The limitations of in situ data for validating satellite-derived spatio-temporal ocean products.

2021 ◽  
Author(s):  
Rory Scarrott ◽  
Fiona Cawkwell ◽  
Mark Jessopp ◽  
Caroline Cusack
Keyword(s):  
Surface Temperature ◽  
Surface Heterogeneity ◽  
Ocean Surface ◽  
Bias Reduction ◽  
Data Validation ◽  
Near Surface ◽  
In Situ Data ◽  
Spatio Temporal ◽  
Image Pixels

<p>The Ocean-surface Heterogeneity MApping (OHMA) algorithm is an objective, replicable approach to map the spatio-temporal heterogeneity of ocean surface waters. It is used to processes hypertemporal, satellite-derived data and produces a single-image surface heterogeneity (SH) dataset for the selected parameter of interest. The product separates regions of dissimilar temporal characteristics. Data validation is challenging because it requires In-situ observations at spatial and temporal resolutions comparable to the hyper-temporal inputs. Validating this spatio-temporal product highlighted the need to optimise existing vessel-based data collection efforts, to maximise exploitation of the rapidly-growing hyper-temporal data resource.</p><p>For this study, the SH was created using hyper-temporal 1km resolution satellite derived Sea Surface Temperature (SST) data acquired in 2011. Underway ship observations of near surface temperature collected on multiple scientific surveys off the Irish coast in 2011 were used to validate the results. The most suitable underway ship SST data were identified in ocean areas sampled multiple times and with representative measurements across all seasons.</p><p>A 3-stage bias reduction approach was applied to identify suitable ocean areas. The first bias reduction addressed temporal bias, i.e., the temporal spread of available In-situ ship transect data across each satellite image pixel. Only pixels for which In-situ data were obtained at least once in each season were selected; resulting in 14 SH image pixels deemed suitable out of a total of 3,677 SH image pixels with available In-situ data. The second bias reduction addressed spatial bias, to reduce the influence of over-sampled areas in an image pixel with a sub-pixel approach. Statistical dispersion measures and statistical shape measures were calculated for each of the sets of sub-pixel values. This gave heterogeneity estimates for each cruise transit of a pixel area. The third bias reduction addressed bias of temporally oversampled seasons. For each transit-derived heterogeneity measure, the values within each season were averaged, before the annual average value was derived across all four seasons in 2011.</p><p>Significant associations were identified between satellite SST-derived SH values, and In-situ heterogeneity measures related to shape; absolute skewness (Spearman’s Rank, n=14, ρ[12]= +0.5755, P<0.05), and kurtosis (Spearman’s Rank, n=14, ρ[12] = 0.5446, P < 0.05). These are a consequence of (i) locally-extreme measurements, and/or (ii) increased presence of sharp transitions detected spatially by In-situ data. However, the number and location of suitable In-situ validation sites precluded a robust validation of the SH dataset (14 pixels located in Irish waters, for a dataset spanning the North Atlantic). This requires more targeted data. The approach would have benefited from more samples over the winter season, which would have enabled more offshore validation sites to be incorporated into the analysis. This is a challenge that faces satellite product developers, who want to deliver spatio-temporal information to new markets. There is a significant opportunity for dedicated, transit-measured (e.g. Ferry box data), validation sites to be established. These could potentially synergise with key nodes in global shipping routes to maximise data collected by vessels of opportunity, and ensure consistent data are collected over the same area at regular intervals.</p>

scholarly journals Comparison of Near-Surface Air Temperatures and MODIS Ice-Surface Temperatures at Summit, Greenland (2008–13)

2014 ◽  
Vol 53 (9) ◽  
pp. 2171-2180 ◽  
Author(s):  
Christopher A. Shuman ◽  
Dorothy K. Hall ◽  
Nicolo E. DiGirolamo ◽  
Thomas K. Mefford ◽  
Michael J. Schnaubelt
Keyword(s):  
Surface Temperature ◽  
Temperature Data ◽  
Quality Data ◽  
High Temporal Resolution ◽  
Near Surface ◽  
Annual Cycles ◽  
In Situ Data ◽  
Ice Surface ◽  
Surface Temperatures

AbstractThe stability of the Moderate Resolution Imaging Spectroradiometer (MODIS) ice-surface temperature (IST) product from Terra was investigated for use as a climate-quality data record. The availability of climate-quality air temperature data TA from a NOAA observatory at Greenland’s Summit Station has enabled this high-temporal-resolution study of MODIS ISTs. During a >5-yr period (July 2008–August 2013), more than 2500 IST values were compared with ±3-min-average TA values from NOAA’s primary 2-m temperature sensor. This enabled an expected small offset between air and ice-sheet surface temperatures (TA > IST) to be investigated over multiple annual cycles. The principal findings of this study show 1) that IST values are slightly colder than the TA values near freezing but that this offset increases as temperature decreases and 2) that there is a pattern in IST–TA differences as the solar zenith angle (SoZA) varies annually. This latter result largely explains the progressive offset from the in situ data at colder temperatures but also indicates that the MODIS cloud mask is less accurate approaching and during the polar night. The consistency of the results over each year in this study indicates that MODIS provides a platform for remotely deriving surface temperature data, with the resulting IST data being most compatible with in situ TA data when the sky is clear and the SoZA is less than ~85°. The ongoing development of the IST dataset should benefit from improved cloud filtering as well as algorithm modifications to account for the progressive offset from TA at colder temperatures.

scholarly journals Ocean-Surface Heterogeneity Mapping (OHMA) to Identify Regions of Change

2021 ◽  
Vol 13 (7) ◽  
pp. 1283
Author(s):  
Rory Gordon Scarrott ◽  
Fiona Cawkwell ◽  
Mark Jessopp ◽  
Caroline Cusack ◽  
Eleanor O’Rourke ◽  
...  
Keyword(s):  
Surface Temperature ◽  
Surface Waters ◽  
Extreme Temperature ◽  
Surface Characteristics ◽  
Surface Heterogeneity ◽  
Ocean Surface ◽  
Future Research ◽  
Temporal Heterogeneity ◽  
Surface Stability ◽  
Spatio Temporal

Mapping heterogeneity of the ocean’s surface waters is important for understanding biogeographical distributions, ocean surface habitat mapping, and ocean surface stability. This article describes the Ocean-surface Heterogeneity MApping (OHMA) algorithm—an objective, replicable approach that uses hypertemporal, satellite-derived datasets to map the spatio-temporal heterogeneity of ocean surface waters. The OHMA produces a suite of complementary datasets—a surface spatio-temporal heterogeneity dataset, and an optimised spatio-temporal classification of the ocean surface. It was demonstrated here using a hypertemporal Sea Surface Temperature image dataset of the North Atlantic. Validation with Underway-derived temperature data showed higher heterogeneity areas were associated with stronger surface temperature gradients, or an increased presence of locally extreme temperature values. Using four exploratory case studies, spatio-temporal heterogeneity values were related to a range of region-specific surface and sub-surface characteristics including fronts, currents and bathymetry. The values conveyed the interactions between these parameters as a single metric. Such over-arching heterogeneity information is virtually impossible to map from in-situ instruments, or less temporally dense satellite datasets. This study demonstrated the OHMA approach is a useful and robust tool to explore, examine, and describe the ocean’s surface. It advances our capability to map biologically relevant measures of ocean surface heterogeneity. It can support ongoing efforts in Ocean Surface Partitioning, and attempts to understand marine species distributions. The study highlighted the need to establish dedicated spatio-temporal ocean validation sites, specifically measured using surface transits, to support advances in hypertemporal ocean data use, and exploitation. A number of future research avenues are also highlighted.

scholarly journals RETRIEVAL OF SEA SURFACE TEMPERATURE OVER POTERAN ISLAND WATER OF INDONESIA WITH LANDSAT 8 TIRS IMAGE: A PRELIMINARY ALGORITHM

2015 ◽  
Vol XL-2/W4 ◽  
pp. 87-90 ◽  
Author(s):  
M. A. Syariz ◽  
L. M. Jaelani ◽  
L. Subehi ◽  
A. Pamungkas ◽  
E. S. Koenhardono ◽  
...  
Keyword(s):  
Surface Temperature ◽  
Sea Surface Temperature ◽  
Brightness Temperature ◽  
Sea Surface ◽  
Coefficient Of Determination ◽  
Landsat 8 ◽  
In Situ Data ◽  
The North ◽  
Thermal Band

The Sea Surface Temperature (SST) retrieval from satellites data Thus, it could provide SST data for a long time. Since, the algorithms of SST estimation by using Landsat 8 Thermal Band are sitedependence, we need to develop an applicable algorithm in Indonesian water. The aim of this research was to develop SST algorithms in the North Java Island Water. The data used are in-situ data measured on April 22, 2015 and also estimated brightness temperature data from Landsat 8 Thermal Band Image (band 10 and band 11). The algorithm was established using 45 data by assessing the relation of measured in-situ data and estimated brightness temperature. Then, the algorithm was validated by using another 40 points. The results showed that the good performance of the sea surface temperature algorithm with coefficient of determination (<i>R</i><sup>2</sup>) and Root Mean Square Error (<i>RMSE</i>) of 0.912 and 0.028, respectively.

scholarly journals Uncertainty Characterization of HOAPS-3.3 Latent Heat Flux Related Parameters

2017 ◽  
Author(s):  
Julian Kinzel ◽  
Marc Schröder ◽  
Karsten Fennig ◽  
Axel Andersson ◽  
Rainer Hollmann
Keyword(s):  
Latent Heat ◽  
Heat Fluxes ◽  
Specific Humidity ◽  
Boreal Winter ◽  
Western Boundary ◽  
Near Surface ◽  
In Situ Data ◽  
Uncertainty Measures ◽  
Global Mean

Abstract. Latent heat fluxes (LHF) are one of the main contributors to the global energy budget. As the density of LHF measurements over the global oceans is generally poor, the potential of remotely sensed LHF for meteorological applications is enormous. However, to date none of the available satellite products include estimates of systematic, random retrieval, and sampling uncertainties, all of which are essential for assessing their quality. Here, this challenge is taken on by applying regionally independent multi-dimensional bias analyses to LHF-related parameters (wind speed U, near-surface specific humidity qa, and sea surface saturation specific humidity qs) of the Hamburg Ocean Atmosphere Parameters and Fluxes from Satellite (HOAPS) climatology. In connection with multiple triple collocation analyses, it is demonstrated how both instantaneous (gridded) uncertainty measures may be assigned to each pixel (grid box). A high-quality in situ data archive including buoys and selected ships serves as the ground reference. Results show that systematic LHF uncertainties range between 15–50 W m-2 with a global mean of 25 W m-2. Local maxima are mainly found over the subtropical ocean basins as well as along the western boundary currents. Investigations indicate that contributions by qa (U) to the overall LHF uncertainty are in the order of 60 % (25 %). From an instantaneous point of view, random retrieval uncertainties are specifically large over the subtropics with a global average of 37 W m-2. In a climatological sense, their magnitudes become negligible, as do respective sampling uncertainties. Time series analyses show footprints of climate events, such as the strong El Niño during 1997/98. Regional and seasonal analyses suggest that largest total (i.e., systematic + instantaneous random) LHF uncertainties are seen over the Gulf Stream and the Indian monsoon region during boreal winter. In light of the uncertainty measures, the observed continuous global mean LHF increase up to 2009 needs to be treated with caution. First intercomparisons to other LHF climatologies (in situ, satellite) reveal overall resemblance with few, yet distinct exceptions.

Evaluating the potential of MODIS-LST for monitoring ground surface temperatures in the Maritime Antarctic (Barton Peninsula, King George Island, Antarctic). 

2021 ◽  
Author(s):  
Alejandro Corbea-Pérez ◽  
Gonçalo Vieira ◽  
Carmen Recondo ◽  
Joana Baptista ◽  
Javier F.Calleja ◽  
...  
Keyword(s):  
Surface Temperature ◽  
Land Surface Temperature ◽  
Land Surface ◽  
Ground Temperature ◽  
King George Island ◽  
Maritime Antarctic ◽  
In Situ Data ◽  
Surface Temperatures ◽  
Barton Peninsula

&lt;p&gt;Land surface temperature is an important factor for permafrost modelling as well as for understanding the dynamics of Antarctic terrestrial ecosystems (Bockheim et al. 2008). In the South Shetland Islands the distribution of permafrost is complex (Vieira et al. 2010) and the use of remote sensing data is essential since the installation and maintenance of an extensive network of ground-based stations are impossible. Therefore, it is important to evaluate the applicability of satellites and sensors by comparing data with in-situ observations. In this work, we present the results from the analysis of land surface temperatures from Barton Peninsula, an ice-free area in King George Island (South Shetlands). We have studied the period from March 1, 2019 to January 31, 2020 using data from the Moderate Resolution Imaging Spectroradiometer (MODIS) Land Surface Temperature (LST) and in-situ data from 6 ground temperature loggers. MOD11A1 and MYD11A1 products, from TERRA and AQUA satellites, respectively, were used, following the application of MODIS quality filters. Given the scarce number of high-quality data as defined by MODIS, all average LST with error &amp;#8804; 2K were included. Dates with surface temperature below -20&amp;#186;C, which are rare in the study area, and dates when the difference between MODIS and in-situ data exceeded 10&amp;#186;C were also examined. In both cases, those days on which MOD09GA/MYD09GA products showed cloud cover were eliminated. Eight in-situ ground temperature measurements per day were available, from which the one nearest to the time of satellite overpass was selected for comparison with MODIS-LST. The results obtained show a better correlation with daytime data than with nighttime data. Specifically, the best results are obtained with daytime data from AQUA (R&lt;sup&gt;2&lt;/sup&gt; between 0.55 and 0.81). With daytime data, correlation between MODIS-LST and in-situ data was verified with relative humidity (RH) values provided by King Sejong weather station, located in the study area. When RH is lower, the correlation between LST and in-situ data improves: we obtained correlation coefficients between 0.6 - 0.7 for TERRA data and 0.8 - 0.9 for AQUA data with RH values lower than 80%. The results suggest that MODIS can be used for temperature estimation in the ice-free areas of the Maritime Antarctic.&lt;/p&gt;&lt;p&gt;References:&lt;/p&gt;&lt;p&gt;Bockheim, J. G., Campbell, I. B., Guglielmin, M., and L&amp;#243;pez- Mart&amp;#305;nez, J.: Distribution of permafrost types and buried ice in ice free areas of Antarctica, in: 9th International Conference on Permafrost, 28 June&amp;#8211;3 July 2008, Proceedings, University of Alaska Press, Fairbanks, USA, 2008, 125&amp;#8211;130.&lt;/p&gt;&lt;p&gt;Vieira, G.; Bockheim, J.; Guglielmin, M.; Balks, M.; Abramov, A. A.; Boelhouwers, J.; Cannone, N.; Ganzert, L.; Gilichinsky, D. A.; Goryachkin, S.; L&amp;#243;pez-Mart&amp;#237;nez, J.; Meiklejohn, I.; Raffi, R.; Ramos, M.; Schaefer, C.; Serrano, E.; Simas, F.; Sletten, R.; Wagner, D. Thermal State of Permafrost and Active-layer Monitoring in the Antarctic: Advances During the International Polar Year 2007-2009. Permafr. Periglac. Process. 2010, 21, 182&amp;#8211;197.&lt;/p&gt;&lt;p&gt;&amp;#160;&lt;/p&gt;&lt;p&gt;Acknowledgements&lt;/p&gt;&lt;p&gt;This work was made possible by an internship at the IGOT, University of Lisbon, Portugal, funded by the Principality of Asturias (code EB20-16).&lt;/p&gt;&lt;p&gt;&amp;#160;&lt;/p&gt;

scholarly journals Spatio-Temporal Validation of AIRS CO2 Observations Using GAW, HIPPO and TCCON

2020 ◽  
Vol 12 (21) ◽  
pp. 3583
Author(s):  
Hui Yang ◽  
Gefei Feng ◽  
Ru Xiang ◽  
Yunjing Xu ◽  
Yong Qin ◽  
...  
Keyword(s):  
Co2 Concentration ◽  
Satellite Observations ◽  
Total Carbon ◽  
Near Surface ◽  
Temporal Validation ◽  
Satellite Sensors ◽  
Spatio Temporal ◽  
The Difference ◽  
Carbon Dioxide Co2

Carbon dioxide (CO2) is a significant atmospheric greenhouse gas and its concentrations can be observed by in situ surface stations, aircraft flights and satellite sensors. This paper investigated the ability of the CO2 satellite observations to monitor, analyze and predict the horizontal and vertical distribution of atmospheric CO2 concentration at global scales. CO2 observations retrieved by an Atmospheric Infrared Sounder (AIRS) were inter-compared with the Global Atmosphere Watch Program (GAW) and HIAPER Pole-to-Pole Observations (HIPPOs), with reference to the measurements obtained using high-resolution ground-based Fourier Transform Spectrometers (FTS) in the Total Carbon Column Observing Network (TCCON) from near-surface level to the mid-to-high troposphere. After vertically integrating the AIRS-retrieved values with the column averaging kernels of TCCON measurements, the AIRS observations are spatio-temporally compared with HIPPO-integrated profiles in the mid-to-high troposphere. Five selected GAW stations are used for comparisons with TCCON sites near the surface of the Earth. The results of AIRS, TCCON (5–6 km), GAW and TCCON (1 km) CO2 measurements from 2007 to 2013 are compared, analyzed and discussed at their respective altitudes. The outcomes indicate that the difference of about 3.0 ppmv between AIRS and GAW or other highly accurate in situ surface measurements is mainly due to the different vertical altitudes, rather than the errors in the AIRS. The study reported here also explores the potential of AIRS satellite observations for analyzing the spatial distribution and seasonal variation of CO2 concentration at global scales.

scholarly journals Evaluation of Collection-6 MODIS Land Surface Temperature Product Using Multi-Year Ground Measurements in an Arid Area of Northwest China

2018 ◽  
Vol 10 (11) ◽  
pp. 1852 ◽  
Author(s):  
Lei Lu ◽  
Tingjun Zhang ◽  
Tiejun Wang ◽  
Xiaoming Zhou
Keyword(s):  
Surface Temperature ◽  
Land Surface Temperature ◽  
Land Surface ◽  
Northwest China ◽  
Bare Soil ◽  
Vegetation Monitoring ◽  
Modis Land Surface Temperature ◽  
Global Circulation Models ◽  
In Situ Data

Moderate Resolution Imaging Spectroradiometer (MODIS) land surface temperature (LST) products are widely used in ecology, hydrology, vegetation monitoring, and global circulation models. Compared to the collection-5 (C5) LST products, the newly released collection-6 (C6) LST products have been refined over bare soil pixels. This study aims to evaluate the C6 MODIS 1-km LST product using multi-year in situ data covering barren surfaces. Evaluation using all in situ data shows that the MODIS C6 LSTs are underestimated with a root-mean-square error (RMSE) of 2.59 K for the site in the Gobi area, 3.05 K for the site in the sand desert area, and 2.86 K for the site in the desert steppe area at daytime. For nighttime LSTs, the RMSEs are 2.01 K, 2.88 K, and 1.80 K for the three sites, respectively. Both biases and RMSEs also show strong seasonal signals. Compared to the error of C5 1-km LSTs, the RMSE of C6 1-km LST product is smaller, especially for daytime LSTs, with a value of 2.24 K compared to 3.51 K. The large errors in the sand desert region are presumably due to the lack of global representativeness of the magnitude of emissivity adjustment and misclassification for the barren surface causing error in emissivities. It indicates that the accuracy of the MODIS C6 LST product might be further improved through emissivity adjustment with globally representative magnitude and accurate land cover classification. From this study, the MODIS C6 1-km LST product is recommended for applications.

scholarly journals Estimate of bias in Aura TES HDO/H<sub>2</sub>O profiles from comparison of TES and in situ HDO/H<sub>2</sub>O measurements at the Mauna Loa observatory

2011 ◽  
Vol 11 (9) ◽  
pp. 4491-4503 ◽  
Author(s):  
J. Worden ◽  
D. Noone ◽  
J. Galewsky ◽  
A. Bailey ◽  
K. Bowman ◽  
...  
Keyword(s):  
Bias Correction ◽  
Trace Gases ◽  
Lower Troposphere ◽  
Vertical Profiles ◽  
Thermal Contrast ◽  
Near Surface ◽  
Mauna Loa ◽  
In Situ Data ◽  
Infrared Radiances

Abstract. The Aura satellite Tropospheric Emission Spectrometer (TES) instrument is capable of measuring the HDO/H2O ratio in the lower troposphere using thermal infrared radiances between 1200 and 1350 cm−1. However, direct validation of these measurements is challenging due to a lack of in situ measured vertical profiles of the HDO/H2O ratio that are spatially and temporally co-located with the TES observations. From 11 October through 5 November 2008, we undertook a campaign to measure HDO and H2O at the Mauna Loa observatory in Hawaii for comparison with TES observations. The Mauna Loa observatory is situated at 3.1 km above sea level or approximately 680 hPa, which is approximately the altitude where the TES HDO/H2O observations show the most sensitivity. Another advantage of comparing in situ data from this site to estimates derived from thermal IR radiances is that the volcanic rock is heated by sunlight during the day, thus providing significant thermal contrast between the surface and atmosphere; this thermal contrast increases the sensitivity to near surface estimates of tropospheric trace gases. The objective of this inter-comparison is to better characterize a bias in the TES HDO data, which had been previously estimated to be approximately 5 % too high for a column integrated value between 850 hPa and 500 hPa. We estimate that the TES HDO profiles should be corrected downwards by approximately 4.8 % and 6.3 % for Versions 3 and 4 of the data respectively. These corrections must account for the vertical sensitivity of the TES HDO estimates. We estimate that the precision of this bias correction is approximately 1.9 %. The accuracy is driven by the corrections applied to the in situ HDO and H2O measurements using flask data taken during the inter-comparison campaign and is estimated to be less than 1 %. Future comparisons of TES data to accurate vertical profiles of in situ measurements are needed to refine this bias estimate.

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