scholarly journals NOAA-AVHRR Orbital Drift Correction: Validating Methods Using MSG-SEVIRI Data as a Benchmark Dataset

2021 ◽  
Vol 13 (5) ◽  
pp. 925
Author(s):  
Yves Julien ◽  
José A. Sobrino
Keyword(s):  
Time Series ◽  
Standard Deviation ◽  
Land Surface ◽  
Correction Method ◽  
Drift Correction ◽  
Noaa Avhrr ◽  
Coarse Spatial Resolution ◽  
Orbital Drift ◽  
Better Than

National Oceanic and Atmospheric Administration–Advanced Very High Resolution Radiometer (NOAA-AVHRR) data provides the possibility to build the longest Land Surface Temperature (LST) dataset to date, starting in 1981 up to the present. However, due to the orbital drift of the NOAA platforms, no LST dataset is available before 2000 and the arrival of newer platforms. Although numerous methods have been developed to correct this orbital drift effect on the LST, a lack of validation has prevented their application. This is the gap we bridge here by using the 15 min temporal resolution of Meteosat Second Generation–Spinning Enhanced Visible and Infra-Red Imager (MSG-SEVIRI) data to simulate drifted and reference LST time series. We then use these time series to validate an orbital drift correction method based on solar zenith angle (SZA) anomalies that we presented in a previous work (C1), as well as two variations of this approach (C0 and C2). Our results show that the C0 method performs better than the two others, although its overall bias absolute value ranges up to 1 K, while standard deviation values remain around 3 K. This is verified for most land covers, for all NOAA platforms, and these statistics remain mostly stable with noise on SZA time series (from 0° to ±10°). With this study, we show that orbital drift correction methods can be thoroughly validated and that such validation should aim toward bias absolute values below 0.1 K and standard deviation values around 1.4 K at coarse spatial resolution. To validate other orbital drift correction approaches, the drifted and reference time series used in this work are freely available for download from the first author’s webpage. This will be the first step toward the building of an orbital-drift-corrected long-term LST dataset.

scholarly journals A global long-term (1981–2000) land surface temperature product for NOAA AVHRR

2020 ◽  
Author(s):  
Jin Ma ◽  
Ji Zhou ◽  
Frank-Michael Göttsche ◽  
Shunlin Liang ◽  
Shaofei Wang ◽  
...  
Keyword(s):  
Surface Temperature ◽  
Land Surface Temperature ◽  
Land Surface ◽  
Radiation Budget ◽  
Mean Bias Error ◽  
Data Set ◽  
Noaa Avhrr ◽  
Orbital Drift

Abstract. Land Surface Temperature (LST) plays an important role in the research of climate change and various land surface processes. Before 2000, global LST products with relatively high temporal and spatial resolutions are scarce, despite of a variety of operational satellite LST products. In this study, a global 0.05° × 0.05° historical LST product is generated from NOAA AVHRR data (1981–2000), which includes three data layers: (1) instantaneous LST, a product generated by integrating several Split-Window Algorithms with a Random Forest (RF-SWA); (2) orbital drift corrected (ODC) LST, a drift corrected version of RF-SWA LST; (3) monthly averages of ODC LST. For an assumed maximum uncertainty in emissivity and column water vapour content of 0.04 and 1.0 g/cm2, respectively and evaluated against the simulation data set, the RF-SWA method has a Mean Bias Error (MBE) of less than 0.10 K and a Standard Deviation (STD) of 1.10 K. To compensate the influence of orbital drift on LST, the retrieved RF-SWA LST was normalized with an improved ODC method. The RF-SWA LST were validated with in-situ LST from Surface Radiation Budget (SURFRAD) sites and water temperatures obtained from the National Data Buoy Center (NDBC). Against the in-situ LST, the RF-SWA LST has a MBE 0.03 K with a range of −1.59 K–2.71 K and STD is 1.18 K with a range of 0.84 K–2.76 K. Since water temperature only changes slowly, the validation of ODC LST was limited to SURFRAD sites, for which the MBE is 0.54 K with a range of −1.05 K to 3.01 K and STD is 3.57 K with a range of 2.34 K to 3.69 K, indicating a good product accuracy. As global historical datasets, the new AVHRR LST products are useful for filling the gaps in long-term LST data. Furthermore, the new LST products can be used as input to related land surface models and environmental applications. Furthermore, in support of the scientific research community, the datasets are freely available at https://doi.org/10.5281/zenodo.3934354 for RF-SWA LST (Ma et al., 2020a); https://doi.org/10.5281/zenodo.3936627 for ODC LST (Ma et al., 2020c); https://doi.org/10.5281/zenodo.3936641 for monthly averaged LST (Ma et al., 2020b).

scholarly journals A global long-term (1981–2000) land surface temperature product for NOAA AVHRR

2020 ◽  
Vol 12 (4) ◽  
pp. 3247-3268
Author(s):  
Jin Ma ◽  
Ji Zhou ◽  
Frank-Michael Göttsche ◽  
Shunlin Liang ◽  
Shaofei Wang ◽  
...  
Keyword(s):  
Surface Temperature ◽  
Land Surface Temperature ◽  
Land Surface ◽  
Surface Radiation ◽  
Radiation Budget ◽  
Mean Bias Error ◽  
Noaa Avhrr ◽  
Orbital Drift

Abstract. Land surface temperature (LST) plays an important role in the research of climate change and various land surface processes. Before 2000, global LST products with relatively high temporal and spatial resolutions are scarce, despite a variety of operational satellite LST products. In this study, a global 0.05∘×0.05∘ historical LST product is generated from NOAA advanced very-high-resolution radiometer (AVHRR) data (1981–2000), which includes three data layers: (1) instantaneous LST, a product generated by integrating several split-window algorithms with a random forest (RF-SWA); (2) orbital-drift-corrected (ODC) LST, a drift-corrected version of RF-SWA LST; and (3) monthly averages of ODC LST. For an assumed maximum uncertainty in emissivity and column water vapor content of 0.04 and 1.0 g cm−2, respectively, evaluated against the simulation dataset, the RF-SWA method has a mean bias error (MBE) of less than 0.10 K and a standard deviation (SD) of 1.10 K. To compensate for the influence of orbital drift on LST, the retrieved RF-SWA LST was normalized with an improved ODC method. The RF-SWA LST were validated with in situ LST from Surface Radiation Budget (SURFRAD) sites and water temperatures obtained from the National Data Buoy Center (NDBC). Against the in situ LST, the RF-SWA LST has a MBE of 0.03 K with a range of −1.59–2.71 K, and SD is 1.18 K with a range of 0.84–2.76 K. Since water temperature only changes slowly, the validation of ODC LST was limited to SURFRAD sites, for which the MBE is 0.54 K with a range of −1.05 to 3.01 K and SD is 3.57 K with a range of 2.34 to 3.69 K, indicating good product accuracy. As global historical datasets, the new AVHRR LST products are useful for filling the gaps in long-term LST data. Furthermore, the new LST products can be used as input to related land surface models and environmental applications. Furthermore, in support of the scientific research community, the datasets are freely available at https://doi.org/10.5281/zenodo.3934354 for RF-SWA LST (Ma et al., 2020a), https://doi.org/10.5281/zenodo.3936627 for ODC LST (Ma et al., 2020c), and https://doi.org/10.5281/zenodo.3936641 for monthly averaged LST (Ma et al., 2020b).

scholarly journals CONSTRUCTION AND ANALYSIS OF LONG-TERM SURFACE TEMPERATURE DATASET IN FUJIAN PROVINCE

2017 ◽  
Vol XLII-2/W7 ◽  
pp. 1223-1227 ◽  
Author(s):  
W. E. Li ◽  
X. Q. Wang ◽  
H. Su
Keyword(s):  
Time Series ◽  
Surface Temperature ◽  
Land Surface ◽  
Rapid Development ◽  
Temporal Distribution ◽  
Heat Fluxes ◽  
Urban Region ◽  
Fujian Province ◽  
Spatio Temporal

Land surface temperature (LST) is a key parameter of land surface physical processes on global and regional scales, linking the heat fluxes and interactions between the ground and atmosphere. Based on MODIS 8-day LST products (MOD11A2) from the split-window algorithms, we constructed and obtained the monthly and annual LST dataset of Fujian Province from 2000 to 2015. Then, we analyzed the monthly and yearly time series LST data and further investigated the LST distribution and its evolution features. The average LST of Fujian Province reached the highest in July, while the lowest in January. The monthly and annual LST time series present a significantly periodic features (annual and interannual) from 2000 to 2015. The spatial distribution showed that the LST in North and West was lower than South and East in Fujian Province. With the rapid development and urbanization of the coastal area in Fujian Province, the LST in coastal urban region was significantly higher than that in mountainous rural region. The LST distributions might affected by the climate, topography and land cover types. The spatio-temporal distribution characteristics of LST could provide good references for the agricultural layout and environment monitoring in Fujian Province.

Global long-term land surface temperature for NOAA AVHRR: extension from 1981-2000 to 1981-2020

2021 ◽  
Author(s):  
Jin Ma ◽  
Ji Zhou
Keyword(s):  
Climate Change ◽  
Surface Temperature ◽  
Land Surface Temperature ◽  
Land Surface ◽  
Global Scale ◽  
Mean Bias Error ◽  
Bias Error ◽  
Important Indicator ◽  
Noaa Avhrr

<p>As an important indicator of land-atmosphere energy interaction, land surface temperature (LST) plays an important role in the research of climate change, hydrology, and various land surface processes. Compared with traditional ground-based observation, satellite remote sensing provides the possibility to retrieve LST more efficiently over a global scale. Since the lack of global LST before, Ma et al., (2020) released a global 0.05 ×0.05  long-term (1981-2000) LST based on NOAA-7/9/11/14 AVHRR. The dataset includes three layers: (1) instantaneous LST, a product generated based on an ensemble of several split-window algorithms with a random forest (RF-SWA); (2) orbital-drift-corrected (ODC) LST, a drift-corrected version of RF-SWA LST at 14:30 solar time; and (3) monthly averages of ODC LST. To meet the requirement of the long-term application, e.g. climate change, the period of the LST is extended from 1981-2000 to 1981-2020 in this study. The LST from 2001 to 2020 are retrieved from NOAA-16/18/19 AVHRR with the same algorithm for NOAA-7/8/11/14 AVHRR. The train and test results based on the simulation data from SeeBor and TIGR atmospheric profiles show that the accuracy of the RF-SWA method for the three sensors is consistent with the previous four sensors, i.e. the mean bias error and standard deviation less than 0.10 K and 1.10 K, respectively, under the assumption that the maximum emissivity and water vapor content uncertainties are 0.04 and 1.0 g/cm<sup>2</sup>, respectively. The preliminary validation against <em>in-situ</em> LST also shows a similar accuracy, indicating that the accuracy of LST from 1981 to 2020 are consistent with each other. In the generation code, the new LST has been improved in terms of land surface emissivity estimation, identification of cloud pixel, and the ODC method in order to generate a more reliable LST dataset. Up to now, the new version LST product (1981-2020) is under generating and will be released soon in support of the scientific research community.</p>

scholarly journals Evaluation of Seven Atmospheric Profiles from Reanalysis and Satellite-Derived Products: Implication for Single-Channel Land Surface Temperature Retrieval

2020 ◽  
Vol 12 (5) ◽  
pp. 791 ◽  
Author(s):  
Jingjing Yang ◽  
Si-Bo Duan ◽  
Xiaoyu Zhang ◽  
Penghai Wu ◽  
Cheng Huang ◽  
...  
Keyword(s):  
Time Series ◽  
Surface Temperature ◽  
Land Surface Temperature ◽  
Land Surface ◽  
Single Channel ◽  
Landsat 8 ◽  
Atmospheric Profiles ◽  
Atmospheric Profile

Land surface temperature (LST) is vital for studies of hydrology, ecology, climatology, and environmental monitoring. The radiative-transfer-equation-based single-channel algorithm, in conjunction with the atmospheric profile, is regarded as the most suitable one with which to produce long-term time series LST products from Landsat thermal infrared (TIR) data. In this study, the performances of seven atmospheric profiles from different sources (the MODerate-resolution Imaging Spectroradiomete atmospheric profile product (MYD07), the Atmospheric Infrared Sounder atmospheric profile product (AIRS), the European Centre for Medium-range Weather Forecasts (ECMWF), the Modern-Era Retrospective analysis for Research and Applications, Version 2 (MERRA2), the National Centers for Environmental Prediction (NCEP)/Global Forecasting System (GFS), NCEP/Final Operational Global Analysis (FNL), and NCEP/Department of Energy (DOE)) were comprehensively evaluated in the single-channel algorithm for LST retrieval from Landsat 8 TIR data. Results showed that when compared with the radio sounding profile downloaded from the University of Wyoming (UWYO), the worst accuracies of atmospheric parameters were obtained for the MYD07 profile. Furthermore, the root-mean-square error (RMSE) values (approximately 0.5 K) of the retrieved LST when using the ECMWF, MERRA2, NCEP/GFS, and NCEP/FNL profiles were smaller than those but greater than 0.8 K when the MYD07, AIRS, and NCEP/DOE profiles were used. Compared with the in situ LST measurements that were collected at the Hailar, Urad Front Banner, and Wuhai sites, the RMSE values of the LST that were retrieved by using the ECMWF, MERRA2, NCEP/GFS, and NCEP/FNL profiles were approximately 1.0 K. The largest discrepancy between the retrieved and in situ LST was obtained for the NCEP/DOE profile, with an RMSE value of approximately 1.5 K. The results reveal that the ECMWF, MERRA2, NCEP/GFS, and NCEP/FNL profiles have great potential to perform accurate atmospheric correction and generate long-term time series LST products from Landsat TIR data by using a single-channel algorithm.

scholarly journals Drifting Effects of NOAA Satellites on Long-Term Active Fire Records of Europe

2019 ◽  
Vol 11 (4) ◽  
pp. 467 ◽  
Author(s):  
Helga Weber ◽  
Stefan Wunderle
Keyword(s):  
Time Series ◽  
Explicit Knowledge ◽  
Regional Scale ◽  
Error Sources ◽  
Fire Cycle ◽  
Active Fire ◽  
Maximum Delay ◽  
Orbital Drift ◽  
The Impact

Explicit knowledge of different error sources in long-term climate records from space is required to understand and mitigate their impacts on resulting time series. Imagery of the heritage Advanced Very High Resolution Radiometer (AVHRR) provides unique potential for climate research dating back to the 1980s, flying onboard a series of successive National Oceanic and Atmospheric Administration (NOAA) and Meteorological Operational (MetOp) satellites. However, the NOAA satellites are affected by severe orbital drift that results in spurious trends in time series. We identified the impact and extent of the orbital drift in 1 km AVHRR long-term active fire data. This record contains data of European fire activity from 1985–2016 and was analyzed on a regional scale and extended across Europe. Inconsistent sampling of the diurnal active fire cycle due to orbital drift with a maximum delay of ∼5 h over NOAA-14 lifetime revealed a ∼90% decline in the number of observed fires. However, interregional results were less conclusive and other error sources as well as interannual variability were more pronounced. Solar illumination, measured by the sun zenith angle (SZA), related changes in background temperatures were significant for all regions and afternoon satellites with major changes in −0.03 to −0.09 K deg − 1 for ▵ B T 34 (p ≤ 0 . 001). Based on example scenes, we simulated the influence of changing temperatures related to changes in the SZA on the detection of active fires. These simulations showed a profound influence of the active fire detection capabilities dependent on biome and land cover characteristics. The strong decrease in the relative changes in the apparent number of active fires calculated over the satellites lifetime highlights that a correction of the orbital drift effect is essential even over short time periods.

scholarly journals Comprehensive Assessment of Performances of Long Time-Series LAI, FVC and GPP Products over Mountainous Areas: A Case Study in the Three-River Source Region, China

2021 ◽  
Vol 14 (1) ◽  
pp. 61
Author(s):  
Wenqi Zhang ◽  
Huaan Jin ◽  
Ainong Li ◽  
Huaiyong Shao ◽  
Xinyao Xie ◽  
...  
Keyword(s):  
Time Series ◽  
Global Change ◽  
Land Surface ◽  
Gross Primary Production ◽  
Source Region ◽  
Mountainous Areas ◽  
Area Index ◽  
Vegetation Activity ◽  
Long Time

Vegetation biophysical products offer unique opportunities to examine long-term vegetation dynamics and land surface phenology (LSP). It is important to understand the time-series performances of various global biophysical products for global change research. However, few endeavors have been dedicated to assessing the performances of long-term change characteristics or LSP extraction derived from different satellite products, especially in mountainous areas with highly fragmented and rugged surfaces. In this paper, we assessed the time-series characteristics and LSP detections of Global LAnd Surface Satellite (GLASS) leaf area index (LAI), fractional vegetation cover (FVC), and gross primary production (GPP) products across the Three-River Source Region (TRSR). The performances of products’ temporal agreements and their statistical relationship as a function of topographic indices and heterogeneous pixels, respectively, were investigated through intercomparison among three products during the period 2000 to 2018. The results show that the phenological differences between FVC and two other products are beyond 10 days over more than 35% of the pixels in TRSR. The long-term trend of FVC diverges significantly from GPP and LAI for 13.96% of the total pixels, and the percentages of mismatched pixels between FVC and two other products are 33.24% in the correlation comparison. Moreover, good agreements are observed between GPP and LAI, both in terms of LSP and interannual variations. Finally, the LSP and long-term dynamics of the three products exhibit poor performances on heterogeneous surfaces and complex topographic areas, which reflects the potential impacts of environmental factors and algorithmic imperfections on the quality and performances of different products. Our study highlights the spatiotemporal disparities in detections of surface vegetation activity in mountainous areas by using different biophysical products. Future global change studies may require multiple high-quality satellite products with long-term stability as data support.

Sign in / Sign up

Export Citation Format

Share Document