Validation and application of the MERIS Terrestrial Chlorophyll Index.

Climate is one of the key variables driving ecosystems at local to global scales. How and to what extent vegetation responds to climate variability is a challenging topic for global change analysis. Earth observation provides an opportunity to study temporal ecosystem dynamics, providing much needed information about the response of vegetation to environmental and climatic change at local to global scales. The European Space Agency (ESA) uses data recorded by the Medium Resolution Imaging Spectrometer (MERlS) in red I near infrared spectral bands to produce an operational product called the MERlS Terrestrial Chlorophyll Index (MTCI). The MTCI is related to the position of the red edge in vegetation spectra and can be used to estimate the chlorophyll content of vegetation. The MTCI therefore provides a powerful product to monitor phenology, stress and productivity. The MTCI needs full validation if it is to be embraced by the user community who require precise and consistent, spatial and temporal comparisons of vegetation condition. This research details experimental investigations into variables that may influence the relationship between the MTCI and vegetation chlorophyll content, namely soil background and sensor view angle, vegetation type and spatial scale. Validation campaigns in the New Forest and at Brooms Barn agricultural study site reinforced the strong correlation between chlorophyll content and MTCI that was evident from laboratory spectroscopy investigations, demonstrating the suitability of the MTCI as a surrogate for field chlorophyll content measurements independent of cover type. However, this relationship was significantly weakened where the leaf area index (LAI) was low, indicating that the MTCI is sensitive to the effects of soil background. In the light of such conclusions, this project then assessed the MTCI as a tool to monitor changes in ecosystem phenology as a function of climatic variability, and the suitability of the MTCI as a surrogate measure of photosynthetic light use efficiency, to model ecosystem gross primary productivity (GPP) at various sites in North America with contrasting vegetation types. Changes in MTCI throughout the growing season demonstrated the potential of the MTCI to estimate vegetation dynamics, characterising the temporal characteristics in both phenology and gross primary productivity

Land Surface Temperature Product Validation Best Practice Protocol Version 1.0 - October, 2017

The Global Climate Observing System (GCOS) has specified the need to systematically generate andvalidate Land Surface Temperature (LST) products. This document provides recommendations on goodpractices for the validation of LST products. Internationally accepted definitions of LST, emissivity andassociated quantities are provided to ensure the compatibility across products and reference data sets. Asurvey of current validation capabilities indicates that progress is being made in terms of up-scaling and insitu measurement methods, but there is insufficient standardization with respect to performing andreporting statistically robust comparisons.Four LST validation approaches are identified: (1) Ground-based validation, which involvescomparisons with LST obtained from ground-based radiance measurements; (2) Scene-based intercomparisonof current satellite LST products with a heritage LST products; (3) Radiance-based validation,which is based on radiative transfer calculations for known atmospheric profiles and land surface emissivity;(4) Time series comparisons, which are particularly useful for detecting problems that can occur during aninstrument's life, e.g. calibration drift or unrealistic outliers due to undetected clouds. Finally, the need foran open access facility for performing LST product validation as well as accessing reference LST datasets isidentified

Quantifying Fundamental Vegetation Traits over Europe Using the Sentinel-3 OLCI Catalogue in Google Earth Engine

Thanks to the emergence of cloud-computing platforms and the ability of machine learning methods to solve prediction problems efficiently, this work presents a workflow to automate spatiotemporal mapping of essential vegetation traits from Sentinel-3 (S3) imagery. The traits included leaf chlorophyll content (LCC), leaf area index (LAI), fraction of absorbed photosynthetically active radiation (FAPAR), and fractional vegetation cover (FVC), being fundamental for assessing photosynthetic activity on Earth. The workflow involved Gaussian process regression (GPR) algorithms trained on top-of-atmosphere (TOA) radiance simulations generated by the coupled canopy radiative transfer model (RTM) SCOPE and the atmospheric RTM 6SV. The retrieval models, named to S3-TOA-GPR-1.0, were directly implemented in Google Earth Engine (GEE) to enable the quantification of the traits from TOA data as acquired from the S3 Ocean and Land Colour Instrument (OLCI) sensor. Following good to high theoretical validation results with normalized root mean square error (NRMSE) ranging from 5% (FAPAR) to 19% (LAI), a three fold evaluation approach over diverse sites and land cover types was pursued: (1) temporal comparison against LAI and FAPAR products obtained from Moderate Resolution Imaging Spectroradiometer (MODIS) for the time window 2016–2020, (2) spatial difference mapping with Copernicus Global Land Service (CGLS) estimates, and (3) direct validation using interpolated in situ data from the VALERI network. For all three approaches, promising results were achieved. Selected sites demonstrated coherent seasonal patterns compared to LAI and FAPAR MODIS products, with differences between spatially averaged temporal patterns of only 6.59%. In respect of the spatial mapping comparison, estimates provided by the S3-TOA-GPR-1.0 models indicated highest consistency with FVC and FAPAR CGLS products. Moreover, the direct validation of our S3-TOA-GPR-1.0 models against VALERI estimates indicated good retrieval performance for LAI, FAPAR and FVC. We conclude that our retrieval workflow of spatiotemporal S3 TOA data processing into GEE opens the path towards global monitoring of fundamental vegetation traits, accessible to the whole research community.We gratefully acknowledge the financial support by the European Space Agency (ESA) for airborne data acquisition and data analysis in the frame of the FLEXSense campaign (ESA Contract No. 4000125402/18/NL/NA). The research was also supported by the Action CA17134 SENSECO (Optical synergies for spatiotemporal sensing of scalable ecophysiological traits) funded by COST (European Cooperation in Science and Technology, www.cost.eu, accessed on: 8 January 2022). This publication is also the result of the project implementation: “Scientific support of climate change adaptation in agriculture and mitigation of soil degradation” (ITMS2014+313011W580) supported by the Integrated Infrastructure Operational Programme funded by the ERDF

Remote sensing of boreal land cover : estimation of forest attributes and extent

Remote sensing provides methods to infer land cover information over large geographical areas at a variety of spatial and temporal resolutions. Land cover is input data for a range of environmental models and information on land cover dynamics is required for monitoring the implications of global change. Such data are also essential in support of environmental management and policymaking. Boreal forests are a key component of the global climate and a major sink of carbon. The northern latitudes are expected to experience a disproportionate and rapid warming, which can have a major impact on vegetation at forest limits. This thesis examines the use of optical remote sensing for estimating aboveground biomass, leaf area index (LAI), tree cover and tree height in the boreal forests and tundra taiga transition zone in Finland. The continuous fields of forest attributes are required, for example, to improve the mapping of forest extent. The thesis focus on studying the feasibility of satellite data at multiple spatial resolutions, assessing the potential of multispectral, -angular and -temporal information, and provides regional evaluation for global land cover data. Preprocessed ASTER, MISR and MODIS products are the principal satellite data. The reference data consist of field measurements, forest inventory data and fine resolution land cover maps. Fine resolution studies demonstrate how statistical relationships between biomass and satellite data are relatively strong in single species and low biomass mountain birch forests in comparison to higher biomass coniferous stands. The combination of forest stand data and fine resolution ASTER images provides a method for biomass estimation using medium resolution MODIS data. The multiangular data improve the accuracy of land cover mapping in the sparsely forested tundra taiga transition zone, particularly in mires. Similarly, multitemporal data improve the accuracy of coarse resolution tree cover estimates in comparison to single date data. Furthermore, the peak of the growing season is not necessarily the optimal time for land cover mapping in the northern boreal regions. The evaluated coarse resolution land cover data sets have considerable shortcomings in northernmost Finland and should be used with caution in similar regions. The quantitative reference data and upscaling methods for integrating multiresolution data are required for calibration of statistical models and evaluation of land cover data sets. The preprocessed image products have potential for wider use as they can considerably reduce the time and effort used for data processing.Kaukokartoituksella voidaan tuottaa tietoa maanpeitteen ominaisuuksista ja muutoksista laajoilla alueilla. Tietoa maanpeitteestä tarvitaan esimerkiksi ympäristömalleihin, ilmastonmuutoksen vaikutusten seurantaan ja päätöksenteon tueksi. Boreaalisilla metsillä on tärkeä merkitys maapallon ilmastolle ja ne ovat tärkeä hiilinielu. Pohjoisten alueiden ilmaston on ennustettu lämpenevän voimakkaasti ilmastonmuutoksen seurauksena, millä voi olla merkittävä vaikutus metsänrajavyöhykkeen kasvillisuuteen. Väitöskirjassa tarkastellaan optisen alueen satelliittikaukokartoituksen käyttöä metsän ominaisuuksien, kuten biomassan ja puuston peittävyyden arviointiin ja kartoitukseen. Tutkimusalueet sijaitsevat eteläisessä Suomessa ja Pohjois-Suomen metsänrajavyöhykkeessä. Keskeisimpinä tavoitteina oli tutkia satelliittikuva-aineistojen käyttökelpoisuutta ja monikulmaisen ja -aikaisen informaation mahdollisuuksia sekä arvioida globaalien maanpeitetuotteiden luotettavuutta. Satelliittikuva-aineistona käytettiin ASTER, MISR ja MODIS -kuvatuotteita ja vertailuaineistona maastomittauksia, inventointiaineistoja ja maanpeitekarttoja. Tutkimustuloksia voidaan hyödyntää maanpeitteen kartoituksessa ja muutostulkinnassa boreaalisilla alueilla. Korkearesoluutioiset aineistot havainnollistavat kuinka heijastuksen ja biomassan välinen riippuvuus on voimakkaampi harvapuustoisissa tunturikoivikoissa kuin havupuuvaltaisissa metsissä, joiden biomassa on suurempi. Käyttämällä yhdessä kuvioittaista maastoaineistoa ja eri resoluutioisia satelliittikuvia voidaan tuottaa biomassa-arvioita laajoille alueille. Metsänrajavyöhykkeessä monikulmaiset aineistot parantavat metsämuuttujien arvioita vähentäen yliarviointia ongelmallisilla avosoilla ja pensastoisilla alueilla. Myös moniaikainen aineisto parantaa kartoitustarkkuutta. Keskikesän kuvat eivät ole välttämättä ihanteellisimpia kasvipeitteen tulkintaan. Globaalit maanpeitetuotteet osoittautuivat Ylä-Lapissa puutteellisiksi ja niitä tulee käyttää varauksella vastaavilla alueilla, esimerkiksi arvioitaessa metsän laajuutta. Tutkimuksessa korostuivat myös kvantitatiivisen maastoaineiston merkitys maanpeiteaineistojen arvioinnissa sekä maasto- ja satelliittikuva-aineiston yhdistämiseen liittyvät kysymykset. Työssä käytetyt esikäsitellyt kuva-aineistot voivat jatkossa vähentää merkittävästi kuvankäsittelyyn käytettävää aikaa

Evaluating Soil Resistance Formulations in Thermal‐Based Two‐Source Energy Balance (TSEB) Model: Implications for Heterogeneous Semiarid and Arid Regions

Relatively small fluctuations in the surface energy balance and evapotranspiration in semiarid and arid regions can be indicative of significant changes to ecosystem health. Therefore, it is imperative to have approaches for monitoring surface fluxes in these regions. The remote sensing‐based two‐source energy balance (TSEB) model is a suitable method for flux estimation over sparsely vegetated semiarid and arid landscapes since it explicitly considers surface energy flux contributions from soil and vegetation. However, previous studies indicate that TSEB generally underestimates sensible heat flux (H) and hence overestimates latent heat flux (LE) or evapotranspiration for these regions unless soil resistance coefficients are modified based on additional ground information. In this study, TSEB is applied over semiarid and arid regions on three continents using the original soil resistance formulation with modified coefficients and a recently developed physically based soil resistance formulation. Model sensitivity analysis demonstrates the high sensitivity of TSEB with original soil resistance formulation to soil resistance coefficients, while TSEB with the new soil resistance formulation has relatively low sensitivity to uncertainties in all coefficients. The performance of TSEB using different soil resistance formulations is evaluated by comparing modeled H against eddy covariance measurements in six semiarid and arid study sites and ranking the error statistics. Our results indicate that incorporating the new soil resistance formulation into TSEB would enhance its utility in flux estimation over heterogeneous landscapes by obviating its reliance on semiempirical coefficients and thus provide more robust fluxes over sparsely vegetated regions without model calibration and/or parameter tuning.info:eu-repo/semantics/publishedVersio