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

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).

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In situ data supporting remote sensing estimation of spruce forest parameters at the ecosystem station Bílý Kříž

Beskydy ◽

10.11118/beskyd201710010075 ◽

2017 ◽

Vol 10 (1-2) ◽

pp. 75-86 ◽

Cited By ~ 3

Author(s):

Lucie Homolová ◽

Růžena Janoutová ◽

Petr Lukeš ◽

Jan Hanuš ◽

Jan Novotný ◽

...

Keyword(s):

Remote Sensing ◽

Leaf Area Index ◽

Leaf Area ◽

Spatial Scales ◽

Carotenoid Content ◽

Area Index ◽

Needle Age ◽

Quantitative Parameters ◽

In Situ Data

Remote sensing offers an effective way of mapping vegetation parameters in a spatially continuous manner, at larger spatial scales and repeatedly in time compared to traditional in situ mapping approaches that are typically accurate, but limited to a few distributed location and few repetitions. In case of forest ecosystems, remote sensing allow to assess quantitative parameters or indicators related to forest health status such as leaf area index, leaf pigment content, chlorophyll fluorescence, etc. Development, calibration and validation of remote sensing-based methods, however, still rely on supportive in situ data. The aim of this contribution is to introduce the individual in situ components in the framework for the retrieval of forest quantitative parameters from airborne imaging spectroscopy data. All measurements were acquired during an extensive in situ/flight campaign that took place at the Norway spruce dominated study site Bílý Kříž (Moravian-Silesian Beskydy Mts., Czech Republic) during August 2016. In addition to airborne remote sensing data acquisition, the in situ activities included terrestrial laser scanning for tree 3D modelling, measurements of needle biochemical and optical properties, leaf area index measurements and spectral measurements of various natural and artificial surfaces. Leaf pigments varied between 25.2 and 49.1 µg cm-2 for chlorophyll a+b content, 4.9 – 10.6 µg cm-2 for carotenoid content depending on needle age and its adaptation to sun illumination, whereas ratio between the two pigments was stable around 4.6 – 5. 3. Specific leaf area of spruce needles varied between 49.3 and 105.8 cm2 g-1, being the highest for the shade adapted needles of the current year. Leaf area index of spruce stands of various age and densities varied between 5.3 and 9. 3.

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Global downscaling of remotely sensed soil moisture using neural networks

Hydrology and Earth System Sciences ◽

10.5194/hess-22-5341-2018 ◽

2018 ◽

Vol 22 (10) ◽

pp. 5341-5356 ◽

Cited By ~ 10

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.

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In Situ Isotopic Analysis of Uraninite Microstructures from the Oklo-Okélobondo Natural Fission Reactors, Gabon

MRS Proceedings ◽

10.1557/proc-713-jj8.5 ◽

2002 ◽

Vol 713 ◽

Cited By ~ 2

Author(s):

Mostafa Fayek ◽

Keld A. Jensen ◽

Rodney C. Ewing ◽

Lee R. Riciputi

Keyword(s):

Spatial Resolution ◽

Spent Nuclear Fuel ◽

Spatial Scales ◽

Ionization Mass ◽

Near Surface ◽

Chain Reactions ◽

Isotopic Analyses ◽

Fission Reactors

ABSTRACTUranium deposits can provide important information on the long-term performance of radioactive waste forms because uraninite (UO2+X) is similar to the UO2 in spent nuclear fuel. The Oklo-Okélobondo U-deposits, Gabon, serve as natural laboratory where the long-term (hundreds to billions of years) migration of uranium and other radionuclides can be studied over large spatial scales (nm to km). The natural fission reactors associated with the Oklo- Okélobondo U-deposits occur over a range of depths (100 to 400 m) and provide a unique opportunity to study the behavior of uraninite in near surface oxidizing environments versus more reducing conditions at depth. Previously, it has been difficult to constrain the timing of interaction between U-rich minerals and post-depositional fluids. These problems are magnified because uraninite is susceptible to alteration, it continuously self-anneals radiation damage, and because these processes are manifested at the nm to μm scale. Uranium, lead and oxygen isotopes can be used to study fluid-uraninite interaction, provided that the analyses are obtained on the micro-scale. Secondary ionization mass spectrometry (SIMS) permits in situ measurement of isotopic ratios with a spatial resolution on the scale of a few μm. Preliminary U-Pb results show that uraninite from all reactor zones are highly discordant with ages aaproaching the timing of fission chain reactions (1945±50 Ma) and resetting events at 1180±47 Ma and 898±46 Ma. Oxygen isotopic analyses show that uraninite from reactors that occur in near surface environments (δ18O= −14.4‰ to −8.5‰) have reacted more extensively with groundwater of meteoric origin relative to reactors located at greater depths (μ18O= −10.2‰ to −7.3‰). This study emphasizes the importance of using in situ high spatial resolution analysis techniques for natural analogue studies.

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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

10.5194/essd-2017-53 ◽

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).

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New Downscaling Approach Using ESA CCI SM Products for Obtaining High Resolution Surface Soil Moisture

Remote Sensing ◽

10.3390/rs12071119 ◽

2020 ◽

Vol 12 (7) ◽

pp. 1119 ◽

Cited By ~ 2

Author(s):

Jovan Kovačević ◽

Željko Cvijetinović ◽

Nikola Stančić ◽

Nenad Brodić ◽

Dragan Mihajlović

Keyword(s):

Soil Moisture ◽

High Resolution ◽

Random Forest ◽

Spatial Resolution ◽

Surface Soil ◽

Initial Step ◽

Surface Soil Moisture ◽

In Situ Data ◽

Random Forest Models

ESA CCI SM products have provided remotely-sensed surface soil moisture (SSM) content with the best spatial and temporal coverage thus far, although its output spatial resolution of 25 km is too coarse for many regional and local applications. The downscaling methodology presented in this paper improves ESA CCI SM spatial resolution to 1 km using two-step approach. The first step is used as a data engineering tool and its output is used as an input for the Random forest model in the second step. In addition to improvements in terms of spatial resolution, the approach also considers the problem of data gaps. The filling of these gaps is the initial step of the procedure, which in the end produces a continuous product in both temporal and spatial domains. The methodology uses combined active and passive ESA CCI SM products in addition to in situ soil moisture observations and the set of auxiliary downscaling predictors. The research tested several variants of Random forest models to determine the best combination of ESA CCI SM products. The conclusion is that synergic use of all ESA CCI SM products together with the auxiliary datasets in the downscaling procedure provides better results than using just one type of ESA CCI SM product alone. The methodology was applied for obtaining SSM maps for the area of California, USA during 2016. The accuracy of tested models was validated using five-fold cross-validation against in situ data and the best variation of model achieved RMSE, R2 and MAE of 0.0518 m3/m3, 0.7312 and 0.0374 m3/m3, respectively. The methodology proved to be useful for generating high-resolution SSM products, although additional improvements are necessary.

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

Remote Sensing ◽

10.3390/rs12071207 ◽

2020 ◽

Vol 12 (7) ◽

pp. 1207 ◽

Cited By ~ 1

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.

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

Biogeosciences ◽

10.5194/bg-6-1927-2009 ◽

2009 ◽

Vol 6 (9) ◽

pp. 1927-1934 ◽

Cited By ~ 16

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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Utility of Satellite Remote Sensing for Land–Atmosphere Coupling and Drought Metrics

Journal of Hydrometeorology ◽

10.1175/jhm-d-16-0171.1 ◽

2017 ◽

Vol 18 (3) ◽

pp. 863-877 ◽

Cited By ~ 7

Author(s):

Joshua K. Roundy ◽

Joseph A. Santanello

Keyword(s):

Remote Sensing ◽

Satellite Remote Sensing ◽

Prediction Models ◽

Water Cycle ◽

Spatial Scales ◽

Complex Nature ◽

Sufficient Information ◽

Tropospheric Temperature ◽

In Situ Data

Abstract Feedbacks between the land and the atmosphere can play an important role in the water cycle, and a number of studies have quantified land–atmosphere (LA) interactions and feedbacks through observations and prediction models. Because of the complex nature of LA interactions, the observed variables are not always available at the needed temporal and spatial scales. This work derives the Coupling Drought Index (CDI) solely from satellite data and evaluates the input variables and the resultant CDI against in situ data and reanalysis products. NASA’s Aqua satellite and retrievals of soil moisture and lower-tropospheric temperature and humidity properties are used as input. Overall, the Aqua-based CDI and its inputs perform well at a point, spatially, and in time (trends) compared to in situ and reanalysis products. In addition, this work represents the first time that in situ observations were utilized for the coupling classification and CDI. The combination of in situ and satellite remote sensing CDI is unique and provides an observational tool for evaluating models at local and large scales. Overall, results indicate that there is sufficient information in the signal from simultaneous measurements of the land and atmosphere from satellite remote sensing to provide useful information for applications of drought monitoring and coupling metrics.

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Assessing Water Stress of Desert Tamarugo Trees Using in situ Data and Very High Spatial Resolution Remote Sensing

Remote Sensing ◽

10.3390/rs5105064 ◽

2013 ◽

Vol 5 (10) ◽

pp. 5064-5088 ◽

Cited By ~ 17

Author(s):

Roberto Chávez ◽

Jan Clevers ◽

Martin Herold ◽

Edmundo Acevedo ◽

Mauricio Ortiz

Keyword(s):

Remote Sensing ◽

Water Stress ◽

Spatial Resolution ◽

High Spatial Resolution ◽

In Situ Data ◽

Very High Spatial Resolution ◽

Very High

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

Remote Sensing ◽

10.3390/rs13091748 ◽

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.

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