Development and Evaluation of a Multi-Year Fractional Surface Water Data Set Derived from Active/Passive Microwave Remote Sensing Data

abstract: The sensitivity of Earth’s wetlands to observed shifts in global precipitation and temperature patterns and their ability to produce large quantities of methane gas are key global change questions. We present a microwave satellite-based approach for mapping fractional surface water (FW) globally at 25-km resolution. The approach employs a land cover-supported, atmospherically-corrected dynamic mixture model applied to 20+ years (1992–2013) of combined, daily, passive/active microwave remote sensing data. The resulting product, known as Surface WAter Microwave Product Series (SWAMPS), shows strong microwave sensitivity to sub-grid scale open water and inundated wetlands comprising open plant canopies. SWAMPS’ FW compares favorably (R[superscript 2] = 91%–94%) with higher-resolution, global-scale maps of open water from MODIS and SRTM-MOD44W. Correspondence of SWAMPS with open water and wetland products from satellite SAR in Alaska and the Amazon deteriorates when exposed wetlands or inundated forests captured by the SAR products were added to the open water fraction reflecting SWAMPS’ inability to detect water underneath the soil surface or beneath closed forest canopies. Except for a brief period of drying during the first 4 years of observation, the inundation extent for the global domain excluding the coast was largely stable. Regionally, inundation in North America is advancing while inundation is on the retreat in Tropical Africa and North Eurasia. SWAMPS provides a consistent and long-term global record of daily FW dynamics, with documented accuracies suitable for hydrologic assessment and global change-related investigations.The final version of this article, as published in Remote Sensing, can be viewed online at: http://www.mdpi.com/2072-4292/7/12/1584

Development and Evaluation of a Multi-Year Fractional Surface Water Data Set Derived from Active/Passive Microwave Remote Sensing Data

The sensitivity of Earth’s wetlands to observed shifts in global precipitation and temperature patterns and their ability to produce large quantities of methane gas are key global change questions. We present a microwave satellite-based approach for mapping fractional surface water (FW) globally at 25-km resolution. The approach employs a land cover-supported, atmospherically-corrected dynamic mixture model applied to 20+ years (1992–2013) of combined, daily, passive/active microwave remote sensing data. The resulting product, known as Surface WAter Microwave Product Series (SWAMPS), shows strong microwave sensitivity to sub-grid scale open water and inundated wetlands comprising open plant canopies. SWAMPS’ FW compares favorably (R2 = 91%–94%) with higher-resolution, global-scale maps of open water from MODIS and SRTM-MOD44W. Correspondence of SWAMPS with open water and wetland products from satellite SAR in Alaska and the Amazon deteriorates when exposed wetlands or inundated forests captured by the SAR products were added to the open water fraction reflecting SWAMPS’ inability to detect water underneath the soil surface or beneath closed forest canopies. Except for a brief period of drying during the first 4 years of observation, the inundation extent for the global domain excluding the coast was largely stable. Regionally, inundation in North America is advancing while inundation is on the retreat in Tropical Africa and North Eurasia. SWAMPS provides a consistent and long-term global record of daily FW dynamics, with documented accuracies suitable for hydrologic assessment and global change-related investigations

Korekce lokálního dopadového úhlu SAR dat pro analýzu časových řad: metoda specifická pro krajinný pokryv

To ensure the highest possible temporal resolution of SAR data, it is necessary to use all the available acquisition orbits and paths of a selected area. This can be a challenge in a mountainous terrain, where the side-looking geometry of space-borne SAR satellites in combination with different slope and aspect angles of terrain can strongly affect the backscatter intensity. These errors/noises caused by terrain need to be eliminated. Although there have been methods described in the literature that address this problem, none of these methods is prepared for operable and easily accessible time series analysis in the mountainous areas. This study deals with a land cover-specific local incidence angle (LIA) correction method for time-series analysis of forests in mountainous areas. The methodology is based on the use of a linear relationship between backscatter and LIA, which is calculated for each image separately. Using the combination of CORINE and Hansen Global Forest databases, a wide range of different LIAs for a specific forest type can be generated for each individual image. The algorithm is prepared and tested in cloud-based platform Google Earth Engine (GEE) using Sentinel-1 open access data, SRTM digital elevation model, and CORINE and Hansen Global Forest databases. The method was tested...K zajištění co nejvyššího možného časového rozlišení dat SAR je nutné použít všechny dostupné dráhy družic nad daným územím. To může představovat výzvu v hornatém terénu, kde boční snímání družic SAR v kombinaci s různými sklony a aspekty terénu může silně ovlivnit intenzitu zpětného radarového rozptylu. Tyto chyby způsobené terénem je třeba odstranit pro možné porovnání dat v čase. Ačkoli v literatuře jsou popsány metody, které se zabývají tímto problémem, žádná z těchto metod není připravena na operativní a snadno přístupnou analýzu časových řad v horských oblastech. Tato studie se zabývá metodou korekce lokálního dopadového úhlu (LIA) pro analýzu časových řad lesů v horských oblastech. Metodika je založena na použití lineární závislosti mezi radarovým zpětným rozptylem a LIA, který se počítá pro každý satelitní snímek zvlášť. Použitím kombinace databází CORINE a Hansen Global Forest můžeme pro každý jednotlivý snímek získat širokou škálu různých LIA pro konkrétní typ lesa. Algoritmus korekce byl připraven v cloudové platformě Google Earth Engine (GEE) s využitím volně dostupných dat Sentinel-1, digitálního modelu terénu SRTM a databází CORINE a Hansen Global...Katedra aplikované geoinformatiky a kartografieDepartment of Applied Geoinformatics and CartographyFaculty of SciencePřírodovědecká fakult

Harmonization of remote sensing land surface products : correction of clear-sky bias and characterization of directional effects

Tese de doutoramento, Ciências Geofísicas e da Geoinformação (Deteção Remota), Universidade de Lisboa, Faculdade de Ciências, 2018Land surface temperature (LST) is the mean radiative skin temperature of an area of land resulting from the mean energy balance at the surface. LST is an important climatological variable and a diagnostic parameter of land surface conditions, since it is the primary variable determining the upward thermal radiation and one of the main controllers of sensible and latent heat fluxes between the surface and the atmosphere. The reliable and long-term estimation of LST is therefore highly relevant for a wide range of applications, including, amongst others: (i) land surface model validation and monitoring; (ii) data assimilation; (iii) hydrological applications; and (iv) climate monitoring. Remote sensing constitutes the most effective method to observe LST over large areas and on a regular basis. Satellite LST products generally rely on measurements in the thermal infrared (IR) atmospheric window, i.e., within the 8-13 micrometer range. Beside the relatively weak atmospheric attenuation under clear sky conditions, this band includes the peak of the Earth’s spectral radiance, considering surface temperature of the order of 300K (leading to maximum emission at approximately 9.6 micrometer, according to Wien’s Displacement Law). The estimation of LST from remote sensing instruments operating in the IR is being routinely performed for nearly 3 decades. Nevertheless, there is still a long list of open issues, some of them to be addressed in this PhD thesis. First, the viewing position of the different remote sensing platforms may lead to variability of the retrieved surface temperature that depends on the surface heterogeneity of the pixel – dominant land cover, orography. This effect introduces significant discrepancies among LST estimations from different sensors, overlapping in space and time, that are not related to uncertainties in the methodologies or input data used. Furthermore, these directional effects deviate LST products from an ideally defined LST, which should correspond to the ensemble directional radiometric temperature of all surface elements within the FOV. In this thesis, a geometric model is presented that allows the upscaling of in situ measurements to the any viewing configuration. This model allowed generating a synthetic database of directional LST that was used consistently to evaluate different parametric models of directional LST. Ultimately, a methodology is proposed that allows the operational use of such parametric models to correct angular effects on the retrieved LST. Second, the use of infrared data limits the retrieval of LST to clear sky conditions, since clouds “close” the atmospheric window. This effect introduces a clear-sky bias in IR LST datasets that is difficult to quantify since it varies in space and time. In addition, the cloud clearing requirement severely limits the space-time sampling of IR measurements. Passive microwave (MW) measurements are much less affected by clouds than IR observations. LST estimates can in principle be derived from MW measurements, regardless of the cloud conditions. However, retrieving LST from MW and matching those estimations with IR-derived values is challenging and there have been only a few attempts so far. In this thesis, a methodology is presented to retrieve LST from passive MW observations. The MW LST dataset is examined comprehensively against in situ measurements and multiple IR LST products. Finally, the MW LST data is used to assess the spatial-temporal patterns of the clear-sky bias at global scale.Fundação para a Ciência e a Tecnologia, SFRH/BD/9646

La variabilité hydrologique et climatique dans les bassins versants de la Sibérie Occidentale (selon les données des stations météorologiques, de ré-analyse météorologique et d'altimétrie satellitaire)

Le caractère fortement marécageux du territoire de la Sibérie Occidentale et la rareté des stations d'observations hydrométéorologiques compliquent le suivi du régime hydrologique des bassins versants. Dans une telle situation, la prise en compte des données de ré-analyse météorologique et d'altimétrie satellitaires, assurant une couverture régulière de l'ensemble du territoire étudié de la plaine de la Sibérie Occidentale, permet de compléter les observations in situ et d'élargir considérablement la portée des recherches, ce qui détermine la pertinence de ce travail. Le premier chapitre décrit les principaux facteurs physiques et géographiques et les lois qui ont déterminé le développement de la zone d'étude et les caractéristiques de son climat. Le deuxième chapitre est consacré aux méthodes d'étude qui sont utilisées dans la présente thèse. Au début une brève description des objets d'étude est fournie, puis les méthodes mêmes de l'étude. Le chapitre expose en détail le principe de fonctionnement de la méthode d'altimétrie satellitaire, résume les méthodes de télédétection, et également expose la méthode de ré-analyse à l'aide de laquelle sont étudiées les caractéristiques climatiques. Le troisième chapitre présente les résultats des travaux accomplis 1) L'analyse des disponibilités en chaleur et en humidité de diverses zones climatiques et hydrologiques marécageuses, 2) L'analyse de la variabilité de la quantité d'eau dans les bassins fluviaux selon les données d'altimétrie satellitaire. L'originalité de cette thèse est d'appliquer une nouvelle approche d'évaluation du niveau d'eau de la zone étudié à l'aide de données d'altimétrie satellitaire en analysant le signal de rétrodiffusion exprimé en décibels.Because of high level of swamping in West Siberia and a sparse network of hydrometeorological stations, monitoring of hydrological regime is hindered. In such a situation, it is necessary to involve model reanalysis and satellite altimetry data that ensure the regular coverage of the whole West Siberian Plain, making it possible to supplement field studies and to considerably expand the area of research. This determines the relevance of the thesis. Chapter 1 describes the physic and geographic factors as well as mechanisms that determined the evolution of the West Siberian Plain. Climate change of various regions has its particular characteristics and differs from the global state. Chapter 2 is devoted to methods used in the study. In the first part of the chapter, a brief description of the classification of remote sensing methods based on different criteria is presented. The second part is devoted to satellite altimetry method and its operating principle. The main result of the thesis as the time dynamics of air temperature, precipitation and spatio-temporel variability of moisture regime from reanalysis data and satellite altimetry are given in the 3rd chapter. The principle originality of the thesis is to apply satellite altimetry data for estimation of spatial and temporal variability of inundated zones based on the analysis of a reflected signal or backscatter coefficient (BSC), expressed in decibels

An exploratory study to improve the predictive capacity of the crop growth monitoring system as applied by the European Commission

The European Union (EU), through its Common Agricultural Policy (CAP), attempts to regulate the common agricultural market to, among others, secure food supplies and provide consumers with food at reasonable prices. Implementation and control of these CAP regulations is executed by the Directorate General for Agriculture (DG VI) of the EU. To manage this common market, to evaluate the consequences of these regulations and to estimate and control the subsidies to be paid, DG VI requires detailed information on planted area, crop yield and production volume.Information on land use, interannual land use changes and yields is routinely collected by the national statistical services, which convey this information to the statistical office of the European Commission, EUROSTAT. Collection and compilation of these agricultural statistics however, is time consuming and laborious; it often takes up to one or two years before this information is available in the EUROSTAT databases. At this late stage, these statistics are of limited use for evaluating policy or to estimate the amount of subsidies to be paid. Hence, more timely and accurate information is needed. To assist DG VI and EUROSTAT to collect this information, the MARS project was initiated, with the aim to develop methods to produce timely statistics on land use, planted area and production volumes for various crops within the EU.The MARS project applies remote sensing imagery and ground surveys to estimate the planted area. Since no proven methods to relate satellite imagery to quantitative crop yields were available at the beginning of the MARS project, a crop growth monitoring system (CGMS) based on the WOFOST crop growth simulation model was developed.In this thesis several variants of the current standard operational version of CGMS are explored. The standard CGMS version assumes that yield per unit area and planted area are independent of each other. In this thesis total production volume instead of yield per unit area is considered, hypothesizing that the annually planted area and the yield per unit area are mutually dependent and should therefore be analyzed simultaneously. It is assumed that weather and economic factors affect production volume variation. However, for two of the major wheat producing countries the analysis fails to demonstrate a relation between the soft wheat production volume and selling or intervention price. Furthermore, for soft wheat, for 5 out of the 10 investigated countries, and for durum wheat, for 3 out of the 4 investigated countries, the expenditure on crop protection agents is not significantly associated with the production volume. These results suggest that these parameters are not generally applicable and should therefore not be applied for production volume prediction. As an alternative to economic factors, the fertilizer application per unit area is examined. The analysis shows that this factor can account for the trend and production volume variation.Next, production volumes of soft and durum wheat are predicted and two types of prediction models were examined. The first type included the planted area in the prediction model, and production volume was predicted in one step. The second type predicted the production volume in two steps: first, yield per unit area was predicted and subsequently, this value was multiplied by an estimate for the planted area. Furthermore, two functions to describe the trend in yield and production volume series were tested: a linear function of time and a linear function of the fertilizer application. A hypothetical and an operational situation were studied. The hypothetical situation assumes that current year's information on planted area and fertilizer consumption is available, whereas the operational situation assumes that these two variables are not available and consequently have to be estimated.Comparison of the results from the one-step model with those from the two-step model demonstrates that in the operational situation in 14 out of 16 crop-country combinations the one-step model predicted more accurately when a linear time trend was applied. When fertilizer application was applied the one-step model in 10 out of 16 crop-country combinations provided more accurate results. Furthermore, when two-step prediction models were applied, crop simulation results were significant in approximately 30% of the cases (5% t-test). However, when models of the one-step type were used, this number increased to more than 80%.Although these results cannot be viewed as a proof that one-step models are really superior, they still give an indication and provide a direction for further research. It corroborates the assumption that variation in planted area and yield per unit area are not independent and therefore variation in production volume should be analyzed using models of the one-step type.Comparison among the one-step model results in the operational situation shows that in 50% of the investigated crop-country combinations the model that applied simulation results plus either a linear time trend or fertilizer application, predicted more accurately than the model that did not apply simulation results. In the hypothetical situation the two-step model that uses the fertilizer application provided the most accurate results. However, analysis also demonstrates that in the operational situation this model yielded the least accurate results. In this situation, the one-step models provided the most accurate results since they are less sensitive to errors in the planted area estimates.Although the prediction results obtained with simulation results are not always more accurate when compared to results derived from trend extrapolations or simple averages, the use of simulation results in combination with a trend function certainly holds a promise for further improvement.Next, a method to estimate daily global radiation was developed and tested. This method uses cloud cover and the temperature range as input. It provides less accurate results than the Ångström-Prescott equation, but the differences are small. This method may be used as an alternative for the Ångström-Prescott method when sunshine duration observations are not available. A hierarchical method is proposed to introduce global radiation in CGMS. If observed global radiation is available it will be used, if only sunshine duration is available the Ångström-Prescott method will be used, if neither radiation nor sunshine is available, the method developed here may be applied. This method was tested and the prediction results were slightly more accurate than the results obtained with the standard operational version of CGMS.Furthermore, an additive and a multiplicative model are compared. An additive model assumes that variation in production volume as a result of weather variation is similar under high production systems and low production systems. The multiplicative model assumes that variation in production volume over the years is proportional to the mean production level. Wheat production volumes for France were predicted at subregional, regional and national level. The predictions at subregional and regional level were aggregated to national values.The results suggest that more accurate predictions of total national production volume can be obtained when predictions executed at regional or subregional level are aggregated into a national value instead of estimating this value in one step. This may be the result of the applied aggregation procedure. Presumably, local weather effects are obscured in the aggregated values. Another explanation could be that errors in the production volumes of the individual regions or subregions compensate each other when summed to a total national value. These results also provide some evidence that aggregated predictions derived from the multiplicative model are more accurate than those derived from the additive model, suggesting that effects of weather on crop growth depend on the magnitude of the annual mean yield.Finally, data obtained from the field surveys executed in the framework of the MARS are analyzed with the aim to increase insight in sowing strategies of rainfed barley in semi-arid regions. The hypothesis is that in CGMS sowing date variation should be accounted for: CGMS assumes per crop and per region one sowing and one flowering date, hypothesizing that sowing and flowering date variation have limited effects on the regional production volume. The results, at least for barley grown under rainfed conditions, support this hypothesis: no association could be demonstrated between (i) sowing date variation and yield per unit area; (ii) sowing date variation and the precipitation amount; (iii) flowering date variation and yield per unit area. Farmers may base their sowing strategy on the fact that sowing at the presumed beginning of the rainy season will give higher yields than when sowing is delayed, provided rainfall during the growing season is sufficient. In dry years, when available water is the main yield-limiting factor, effects of sowing date variation on yield are not noticeable. The need to synchronize seasonal rainfall and phenology of the selected barley cultivars may also limit the possibilities to postpone sowing.EvaluationThe principal objective of this study was to explore possibilities to improve CGMS in such a way that it may be applied for quantitative yield prediction for all EU member states. Various options have been explored. Although some interesting results have been obtained, only two concrete suggestions for such an improvement can be given: (i) predictions should be executed at lower administrative level and subsequently aggregated to national values, (ii) planted area should be included in the analysis and prediction model. More research is needed to identify tangible points for improvements in CGMS.</p