The EnMAP Satellite –Data Product Validation Activities

2021 ◽  
Author(s):  
Maximilian Brell ◽  
Luis Guanter ◽  
Karl Segl ◽  
Daniel Scheffler ◽  
Niklas Bohn ◽  
...  
Keyword(s):  
Satellite Data ◽  
Data Product

A method for the assessment of forest regrowth site index based on Earth observations and modelling

2020 ◽  
Author(s):  
Vasily Zharko ◽  
Sergey Bartalev ◽  
Mikhail Bogodukhov
Keyword(s):  
Satellite Data ◽  
Forest Type ◽  
Site Index ◽  
Forest Growth ◽  
Joint Analysis ◽  
Forest Change ◽  
Russian Science ◽  
Data Product ◽  
Forest Growth Model ◽  
Forest Regrowth

<p>Presented is a method for the estimation of a productivity class/site index of the forest regrowth after stand replacement natural and human induced disturbances. The method uses Global Forest Change project data on spatial distribution of forest loss sites (including the information about the date of the disturbance) with a 30 m resolution based on Landsat data. Joint analysis of this data resampled to 100 m spatial resolution together with a Russian Land Cover map for 2016 developed based on 100 m PROBA-V data is used to identify reforestation sites and to determine the forest type. Based on this information an appropriate forest growth model is chosen to simulate forest characteristics' dynamics for different site indexes. Finally information on forest characteristics from satellite data-based products is compared to the modeling results for the forest age, computed as a difference between the date of the disturbance and the date of the satellite data product. Reforestation site is assigned a productivity class that yields the best consistency between modeling results and existing satellite data products information.</p><p>Application of the presented method was tested over the European part of Russia using a 100 m global growing stock volume (GSV) map developed within Globbiomass project and lidar vegetation canopy height measurements from ICESat-2/ATLAS system (ATL08 data product). It was found that ICESat-2/ATLAS data is better suited for the proposed approach.</p><p>Presented method is aimed at the development of a reference dataset on forest parameters since obtained information on forest type, age and site index together can be used to estimate other crucial characteristics, including GSV, mean height, mean stem diameter, basal area, productivity, growth and mortality parameters, using the appropriate model. It is also worth mentioning that proposed approach allows estimation of characteristics of young forests which are rarely represented in the field survey-based reference datasets.</p><p>This work was supported by the Russian Science Foundation [grant number 19-77-30015]. Data processing and analysis was carried out using resources of the Centre for collective use ‘IKI-Monitoring’ developed by the Space Research Institute of the Russian Academy of Sciences.</p>

scholarly journals Snowfall Rates from Satellite Data Help Weather Forecasters

Eos ◽  
2018 ◽  
Vol 99 ◽  
Author(s):  
Ralph Ferraro ◽  
Huan Meng ◽  
Brad Zavodsky ◽  
Sheldon Kusselson ◽  
Deirdre Kann ◽  
...  
Keyword(s):  
Satellite Data ◽  
Weather Data ◽  
Data Product ◽  
Weather Forecasters

A new data product calculates snowfall rates from weather data beamed directly from several satellites, helping meteorologists provide fast, accurate weather reports and forecasts.

Satellite Data Product Processing

1990 ◽  
pp. 166-179
Author(s):  
P. Krishna Rao ◽  
Susan J. Holmes ◽  
Ralph K. Anderson ◽  
Jay S. Winston ◽  
Paul E. Lehr
Keyword(s):  
Satellite Data ◽  
Data Product

scholarly journals Underway sampling of marine inherent optical properties on the Tara Oceans expedition as a novel resource for ocean color satellite data product validation

2013 ◽  
Vol 7 ◽  
pp. 40-51 ◽  
Author(s):  
P. Jeremy Werdell ◽  
Christopher W. Proctor ◽  
Emmanuel Boss ◽  
Thomas Leeuw ◽  
Mustapha Ouhssain
Keyword(s):  
Optical Properties ◽  
Satellite Data ◽  
Ocean Color ◽  
Inherent Optical Properties ◽  
Data Product

scholarly journals Estimating Root Zone Soil Moisture Across the Eastern United States with Passive Microwave Satellite Data and a Simple Hydrologic Model

2019 ◽  
Vol 11 (17) ◽  
pp. 2013 ◽  
Author(s):  
Douglas Baldwin ◽  
Salvatore Manfreda ◽  
Henry Lin ◽  
Erica A.H. Smithwick
Keyword(s):  
Soil Moisture ◽  
Spatial Resolution ◽  
Satellite Data ◽  
Spatial Model ◽  
Root Zone ◽  
Hydrologic Model ◽  
Passive Microwave ◽  
Data Product ◽  
Data Products ◽  
Shale Hills

Root zone soil moisture (RZSM) affects many natural processes and is an important component of environmental modeling, but it is expensive and challenging to monitor for relatively small spatial extents. Satellite datasets offer ample spatial coverage of near-surface soil moisture content at up to a daily time-step, but satellite-derived data products are currently too coarse in spatial resolution to use directly for many environmental applications, such as those for small catchments. This study investigated the use of passive microwave satellite soil moisture data products in a simple hydrologic model to provide root zone soil moisture estimates across a small catchment over a two year time period and the Eastern U.S. (EUS) at a 1 km resolution over a decadal time-scale. The physically based soil moisture analytical relationship (SMAR) was calibrated and tested with the Advanced Microwave Scanning Radiometer (AMSRE), Soil Moisture Ocean Salinity (SMOS), and Soil Moisture Active Passive (SMAP) data products. The SMAR spatial model relies on maps of soil physical properties and was first tested at the Shale Hills experimental catchment in central Pennsylvania. The model met a root mean square error (RMSE) benchmark of 0.06 cm3 cm−3 at 66% of the locations throughout the catchment. Then, the SMAR spatial model was calibrated at up to 68 sites (SCAN and AMERIFLUX network sites) that monitor soil moisture across the EUS region, and maps of SMAR parameters were generated for each satellite data product. The average RMSE for RZSM estimates from each satellite data product is <0.06 cm3 cm−3. Lastly, the 1 km EUS regional RZSM maps were tested with data from the Shale Hills, which was set aside for validating the regional SMAR, and the RMSE between the RZSM predictions and the catchment average is 0.042 cm3 cm−3. This study offers a promising approach for generating long time-series of regional RZSM maps with the same spatial resolution of soil property maps.

scholarly journals On sampling bias adjustment for sparsely observing satellite instruments for the example of carbonyl sulfide (OCS)

2018 ◽  
Author(s):  
Corinna Kloss ◽  
Marc von Hobe ◽  
Michael Höpfner ◽  
Kaley A. Walker ◽  
Martin Riese ◽  
...  
Keyword(s):  
Regression Model ◽  
Satellite Data ◽  
Carbonyl Sulfide ◽  
Sampling Bias ◽  
Adjustment Factor ◽  
Bias Adjustment ◽  
Adjustment Procedure ◽  
Mixing Ratios ◽  
Data Product ◽  
Spatio Temporal

Abstract. When computing climatological averages of atmospheric trace gas mixing ratios obtained from satellite-based measurements, sampling biases arise if data coverage is not uniform in space and time. Complete homogeneous spatio-temporal coverage is essentially impossible to achieve. Solar occultation measurements, by virtue of satellite orbits and the requirement of direct observation of the sun through the atmosphere, result in particularly sparse spatial coverage. In this study, a method is presented to adjust for such sampling biases when calculating climatological means. The method is demonstrated using carbonyl sulfide (OCS) measurements at 16 km altitude from the ACE-FTS (Atmospheric Chemistry Experiment Fourier Transform 15 Spectrometer). At this altitude, OCS mixing ratios show a steep gradient between the poles and equator. ACE-FTS measurements, which are provided as vertically resolved profiles, and integrated stratospheric OCS columns are used in this study. The bias adjustment procedure requires no additional observations other than the satellite data product itself and is expected to be generally applicable when constructing climatologies of long-lived tracers from sparsely and heterogeneously sampled satellite data. In a first step of the adjustment procedure, a regression model is used to fit a 2-D surface to all available ACE-FTS OCS measurements as a function of day-of-year and latitude. The regression model fit is used to calculate an adjustment factor, 20 which is then used to adjust each measurement individually. The mean of the adjusted measurement points of a chosen spatio-temporal frame is then used as the bias-free climatological value. When applying the adjustment factor to seasonal averages in 30° zones, the maximum spatio-temporal sampling bias adjustment was 11 % for OCS mixing ratios at 16 km and 5 % for the stratospheric OCS column. The adjustments were validated against the much denser and more homogeneous OCS data product from the limb-sounding MIPAS (Michelson Interferometer for Passive Atmospheric Sounding) instrument, and both the direction and sign of the adjustments were in agreement with the adjustment of the ACE-FTS data.

scholarly journals Knowledge based system for Satellite data product selection

2014 ◽  
Vol XL-8 ◽  
pp. 1233-1236 ◽  
Author(s):  
R. Goyal ◽  
T. Jayasudha ◽  
P. Pandey ◽  
D. Rama Devi ◽  
A. Rebecca ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Satellite Data ◽  
Effective Means ◽  
Remote Sensing Data ◽  
Base System ◽  
Product Selection ◽  
Domain Experts ◽  
Data Product ◽  
Knowledge Based ◽  
Sensing Applications

In recent years, the use of satellite data for geospatial applications has multiplied and contributed significantly towards development of the society. Satellite data requirements, in terms of spatial and spectral resolution, periodicity of data, level of correction and other parameters, vary for different applications. For major applications, remote sensing data alone may not suffice and may require additional data like field data. An application user, even though being versatile in his application, may not know which satellite data is best suited for his application, how to use the data and what information can be derived from the data. Remote sensing domain experts have the proficiency of using appropriate data for remote sensing applications. <br><br> Entrenching domain expertise into the system and building a knowledge base system for satellite data product selection is vital. Non specialist data users need a user-friendly software which guides them to the most suitable satellite data product on the basis of their application. Such tool will aid the usage for apt remote sensed data for various sectors of application users. Additionally, the consumers will be less concerned about the technical particulars of the platforms that provide satellite data, instead focusing on the content and values in the data product, meeting the timelines and ease of access. Embedding knowledge is a popular and effective means of increasing the power of using a system. This paper describes a system, driven by the built-in knowledge of domain experts, for satellite data products selection for geospatial applications.

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