scholarly journals Retrieval and Uncertainty Analysis of Land Surface Reflectance Using a Geostationary Ocean Color Imager

2022 ◽  
Vol 14 (2) ◽  
pp. 360
Author(s):  
Kyeong-Sang Lee ◽  
Eunkyung Lee ◽  
Donghyun Jin ◽  
Noh-Hun Seong ◽  
Daeseong Jung ◽  
...  
Keyword(s):  
Uncertainty Analysis ◽  
Surface Properties ◽  
Spatial Resolution ◽  
Zenith Angle ◽  
Land Surface ◽  
Solar Zenith Angle ◽  
Ocean Color ◽  
Surface Reflectance ◽  
Temporal And Spatial

Land surface reflectance (LSR) is well known as an essential variable to understand land surface properties. The Geostationary Ocean Color Imager (GOCI) be able to observe not only the ocean but also the land with the high temporal and spatial resolution thanks to its channel specification. In this study, we describe the land atmospheric correction algorithm and present the quality of results through comparison with Moderate Resolution Imaging Spectroradiometer (MODIS) and in-situ data for GOCI-II. The GOCI LSR shows similar spatial distribution and quantity with MODIS LSR for both healthy and unhealthy vegetation cover. Our results agreed well with in-situ-based reference LSR with a high correlation coefficient (>0.9) and low root mean square error (<0.02) in all 8 GOCI channels. In addition, seasonal variation according to the solar zenith angle and phenological dynamics in time-series was well presented in both reference and GOCI LSR. As the results of uncertainty analysis, the estimated uncertainty in GOCI LSR shows a reasonable range (<0.04) even under a high solar zenith angle over 70°. The proposed method in this study can be applied to GOCI-II and can provide continuous satellite-based LSR products having a high temporal and spatial resolution for analyzing land surface properties.

scholarly journals Global downscaling of remotely sensed soil moisture using neural networks

2018 ◽  
Vol 22 (10) ◽  
pp. 5341-5356 ◽  
Author(s):  
Seyed Hamed Alemohammad ◽  
Jana Kolassa ◽  
Catherine Prigent ◽  
Filipe Aires ◽  
Pierre Gentine
Keyword(s):  
Soil Moisture ◽  
Spatial Resolution ◽  
Land Surface ◽  
Vegetation Index ◽  
Spatial Scales ◽  
Ancillary Data ◽  
Repeat Time ◽  
Spatiotemporal Scales ◽  
Better Than

Abstract. Characterizing soil moisture at spatiotemporal scales relevant to land surface processes (i.e., of the order of 1 km) is necessary in order to quantify its role in regional feedbacks between the land surface and the atmospheric boundary layer. Moreover, several applications such as agricultural management can benefit from soil moisture information at fine spatial scales. Soil moisture estimates from current satellite missions have a reasonably good temporal revisit over the globe (2–3-day repeat time); however, their finest spatial resolution is 9 km. NASA's Soil Moisture Active Passive (SMAP) satellite has estimated soil moisture at two different spatial scales of 36 and 9 km since April 2015. In this study, we develop a neural-network-based downscaling algorithm using SMAP observations and disaggregate soil moisture to 2.25 km spatial resolution. Our approach uses the mean monthly Normalized Differenced Vegetation Index (NDVI) as ancillary data to quantify the subpixel heterogeneity of soil moisture. Evaluation of the downscaled soil moisture estimates against in situ observations shows that their accuracy is better than or equal to the SMAP 9 km soil moisture estimates.

scholarly journals GOCI Yonsei Aerosol Retrieval (YAER) algorithm and validation during DRAGON-NE Asia 2012 campaign

2015 ◽  
Vol 8 (9) ◽  
pp. 9565-9609 ◽  
Author(s):  
M. Choi ◽  
J. Kim ◽  
J. Lee ◽  
M. Kim ◽  
Y. Je Park ◽  
...  
Keyword(s):  
Optical Properties ◽  
Land Surface ◽  
Pearson Correlation ◽  
Ocean Color ◽  
Single Scattering ◽  
Surface Reflectance ◽  
Robotic Networks ◽  
Deep Blue ◽  
Aerosol Retrieval ◽  
Highly Correlated

Abstract. The Geostationary Ocean Color Imager (GOCI) onboard the Communication, Ocean, and Meteorology Satellites (COMS) is the first multi-channel ocean color imager in geostationary orbit. Hourly GOCI top-of-atmosphere radiance has been available for the retrieval of aerosol optical properties over East Asia since March 2011. This study presents improvements to the GOCI Yonsei Aerosol Retrieval (YAER) algorithm over ocean and land together with validation results during the DRAGON-NE Asia 2012 campaign. Optical properties of aerosol are retrieved from the GOCI YAER algorithm including aerosol optical depth (AOD) at 550 nm, fine-mode fraction (FMF) at 550 nm, single scattering albedo (SSA) at 440 nm, Angstrom exponent (AE) between 440 and 860 nm, and aerosol type from selected aerosol models in calculating AOD. Assumed aerosol models are compiled from global Aerosol Robotic Networks (AERONET) inversion data, and categorized according to AOD, FMF, and SSA. Nonsphericity is considered, and unified aerosol models are used over land and ocean. Different assumptions for surface reflectance are applied over ocean and land. Surface reflectance over the ocean varies with geometry and wind speed, while surface reflectance over land is obtained from the 1–3 % darkest pixels in a 6 km × 6 km area during 30 days. In the East China Sea and Yellow Sea, significant area is covered persistently by turbid waters, for which the land algorithm is used for aerosol retrieval. To detect turbid water pixels, TOA reflectance difference at 660 nm is used. GOCI YAER products are validated using other aerosol products from AERONET and the MODIS Collection 6 aerosol data from "Dark Target (DT)" and "Deep Blue (DB)" algorithms during the DRAGON-NE Asia 2012 campaign from March to May 2012. Comparison of AOD from GOCI and AERONET gives a Pearson correlation coefficient of 0.885 and a linear regression equation with GOCI AOD =1.086 × AERONET AOD – 0.041. GOCI and MODIS AODs are more highly correlated over ocean than land. Over land, especially, GOCI AOD shows better agreement with MODIS DB than MODIS DT because of the choice of surface reflectance assumptions. Other GOCI YAER products show lower correlation with AERONET than AOD, but are still qualitatively useful.

Accuracy of Satellite Land Surface Reflectance Determination

1991 ◽  
Vol 30 (7) ◽  
pp. 960-972 ◽  
Author(s):  
O. Arino ◽  
G. Dedieu ◽  
P. Y. Deschamps
Keyword(s):  
Land Surface ◽  
Surface Albedo ◽  
Maximum Error ◽  
Surface Reflectance ◽  
Relative Uncertainty ◽  
Calibration Uncertainty ◽  
Maximum Bias ◽  
Calibration Accuracy ◽  
The Difference

Abstract An accuracy budget of the surface reflectance determination from Meteosat geostationary satellite data is performed. Error analysis allows identification of three main problems: calibration uncertainty of the Meteosat instrument, atmospheric corrections, and surface effects (spectral and directional). Calibration accuracy is 10%, leading to a 10% relative uncertainty on reflectance. Spectral effects of the surface lead to a maximum bias of 0.01 for a vegetated surface as sensed by Meteosat, while directional effects can lead to a bias of 0.035 between two measurements taken at two different sun zenith and azimuth angles at the same view angle over savannas. The maximum error due to the atmosphere is estimated to be of the order of 0.03 in reflectance for a surface reflectance of 0.40 and 0.01 for, a surface reflectance of 0.10. Validation with in situ measurement is within the expected error over savanna. But the difference is still high over the southwest France site of HAPEX-MOBILHY, certainly due to the joint spectral and directional errors. Comparisons with surface albedo maps from literature show the same spatial and spatial evolutions with a better spatial and temporal determination in our results.

Evaluation of TROPOMI Tropospheric NO2 VCDs over China

2020 ◽  
Author(s):  
Kai Qin ◽  
Qin He ◽  
Jincheng Shi
Keyword(s):  
Wind Speed ◽  
Spatial Resolution ◽  
Zenith Angle ◽  
Solar Zenith Angle ◽  
Eastern China ◽  
Cloud Fraction ◽  
Industrial Facilities ◽  
Monitoring Instrument ◽  
Tropospheric No2

&lt;p&gt;The Tropospheric Monitoring Instrument (TROPOMI) with a higher spatial resolution is a push broom UVIS spectrometer carried on the S5P satellite which was launched on October 13th, 2017.&amp;#160; But compared to the widely used OMI and GOME-2, TROPOMI NO2 products have not been extensively used in China. To evaluate the TROPOMI NO2 products, we present a comparison between TROPOMI NO2 products and MAX-DOAS observations in Xuzhou, eastern China from April 2018 to September 2019. We find a high correlation, but a clear underestimation. We find that solar zenith angle, viewing zenith angle, the cloud fraction and wind speed will affect the evaluation results. We examine the retrievals of TROPOMI tropospheric NO2 over China, contrasting them with the retrievals of OMI. We find that TROPOMI has better ability to resolve smallscale plumes and distinguish the distribution of NO2 concentration on a city scale. Our goal is to support the application of TROPOMI for NO2 observations and deriving emissions from urban or industrial facilities over China.&lt;/p&gt;

Global Land Surface Temperature from historical NOAA AVHRR datasets (1981-2000)

2020 ◽  
Author(s):  
Jin Ma ◽  
Ji Zhou ◽  
Frank-Michael Göttsche ◽  
Shaofei Wang
Keyword(s):  
Climate Change ◽  
Remote Sensing ◽  
Surface Temperature ◽  
Spatial Resolution ◽  
Land Surface Temperature ◽  
Land Surface ◽  
Microwave Remote Sensing ◽  
Community Land Model ◽  
Noaa Avhrr

&lt;p&gt;As one of the most important indicators in the energy exchange between land and atmosphere, Land Surface Temperature (LST) plays an important role in the research of climate change and various land surface processes. In contrast to &lt;em&gt;in-situ&lt;/em&gt; measurements, satellite remote sensing provides a practical approach to measure global and local land surface parameters. Although passive microwave remote sensing offers all-weather observation capability, retrieving LST from thermal infra-red data is still the most common approach. To date, a variety of global LST products have been published by the scientific community, e.g. MODIS and (A)ASTR /SLSTR LST products, and used in a broad range of research fields. Several global and regional satellite retrieved LSTs are available since 1995. However, the temporal-spatial resolution before 2000 is generally considerably lower than that after 2000. According to the latest IPCC report, 1983 &amp;#8211; 2012 are the warmest 30 years for nearly 1400 years. Therefore, for global climate change research, it is meaningful to extend the time series of global LST products with a relatively higher temporal-spatial resolution to before 2000, e.g. that of NOAA AVHRR. In this study, global daily NOAA AVHRR LST products with 5-km spatial resolution were generated for 1981-2000. The LST was retrieved using an ensemble of RF-SWAs (Random Forest and Split-Window Algorithm). For a maximum uncertainty in emissivity and water vapor content of 0.04 and 1.0 g/cm&lt;sup&gt;2&lt;/sup&gt;, respectively, the training and testing with simulated datasets showed a retrieval accuracy with MBE of less than 0.1 K and STD of 1.1 K. The generated RF-SWA LST product was also evaluated against &lt;em&gt;in-situ&lt;/em&gt; measurements: for water sites of the National Data Buoy Center (NDBC) between 1981 and 2000, it showed an accuracy similar to that for the simulated data, with a small MBE of less than 0.1 K and a STD between 0.79 K and 1.02 K. For SURFRAD data collected between 1995 and 2000, the MBE is -0.03 K with a range of -1.20 K &amp;#8211; 0.54 K and a STD with a mean of 2.55 K and a range of 2.08 K &amp;#8211; 3.0 K (site dependent). As a new global historical dataset, the RF-SWA LST product can help to close the gap in long-term LST data available to climate research. Furthermore, the data can be used as input to land surface process models, e.g. the Community Land Model (CLM). In support of the scientific research community, the RF-SWA LST product will be freely available at the National Earth System Science Data Center of China (http://www.geodata.cn/).&lt;/p&gt;

scholarly journals Estimation of Diurnal Cycle of Land Surface Temperature at High Temporal and Spatial Resolution from Clear-Sky MODIS Data

2014 ◽  
Vol 6 (4) ◽  
pp. 3247-3262 ◽  
Author(s):  
Si-Bo Duan ◽  
Zhao-Liang Li ◽  
Bo-Hui Tang ◽  
Hua Wu ◽  
Ronglin Tang ◽  
...  
Keyword(s):  
Surface Temperature ◽  
Spatial Resolution ◽  
Land Surface Temperature ◽  
Diurnal Cycle ◽  
Land Surface ◽  
Modis Data ◽  
Clear Sky ◽  
Temporal And Spatial
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