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 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).

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

<p>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. </p><p>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. </p><p>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  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. </p>

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 Physically based retrieval of crop characteristics for improved water use estimates

2009 ◽  
Vol 13 (5) ◽  
pp. 663-674 ◽  
Author(s):  
K. Richter ◽  
W. J. Timmermans
Keyword(s):  
Sustainable Management ◽  
Test Site ◽  
Balance Model ◽  
Radiative Transfer Model ◽  
Irrigated Agriculture ◽  
Transfer Model ◽  
Retrieval Performance ◽  
Area Index ◽  
Physically Based

Abstract. The increasing scarcity of water from local to global scales requires the efficient monitoring of this valuable resource, especially in the context of a sustainable management in irrigated agriculture. In this study, a two-source energy balance model (TSEB) was applied to the Barrax test site. The inputs of leaf area index (LAI) and fractional vegetation cover (fCover) were estimated from CHRIS imagery by using the traditional scaled NDVI and a look-up table (LUT) inversion approach. The LUT was constructed by using the well established SAILH + PROSPECT radiative transfer model. Simulated fluxes were compared with tower measurements and vegetation characteristics were evaluated with in situ LAI and fCover measurements of a range of crops from the SPARC campaign 2004. Results showed a better retrieval performance for the LUT approach for canopy parameters, affecting flux predictions that were related to land use.

scholarly journals Spatial Distribution of Calibrated WOFOST Parameters and Their Influence on the Performances of a Regional Yield orecasting System

2013 ◽  
Vol 2 (4) ◽  
pp. 12 ◽  
Author(s):  
Bakary Djaby ◽  
Allard De Wit ◽  
Louis Kouadio ◽  
Moussa El Jarroudi ◽  
Bernard Tychon
Keyword(s):  
Winter Wheat ◽  
Air Temperature ◽  
Western Europe ◽  
Test Site ◽  
Qualitative Assessment ◽  
Growth Monitoring ◽  
Area Index ◽  
Crop Parameters ◽  
Two Parameters ◽  
Crop Variety

We investigate in this study (i) a redefinition of crop variety zonations at a spatial scale of 10x10 km, and (ii) the influence of recalibrated crop parameters on regional yield forecasting of winter wheat and grain maize in western Europe. The baseline zonation and initial crop parameter set was derived from the operational European crop growth monitoring system (CGMS) which involves the agrometeorological model WOFOST. Air temperature data from 325 weather stations over the 1992-2007 period were used to define new zonations in a 300 x 300 km test site. Two parameters which influenced mostly the phenological development stages (i.e. TSUM1 and TSUM2, the effective air temperature sums from emergence to anthesis, and from anthesis to maturity, respectively) were chosen and calibrated. The CGMS was finally run based on these new recalibrated parameters and simulated crop status indicators were compared with official statistics over the 2000-2007 period. Our results showed that the days of anthesis and maturity were simulated with coefficients of determination (R2) ranging from 0.22 to 0.87 for both crops over the study site. A qualitative assessment of maximum leaf area index and harvest index also revealed a more consistent spatial pattern than the initial zonation in the simulation results. Finally, recalibrated TSUM1 and TSUM2 led to improved relationships between official yield and simulated crop indicators (significant R2 in 17 out of 28 and in 14 out of 59 NUTS3 regions with respect to the best predictor for grain maize and winter wheat, respectively).

scholarly journals Assessing the Effect of Real Spatial Resolution of In Situ UAV Multispectral Images on Seedling Rapeseed Growth Monitoring

2020 ◽  
Vol 12 (7) ◽  
pp. 1207 ◽  
Author(s):  
Jian Zhang ◽  
Chufeng Wang ◽  
Chenghai Yang ◽  
Tianjin Xie ◽  
Zhao Jiang ◽  
...  
Keyword(s):  
Spatial Resolution ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Vegetation Indices ◽  
Crop Growth ◽  
Growth Monitoring ◽  
Multispectral Images ◽  
Area Index ◽  
Uav Images

The spatial resolution of in situ unmanned aerial vehicle (UAV) multispectral images has a crucial effect on crop growth monitoring and image acquisition efficiency. However, existing studies about optimal spatial resolution for crop monitoring are mainly based on resampled images. Therefore, the resampled spatial resolution in these studies might not be applicable to in situ UAV images. In order to obtain optimal spatial resolution of in situ UAV multispectral images for crop growth monitoring, a RedEdge Micasense 3 camera was installed onto a DJI M600 UAV flying at different heights of 22, 29, 44, 88, and 176m to capture images of seedling rapeseed with ground sampling distances (GSD) of 1.35, 1.69, 2.61, 5.73, and 11.61 cm, respectively. Meanwhile, the normalized difference vegetation index (NDVI) measured by a GreenSeeker (GS-NDVI) and leaf area index (LAI) were collected to evaluate the performance of nine vegetation indices (VIs) and VI*plant height (PH) at different GSDs for rapeseed growth monitoring. The results showed that the normalized difference red edge index (NDRE) had a better performance for estimating GS-NDVI (R2 = 0.812) and LAI (R2 = 0.717), compared with other VIs. Moreover, when GSD was less than 2.61 cm, the NDRE*PH derived from in situ UAV images outperformed the NDRE for LAI estimation (R2 = 0.757). At oversized GSD (≥5.73 cm), imprecise PH information and a large heterogeneity within the pixel (revealed by semi-variogram analysis) resulted in a large random error for LAI estimation by NDRE*PH. Furthermore, the image collection and processing time at 1.35 cm GSD was about three times as long as that at 2.61 cm. The result of this study suggested that NDRE*PH from UAV multispectral images with a spatial resolution around 2.61 cm could be a preferential selection for seedling rapeseed growth monitoring, while NDRE alone might have a better performance for low spatial resolution images.

scholarly journals Comparison of Crop Trait Retrieval Strategies Using UAV-Based VNIR Hyperspectral Imaging

2021 ◽  
Vol 13 (9) ◽  
pp. 1748
Author(s):  
Asmaa Abdelbaki ◽  
Martin Schlerf ◽  
Rebecca Retzlaff ◽  
Miriam Machwitz ◽  
Jochem Verrelst ◽  
...  
Keyword(s):  
Hybrid Methods ◽  
Growing Season ◽  
Statistical Modelling ◽  
Hyperspectral Data ◽  
Learning Approaches ◽  
Agricultural Field ◽  
Illumination Variation ◽  
Area Index ◽  
In Situ Data

Hyperspectral cameras onboard unmanned aerial vehicles (UAVs) have recently emerged for monitoring crop traits at the sub-field scale. Different physical, statistical, and hybrid methods for crop trait retrieval have been developed. However, spectra collected from UAVs can be confounded by various issues, including illumination variation throughout the crop growing season, the effect of which on the retrieval performance is not well understood at present. In this study, four retrieval methods are compared, in terms of retrieving the leaf area index (LAI), fractional vegetation cover (fCover), and canopy chlorophyll content (CCC) of potato plants over an agricultural field for six dates during the growing season. We analyzed: (1) The standard look-up table method (LUTstd), (2) an improved (regularized) LUT method that involves variable correlation (LUTreg), (3) hybrid methods, and (4) random forest regression without (RF) and with (RFexp) the exposure time as an additional explanatory variable. The Soil–Leaf–Canopy (SLC) model was used in association with the LUT-based inversion and hybrid methods, while the statistical modelling methods (RF and RFexp) relied entirely on in situ data. The results revealed that RFexp was the best-performing method, yielding the highest accuracies, in terms of the normalized root mean square error (NRMSE), for LAI (5.36%), fCover (5.87%), and CCC (15.01%). RFexp was able to reduce the effects of illumination variability and cloud shadows. LUTreg outperformed the other two retrieval methods (hybrid methods and LUTstd), with an NRMSE of 9.18% for LAI, 10.46% for fCover, and 12.16% for CCC. Conversely, LUTreg led to lower accuracies than those derived from RF for LAI (5.51%) and for fCover (6.23%), but not for CCC (16.21%). Therefore, the machine learning approaches—in particular, RF—appear to be the most promising retrieval methods for application to UAV-based hyperspectral data.

scholarly journals Retrieving and Validating Leaf and Canopy Chlorophyll Content at Moderate Resolution: A Multiscale Analysis with the Sentinel-3 OLCI Sensor

2021 ◽  
Vol 13 (8) ◽  
pp. 1419
Author(s):  
Charlotte De Grave ◽  
Luca Pipia ◽  
Bastian Siegmann ◽  
Pablo Morcillo-Pallarés ◽  
Juan Pablo Rivera-Caicedo ◽  
...  
Keyword(s):  
Chlorophyll Content ◽  
Spatial Resolution ◽  
Spatial Scales ◽  
Fluorescence Signal ◽  
Scaling Analysis ◽  
Terrestrial Vegetation ◽  
Area Index ◽  
In Situ Data ◽  
Linear Behavior

ESA’s Eighth Earth Explorer mission “FLuorescence EXplorer” (FLEX) will be dedicated to the global monitoring of the chlorophyll fluorescence emitted by vegetation. In order to properly interpret the measured fluorescence signal, essential vegetation variables need to be retrieved concomitantly. FLEX will fly in tandem formation with Sentinel-3 (S3), which conveys the Ocean and Land Color Instrument (OLCI) that is designed to characterize the atmosphere and the terrestrial vegetation at a spatial resolution of 300 m. In support of FLEX’s preparatory activities, this paper presents a first validation exercise of OLCI vegetation products against in situ data coming from the 2018 FLEXSense campaign. During this campaign, leaf chlorophyll content (LCC) and leaf area index (LAI) measurements were collected over croplands, while HyPlant DUAL images of the area were acquired at a 3 m spatial resolution. A multiscale validation strategy was pursued. First, estimates of these two variables, together with the combined canopy chlorophyll content (CCC = LCC × LAI), were obtained at the HyPlant spatial resolution and were compared against the in situ measurements. Second, the fine-scale retrieval maps from HyPlant were coarsened to the S3 spatial scale as a reference to assess the quality of the OLCI vegetation products. As an intermediary step, vegetation products extracted from Sentinel-2 data were used to compare retrievals at the in-between spatial resolution of 20 m. For all spatial scales, CCC delivered the most accurate estimates with the smallest prediction error obtained at the 300 m resolution (R2 of 0.74 and RMSE = 26.8 μg cm−2). Results of a scaling analysis suggest that CCC performs well at the different tested spatial resolutions since it presents a linear behavior across scales. LCC, on the other hand, was poorly retrieved at the 300 m scale, showing overestimated values over heterogeneous pixels. The introduction of a new LCC model integrating mixed reflectance spectra in its training enabled to improve by 16% the retrieval accuracy for this variable (RMSE = 10 μg cm−2 for the new model versus RMSE = 11.9 μg cm−2 for the former model).

scholarly journals LUCAS Copernicus 2018: Earth-observation-relevant in situ data on land cover and use throughout the European Union

2021 ◽  
Vol 13 (3) ◽  
pp. 1119-1133
Author(s):  
Raphaël d'Andrimont ◽  
Astrid Verhegghen ◽  
Michele Meroni ◽  
Guido Lemoine ◽  
Peter Strobl ◽  
...  
Keyword(s):  
Land Use ◽  
European Union ◽  
High Resolution ◽  
Land Cover ◽  
Earth Observation ◽  
The European Union ◽  
In Situ Data ◽  
Cardinal Directions ◽  
High Resolution Satellite Imagery

Abstract. The Land Use/Cover Area frame Survey (LUCAS) is an evenly spaced in situ land cover and land use ground survey exercise that extends over the whole of the European Union. LUCAS was carried out in 2006, 2009, 2012, 2015, and 2018. A new LUCAS module specifically tailored to Earth observation (EO) was introduced in 2018: the LUCAS Copernicus module. The module surveys the land cover extent up to 51 m in four cardinal directions around a point of observation, offering in situ data compatible with the spatial resolution of high-resolution sensors. However, the use of the Copernicus module being marginal, the goal of the paper is to facilitate its uptake by the EO community. First, the paper summarizes the LUCAS Copernicus protocol to collect homogeneous land cover on a surface area of up to 0.52 ha. Secondly, it proposes a methodology to create a ready-to-use dataset for Earth observation land cover and land use applications with high-resolution satellite imagery. As a result, a total of 63 364 LUCAS points distributed over 26 level-2 land cover classes were surveyed on the ground. Using homogeneous extent information in the four cardinal directions, a polygon was delineated for each of these points. Through geospatial analysis and by semantically linking the LUCAS core and Copernicus module land cover observations, 58 426 polygons are provided with level-3 land cover (66 specific classes including crop type) and land use (38 classes) information as inherited from the LUCAS core observation. The open-access dataset supplied with this paper (https://doi.org/10.6084/m9.figshare.12382667.v4 d'Andrimont, 2020) provides a unique opportunity to train and validate decametric sensor-based products such as those obtained from the Copernicus Sentinel-1 and Sentinel-2 satellites. A follow-up of the LUCAS Copernicus module is already planned for 2022. In 2022, a simplified version of the LUCAS Copernicus module will be carried out on 150 000 LUCAS points for which in situ surveying is planned. This guarantees a continuity in the effort to find synergies between statistical in situ surveying and the need to collect in situ data relevant for Earth observation in the European Union.

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