SMART – Space monitoring of Arctic Tundra landscapes

2021 ◽  
Author(s):  
Jennifer Sobiech-Wolf ◽  
Tobias Ullmann ◽  
Wolfgang Dierking
Keyword(s):  
Remote Sensing ◽  
Land Cover ◽  
Land Surface ◽  
In Situ Measurements ◽  
German Research Foundation ◽  
Marine Research ◽  
In Situ Data ◽  
Spatial Coverage ◽  
Infrared Measurements

<p>Satellite remote sensing as well as in-situ measurements are common tools to monitor the state of Arctic environments. However, remote sensing products often lack sufficient temporal and/or spatial resolution, and in-situ measurements can only describe the environmental conditions on a very limited spatial scale. Therefore, we conducted an air-borne campaign to connect the detailed in-situ data with poor spatial coverage to coarse satellite images. The SMART campaign is part of the ongoing project „Characterization of Polar Permafrost Landscapes by Means of Multi-Temporal and Multi-Scale Remote Sensing, and In-Situ Measurements“, funded by the German Research Foundation (DFG).  The focus of the project is to close the gap between in-situ measurements and space-borne images in polar permafrost landscapes. The airborne campaign SMART was conducted in late summer 2018 in north-west Canada, focussing on the Mackenzie-Delta region, which is underlain by permafrost and rarely inhabited. The land cover is either dominated by open Tundra landscapes or by boreal forests. The Polar-5 research-aircraft from the Alfred Wegener Institute, Helmholtz Center for Polar and Marine Research, Germany, was equipped with a ground penetrating radar, a hyperspectral camara, a laserscanner, and an infrared temperature sensor amongst others. In parallel to the airborne acquisition, a team collected in-situ data on ground, including manual active layer depth measurements, geophysical surveying using 2D Electric Resistivity Tomography (ERT), GPR, and mapping of additional land cover properties. The database was completed by a variety of satellite data from different platforms, e.g. MODIS, Landsat, TerraSAR-X and Sentinel-1.  As part of the project, we analysed the performance of MODIS Land surfaces temperature products compared to our air-borne infrared measurements and evaluated, how long the land surface temperatures of this Arctic environment can be considered as stable. It turned out that the MODIS data differ up to 2°C from the air-borne measurements. If this is due to the spatial difference of the measurements or a result of data processing of the MODIS LST products is part of ongoing analysis.</p>

scholarly journals Verification of Accuracy of Unmanned Aerial Vehicle (UAV) Land Surface Temperature Images Using In-Situ Data

2020 ◽  
Vol 12 (2) ◽  
pp. 288 ◽  
Author(s):  
Bonggeun Song ◽  
Kyunghun Park
Keyword(s):  
Land Cover ◽  
Unmanned Aerial Vehicle ◽  
Land Surface ◽  
Urban Areas ◽  
Thermal Characteristics ◽  
In Situ Measurements ◽  
Atmospheric Conditions ◽  
In Situ Data ◽  
Aerial Vehicle

The accuracy of land surface temperatures (LSTs) acquired by an unmanned aerial vehicle (UAV) was verified by comparison with in-situ LSTs of various land cover materials at the Changwon National University Campus, Changwon City, South Korea. UAV imaging and in-situ measurements were performed on 31 July and 2 August 2019. During the in-situ measurements, LST was measured at 160 points using an infrared thermometer. The linear regression model between the UAV and in-situ measurements exhibited a very high correlation on both days, with R2 values greater than 0.7004. The root mean square error (RMSE), however, was 4.030 °C on 31 July and 5.446 °C on 2 August and it also varied depending on the land cover type. These results may depend on various factors, such as the field of view and performance of the TIR (Thermal infrared radiance) camera, as well as the weather and atmospheric conditions. Accurately diagnosing the thermal characteristics of urban areas based on the spatial elements can be used to accurately analyze the thermal characteristics of urban areas and to make effective policy decisions. Techniques for verifying and improving the accuracy of UAV TIR LST data for various land cover materials are required to enable precise investigation of the thermal characteristics of urban areas.

A Sentinel-1 based European crop parcel map using 2018 in-situ LUCAS Copernicus observations

2020 ◽  
Author(s):  
Verhegghen Astrid ◽  
d'Andrimont Raphaël ◽  
Lemoine Guido ◽  
Strobl Peter ◽  
van der Velde Marijn
Keyword(s):  
Remote Sensing ◽  
European Union ◽  
Land Cover ◽  
Growing Season ◽  
Earth Observation ◽  
Soft Wheat ◽  
High Quality ◽  
In Situ Data ◽  
Crop Type

<p>Efficient near-real time and wall-to-wall land monitoring is now possible with unprecedented detail because of the fleet of Copernicus Sentinel satellites. This remote sensing paradigm is the consequence of the freely accessible, global, Copernicus data, combined with affordable cloud computing. However, to translate this capacity in accurate products, and to truly benefit from the high spatial detail (~10m) and temporal resolution (~5 days in constellation) of the Sentinels 1 and 2, high quality and timely in-situ data remains crucial. Robust operational monitoring systems are in need of both training and validation data. </p><p>Here, we demonstrate the potential of Sentinel 1 observations and complementary high-quality in-situ data to generate a crop type map at continental scale. In 2018, the Land Cover and Land Use Area frame Survey (LUCAS) carried out in the European Union contained a specific Copernicus module corresponding to 93.091 polygons surveyed in-situ. In contrast to the usual LUCAS point observation, the Copernicus protocol provides data on the extent of homogeneous land cover for a maximum size of 100 x 100 m, making it meaningful for remote sensing applications. After filtering the polygons to retrieve only high quality sample, a sample was selected to explore the accuracy of crop type maps at different moments of the 2018 growing season over Europe. The time series of 10 days VV and VH were classified using Random Forest models. The crops that were mapped correspond to the 13 major crops in Europe and are those that are monitored and forecast by the JRC MARS activities (soft wheat, maize, rapeseed, barley, potatoes, ...). Overall, reasonable accuracies were obtained (~80%). Although no a priori parcel delineation was used, it was encouraging to observe the relative homogeneity of pixel classification results within the same parcel. In the context of forecasting, we specifically assessed at what time in the growing season accuracies moved beyond a set threshold for the different crops. This ranged from May for winter crops such as soft wheat, and September for summer crops such as maize. </p><p>Our results contribute to the discussion regarding the usefulness, benefits, as well as weaknesses, of the newly acquired LUCAS Copernicus data. Doing so, this study demonstrates the potential of in-situ surveys such as LUCAS Copernicus module  specifically targeted for Earth Observation applications. Future improvements to the LUCAS Copernicus survey methodology are suggested. Importantly, now that LUCAS has been postponed to 2022, and aligned with the Copernicus space program, we advocate for a European Union wide systematic and representative in-situ sample campaign relevant for Earth Observation applications, beyond the traditional LUCAS survey. </p>

scholarly journals Bias-correcting carbon fluxes derived from land-surface satellite data for retrospective and near-real-time assimilation systems

2021 ◽  
Vol 21 (12) ◽  
pp. 9609-9628
Author(s):  
Brad Weir ◽  
Lesley E. Ott ◽  
George J. Collatz ◽  
Stephan R. Kawa ◽  
Benjamin Poulter ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Satellite Data ◽  
Growth Rates ◽  
Land Surface ◽  
Surface Fluxes ◽  
Short Latency ◽  
Satellite Observations ◽  
Seasonal Forecasts ◽  
Spatial Coverage

Abstract. The ability to monitor and understand natural and anthropogenic variability in atmospheric carbon dioxide (CO2) is a growing need of many stakeholders across the world. Systems that assimilate satellite observations, given their short latency and dense spatial coverage, into high-resolution global models are valuable, if not essential, tools for addressing this need. A notable drawback of modern assimilation systems is the long latency of many vital input datasets; for example, inventories, in situ measurements, and reprocessed remote-sensing data can trail the current date by months to years. This paper describes techniques for bias-correcting surface fluxes derived from satellite observations of the Earth's surface to be consistent with constraints from inventories and in situ CO2 datasets. The techniques are applicable in both short-term forecasts and retrospective simulations, thus taking advantage of the coverage and short latency of satellite data while reproducing the major features of long-term inventory and in situ records. Our approach begins with a standard collection of diagnostic fluxes which incorporate a variety of remote-sensing driver data, viz. vegetation indices, fire radiative power, and nighttime lights. We then apply an empirical sink so that global budgets of the diagnostic fluxes match given atmospheric and oceanic growth rates for each year. This step removes coherent, systematic flux errors that produce biases in CO2 which mask the signals an assimilation system hopes to capture. Depending on the simulation mode, the empirical sink uses different choices of atmospheric growth rates: estimates based on observations in retrospective mode and projections based on seasonal forecasts of sea surface temperature in forecasting mode. The retrospective fluxes, when used in simulations with NASA's Goddard Earth Observing System (GEOS), reproduce marine boundary layer measurements with comparable skill to those using fluxes from a modern inversion system. The forecasted fluxes show promising accuracy in their application to the analysis of changes in the carbon cycle as they occur.

Risk of Ground Movement in Faridabad, India – Investigated using Remote Sensing and In-Situ Data

2021 ◽  
Author(s):  
Shagun Garg ◽  
Vamshi Karanam ◽  
Mahdi Motagh ◽  
Indu Jayaluxmi
Keyword(s):  
Remote Sensing ◽  
High Risk ◽  
Population Density ◽  
Land Surface ◽  
Underground Water ◽  
Ground Movement ◽  
Geographical Information ◽  
Groundwater Levels ◽  
In Situ Data

<p>Land surface elevation changes can cause damage to infrastructure and other resources; thus, its monitoring is crucial for the safety and economics of the city. Long-term excessive extraction of underground water is one of the factors that causes ground to sink. Faridabad, the industrial hub of Haryana, a state in north India is staring a severe water crisis in the near future and has already been declared as a dark zone with regard to groundwater resources. At many places, the underground water table has dropped more than 150m. The plummeting groundwater levels and the geology of this region make it prone to subsidence.</p><p>Continuous monitoring of land surface elevations using traditional surveying techniques can be time-consuming and labor-intensive. Several studies have shown the potential of remote sensing techniques in monitoring the changes in topography to an mm level accuracy. In this study, we used the elevation change map (derived using 200+ sentinel -1 images), subsidence gradient, groundwater in-situ data, population, population density, land cover, and lithology. These information were then processed and analyzed in a geographical information system to perform a hazard vulnerability and risk assessment. The final risk map was classified into three different classes viz high, medium, and low risk pertaining to ground movement.</p><p>The results indicate that the high-risk zone covers an area of more than 2.5 square kilometers. New Industrial Town (NIT) in Faridabad with an estimated population of more than 1.5 million, is found to be at high risk of ground movement. Groundwater levels in this area are currently depleting by more than 5m/year. Some other areas which are under high risk are the Dabua colony, Sanjay Gandhi Memorial Nagar, and Gandhi colony. All these regions have a high population density and demand urgent government attention.</p>

Long-term trends of surface reflectance derived from models, satellite and in-situ observations over polar areas

2020 ◽  
Author(s):  
Christian Lanconelli ◽  
Fabrizio Cappucci ◽  
Bernardo Mota ◽  
Nadine Gobron ◽  
Amelie Driemel ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Surface Temperature ◽  
Land Surface Temperature ◽  
Land Surface ◽  
Kendall Test ◽  
Surface Radiation ◽  
In Situ Data ◽  
In Situ Observations

<div> <p>Nowadays, an increasingly amount of remote sensing and in-situ data are extending over decades. They contribute to increase the relevance of long-term studies aimed to deduce the mechanisms underlying the climate change dynamics. The aim of this study is to investigate the coherence between trends of different long-term climate related variables including the surface albedo (A) and land surface temperature (LST) as obtained by remote sensing platforms, models and in-situ observations. </p> </div><div> <p>Directional-hemispherical and bi-hemispherical broadband surface reflectances as derived from MODIS-MCD43 (v006) and MISR, and the analogous products of the Copernicus Global Land (CGLS) and C3S services derived from SPOT-VEGETATION, PROBA-V and AVHRR (v0 and v1), have been harmonized and, together with the ECMWF ERA-5 model, assessed with respect ground data taken over polar areas, over a temporal window spanning the last 20 years.  </p> </div><div> <p>The benchmark was established using in-situ measurements provided from the Baseline Surface Radiation Network (BSRN) over four Arctic and four Antarctic sites. The 1-minute resolution datasets of broadband upwelling and down-welling radiation, have been reduced to directional- and bi-hemispherical reflectances, with the same time scale of satellite products (1-day, 10-days, monthly).  </p> </div><div> <p>A similar approach was used to investigate the fitness for purpose of Land Surface Temperature as derived by MODIS (MOD11), ECMWF ERA-5, with respect to the brightness temperature derived using BSRN measurements over the longwave band.  </p> </div><div> <p>The entire temporal series are decomposed into seasonal and residual components, and then the presence of monotonic significant trends are assessed using the non-parametric Kendall test. Preliminary results shown a strong correlation between negative albedo trends and positive LST trends, especially in arctic regions. </p> </div>

scholarly journals Developing an improved soil moisture dataset by blending passive and active microwave satellite-based retrievals

2010 ◽  
Vol 7 (5) ◽  
pp. 6699-6724 ◽  
Author(s):  
Y. Y. Liu ◽  
R. M. Parinussa ◽  
W. A. Dorigo ◽  
R. A. M. de Jeu ◽  
W. Wagner ◽  
...  
Keyword(s):  
Soil Moisture ◽  
Land Surface ◽  
Land Surface Model ◽  
Cumulative Distribution ◽  
Surface Model ◽  
Carbon Cycles ◽  
In Situ Data ◽  
Spatial Coverage ◽  
Global Soil

Abstract. Combining information derived from satellite-based passive and active microwave sensors has the potential to offer improved retrievals of surface soil moisture variations at global scales. Here we propose a technique to take advantage of retrieval characteristics of passive (AMSR-E) and active (ASCAT) microwave satellite estimates over sparse-to-moderately vegetated areas to obtain an improved soil moisture product. To do this, absolute soil moisture values from AMSR-E and relative soil moisture derived from ASCAT are rescaled against a reference land surface model date set using a cumulative distribution function (CDF) matching approach. While this technique imposes the bias of the reference to the rescaled satellite products, it adjusts both satellite products to the same range and almost preserves the correlation between satellite products and in situ measurements. Comparisons with in situ data demonstrated that over the regions where the correlation coefficient between rescaled AMSR-E and ASCAT is above 0.65 (hereafter referred to as transitional regions), merging the different satellite products together increases the number of observations while minimally changing the accuracy of soil moisture retrievals. These transitional regions also delineate the boundary between sparsely and moderately vegetated regions where rescaled AMSR-E and ASCAT are respectively used in the merged product. Thus the merged product carries the advantages of better spatial coverage overall and increased number of observations particularly for the transitional regions. The combination approach developed in this study has the potential to be applied to existing microwave satellites as well as to new microwave missions. Accordingly, a long-term global soil moisture dataset can be developed and extended, enhancing basic understanding of the role of soil moisture in the water, energy and carbon cycles.

scholarly journals Validation and Comparison of Water Quality Products in Baltic Lakes Using Sentinel-2 MSI and Sentinel-3 OLCI Data

Sensors ◽  
2020 ◽  
Vol 20 (3) ◽  
pp. 742 ◽  
Author(s):  
Tuuli Soomets ◽  
Kristi Uudeberg ◽  
Dainis Jakovels ◽  
Agris Brauns ◽  
Matiss Zagars ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Water Quality ◽  
Inland Waters ◽  
Water Type ◽  
Water Types ◽  
In Situ Data ◽  
Spatial Coverage ◽  
Level 2 ◽  
Sentinel 2

Inland waters, including lakes, are one of the key points of the carbon cycle. Using remote sensing data in lake monitoring has advantages in both temporal and spatial coverage over traditional in-situ methods that are time consuming and expensive. In this study, we compared two sensors on different Copernicus satellites: Multispectral Instrument (MSI) on Sentinel-2 and Ocean and Land Color Instrument (OLCI) on Sentinel-3 to validate several processors and methods to derive water quality products with best performing atmospheric correction processor applied. For validation we used in-situ data from 49 sampling points across four different lakes, collected during 2018. Level-2 optical water quality products, such as chlorophyll-a and the total suspended matter concentrations, water transparency, and the absorption coefficient of the colored dissolved organic matter were compared against in-situ data. Along with the water quality products, the optical water types were obtained, because in lakes one-method-to-all approach is not working well due to the optical complexity of the inland waters. The dynamics of the optical water types of the two sensors were generally in agreement. In most cases, the band ratio algorithms for both sensors with optical water type guidance gave the best results. The best algorithms to obtain the Level-2 water quality products were different for MSI and OLCI. MSI always outperformed OLCI, with R2 0.84–0.97 for different water quality products. Deriving the water quality parameters with optical water type classification should be the first step in estimating the ecological status of the lakes with remote sensing.

scholarly journals Remote Sensing Satellite Imagery and In-Situ Data for Identifying Geothermal Potential Sites: Jaboi, Indonesia

2020 ◽  
Vol 9 (2) ◽  
pp. 237-245
Author(s):  
Muhammad Isa ◽  
Dwiky Pobri Cesarian ◽  
Ismail Ahmad Abir ◽  
Elin Yusibani ◽  
Muhammad Syukri Surbakti ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Land Surface ◽  
Drainage Density ◽  
Ground Temperature ◽  
Landsat 8 ◽  
Large Area ◽  
In Situ Data ◽  
Geothermal Potential ◽  
Electrical Sounding

Remote sensing makes it possible to map potential geothermal site for a large area effectively using thermal infrared. The purpose of the present research is to overlay ground temperature, resistivity and satellite retrieved temperature in identifying geothermal potential site in Jaboi, Sabang-Indonesia. The data of acquisition of the DEM imagery was January 3rd, 2009 and the Landsat 8 imagery is July 18th, 2017. The satellite data were applied to extract the land surface temperature and land classification across. Two supporting data in situ were used to validate the results from remote sensing. First dataset was ground temperature measurements with total 114 points and second dataset was vertical electrical sounding (VES) with total of 51 points. Satellite, VES and ground temperature data were processed and analysed using the Envi 5.3, PCI Geomatica 2016 and ArcMap 10.4. The results from each data were integrated to produce a map shows geothermal potential. Its integration produced four areas which were considered to have high geothermal potential. However, these areas vary in term of the clustering of the features of interest, for example lineament and drainage density of the area, high temperature in the surface area, fault existence and low resistivity subsurface. All the features must take into consideration to rank potential area which has higher potential. Finally, a map of geothermal potential across were successfully created as an insight for future reference. ©2020. CBIORE-IJRED. All rights reserved

scholarly journals HSRL-2 aerosol optical measurements and microphysical retrievals vs. airborne in situ measurements during DISCOVER-AQ 2013: an intercomparison study

2016 ◽  
Author(s):  
Patricia Sawamura ◽  
Richard H. Moore ◽  
Sharon P. Burton ◽  
Eduard Chemyakin ◽  
Detlef Müller ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Surface Area ◽  
Mie Theory ◽  
In Situ Measurements ◽  
Optical Measurements ◽  
High Spectral Resolution ◽  
Size Distributions ◽  
In Situ Data ◽  
Intercomparison Study

Abstract. Over 700 vertically-resolved retrievals of effective radii, number, volume, and surface-area concentrations of aerosols obtained from inversion of airborne multiwavelength High Spectral Resolution Lidar (HSRL-2) measurements are compared to vertically resolved airborne in situ measurements obtained during DISCOVER-AQ campaign from 2013 in California and Texas. In situ measurements of dry and humidified scattering, dry absorption, and dry size distributions are used to estimate hygroscopic adjustments which, in turn, are applied to the dry in situ measurements before comparison to HSRL-2 measurements and retrievals. The HSRL-2 retrievals of size parameters agree well with the in situ measurements once the hygroscopic adjustments are applied to the latter, with biases smaller than 25 % for surface-area concentrations, and smaller than 10 % for volume concentration. A closure study is performed by comparing the extinction and backscatter measured with the HSRL-2 with those calculated from the in situ size distributions and Mie theory, once refractive indices (at ambient RH) and hygroscopic adjustments are calculated and applied. The results of this closure study revealed discrepancies between the HSRL-2 optical measurements and those calculated from in situ measurements, in both California and Texas datasets, with the aerosol extinction and backscatter coefficients measured with the HSRL-2 being larger than those calculated from the adjusted in situ measurements and Mie theory. These discrepancies are further investigated and discussed in light of the many challenges often present in closure studies between in situ and remote sensing systems, such as: limitations in covering the same size range of particles with in situ and remote sensing instruments, as well as simplified parameterizations and assumptions used when dry in situ data are adjusted to account for aerosol hygroscopicity.

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