scholarly journals Evolution of soil and plant parameters on the agricultural Gebesee test site: a database for the set-up and validation of EO-LDAS and other satellite-aided retrieval models

2017 ◽  
Author(s):  
Sina C. Truckenbrodt ◽  
Christiane C. Schmullius
Keyword(s):  
Reference Data ◽  
Spatial Scales ◽  
Test Site ◽  
Earth Observation ◽  
Reference Database ◽  
Observation Data ◽  
Monitoring Methods ◽  
Retrieval Models ◽  
In Situ Data

Abstract. Ground reference data are a prerequisite for the calibration, update and validation of retrieval models facilitating the monitoring of land parameters based on Earth Observation data. Here, we describe the acquisition of a comprehensive ground reference database which was elaborated to test and validate the recently developed Earth Observation Land Data Assimilation System (EO-LDAS). In situ data was collected for seven crop types (winter barley, winter wheat, spring wheat, durum, winter rape, potato and sugar beet) cultivated on the agricultural Gebesee test site, central Germany, in 2013 and 2014. The database contains information on hyperspectral surface reflectance, the evolution of biophysical and biochemical plant parameters, phenology, surface conditions, atmospheric states, and a set of ground control points. Ground reference data was gathered with an approximately weekly resolution and on different spatial scales to investigate variations within and between acreages. In situ data collected less than 1 day apart from satellite acquisitions (RapidEye, SPOT5, Landsat-7 and -8) with a cloud coverage ≤ 25 % is available for 10 and 16 days in 2013 and 2014, respectively. The measurements show that the investigated growing seasons were characterized by distinct meteorological conditions causing interannual variations in the parameter evolution. In the article, the experimental design of the field campaigns, and methods employed in the determination of all parameters are described in detail. Insights into the database are provided and potential fields of application are discussed. We hope these data will contribute to a further development of crop monitoring methods based on remote sensing techniques. The database is freely available at PANGAEA (doi:10.1594/PANGAEA.874251).

scholarly journals Seasonal evolution of soil and plant parameters on the agricultural Gebesee test site: a database for the set-up and validation of EO-LDAS and satellite-aided retrieval models

2018 ◽  
Vol 10 (1) ◽  
pp. 525-548 ◽  
Author(s):  
Sina C. Truckenbrodt ◽  
Christiane C. Schmullius
Keyword(s):  
Remote Sensing ◽  
Reference Data ◽  
Spatial Scales ◽  
Test Site ◽  
Earth Observation ◽  
Reference Database ◽  
Observation Data ◽  
Retrieval Models ◽  
In Situ Data

Abstract. Ground reference data are a prerequisite for the calibration, update, and validation of retrieval models facilitating the monitoring of land parameters based on Earth Observation data. Here, we describe the acquisition of a comprehensive ground reference database which was created to test and validate the recently developed Earth Observation Land Data Assimilation System (EO-LDAS) and products derived from remote sensing observations in the visible and infrared range. In situ data were collected for seven crop types (winter barley, winter wheat, spring wheat, durum, winter rape, potato, and sugar beet) cultivated on the agricultural Gebesee test site, central Germany, in 2013 and 2014. The database contains information on hyperspectral surface reflectance factors, the evolution of biophysical and biochemical plant parameters, phenology, surface conditions, atmospheric states, and a set of ground control points. Ground reference data were gathered at an approximately weekly resolution and on different spatial scales to investigate variations within and between acreages. In situ data collected less than 1 day apart from satellite acquisitions (RapidEye, SPOT 5, Landsat-7 and -8) with a cloud coverage  ≤  25 % are available for 10 and 15 days in 2013 and 2014, respectively. The measurements show that the investigated growing seasons were characterized by distinct meteorological conditions causing interannual variations in the parameter evolution. Here, the experimental design of the field campaigns, and methods employed in the determination of all parameters, are described in detail. Insights into the database are provided and potential fields of application are discussed. The data will contribute to a further development of crop monitoring methods based on remote sensing techniques. The database is freely available at PANGAEA (https://doi.org/10.1594/PANGAEA.874251).

scholarly journals How valuable are citizen science data for a space-borne crop growth monitoring? – The reliability of self-appraisals

2020 ◽  
Author(s):  
Sina C. Truckenbrodt ◽  
Friederike Klan ◽  
Erik Borg ◽  
Klaus-Dieter Missling ◽  
Christiane C. Schmullius
Keyword(s):  
Management Practices ◽  
Test Site ◽  
Uncertainty Assessment ◽  
Earth Observation ◽  
Growth Monitoring ◽  
State Variables ◽  
Science Data ◽  
Area Index ◽  
In Situ Data

<p>Space-borne Earth Observation (EO) data depicting vegetation covered land surfaces contain insufficient information for an unambiguous interpretation of the spectral signal in terms of variables that characterize the vegetation state (e.g., leaf area index, leaf chlorophyll content and proportion of senescent material). For the retrieval of vegetation properties from EO data, an optimal estimate of the state variables needs to be found. The uncertainty of such an estimate can be reduced by combining EO data and in situ data. Information provided by citizens represents a valuable and mostly inexpensive source for in situ data. Since the quality of such data can be diverse, the consideration of uncertainties is of great importance.</p><p>In this contribution, we present a concept for the elicitation of local knowledge from citizens with respect to the application of management practices (e.g., sowing and harvesting date, irrigation) and the instantaneous state of crops. The concept includes the acquisition of in situ data as well as an uncertainty assessment (precision and/or accuracy). The latter involves a profiling in which inherent uncertainties are quantified for individual citizens. This concept was tested for agricultural fields of the Durable Environmental Multidisciplinary Monitoring Information Network (DEMMIN) test site in Northeast Germany. Within the frame of several field seminars, students were requested to assess management practices and the instantaneous state of crops. Furthermore, they assessed their own ability to create valid data. They filled in pseudonymized questionnaires from which we created corresponding datasets. At the same day, field data were collected with appropriate equipment and can be used as reference against which student estimates can be compared. The level of compliance between estimated and measured data was determined on an individual basis.</p><p>The results of this analysis will be presented. Conclusions will be drawn regarding the ability of the students to evaluate their own skills. In addition, we will demonstrate an approach for a digital ascertainment of in situ data. In the future, this approach will be used to collect in situ data for the setup of refined prior information within the frame of the Earth Observation Land Data Assimilation System (EO-LDAS).</p>

scholarly journals Fiducial Reference Measurements for Satellite Ocean Colour (FRM4SOC)

2020 ◽  
Vol 12 (8) ◽  
pp. 1322 ◽  
Author(s):  
Andrew Clive Banks ◽  
Riho Vendt ◽  
Krista Alikas ◽  
Agnieszka Bialek ◽  
Joel Kuusk ◽  
...  
Keyword(s):  
White Paper ◽  
Uncertainty Estimation ◽  
Earth Observation ◽  
Observation Data ◽  
Ocean Colour ◽  
Satellite Validation ◽  
The Earth ◽  
Earth Observation Data ◽  
Reference Measurements

Earth observation data can help us understand and address some of the grand challenges and threats facing us today as a species and as a planet, for example climate change and its impacts and sustainable use of the Earth’s resources. However, in order to have confidence in earth observation data, measurements made at the surface of the Earth, with the intention of providing verification or validation of satellite-mounted sensor measurements, should be trustworthy and at least of the same high quality as those taken with the satellite sensors themselves. Metrology tells us that in order to be trustworthy, measurements should include an unbroken chain of SI-traceable calibrations and comparisons and full uncertainty budgets for each of the in situ sensors. Until now, this has not been the case for most satellite validation measurements. Therefore, within this context, the European Space Agency (ESA) funded a series of Fiducial Reference Measurements (FRM) projects targeting the validation of satellite data products of the atmosphere, land, and ocean, and setting the framework, standards, and protocols for future satellite validation efforts. The FRM4SOC project was structured to provide this support for evaluating and improving the state of the art in ocean colour radiometry (OCR) and satellite ocean colour validation through a series of comparisons under the auspices of the Committee on Earth Observation Satellites (CEOS). This followed the recommendations from the International Ocean Colour Coordinating Group’s white paper and supports the CEOS ocean colour virtual constellation. The main objective was to establish and maintain SI traceable ground-based FRM for satellite ocean colour and thus make a fundamental contribution to the European system for monitoring the Earth (Copernicus). This paper outlines the FRM4SOC project structure, objectives and methodology and highlights the main results and achievements of the project: (1) An international SI-traceable comparison of irradiance and radiance sources used for OCR calibration that set measurement, calibration and uncertainty estimation protocols and indicated good agreement between the participating calibration laboratories from around the world; (2) An international SI-traceable laboratory and outdoor comparison of radiometers used for satellite ocean colour validation that set OCR calibration and comparison protocols; (3) A major review and update to the protocols for taking irradiance and radiance field measurements for satellite ocean colour validation, with particular focus on aspects of data acquisition and processing that must be considered in the estimation of measurement uncertainty and guidelines for good practice; (4) A technical comparison of the main radiometers used globally for satellite ocean colour validation bringing radiometer manufacturers together around the same table for the first time to discuss instrument characterisation and its documentation, as needed for measurement uncertainty estimation; (5) Two major international side-by-side field intercomparisons of multiple ocean colour radiometers, one on the Atlantic Meridional Transect (AMT) oceanographic cruise, and the other on the Acqua Alta oceanographic tower in the Gulf of Venice; (6) Impact and promotion of FRM within the ocean colour community, including a scientific road map for the FRM-based future of satellite ocean colour validation and vicarious calibration (based on the findings of the FRM4SOC project, the consensus from two major international FRM4SOC workshops and previous literature, including the IOCCG white paper on in situ ocean colour radiometry).

scholarly journals Copernicus: the European Earth Observation programme

2020 ◽  
Author(s):  
S. Jutz ◽  
M.P. Milagro-Pérez
Keyword(s):  
Numerical Models ◽  
Earth Observation ◽  
Environmental Data ◽  
Observation Data ◽  
The European Union ◽  
Global Environmental ◽  
In Situ Sensors ◽  
Earth Observation Data ◽  
Observation Programme

<span>The European Union-led Copernicus programme, born with the aim of developing space-based global environmental monitoring services to ensure a European autonomous capacity for Earth Observation, comprises a Space Component, Core Services, and In-situ measurements. The Space Component, coordinated by ESA, has seven Sentinel satellites in orbit, with further missions planned, and is complemented by contributing missions, in-situ sensors and numerical models, and delivers many terabytes of accurate climate and environmental data, free and open, every day to hundreds of thousands of users. This makes Copernicus the biggest provider of Earth Observation data in the world.</span>

scholarly journals Earth Observation Data for Agricultural Drought Monitoring in the Pannonian Basin

2020 ◽  
Author(s):  
Laura Crocetti ◽  
Milan Fischer ◽  
Matthias Forkel ◽  
Aleš Grlj ◽  
Wai-Tim Ng ◽  
...  
Keyword(s):  
Machine Learning ◽  
Soil Moisture ◽  
Pannonian Basin ◽  
Spatial Scales ◽  
European Space Agency ◽  
Earth Observation ◽  
Machine Learning Algorithms ◽  
Agricultural Drought ◽  
Drought Monitoring ◽  
Observation Data

&lt;p&gt;The Pannonian Basin is a region in the southeastern part of Central Europe that is heavily used for agricultural purposes. It is geomorphological defined as the plain area that is surrounded by the Alps in the west, the Dinaric Alps in the Southwest, and the Carpathian mountains in the North, East and Southeast. In recent decades, the Pannonian Basin has experienced several drought episodes, leading to severe impacts on the environment, society, and economy. Ongoing human-induced climate change, characterised by increasing temperature and potential evapotranspiration as well as changes in precipitation distribution will further exacerbate the frequency and intensity of extreme events. Therefore, it is important to monitor, model, and forecast droughts and their impact on the environment for a better adaption to the changing weather and climate extremes. The increasing availability of long-term Earth observation (EO) data with high-resolution, combined with the progress in machine learning algorithms and artificial intelligence, are expected to improve the drought monitoring and impact prediction capacities.&lt;/p&gt;&lt;p&gt;Here, we assess novel EO-based products with respect to drought processes in the Pannonian Basin. To identify meteorological and agricultural drought, the Standardized Precipitation-Evapotranspiration Index was computed from the ERA5 meteorological reanalysis and compared with drought indicators based on EO time series of soil moisture and vegetation like the Soil Water Index or the Normalized Difference Vegetation Index. We suggest that at resolution representing the ERA5 reanalysis (~0.25&amp;#176;) or coarser, both meteorological as well as EO data can identify drought events similarly well. However, at finer spatial scales (e.g. 1 km) the variability of biophysical properties between fields cannot be represented by meteorological data but can be captured by EO data. Furthermore, we analyse historical drought events and how they occur in different EO datasets. It is planned to enhance the forecasting of agricultural drought and estimating drought impacts on agriculture through exploiting the potential of EO soil moisture and vegetation data in a data-driven machine learning framework.&lt;/p&gt;&lt;p&gt;This study is funded by the DryPan project of the European Space Agency (https://www.eodc.eu/esa-drypan/).&lt;/p&gt;

A Sentinel-1 based European crop parcel map using 2018 in-situ LUCAS Copernicus observations

2020 ◽  
Author(s):  
Verhegghen Astrid ◽  
d'Andrimont Raphaël ◽  
Lemoine Guido ◽  
Strobl Peter ◽  
van der Velde Marijn
Keyword(s):  
Remote Sensing ◽  
European Union ◽  
Land Cover ◽  
Growing Season ◽  
Earth Observation ◽  
Soft Wheat ◽  
High Quality ◽  
In Situ Data ◽  
Crop Type

&lt;p&gt;Efficient near-real time and wall-to-wall land monitoring is now possible with unprecedented detail because of the fleet of Copernicus Sentinel satellites. This remote sensing paradigm is the consequence of the freely accessible, global, Copernicus data, combined with affordable cloud computing. However, to translate this capacity in accurate products, and to truly benefit from the high spatial detail (~10m) and temporal resolution (~5 days in constellation) of the Sentinels 1 and 2, high quality and timely in-situ data remains crucial. Robust operational monitoring systems are in need of both training and validation data.&amp;#160;&lt;/p&gt;&lt;p&gt;Here, we demonstrate the potential of Sentinel 1 observations and complementary high-quality in-situ data to generate a crop type map at continental scale. In 2018, the Land Cover and Land Use Area frame Survey (LUCAS) carried out in the European Union contained a specific Copernicus module corresponding to 93.091 polygons surveyed in-situ. In contrast to the usual LUCAS point observation, the Copernicus protocol provides data on the extent of homogeneous land cover for a maximum size of 100 x 100 m, making it meaningful for remote sensing applications. After filtering the polygons to retrieve only high quality sample, a sample was selected to explore the accuracy of crop type maps at different moments of the 2018 growing season over Europe. The time series of 10 days VV and VH were classified using Random Forest models. The crops that were mapped correspond to the 13 major crops in Europe and are those that are monitored and forecast by the JRC MARS activities (soft wheat, maize, rapeseed, barley, potatoes, ...). Overall, reasonable accuracies were obtained (~80%). Although no a priori parcel delineation was used, it was encouraging to observe the relative homogeneity of pixel classification results within the same parcel. In the context of forecasting, we specifically assessed at what time in the growing season accuracies moved beyond a set threshold for the different crops. This ranged from May for winter crops such as soft wheat, and September for summer crops such as maize.&amp;#160;&lt;/p&gt;&lt;p&gt;Our results contribute to the discussion regarding the usefulness, benefits, as well as weaknesses, of the newly acquired LUCAS Copernicus data. Doing so, this study demonstrates the potential of in-situ surveys such as LUCAS Copernicus module&amp;#160; specifically targeted for Earth Observation applications. Future improvements to the LUCAS Copernicus survey methodology are suggested. Importantly, now that LUCAS has been postponed to 2022, and aligned with the Copernicus space program, we advocate for a European Union wide systematic and representative in-situ sample campaign relevant for Earth Observation applications, beyond the traditional LUCAS survey.&amp;#160;&lt;/p&gt;

Management of the Test Site/In-Situ Data Integrated into Space Information

2016 ◽  
Vol 4 (1) ◽  
pp. 1-9 ◽  
Author(s):  
Nargiz E. Samadova ◽  
Rustam B. Rustamov
Keyword(s):  
Test Site ◽  
In Situ Data

scholarly journals Satellite views of global phytoplankton community distributions using an empirical algorithm and a numerical model

2013 ◽  
Vol 10 (1) ◽  
pp. 1083-1109 ◽  
Author(s):  
C. S. Rousseaux ◽  
T. Hirata ◽  
W. W. Gregg
Keyword(s):  
Seasonal Variation ◽  
Data Base ◽  
Observation Data ◽  
Independent Observation ◽  
In Situ Data ◽  
The North ◽  
Model Versus ◽  
Mean Differences ◽  
The North Pacific

Abstract. We compared the functional response of a biogeochemical data assimilation model versus an empirical satellite-derived algorithm in describing the variation of four phytoplankton (diatoms, cyanobacteria, coccolithophores and chlorophytes) groups globally and in 12 major oceanographic basins. Global mean differences of all groups were within ~ 15% of an independent observation data base for both approaches except for satellite-derived chlorophytes. Diatoms and cyanobacteria concentrations were significantly (p < 0.05) correlated with the independent observation data base for both methods. Coccolithophore concentrations were only correlated with the in situ data for the model approach and the chlorophyte concentration was only significantly correlated to the in situ data for the satellite-derived approach. Using monthly means from 1998–2007, the seasonal variation from the satellite-derived approach and model were significantly correlated in 11 regions for diatoms and in 9 for coccolithophores but only in 3 and 2 regions for cyanobacteria and chlorophytes. Most disagreement on the seasonal variation of phytoplankton composition occurred in the North Pacific and Antarctic where, except for diatoms, no significant correlation could be found between the monthly mean concentrations derived from both approaches. In these two regions there was also an overestimate of diatom concentration by the model of ~ 60% whereas the satellite-derived approach was closer to in situ data (8–26% underestimate). Chlorophytes were the group for which both approaches differed most and that was furthest from the in situ data. These results highlight the strengths and weaknesses of both approaches and allow us to make some suggestions to improve our approaches to understanding phytoplankton dynamics and distribution.

Modeling of channel processes in large rivers with the use of field data

2021 ◽  
Vol 9 (1) ◽  
pp. 87-102
Author(s):  
A.T. Zinoviev ◽  
◽  
A.V. Dyachenko ◽  
K.B. Koshelev ◽  
K.V. Marusin ◽  
...  
Keyword(s):  
Sediment Transport ◽  
Anthropogenic Impacts ◽  
Three Dimensional ◽  
Large Rivers ◽  
Observation Data ◽  
Channel Processes ◽  
Channel Deformations ◽  
Study Section ◽  
In Situ Data

The paper deals with mathematical description of channel processes occurring in long sections of large rivers with a complex morphometry. To forecast negative manifestations of channel deformations, a computer model of river sediment transport in the study section is proposed. It is based on a three-dimensional (3D) / two-dimensional horizontal (2DH) flow model, a 2DH model of bed sediment transport and observation data. Comparative analysis of simulation results of channel processes in the Ob river section at the Barnaul water intakes and in situ data makes it possible to evaluate forecast capabilities of the designed model, in particular, for quantitative assessment of changes in channel topography of the study section caused by natural and anthropogenic impacts.

scholarly journals Testing the relationship between the solar radiation dose and surface DMS concentrations using in situ data

2009 ◽  
Vol 6 (9) ◽  
pp. 1927-1934 ◽  
Author(s):  
C. J. Miles ◽  
T. G. Bell ◽  
T. M. Lenton
Keyword(s):  
Radiation Dose ◽  
Solar Radiation ◽  
Spatial Scales ◽  
Mixed Layer Depth ◽  
Light Attenuation ◽  
Climate Feedback ◽  
Solar Ultraviolet Radiation ◽  
In Situ Data ◽  
Linear Relationships

Abstract. The proposed strong positive relationship between dimethylsulphide (DMS) concentration and the solar radiation dose (SRD) received into the surface ocean is tested using data from the Atlantic Meridional Transect (AMT) programme. In situ, daily data sampled concurrently with DMS concentrations is used for the component variables of the SRD (mixed layer depth, MLD, surface insolation, I0, and a light attenuation coefficient, k) to calculate SRDinsitu. This is the first time in situ data for all of the components, including k, has been used to test the SRD-DMS relationship over large spatial scales. We find a significant correlation (ρ=0.55 n=65 p<0.01) but the slope of this relationship (0.006 nM/W m−2) is less than previously found at the global (0.019 nM/W m−2) and regional scales (Blanes Bay, Mediterranean, 0.028 nM/W m−2; Sargasso Sea 0.017 nM/W m−2). The correlation is improved (ρ=0.74 n=65 p<0.01) by replacing the in situ data with an estimated I0 (which assumes a constant 50% removal of the top of atmosphere value; 0.5×TOA), a MLD climatology and a fixed value for k following previous work. Equally strong, but non-linear relationships are also found between DMS and both in situ MLD (ρ=0.61 n=65 p<0.01) and the estimated I0 (ρ=0.73 n=65 p<0.01) alone. Using a satellite-retrieved, cloud-adjusted surface UVA irradiance to calculate a UV radiation dose (UVRD) with a climatological MLD also provides an equivalent correlation (ρ=0.67 n=54 p<0.01) to DMS. With this data, MLD appears the dominant control upon DMS concentrations and remains a useful shorthand to prediction without fully resolving the biological processes involved. However, the implied relationship between the incident solar/ultraviolet radiation (modulated by MLD), and sea surface DMS concentrations, is critical for closing a climate feedback loop.

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