Types Of Satellite Data Research Articles

A Comparison of WorldView-2 and Landsat 8 Images for the Classification of Forests Affected by Bark Beetle Outbreaks Using a Support Vector Machine and a Neural Network: A Case Study in the Sumava Mountains

The objective of this paper is to assess WorldView-2 (WV2) and Landsat OLI (L8) images in the detection of bark beetle outbreaks in the Sumava National Park. WV2 and L8 images were used for the classification of forests infected by bark beetle outbreaks using a Support Vector Machine (SVM) and a Neural Network (NN). After evaluating all the available results, the SVM can be considered the best method used in this study. This classifier achieved the highest overall accuracy and Kappa index for both classified images. In the cases of WV2 and L8, total overall accuracies of 86% and 71% and Kappa indices of 0.84 and 0.66 were achieved with SVM, respectively. The NN algorithm using WV2 also produced very promising results, with over 80% overall accuracy and a Kappa index of 0.79. The methods used in this study may be inspirational for testing other types of satellite data (e.g., Sentinel-2) or other classification algorithms such as the Random Forest Classifier.

Satellite-Based Reconstruction of the Volcanic Deposits during the December 2015 Etna Eruption

Satellite-derived data, including an estimation of the eruption rate, proximal volcanic deposits and lava flow morphometric parameters (area, maximum length, thickness, and volume) are provided for the eruption that occurred at Mt Etna on 6–8 December 2015. This eruption took place at the New Southeast Crater (NSEC), the youngest of the summit craters of Etna, shortly after a sequence of four violent paroxysmal events took place in 65 h (3–5 December) at “Voragine”, the oldest summit crater. Multispectral SEVIRI images at 15 min sampling time have been used to compute time-averaged eruption rate curves, while tri-stereo Pléiades images, at 50 cm spatial resolution, provided the pre-eruptive topography and topographic changes due to volcanic deposits. In addition to the two types of satellite data, other parameters have been inferred, such as probable vesicularity and pyroclastic deposits.

APLIKASI TEKNOLOGI PENGINDERAAN JAUH DAN SISTEM INFORMASI GEOGRAFIS DALAM PEMANTAUAN POTENSI SUMBERDAYA PESISIR DAN LAUT DI KABUPATEN PANGKEP

Pangkep Regency has a large coastal area resource potential which is characterized by a long shoreline line of around 49.71 Km. Potential is spread along the coast which requires up-to-date information, namely position and area which is the basis for optimally and sustainably planning and managing coastal areas. The aim of this study is to map the potential of coastal ecosystem resources and coastal facilities along with their respective areas. This research uses a method with the Remote Sensing Technology and Geographic Information System (GIS) approach. The type of satellite data used is Spot-6 observation of 2018. The results obtained up-to-date data and information related to coastal area ecosystems and coastal facilities, namely mangrove ecosystems (327.22 ha), seagrass ecosystems (197.3 ha), coral reef ecosystems (0 Ha), artificial ecosystems in the form of ponds (13,792 , 57 ha) and beach facilities in the form of seven fishing ports and piers. This data is expected to be a material consideration for policy makers of the Pangkep Regency government in developing and developing their coastal areas in the future.

<italic>A Priori</italic> Surface Reflectance-Based Cloud Shadow Detection Algorithm for Landsat 8 OLI

Prior knowledge of the background land surface reflectance (LSR) constitutes one of the most important factors affecting the precision of cloud shadow detection. To resolve this problem, a surface reflectance-based cloud shadow detection (SRCSD) algorithm is proposed for multitemporal Landsat images. Monthly surface reflectance data sets constructed from MODIS surface reflectance products (MOD09A1) were used to provide the background LSR for cloud shadow detection. Based on the background LSR, the possible variation in the top of atmosphere (TOA) reflectance for each clear pixel can be estimated using the radiative transfer equation under different atmospheric conditions. If a pixel has a smaller TOA reflectance than the minimum value of the possible range under clear conditions, it is identified as being shadow covered. One hundred and twenty-five Landsat 8 Operational Land Imager scenes covered by various surface types were selected to evaluate the feasibility of the algorithm. A validation using manual cloud shadow masks showed that the average producer’s accuracy and user’s accuracy were approximately 0.805 and 0.893, respectively. A comparison of the results of the SRCSD algorithm with those of an object-based cloud shadow detection algorithm (Fmask) recently developed for Landsat images revealed that SRCSD generally detects cloud shadows better than Fmask. The most significant improvement of the SRCSD algorithm is the better detection capability for thin and broken cloud shadows, and this algorithm can be extended to multiple types of satellite data after proper modification.

Up and out: A multifaceted approach to characterizing urbanization in Greater Saigon, 2000–2009

In this case study of Greater Saigon, two types of satellite data are used to estimate the rate of change in urban spatial expansion, both horizontally and volumetrically (horizontal and vertical components), and integrates them with socioeconomic data to examine the correlates and potential causes of both kinds of change. We employ new data products – the Global Human Settlement Layer (GHSL) derived largely from Landsat and a Dense Sampling Method (DSM) product based on QuikSCAT – in combination with data from the 1999 and 2009 censuses of Vietnam. Unlike past studies, we examine horizontal and volumetric changes in urban form and pay particular attention to the role of migration in locations experiencing those different types of change. We find these two types of urban change occur at different rates and in different localities, with the highest rates of horizontal change occurring to the north of administrative Saigon. In contrast, we find the highest rates of volumetric change in the areas north of the central districts but mostly within administrative Saigon. In-migration is strongly associated with horizontal change, whereas increases in population density appears to drive volumetric change, controlling for other factors. Positive volumetric change is associated with necessary amenities of modern urban living, often in high-rise buildings found in dense population centers like Saigon: the increasing presence in households of computers, air conditioners, piped water, and gas fuel. Use of these new integrated data hold promise to shed new light on both the built-environment and social dimensions of urbanization in low- and middle-income settings.

An Integrated Spatio-Spectral–Temporal Sparse Representation Method for Fusing Remote-Sensing Images With Different Resolutions

Different spectral, spatial, and temporal features have been widely used in the remote-sensing image analysis. The further development of multiple sensor remote-sensing technologies has made it necessary to explore new methods of remote-sensing image fusion using different optical image data sets which provide complementary image properties and a tradeoff among spatial, spectral, and temporal resolutions. However, due to problems in assessing correlations between different types of satellite data with different resolutions, a few efforts have been made to explore spatio-spectral–temporal features. For this purpose, we propose a novel sparse representation model to generate synthesized frequent high-spectral and high-spatial resolution data by blending multiple types: spatio-temporal data fusion, spectral–temporal data fusion, spatio-spectral data fusion, and spatio-spectral–temporal data fusion. The proposed method exploits high-spectral correlation across spectral domains and high self-similarity across spatial domains to learn the spatio-spectral fusion basis. Then, it associates temporal changes using a local constraint sparse representation. The integrated spatio-spectral–temporal sparse representation model based on the learned spectral–spatial and temporal change features strengthens the model’s ability to provide high-resolution data needed to address demanding work in real-world applications. Finally, the proposed method is not restricted to a certain type of data, but it can associate any type of remote-sensing data and be applied to dynamic changes in heterogeneous landscapes. The experimental results illustrate the effectiveness and efficiency of the proposed method.

Capabilities of Multispectral Satellite Data in an Assessment of the Status of Abandoned Fire Hazardous and Rewetting Peat Extraction Lands

The capabilities of several multispectral satellite data types to identify the status of peatlands affected by peat extraction and abandoned deposits are examined to assess potential fire dangers and rewetting effectiveness. The available level of detail of describing land/vegetation cover for monitoring abandoned peat extraction sites using Spot-5 HRG, Spot-6 HRG, and Landsat-7 ETM+ satellite images has been demonstrated using the example of peatlands in the Meschera National Park (Vladimir oblast). The results reflect the pros and cons of using different data types to analyze the status of abandoned peat-extraction lands for purposes of peatland inventory, land-cover monitoring, and the prioritization of sites subject to rewetting and mire restoration, as well as for an evaluation of the effectiveness of these measures.

IMAGING GEOMETRY AND POSITIONING ACCURACY OF DUAL SATELLITE STEREO IMAGES: A REVIEW

Abstract. Many previous studies have demonstrated the potential applications of stereo pairs obtained from high-resolution satellites for accurate geo-positioning, but it should be noted that they are based on the conventional use of single-satellite stereo images produced by integrating two images taken by the same satellite. Considering the emergence of various types of satellite data and the requirements to combine these data, it is important to investigate dual-satellite stereo images that integrate two images taken by different satellites. This study reviews the image geometry and positioning accuracy of dual-satellite stereo images based on several important studies, including comparisons with conventional single-satellite stereo images. In particular, the geometric limitation of dual-satellite stereo images often leads to very weak geometry, which degrades the positioning accuracy according to theoretical and experimental studies. Optimal ray intersection can improve the positioning accuracy using dual-satellite stereo images. This review improves our understanding of satellite stereo geometry and the handling of the dual-satellite stereo images, which are expected to become more common in real mapping applications.

Development and evaluation of a lookup-table-based approach to data fusion for seasonal wetlands monitoring: An integrated use of AMSR series, MODIS, and Landsat

Broad scale monitoring of inland waters is essential to research on carbon and water cycles, and for application in the monitoring of disasters including floods and droughts on various spatial and temporal scales. Satellite remote sensing using spatiotemporal data fusion (STF) has recently attracted attention as a way of simultaneously describing spatial heterogeneity and tracking the temporal variability of inland waters. However, existing STF approaches have limitations in describing abrupt temporal changes, integrating “dissimilar” datasets (i.e., fusions between microwave and optical data), and compiling long-term, frequent STF datasets. To overcome these limitations, in this study we developed and evaluated a lookup table (LUT)-based STF, termed database unmixing (DBUX), using multiple types of satellite data (AMSR series, MODIS, and Landsat), and applied it to semi-arid seasonal wetlands in Namibia. The results show that DBUX is: 1) flexible in integrating optical data (MODIS or Landsat) with microwave (AMSR series) and seasonal (day of year) information; 2) able to generate long-term, frequent Landsat-like datasets; and 3) more reliable than an existing approach (spatial and temporal adaptive reflectance fusion model; STARFM) for tracking dynamic temporal variations in seasonal wetlands. Water maps retrieved from the resulting STF dataset for the wetlands had a 30-m spatial resolution and a temporal frequency of 1 or 2days, and the dataset covered from 2002 to 2015. The time series water maps accurately described both seasonal and interannual changes in the wetlands, and could act as a basis for understanding the hydrological features of the region. Further studies are required to enable application of DBUX in other regions, and for other landscapes with different satellite sensor combinations.

Collecting Empirically Derived SAR Characteristic Values over One Year of Sea Ice Environments for Use in Data Assimilation

Abstract A new tool has been developed to calculate dynamic, state-specific tie points, to aid in the assimilation of various types of satellite data into Environment and Climate Change Canada’s Regional Ice Ocean Prediction System. These tie points are referred to as characteristic values (CVs). In this study, CVs are calculated for RadarSat-2 ScanSAR-Wide-A HH-HV backscatter data from October 2010 to September 2011. In this collection, the mean, standard deviation, and percentile distribution of backscatter at locations and times identified as being either ice or open water are represented over different relevant categories affecting the signal. The resulting water CVs are compared with modeled backscatter values, and are in close agreement at midrange wind speeds (5–14 m s−1), where wind slicks are not present. When compared against previously reported values, the ice CVs correspond best for ice conditions with fairly uniform backscatter distributions, such as the Arctic during the spring. When the ice and water CVs are compared to each other, the best cases for the assimilation of RadarSat-2 data are evident. In these cases, the CV distributions at a given incidence angle and wind speed are well separated from each other, such as in the far range (40°–50°) at midrange wind speeds. This set of CVs will be used for an initial assimilation of binary ice and open water retrievals. Future work will include a more complex treatment of ice CVs to address mixed ice types, and the application of CVs to other types of satellite data, including those from passive microwave sensors.

Impact of Satellite Remote Sensing Data on Simulations of Coastal Circulation and Hypoxia on the Louisiana Continental Shelf

We estimated surface salinity flux and solar penetration from satellite data, and performed model simulations to examine the impact of including the satellite estimates on temperature, salinity, and dissolved oxygen distributions on the Louisiana continental shelf (LCS) near the annual hypoxic zone. Rainfall data from the Tropical Rainfall Measurement Mission (TRMM) were used for the salinity flux, and the diffuse attenuation coefficient (Kd) from Moderate Resolution Imaging Spectroradiometer (MODIS) were used for solar penetration. Improvements in the model results in comparison with in situ observations occurred when the two types of satellite data were included. Without inclusion of the satellite-derived surface salinity flux, realistic monthly variability in the model salinity fields was observed, but important inter-annual variability was missed. Without inclusion of the satellite-derived light attenuation, model bottom water temperatures were too high nearshore due to excessive penetration of solar irradiance. In general, these salinity and temperature errors led to model stratification that was too weak, and the model failed to capture observed spatial and temporal variability in water-column vertical stratification. Inclusion of the satellite data improved temperature and salinity predictions and the vertical stratification was strengthened, which improved prediction of bottom-water dissolved oxygen. The model-predicted area of bottom-water hypoxia on the Louisiana shelf, an important management metric, was substantially improved in comparison to observed hypoxic area by including the satellite data.

Developing statistical models for estimating chlorophyll-a and total suspended solid levels at an estuarine reservoir with nutrient inputs from satellite observations

Providing high-resolution monitoring data is essential in promoting decision-making activities for surface water quality protection such as scientific modeling and data analysis. In this study, statistical models using multiple linear regression (MLR) were developed to estimate Chlorophyll-a (Chl-a) and total suspended solid (TSS) concentrations in a mesotrophic reservoir, the Yeongam Reservoir in Korea, from satellite observations. Two types of satellite data that covered different spectral regions, for wavelengths in 412–865nm (for the Geostationary Ocean Color Imager) and those in 405–14,385nm (for the Moderate Resolution Imaging Spectroradiometer), were used as inputs for statistical models, after bias correction. The MLR models for Chl-a and TSS were initially constructed and evaluated with 39 image data-sets obtained during 2011–2014. Subsequently, they compared with their corresponding algorithms that were developed under different environmental settings as well as the CE-QUAL-W2 model, a numerical model of reservoir water quality. Sensitivity analysis showed that specific red and near-infrared wavelengths significantly contributed to improve the accuracy of the Chl-a and TSS estimates, respectively, along with those of blue and green bands typically used. The constructed MLR models showed better performance than the simulation model as well as the classical and recent bio-optical algorithms on average. In particular, poor prediction performance for total nitrogen and total phosphorus caused by a lack of adequate input data and description of transport mechanisms appeared to lower the accuracy of the Chl-a and TSS estimates in the simulation model. Therefore, these results demonstrate that statistical models developed from satellite observations can be used to rapidly screen local water quality and to provide high-quality data for reservoir water quality management.

River plume dynamics in the Kara Sea from altimetry-based lagrangian model, satellite salinity and chlorophyll data

Abstract Two largest Siberian rivers — Ob and Yenisei inflow in the Arctic Ocean in the Kara Sea basin. Mixing of fresh river waters and saline sea waters leads to the formation of the large river plume with typical thickness about ~ 10 m, which can cover a considerable part of the basin. The paper is devoted to the study of interannual variability of the river plume dynamics in the Kara Sea using multi-satellite observations. First, satellite salinity (Aquarius) and chlorophyll-A (MODIS) data for 2011–2014 along with hydrological observations are used in order to identify areas covered by the plume and to validate the satellite measurements. It is shown that both types of satellite data are consistent with in situ observations and provide realistic information about propagation of fresh and turbid plume waters. Second, we introduce particle-tracking model for pathways calculation of the plume propagation that is based on satellite altimetry and wind data reanalysis. The model is successfully validated using Aquarius and MODIS data. Model calculations are used to describe the significant interannual variability of the river plume pathways over twenty-year period (1993–2013) and identify the driving mechanisms of its propagation. It is shown that the plume self-organizing geostrophic circulation, which is induced by the differences in the buoyancy of light plume water and dense ocean water, and changes of prevailing wind direction are the main factors controlling the plume dynamics and its interannual variability.

Costs and benefits of satellite-based tools for irrigation management

This paper presents the results of a collaborative work with farmers and a cost-benefit analysis of geospatial technologies applied to irrigation water management in the semi-arid agricultural area in Lower Austria. We use Earth observation (EO) data to estimate crop evapotranspiration (ET) and webGIS technologies to deliver maps and irrigation advice to farmers. The study reports the technical and qualitative evaluation performed during a demonstration phase in 2013 and provides an outlook to future developments. The calculation of the benefits is based on a comparison of the irrigation volumes estimated from satellite vs. the irrigation supplied by the farmers. In most cases, the amount of water supplied was equal to the maximum amount of water required by crops. At the same time high variability was observed for the different irrigation units and crop types. Our data clearly indicates that economic benefits could be achieved by reducing irrigation volumes, especially for water-intensive crops. Regarding the qualitative evaluation, most of the farmers expressed a very positive interest in the provided information. In particular, information related to crop ET was appreciated as this helps to make better informed decisions on irrigation. The majority of farmers (54%) also expressed a general willingness to pay, either directly or via cost sharing, for such a service. Based on different cost scenarios, we calculated the cost of the service. Considering 20,000 ha regularly irrigated land, the advisory service would cost between 2.5 and 4.3 €/ha per year depending on the type of satellite data used. For comparison, irrigation costs range between 400 and 1000 €/ha per year for a typical irrigation volume of 2,000 cubic meters per ha. With a correct irrigation application, more than 10% of the water and energy could be saved in water-intensive crops, which is equivalent to an economic benefit of 40-100 €/ha per year.

Contrasting glacier variations of Glaciar Perito Moreno and Glaciar Ameghino, Southern Patagonia Icefield

AbstractGlaciar Perito Moreno (GPM) and Glaciar Ameghino (GA), Southern Patagonia Icefield, are in contact in the accumulation area, but have shown contrasting frontal variations in the past few decades. To investigate recent changes of the two glaciers and processes controlling the different responses to similar climate conditions, we measured surface elevation change from 2000 to 2008 and terminus positions from 1999 to 2012 using several types of satellite data. GPM shows no significant changes in terminus position and 0.4 ± 0.3 m a–1 thickening over the period, whereas GA retreated 55 ± 2 m a–1 and thinned 2.6 ± 0.3 m a–1. Mass-balance measurements over the period 1999/2000 show that accumulation at GPM was ten times greater than that at GA, but ablation was only three times greater. The mass-balance–altitude profile is similar for the two glaciers; differences in the mass-balance distribution are caused by differences in the accumulation–area ratio (AAR). Our results suggest that the AAR and the calving flux exert strong control on the evolution of glaciers in the region.

Operational multi-sensor monitoring of turbidity for the entire Mekong Delta

An operational satellite-based approach was implemented to monitor turbidity and organic absorption in the Mekong river system. Using physics-based algorithms linked together in a fully automated processing chain, more than 300 Landsat Enhanced Thematic Mapper (ETM) scenes and 1000 MODIS scenes, representing five years of data, were used to produce standardized, quantitative time series of turbidity and organic absorption across Vietnam, Thailand, Cambodia, Laos, and China. To set up this system, the specific inherent optical properties (SIOPs) of the Mekong river system were determined through three separate field campaigns, laboratory analysis, and subsequent optical closure calculations. Following this, a range of satellite data types was tested using the derived Mekong-specific inherent optical properties, including Moderate Resolution Imaging Spectroradiometer (MODIS) 500 m data, Landsat ETM, Medium Resolution Imaging Spectrometer (MERIS), Satellite Pour l’Observation de la Terre (SPOT) 5, RapidEye, Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER), and QuickBird. The satellite-based turbidity estimates were coincident with available field data, and comparisons showed them to be in good agreement. Overall, the derived SIOPs were suitable for water-quality monitoring of the Mekong, and the MODIS, MERIS, Landsat, and RapidEye sensors were found to be the most radiometrically stable and thereby suitable for ongoing operational processing. The implemented system delivers consistent results across the different satellite sensors and over time, but is limited to where the spatial resolution of the sensor is still able to resolve the river width. The system is currently applicable for the entire Mekong river system, both for near-real-time monitoring and for analysis of historical data archive.

Application of remote sensing to understanding fire regimes and biomass burning emissions of the tropical Andes

AbstractIn the tropical Andes, there have been very few systematic studies aimed at understanding the biomass burning dynamics in the area. This paper seeks to advance on our understanding of burning regimes in this region, with the first detailed and comprehensive assessment of fire occurrence and the derived gross biomass burning emissions of an area of the Peruvian tropical Andes. We selected an area of 2.8 million hectares at altitudes over 2000 m. We analyzed fire occurrence over a 12 year period with three types of satellite data. Fire dynamics showed a large intra‐annual and interannual variability, with most fires occurring May–October (the period coinciding with the dry season). Total area burned decreased with increasing rainfall until a given rainfall threshold beyond which no relationship was found. The estimated fire return interval (FRI) for the area is 37 years for grasslands, which is within the range reported for grasslands, and 65 years for forests, which is remarkably shorter than other reported FRI in tropical moist forests. The greatest contribution (60–70%, depending on the data source) to biomass burning emissions came from burned montane cloud forests (4.5 million Mg CO2 over the study period), despite accounting for only 7.4–10% of the total burned area. Gross aboveground biomass emissions (7.55 ± 2.14 Tg CO2; 0.43 ± 0.04 Tg CO; 24,012 ± 2685 Mg CH4 for the study area) were larger than previously reported for the tropical Andes.

The assimilation of satellite‐derived data into a one‐dimensional lower trophic level marine ecosystem model

Lower trophic level marine ecosystem models are highly dependent on the parameter values given to key rate processes, however many of these are either unknown or difficult to measure. One solution to this problem is to apply data assimilation techniques that optimize key parameter values, however in many cases in situ ecosystem data are unavailable on the temporal and spatial scales of interest. Although multiple types of satellite-derived data are now available with high temporal and spatial resolution, the relative advantages of assimilating different satellite data types are not well known. Here these issues are examined by implementing a lower trophic level model in a one-dimensional data assimilative (variational adjoint) model testbed. A combination of experiments assimilating synthetic and actual satellite-derived data, including total chlorophyll, size-fractionated chlorophyll and particulate organic carbon (POC), reveal that this is an effective tool for improving simulated surface and subsurface distributions both for assimilated and unassimilated variables. Model-data misfits were lowest when parameters were optimized individually at specific sites; however, this resulted in unrealistic overtuned parameter values that deteriorated model skill at times and depths when data were not available for assimilation, highlighting the importance of assimilating data from multiple sites simultaneously. Finally, when chlorophyll data were assimilated without POC, POC simulations still improved, however the reverse was not true. For this two-phytoplankton size class model, optimal results were obtained when satellite-derived size-differentiated chlorophyll and POC were both assimilated simultaneously.

ESTIMATION OF INUNDATED AREAS DURING THE 2011 FLOODS IN THE CHAO PHRAYA RIVER BASIN USING SAR AND OPTICAL SATELLITE IMAGERY

In 2011, massive flooding and inundation in the Chao Phraya River basin, in Thailand, caused serious damage to various activities for a prolonged period of time. Although snapshot images of the inundated area are available, detailed information including temporal changes of the inundated areas and the relationship with meteorological and hydrological conditions are not well documented, particularly for the middle and upper sections of the basin. Therefore, we conducted an analysis using two types of satellite data, HJ-1A and Envisat, to better understand behavior of the large-scale inundation occurred in 2011, focusing on the middle section of the Chao Phraya River basin. In the analysis, water surface in selected domains was extracted using the NDWI value calculated from HJ-1A data. The threshold value of the Envisat ASAR image was then adjusted so that the inundated area estimated from Envisat gives the closest possible match with that estimated from HJ-1A. Finally, the inundated area was estimated for the whole study domain based on the same threshold value from the Envisat data. Results indicated that the inundated area began to extend along the Yom and Nan rivers in early August and continued to spread down to the Nakhon Sawan city area until October. A significant increase in inundated areas occurred between September 2 and September 13, during which higher rainfall intensity was observed. Even after the water level in rivers receded below the bank-full elevation, large areas were left inundated along rivers, particularly over low-lying marsh and paddy fields. In addition, several areas located far from rivers were also inundated, which was likely a consequence of water ponding in paddy fields.

GOCE gravity gradient data for lithospheric modeling

The Gravity field and steady-state Ocean Circulation Explorer (GOCE) is the European Space Agency's (ESA) satellite gravity mission to determine the Earth's mean gravity field. GOCE delivers gravity gradients, a new type of satellite data. We study how these data can improve modeling of the Earth's lithosphere. We discuss the use of the original GOCE gravity gradients versus the use of gravity gradients in grids at satellite altitude or close the Earth's surface and conclude that grids are easier to handle than the original data because one does not have to deal with very different error characteristics of the different gradients, given in a rotating frame at varying heights. The downward continuation to the surface enhances signal and better reflects the near-surface geology. But this does not outweigh the amplification of noise and omission errors, which is why we recommend using the field at mean satellite altitude for lithospheric modeling. The North-East Atlantic region is ideal to analyze the additional value of GOCE gravity gradients because it is a well-studied region in terms of regional geophysics. We calculated the gradient sensitivity for crustal depth slices using a 3D lithospheric model. This reveals that especially interfaces with large density contrasts have a distinct signal in the gravity gradients, but that they are quite insensitive to intra-crustal density sources, which can have quite a large effect on surface gravity data. We also show that the satellite gradients have a depth sensitivity well suited to study the upper mantle density structure, making them complementary to gravity and seismic tomography. In the underexplored Rub’al-Khali area the GOCE vertical gradient was used to invert for crustal thickness. The updated Moho model gives a good fit to four of the six gradients and independent depths from seismic stations. The Moho model was used to update the heat flow model and source rock maturity maps, which are generally consistent with known source rock maturity trends in the surrounding regions. GOCE gradients are therefore useful to map crustal thickness and deep regional structures for frontier areas. In combination with other data, heat flow can be modeled which is essential for basin maturity evaluation.