (CSK) satellite data fusion. The results show the feasibility of overcoming the limitation of the nominal 3 m resolution generated by a series of multi-temporal stripmap SAR data observed in all the polarisations. The CSK satellite system does not allow the observation of cross-polar data in the spotlight acquisition mode because only co-polar data are available. In this work, a fully polarimetric scattering matrix is estimated by using two spotlights in the horizontal horizontal (HH) and vertical vertical (VV) polarisations and two stripmaps in the horizontal vertical (HV) and vertical horizontal (VH) polarisations. The stripmaps were resampled and super-resolved by using the amplitude and phase estimation of sinusoids (APES) filter to achieve the spotlight resolution. The results show that the proposed strip-spot approach has immediate operative applications.

Downscaling of Surface Soil Moisture Retrieval by Combining MODIS/Landsat and In Situ Measurements

Soil moisture, especially surface soil moisture (SSM), plays an important role in the development of various natural hazards that result from extreme weather events such as drought, flooding, and landslides. There have been many remote sensing methods for soil moisture retrieval based on microwave or optical thermal infrared (TIR) measurements. TIR remote sensing has been popular for SSM retrieval due to its fine spatial and temporal resolutions. However, because of limitations in the penetration of optical TIR radiation and cloud cover, TIR methods can only be used under clear sky conditions. Microwave SSM retrieval is based on solid physical principles, and has advantages in cases of cloud cover, but it has low spatial resolution. For applications at the local scale, SSM data at high spatial and temporal resolutions are important, especially for agricultural management and decision support systems. Current remote sensing measurements usually have either a high spatial resolution or a high temporal resolution, but not both. This study aims to retrieve SSM at both high spatial and temporal resolutions through the fusion of Moderate Resolution Imaging Spectroradiometer (MODIS) and Land Remote Sensing Satellite (Landsat) data. Based on the universal triangle trapezoid, this study investigated the relationship between land surface temperature (LST) and the normalized difference vegetation index (NDVI) under different soil moisture conditions to construct an improved nonlinear model for SSM retrieval with LST and NDVI. A case study was conducted in Iowa, in the United States (USA) (Lat: 42.2°~42.7°, Lon: −93.6°~−93.2°), from 1 May 2016 to 31 August 2016. Daily SSM in an agricultural area during the crop-growing season was downscaled to 120-m spatial resolution by fusing Landsat 8 with MODIS, with an R2 of 0.5766, and RMSE from 0.0302 to 0.1124 m3/m3.

Retrieval of High Resolution Surface Net Radiation for Urban Area Using Satellite and CFD Model Data Fusion

Retrieval of High Resolution Surface Net Radiation for Urban Area Using Satellite and CFD Model Data Fusion

Aircraft monitoring by the fusion of satellite and ground ADS-B data

The Automatic Dependent Surveillance- Broadcast (ADS-B) system is today a standard equipment on civil aircraft, transmitting periodically data packages containing information of key data such as aircraft ID, position, altitude and intention. It is designed for terrestrial based ground station to monitor air traffic flow in certain regions. Space based ADS-B is the idea to place sensitive receivers on board satellites in orbit, which can receive ADS-B packages and relay them the relevant ground stations. The terrestrial ADS-B receiver has been widely applied for airport information system, help monitor and control traffic flow, etc. However, its coverage is strongly limited by sea or mountain conditions. This paper first introduces the CubeSat mission, then discusses the integrated application of ADS-B data received from ground stations and from satellites, analyze their characteristics with statistical results of comparison, and explore the technologies to fuse these two different data resources for an integrated application. The satellite data is based on a Chinese CubeSat, STU-2C, being launched into space on Sept 25th 2015. The ADS-B data received from two different resources have shown a good complementary each other, such as to increase the coverage of space for air traffic, and to monitor the whole space in a better and complete way.

Fusion of Multi-Source Satellite Data and DEMs to Create a New Glacier Inventory for Novaya Zemlya

Monitoring glacier changes in remote Arctic regions are strongly facilitated by satellite data. This is especially true for the Russian Arctic where recently increased optical and SAR satellite imagery (Landsat 8 OLI, Sentinel 1/2), and digital elevation models (TanDEM-X, ArcticDEM) are becoming available. These datasets offer new possibilities to create high-quality glacier inventories. Here, we present a new glacier inventory derived from a fusion of multi-source satellite data for Novaya Zemlya in the Russian Arctic. We mainly used Landsat 8 OLI data to automatically map glaciers with the band ratio method. Missing debris-covered glacier parts and misclassified lakes were manually corrected. Whereas perennial snow fields were a major obstacle in glacier identification, seasonal snow was identified and removed using Landsat 5 TM scenes from the year 1998. Drainage basins were derived semi-automatically using the ArcticDEM (gap-filled by the ASTER GDEM V2) and manually corrected using fringes from ALOS PALSAR. The new glacier inventory gives a glacierized area of 22,379 ± 246.16 km2 with 1474 glacier entities >0.05 km2. The region is dominated by large glaciers, as 909 glaciers <0.5 km2 (62% by number) cover only 156 ± 1.7 km2 or 0.7% of the area, whereas 49 glaciers >100 km2 (3.3% by number) cover 18,724 ± 205.9 km2 or 84%. In total, 41 glaciers are marine terminating covering an area of 16,063.7 ± 118.8 km2. The mean elevation is 596 m for all glaciers in the study region (528 m in the northern part, 641 in the southern part). South-east (north-west) facing glaciers cover >35% (20%) of the area. For the smaller glaciers in the southern part we calculated an area loss of ~5% (52.5 ± 4.5 km2) from 2001 to 2016.

Dynamic estimation of water hyacinth area using fusion of satellite and GPS sensors

The interaction of water hyacinth area with growth is known to be strongly influenced by area size, but little is known about the interdependent role that size and time have on dynamic estimation of water hyacinth area. We report on the fusion of satellite and GPS sensor data into area growth model as a function of area and time. We employ a multi-sensor fusion technique that is able to generate uniform data of fitting area growth model with complete control of area and time. Evidence of an overall Goodness of Fit Index of 0.9753 was obtained by using conventional statistic analysis. These findings suggest that the multi-sensor fusion technique readily supports area growth model development with highly resolution. The differential equation is good at describing the spatial spread of water hyacinth. Moreover, it was found that area growth model enjoy an appreciable advantage when it comes to harvesting water hyacinth.

Investigating water use over the Choptank River Watershed using a multisatellite data fusion approach

AbstractThe health of the Chesapeake Bay ecosystem has been declining for several decades due to high levels of nutrients and sediments largely tied to agricultural production systems. Therefore, monitoring of agricultural water use and hydrologic connections between crop lands and Bay tributaries has received increasing attention. Remote sensing retrievals of actual evapotranspiration (ET) can provide valuable information in support of these hydrologic modeling efforts, spatially and temporally describing consumptive water use by crops and natural vegetation and quantifying response to expansion of irrigated area occurring with Bay watershed. In this study, a multisensor satellite data fusion methodology, combined with a multiscale ET retrieval algorithm, was applied over the Choptank River watershed located within the Lower Chesapeake Bay region on the Eastern Shore of Maryland, USA to produce daily 30 m resolution ET maps. ET estimates directly retrieved on Landsat satellite overpass dates have high accuracy with relative error (RE) of 9%, as evaluated using flux tower measurements. The fused daily ET time series have reasonable errors of 18% at the daily time step ‐ an improvement from 27% errors using standard Landsat‐only interpolation techniques. Annual water consumption by different land cover types was assessed, showing reasonable distributions of water use with cover class. Seasonal patterns in modeled crop transpiration and soil evaporation for dominant crop types were analyzed, and agree well with crop phenology at field scale. Additionally, effects of irrigation occurring during a period of rainfall shortage were captured by the fusion program. These results suggest that the ET fusion system will have utility for water management at field and regional scales over the Eastern Shore. Further efforts are underway to integrate these detailed water use data sets into watershed‐scale hydrologic models to improve assessments of water quality and inform best management practices to reduce nutrient and sediment loads to the Chesapeake Bay.

Analytical review of European projects LUCAS and CORINE for monitoring and validation of land cover and landuse on the basis of satellite and ground observations and experience of land cover mapping in Ukraine

Analytical review of the European project CORINE (Coordination of Information on the Environment) Land Cover (CLC) and LUCAS (Land Use/Cover Area Frame Statistical Survey) and their use for the monitoring and validation of land cover and land use is fulfilled. The experience of land cover mapping in Ukraine on the basis of developed in the Space Research Institute of NAS of Ukraine and SSA of Ukraine neural network classification methods, heterogeneous satellite data fusion methods and implementation of appropriate automated information technologies within the SIGMA and Sen2Agri European projects are considered.

Object-Based Analysis and Fusion of Optical and SAR Satellite Data for Dwelling Detection in Refugee Camps

This study investigates the potential of very high spatial resolution (VHSR) optical WorldView-2 (WV-2) and single-polarized TerraSAR-X (TSX) synthetic aperture radar (SAR) satellite data for an automated detection of different dwelling types in a refugee camp by means of object-based image analysis (OBIA). First, the optical data and SAR data are analyzed independently, and then a fusion of both data sets is performed applying two different approaches: 1) an overlay operation-based procedure integrating the independent results of the optical- and SAR-based dwelling detection, and 2) a feature-based analysis approach taking advantage of the conjoint analysis of both data sets. The results of the single-sensor and the data fusion approaches are evaluated in detail on the basis of object-based and area-based accuracy assessments. Advantages and limitations of the analysis approaches are discussed. The accuracy rates reveal that the use of optical satellite data shows promising results regardless of the dwelling material, while the SAR data are suitable for the detection of metal sheet dwellings only. In complex camp areas, with closely spaced containers, the results of the independent analyses can be improved significantly by the proposed fusion approaches. The combination of SAR and optical data allows for the separation of contiguous dwellings in cases this was not possible by the optical sensor information

Fusion of satellite, aircraft, and UAV data for automatic disaster damage assessment

Post-disaster damage assessment is urgent for rapid save and rescue missions. Satellite and airborne imagery platforms take vertical images, in which only buildings’ roofs are observable. Unmanned ...

Drought monitoring using high resolution soil moisture through multi-sensor satellite data fusion over the Korean peninsula

Droughts, typically caused by the deficiencies of precipitation and soil moisture, affect water resources and agriculture. As soil moisture is of key importance in understanding the interaction between the atmosphere and Earth’s surface, it can be used to monitor droughts. In this study, a High resolution Soil Moisture Drought Index (HSMDI) was proposed and evaluated for meteorological, agricultural, and hydrological droughts. HSMDI was developed using the 1km downscaled soil moisture data produced from the Advanced Microwave Scanning Radiometer on the Earth Observing System (AMSR-E) from 2003 to 2011 (March to November) over the Korean peninsula. Seven products from the Moderate Resolution Imaging Spectroradiometer (MODIS) and Tropical Rainfall Measuring Mission (TRMM) satellite sensors were used to downscale AMSR-E soil moisture based on random forest machine learning. The downscaled 1km soil moisture was correlated well with both in situ and AMSR-E soil moisture with the mean coefficient of determination (R2) of 0.29 and 0.59, respectively. The Standardized Precipitation Index (SPI) with time scales from 1 to 12 months, crop yields (for sesame, highland radish, and highland napa cabbage) and streamflow data were used to validate HSMDI for various types of droughts. The results showed that HSMDI depicted meteorological drought well, especially during the dry season, with a similar pattern with the 3-month SPI. However, the performance fluctuated a bit during the wet season possibly due to the limited availability of optical sensor data and heterogeneous land covers around the stations. HSMDI also showed high correlation with crop yield data, in particular the highland radish and napa cabbage cultivated in non-irrigated regions with a mean R2 of 0.77. However, HSMDI did not monitor streamflow well for hydrological drought presenting a various range of correlations with streamflow data (from 0.03 to 0.83).

Fusion of satellite, aircraft, and UAV data for automatic disaster damage assessment

ABSTRACTPost-disaster damage assessment is urgent for rapid save and rescue missions. Satellite and airborne imagery platforms take vertical images, in which only buildings’ roofs are observable. Unmanned aerial vehicles (UAVs) provide an alternative platform that can obtain oblique images from a scene but suffer from occlusion (hiding from view) and a smaller field of view. As imagery from each platform has several pros and cons, a fusion framework is required. A new framework is proposed in this article, based on building environment and image processing techniques to utilize the advantages of different platforms while avoiding the disadvantages. In this approach, a facade is detected in UAV oblique images by fusing a geometrical transformation and environmental information from vertical and oblique images. Fusion of roof and facade features is used to improve damage scale estimation. Damage is estimated automatically for both roof-only and facade-plus-roof images that are sourced from satellite and fused data respectively. The proposed approach is applied to several different datasets including the Haiti earthquake of 2010, Hurricane Irene of 2011, Hurricane Sandy of 2012 and the Illinois tornadoes of 2015. The proposed automatic fusion framework led to enhanced damage scale estimation in comparison with the roof-only damage assessment in these cases.

REMOTE SENSING APPLICATIONS IN THE FRAME OF “URBAN GEOLOGY” PROJECT

In the frame of the “Urban Geology” project of IGME a lot of remote sensing applications were carried out: DSMs creation and accuracy verification, orthorectification of very high resolution satellite data, data fusion, multitemporal and multisensor image analysis, land cover and land use change detection e.t.c. The applications that took place in the pilot case of Nafplio are presented in this study

Ocena możliwości wykorzystania satelitarnych danych optycznych i radarowych do identyfikacji typów użytków rolnych

Fusion of satellite data from different sources is a technique commonly used in studies focused on land cover and land use. Combining images of various spectral bands allows to increase objects differentiation and thereby improve overall classification accuracy. In this study, I focused on crops maps creation using integrated optical and radar data. Landsat 8 multispectral data from OLI sensor and Sentinel-1A SAR ( Synthetic Aperture Radar ) data were applied here. The study was performed for a test area of the Żywiec Basin, which is a part of the Polish Carpathians. The advantage of this small, agricultural region was that it is covered by a mosaic of different-size cultivated fields. I tested six methods of satellite data integration ( IHS, HPF, PCA, Brovey, Ehlers and wavelet transforms ) and two classification algorithms ( Support Vector Machines and Random Forest ). The results demonstrated that the use of integrated optical and radar data is effective for crops classification – the highest overall accuracy achieved in this study was equal to 87.9% and was obtained for Random Forest classification and Ehlers fusion.

Comparison and Fusion of Satellite, Airborne, and Terrestrial Gravity Field Data Using Wavelet Decomposition

AbstractA multiresolution wavelet analysis of gravity datasets obtained from satellite, airborne, and terrestrial platforms was conducted to estimate their spectral differences and develop a fused free-air gravity model. In this study, fifth-generation Gravity field and steady-state Ocean Circulation Explorer (GOCE) satellite model (DIRR5 and TIMR5), airborne, and terrestrial gravity data were compared to estimate their differences within various spectral bands using wavelet decomposition. The datasets were combined in a two-dimensional approximation while taking into account their strongest spectral content to derive a fused gravity model that shows improved long and medium wavelengths over those of the original terrestrial gravity dataset. The studies were focused on two distinct areas, namely, the relatively smooth gravity field along the coast of the southeastern United States and the northern Gulf of Mexico and the highly variable gravity field of eastern Alaska. Results show that changes brought by ...

Monitoring daily evapotranspiration over two California vineyards using Landsat 8 in a multi-sensor data fusion approach

Abstract California's Central Valley grows a significant fraction of grapes used for wine production in the United States. With increasing vineyard acreage, reduced water availability in much of California, and competing water use interests, it is critical to be able to monitor regional water use and evapotranspiration (ET) over large areas, but also in detail at individual field scales to improve water management within these viticulture production systems. This can be achieved by integrating remote sensing data from multiple satellite systems with different spatiotemporal characteristics. In this research, we evaluate the utility of a multi-scale system for monitoring ET as applied over two vineyard sites near Lodi, California during the 2013 growing season, leading into the drought in early 2014. The system employs a multi-sensor satellite data fusion methodology (STARFM: Spatial and Temporal Adaptive Reflective Fusion Model) combined with a multi-scale ET retrieval algorithm based on the Two-Source Energy Balance (TSEB) land-surface representation to compute daily ET at 30 m resolution. In this system, TSEB is run using thermal band imagery from the Geostationary Environmental Operational Satellites (GOES; 4-km spatial resolution, hourly temporal sampling), the Moderate Resolution Imaging Spectroradiometer (MODIS) data (1 km resolution, daily acquisition) and the new Landsat 8 satellite (sharpened to 30 m resolution, ~ 16 day acquisition). Estimates of daily ET generated in two neighboring fields of Pinot noir vines of different age agree with ground-based flux measurements acquired in-field during most of the 2013 season with relative mean absolute errors on the order of 19–23% (root mean square errors of approximately 1 mm d − 1 ), reducing to 14–20% at the weekly timestep relevant for irrigation management (~ 5 mm wk − 1 ). A model overestimation of ET in the early season was detected in the younger vineyard, perhaps relating to an inter-row grass cover crop. Spatial patterns of cumulative ET generally correspond to measured yield maps and indicate areas of variable crop moisture, soil condition, and yield within the vineyards that could require adaptive management. The results suggest that multi-sensor remote sensing observations provide a unique means for monitoring crop water use and soil moisture status at field-scales over extended growing regions, and may have value in supporting operational water management decisions in vineyards and other high value crops.

Data Fusion Enables Better Recognition of Ceiling and Visibility Hazards in Aviation

Abstract Low cloud ceilings and poor visibility claim the lives of more general aviation (GA) pilots and passengers than any other cause of weather-related GA accidents. Experience shows that instrument-rated pilots as well as those rated only for visual flight are vulnerable to low ceiling and visibility (C&V), making total avoidance the most powerful strategy available to a GA pilot dealing with these hazards. The weather awareness needed for avoidance begins with the recognition of current conditions. This article outlines how fusion of surface, satellite, and terrain data yields a graphical analysis product that enables GA pilots, dispatchers, and weather briefers to better visualize the areal distribution of recent and current C&V conditions across the contiguous U.S. The product is available at www.aviationweather.gov/adds/cv and indicates ceilings less than 1,000 ft above ground level, visibilities less than 3 statute miles, and regions where terrain obscuration is possible. The product is also viewable in the context of interactive geographic information system data via the experimental Helicopter Emergency Medical Services Tool available at http://weather.aero/tools/desktopapps/hemstool. The authors summarize verification results and outline work toward a next-generation product that incorporates the use of model forecast data and weather camera imagery to improve information in data-sparse regions. This next-generation product is in development for initial use in Alaska.

Evaluating the Efficiency of Multisensor Satellite Data Fusion Based on the Accuracy Level of Land Cover/Use Classification

The term imagery fusion has been used to describe a variety of combining operations performed to increase the ground resolution of multispectral data. The objective of this study was to characterize and evaluate the impact of different pixel-level fusion methods on the accuracy level of land cover/use classification. Panchromatic FORMOSAT-2 data and multispectral SPOT-6 data were considered, and Brovey, Ehlers, and principal component analysis (PCA) algorithms were used as pixel- level fusion algorithms. The improvement in the accuracy of the fused images relative to the original images was determined. The land cover/use categories were classified into five groups by using a maximum likelihood algorithm. To verify and assess the accuracy of classification, training sites were selected for all land cover/use themes. The classification accuracy was calculated for all images by using error matrices. The greatest improvement in land cover/use classification was obtained by using the Brovey algorithm; the overall accuracy was 93.68% and the kappa coefficient was 0.9115. The next greatest improvement was obtained using the Ehlers algorithm, and the overall accuracy and kappa coefficient were 89.54% and 0.8620, respectively. Finally, the least accurate classification was obtained by using the PCA algorithm; the overall accuracy was 88.36% and the kappa coefficient was 0.8247. Comparing the fused images with the original images, the overall accuracy of 86.36% and kappa coefficient value of 0.8036, which were obtained for the original images, were used as benchmarks.

Integrated satellite data fusion and mining for monitoring lake water quality status of the Albufera de Valencia in Spain

Lake eutrophication is a critical issue in the interplay of water supply, environmental management, and ecosystem conservation. Integrated sensing, monitoring, and modeling for a holistic lake water quality assessment with respect to multiple constituents is in acute need. The aim of this paper is to develop an integrated algorithm for data fusion and mining of satellite remote sensing images to generate daily estimates of some water quality parameters of interest, such as chlorophyll a concentrations and water transparency, to be applied for the assessment of the hypertrophic Albufera de Valencia. The Albufera de Valencia is the largest freshwater lake in Spain, which can often present values of chlorophyll a concentration over 200 mg m−3 and values of transparency (Secchi Disk, SD) as low as 20 cm. Remote sensing data from Moderate Resolution Imaging Spectroradiometer (MODIS) and Landsat Thematic Mapper (TM) and Enhance Thematic Mapper (ETM+) images were fused to carry out an integrative near-real time water quality assessment on a daily basis. Landsat images are useful to study the spatial variability of the water quality parameters, due to its spatial resolution of 30 m, in comparison to the low spatial resolution (250/500 m) of MODIS. While Landsat offers a high spatial resolution, the low temporal resolution of 16 days is a significant drawback to achieve a near real-time monitoring system. This gap may be bridged by using MODIS images that have a high temporal resolution of 1 day, in spite of its low spatial resolution. Synthetic Landsat images were fused for dates with no Landsat overpass over the study area. Finally, with a suite of ground truth data, a few genetic programming (GP) models were derived to estimate the water quality using the fused surface reflectance data as inputs. The GP model for chlorophyll a estimation yielded a R2 of 0.94, with a Root Mean Square Error (RMSE) = 8 mg m−3, and the GP model for water transparency estimation using Secchi disk showed a R2 of 0.89, with an RMSE = 4 cm. With this effort, the spatiotemporal variations of water transparency and chlorophyll a concentrations may be assessed simultaneously on a daily basis throughout the lake for environmental management.