scholarly journals Filling the gaps in meteorological continuous data measured at FLUXNET sites with ERA-interim reanalysis

2015 ◽  
Vol 8 (1) ◽  
pp. 23-55 ◽  
Author(s):  
N. Vuichard ◽  
D. Papale
Keyword(s):  
Standard Deviation ◽  
Land Surface ◽  
Meteorological Data ◽  
Internal Variability ◽  
Fair Use ◽  
Meteorological Variable ◽  
Continuous Data ◽  
High Temporal Resolution ◽  
In Situ Data

Abstract. Exchanges of carbon, water and energy between the land surface and the atmosphere are monitored by eddy covariance technique at the ecosystem level. Currently, the FLUXNET database contains more than 500 sites registered and up to 250 of them sharing data (Free Fair Use dataset). Many modelling groups use the FLUXNET dataset for evaluating ecosystem model's performances but it requires uninterrupted time series for the meteorological variables used as input. Because original in-situ data often contain gaps, from very short (few hours) up to relatively long (some months), we develop a new and robust method for filling the gaps in meteorological data measured at site level. Our approach has the benefit of making use of continuous data available globally (ERA-interim) and high temporal resolution spanning from 1989 to today. These data are however not measured at site level and for this reason a method to downscale and correct the ERA-interim data is needed. We apply this method on the level 4 data (L4) from the LaThuile collection, freely available after registration under a Fair-Use policy. The performances of the developed method vary across sites and are also function of the meteorological variable. On average overall sites, the bias correction leads to cancel from 10 to 36% of the initial mismatch between in-situ and ERA-interim data, depending of the meteorological variable considered. In comparison to the internal variability of the in-situ data, the root mean square error (RMSE) between the in-situ data and the un-biased ERA-I data remains relatively large (on average overall sites, from 27 to 76% of the standard deviation of in-situ data, depending of the meteorological variable considered). The performance of the method remains low for the wind speed field, in particular regarding its capacity to conserve a standard deviation similar to the one measured at FLUXNET stations. The ERA-interim reanalysis data debiased at FLUXNET sites can be downloaded from the PANGAEA data center (http://doi.pangaea.de/10.1594/PANGAEA.838234).

scholarly journals Filling the gaps in meteorological continuous data measured at FLUXNET sites with ERA-Interim reanalysis

2015 ◽  
Vol 7 (2) ◽  
pp. 157-171 ◽  
Author(s):  
N. Vuichard ◽  
D. Papale
Keyword(s):  
Standard Deviation ◽  
Land Surface ◽  
Meteorological Data ◽  
Fair Use ◽  
Meteorological Variable ◽  
Continuous Data ◽  
High Temporal Resolution ◽  
Data Set ◽  
In Situ Data

Abstract. Exchanges of carbon, water and energy between the land surface and the atmosphere are monitored by eddy covariance technique at the ecosystem level. Currently, the FLUXNET database contains more than 500 registered sites, and up to 250 of them share data (free fair-use data set). Many modelling groups use the FLUXNET data set for evaluating ecosystem models' performance, but this requires uninterrupted time series for the meteorological variables used as input. Because original in situ data often contain gaps, from very short (few hours) up to relatively long (some months) ones, we develop a new and robust method for filling the gaps in meteorological data measured at site level. Our approach has the benefit of making use of continuous data available globally (ERA-Interim) and a high temporal resolution spanning from 1989 to today. These data are, however, not measured at site level, and for this reason a method to downscale and correct the ERA-Interim data is needed. We apply this method to the level 4 data (L4) from the La Thuile collection, freely available after registration under a fair-use policy. The performance of the developed method varies across sites and is also function of the meteorological variable. On average over all sites, applying the bias correction method to the ERA-Interim data reduced the mismatch with the in situ data by 10 to 36 %, depending on the meteorological variable considered. In comparison to the internal variability of the in situ data, the root mean square error (RMSE) between the in situ data and the unbiased ERA-I (ERA-Interim) data remains relatively large (on average over all sites, from 27 to 76 % of the standard deviation of in situ data, depending on the meteorological variable considered). The performance of the method remains poor for the wind speed field, in particular regarding its capacity to conserve a standard deviation similar to the one measured at FLUXNET stations. The ERA-Interim reanalysis data de-biased at FLUXNET sites can be downloaded from the PANGAEA data centre (http://doi.pangaea.de/10.1594/PANGAEA.838234).

scholarly journals A New Approach to Defining Uncertainties for MODIS Land Surface Temperature

2019 ◽  
Vol 11 (9) ◽  
pp. 1021 ◽  
Author(s):  
Darren Ghent ◽  
Karen Veal ◽  
Tim Trent ◽  
Emma Dodd ◽  
Harjinder Sembhi ◽  
...  
Keyword(s):  
Standard Deviation ◽  
Surface Temperature ◽  
Land Surface Temperature ◽  
Land Surface ◽  
Uncertainty Budget ◽  
In Situ Data ◽  
Uncertainty Model ◽  
Absolute Bias ◽  
Moderate Resolution Imaging Spectroradiometer

The accuracy of land surface temperature (LST) observations is critical to many applications. Any observation of LST is subject to incomplete knowledge, so an accurate assessment of the uncertainty budget is critical. We present a comprehensive and consistent approach to determining an uncertainty budget for LST products. We apply this approach to the Moderate Resolution Imaging Spectroradiometer (MODIS) instrument on-board the Aqua satellite. In order to generate the uncertainty model, a new implementation of the generalised split-window algorithm is applied, in which retrieval coefficients are categorised by viewing angle and water vapour. Validation of the LST against in situ data shows a mean absolute bias of 0.37 K for daytime and 0.73 K for nighttime. The average standard deviation per site is 1.53 K for daytime and 1.21 K for nighttime. Uncertainties from the implemented model are estimates in their own right and are also validated. We do this by comparing the standard deviation of the differences between the satellite and in situ LSTs, and the total uncertainties of the validation matchups. We show that the uncertainty model provides a good fit. Our approach offers a framework for quantifying uncertainties for LST that is equally applicable across different sensors and different retrieval approaches.

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

<p>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 ≤ 2K were included. Dates with surface temperature below -20ºC, which are rare in the study area, and dates when the difference between MODIS and in-situ data exceeded 10ºC 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<sup>2</sup> 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.</p><p>References:</p><p>Bockheim, J. G., Campbell, I. B., Guglielmin, M., and López- Martınez, J.: Distribution of permafrost types and buried ice in ice free areas of Antarctica, in: 9th International Conference on Permafrost, 28 June–3 July 2008, Proceedings, University of Alaska Press, Fairbanks, USA, 2008, 125–130.</p><p>Vieira, G.; Bockheim, J.; Guglielmin, M.; Balks, M.; Abramov, A. A.; Boelhouwers, J.; Cannone, N.; Ganzert, L.; Gilichinsky, D. A.; Goryachkin, S.; López-Martínez, 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, 182–197.</p><p> </p><p>Acknowledgements</p><p>This work was made possible by an internship at the IGOT, University of Lisbon, Portugal, funded by the Principality of Asturias (code EB20-16).</p><p> </p>

scholarly journals An Effective Similar-Pixel Reconstruction of the High-Frequency Cloud-Covered Areas of Southwest China

2019 ◽  
Vol 11 (3) ◽  
pp. 336 ◽  
Author(s):  
Wenping Yu ◽  
Junlei Tan ◽  
Mingguo Ma ◽  
Xiaolu Li ◽  
Xiaojun She ◽  
...  
Keyword(s):  
Land Surface ◽  
High Frequency ◽  
Southwest China ◽  
Global Climate ◽  
Average Error ◽  
Balance Theory ◽  
High Temporal Resolution ◽  
Retrieval Accuracy ◽  
Southwest Region

With advantages of multispatial resolutions, a high retrieval accuracy, and a high temporal resolution, the satellite-derived land surface temperature (LST) products are very important LST sources. However, the greatest barrier to their wide application is the invalid values produced by large quantities of cloudy pixels, especially for regions frequently swathed in clouds. In this study, an effective method based on the land energy balance theory and similar pixels (SP) method was developed to reconstruct the LSTs over cloudy pixels for the widely used MODIS LST (MOD11A1). The southwest region of China was selected as the study area, where extreme drought has frequently occurred in recent years in the context of global climate change and which commonly exhibits cloudy and foggy weather. The validation results compared with in situ LSTs showed that the reconstructed LSTs have an average error < 1.00 K (0.57 K at night and -0.14 K during the day) and an RMSE < 3.20 K (1.90 K at night and 3.16 K in the daytime). The experiment testing the SP interpolation indicated that the spatial structure of the LST has a greater effect on the SP performance than the size of the data-missing area, which benefits the LST reconstruction in the area frequently covered by large clouds.

scholarly journals Kalman filter physical retrieval of surface emissivity and temperature from geostationary infrared radiances

2013 ◽  
Vol 6 (12) ◽  
pp. 3613-3634 ◽  
Author(s):  
G. Masiello ◽  
C. Serio ◽  
I. De Feis ◽  
M. Amoroso ◽  
S. Venafra ◽  
...  
Keyword(s):  
Kalman Filter ◽  
High Temporal Resolution ◽  
Target Area ◽  
Surface Emissivity ◽  
In Situ Data ◽  
Infrared Imager ◽  
Filter Approach ◽  
Infrared Radiances

Abstract. The high temporal resolution of data acquisition by geostationary satellites and their capability to resolve the diurnal cycle allows for the retrieval of a valuable source of information about geophysical parameters. In this paper, we implement a Kalman filter approach to apply temporal constraints on the retrieval of surface emissivity and temperature from radiance measurements made from geostationary platforms. Although we consider a case study in which we apply a strictly temporal constraint alone, the methodology will be presented in its general four-dimensional, i.e., space-time, setting. The case study we consider is the retrieval of emissivity and surface temperature from SEVIRI (Spinning Enhanced Visible and Infrared Imager) observations over a target area encompassing the Iberian Peninsula and northwestern Africa. The retrievals are then compared with in situ data and other similar satellite products. Our findings show that the Kalman filter strategy can simultaneously retrieve surface emissivity and temperature with an accuracy of ± 0.005 and ± 0.2 K, respectively.

scholarly journals Multispectral Image-Based Estimation of Drought Patterns and Intensity around Lake Chad, Africa

2019 ◽  
Vol 11 (21) ◽  
pp. 2534 ◽  
Author(s):  
Willibroad Gabila Buma ◽  
Sang-Il Lee
Keyword(s):  
Remote Sensing ◽  
Land Surface ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Meteorological Data ◽  
Condition Index ◽  
Drought Monitoring ◽  
World Population ◽  
Lake Chad ◽  
In Situ Data

As the world population keeps increasing and cultivating more land, the extraction of vegetation conditions using remote sensing is important for monitoring land changes in areas with limited ground observations. Water supply in wetlands directly affects plant growth and biodiversity, which makes monitoring drought an important aspect in such areas. Vegetation Temperature Condition Index (VTCI) which depends on thermal stress and vegetation state, is widely used as an indicator for drought monitoring using satellite data. In this study, using clear-sky Landsat multispectral images, VTCI was derived from Land Surface Temperature (LST) and the Normalized Difference Vegetation Index (NDVI). Derived VTCI was used to observe the drought patterns of the wetlands in Lake Chad between 1999 and 2018. The proportion of vegetation from WorldView-3 images was later introduced to evaluate the methods used. With an overall accuracy exceeding 90% and a kappa coefficient greater than 0.8, these methods accurately acquired vegetation training samples and adaptive thresholds, allowing for accurate estimations of the spatially distributed VTCI. The results obtained present a coherent spatial distribution of VTCI values estimated using LST and NDVI. Most areas during the study period experienced mild drought conditions, though severe cases were often seen around the northern part of the lake. With limited in-situ data in this area, this study presents how VTCI estimations can be developed for drought monitoring using satellite observations. This further shows the usefulness of remote sensing to improve the information about areas that are difficult to access or with poor availability of conventional meteorological data.

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 GPCC's new land surface precipitation climatology based on quality-controlled in situ data and its role in quantifying the global water cycle

2013 ◽  
Vol 115 (1-2) ◽  
pp. 15-40 ◽  
Author(s):  
Udo Schneider ◽  
Andreas Becker ◽  
Peter Finger ◽  
Anja Meyer-Christoffer ◽  
Markus Ziese ◽  
...  
Keyword(s):  
Land Surface ◽  
Water Cycle ◽  
Surface Precipitation ◽  
Precipitation Climatology ◽  
In Situ Data ◽  
Global Water Cycle ◽  
Global Water

scholarly journals Determination of land surface heat fluxes over heterogeneous landscape of the Tibetan Plateau by using the MODIS and in-situ data

2011 ◽  
Vol 11 (7) ◽  
pp. 19617-19638 ◽  
Author(s):  
Y. Ma ◽  
L. Zhong ◽  
B. Wang ◽  
W. Ma ◽  
X. Chen ◽  
...  
Keyword(s):  
Heat Flux ◽  
Tibetan Plateau ◽  
Land Surface ◽  
Surface Heat ◽  
Heat Fluxes ◽  
The Tibetan Plateau ◽  
Surface Heat Fluxes ◽  
In Situ Data ◽  
Plateau Area

Abstract. In this study, a parameterization methodology based on MODIS (Moderate Resolution Imaging Spectroradiometer) and in-situ data is proposed and tested for deriving the regional surface reflectance, surface temperature, net radiation flux, soil heat flux, sensible heat flux and latent heat flux over heterogeneous landscape. As a case study, the methodology was applied to the Tibetan Plateau area. Four images of MODIS data (30 January 2007, 15 April 2007, 1 August 2007 and 25 October 2007) were used in this study for the comparison among winter, spring, summer and autumn. The derived results were also validated by using the "ground truth" measured in the stations of the Tibetan Observation and Research Platform (TORP). The results show that the derived surface variables (surface reflectance and surface temperature) and surface heat fluxes (net radiation flux, soil heat flux, sensible heat flux and latent heat flux) in four different seasons over the Tibetan Plateau area are in good accordance with the land surface status. These parameters show a wide range due to the strong contrast of surface features over the Tibetan Plateau. Also, the estimated land surface variables and surface heat fluxes are in good agreement with the ground measurements, and all their absolute percent difference (APD) is less than 10 % in the validation sites. It is therefore concluded that the proposed methodology is successful for the retrieval of land surface variables and surface heat fluxes using the MODIS and in-situ data over the Tibetan Plateau area. The shortage and further improvement of the methodology were also discussed.

scholarly journals Prototype Spatio-temporal Predictive System of pest development of the codling moth, Cydia pomonella, in Kazakhstan

2020 ◽  
Vol 13 (1) ◽  
pp. 1-12
Author(s):  
A. Afonin ◽  
B. Kopzhassarov ◽  
E. Milyutina ◽  
E. Kazakov ◽  
A. Sarbassova ◽  
...  
Keyword(s):  
Land Surface ◽  
Cydia Pomonella ◽  
Codling Moth ◽  
Moth Population ◽  
Development Stages ◽  
In Situ Data ◽  
Weather Stations ◽  
Spatio Temporal ◽  
Accumulated Degree Days

SummaryA prototype for pest development stages forecasting is developed in Kazakhstan exploiting data from the geoinformation technologies and using codling moth as a model pest in apples. The basic methodology involved operational thermal map retrieving based on MODIS land surface temperature products and weather stations data, their recalculation into accumulated degree days maps and then into maps of the phases of the codling moth population dynamics. The validation of the predicted dates of the development stages according to the in-situ data gathered in the apple orchards showed a good predictivity of the forecast maps. Predictivity of the prototype can be improved by using daily satellite sensor datasets and their calibration with data received from a network of weather stations installed in the orchards.

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