591 research outputs found

    Monitoring Snow Cover and Snowmelt Dynamics and Assessing their Influences on Inland Water Resources

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    Snow is one of the most vital cryospheric components owing to its wide coverage as well as its unique physical characteristics. It not only affects the balance of numerous natural systems but also influences various socio-economic activities of human beings. Notably, the importance of snowmelt water to global water resources is outstanding, as millions of populations rely on snowmelt water for daily consumption and agricultural use. Nevertheless, due to the unprecedented temperature rise resulting from the deterioration of climate change, global snow cover extent (SCE) has been shrinking significantly, which endangers the sustainability and availability of inland water resources. Therefore, in order to understand cryo-hydrosphere interactions under a warming climate, (1) monitoring SCE dynamics and snowmelt conditions, (2) tracking the dynamics of snowmelt-influenced waterbodies, and (3) assessing the causal effect of snowmelt conditions on inland water resources are indispensable. However, for each point, there exist many research questions that need to be answered. Consequently, in this thesis, five objectives are proposed accordingly. Objective 1: Reviewing the characteristics of SAR and its interactions with snow, and exploring the trends, difficulties, and opportunities of existing SAR-based SCE mapping studies; Objective 2: Proposing a novel total and wet SCE mapping strategy based on freely accessible SAR imagery with all land cover classes applicability and global transferability; Objective 3: Enhancing total SCE mapping accuracy by fusing SAR- and multi-spectral sensor-based information, and providing total SCE mapping reliability map information; Objective 4: Proposing a cloud-free and illumination-independent inland waterbody dynamics tracking strategy using freely accessible datasets and services; Objective 5: Assessing the influence of snowmelt conditions on inland water resources

    Towards Daily High-resolution Inundation Observations using Deep Learning and EO

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    Satellite remote sensing presents a cost-effective solution for synoptic flood monitoring, and satellite-derived flood maps provide a computationally efficient alternative to numerical flood inundation models traditionally used. While satellites do offer timely inundation information when they happen to cover an ongoing flood event, they are limited by their spatiotemporal resolution in terms of their ability to dynamically monitor flood evolution at various scales. Constantly improving access to new satellite data sources as well as big data processing capabilities has unlocked an unprecedented number of possibilities in terms of data-driven solutions to this problem. Specifically, the fusion of data from satellites, such as the Copernicus Sentinels, which have high spatial and low temporal resolution, with data from NASA SMAP and GPM missions, which have low spatial but high temporal resolutions could yield high-resolution flood inundation at a daily scale. Here a Convolutional-Neural-Network is trained using flood inundation maps derived from Sentinel-1 Synthetic Aperture Radar and various hydrological, topographical, and land-use based predictors for the first time, to predict high-resolution probabilistic maps of flood inundation. The performance of UNet and SegNet model architectures for this task is evaluated, using flood masks derived from Sentinel-1 and Sentinel-2, separately with 95 percent-confidence intervals. The Area under the Curve (AUC) of the Precision Recall Curve (PR-AUC) is used as the main evaluation metric, due to the inherently imbalanced nature of classes in a binary flood mapping problem, with the best model delivering a PR-AUC of 0.85

    On Parameterizing Soil Evaporation in a Direct Remote Sensing Model of ET: PT-JPL

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    Remote sensing models that measure evapotranspiration directly from the Penman-Monteith or Priestley-Taylor equations typically estimate the soil evaporation component over large areas using coarse spatial resolution relative humidity (RH) from geospatial climate datasets. As a result, the models tend to underperform in dry areas at local scales where moisture status is not well represented by surrounding areas. Earth observation sensors that monitor large-scale global dynamics (e.g., MODIS) afford comparable spatial coverage and temporal frequency, but at a higher spatial resolution than geospatial climate datasets. In this study, we compared soil evaporation parameterized with optical and thermal indices derived from MODIS to RH-based soil evaporation as implemented in the Priestley Taylor-Jet Propulsion Laboratory (PT-JPL) model. We evaluated the parameterizations by subtracting PT-JPL transpiration from observation-based flux tower evapotranspiration in agricultural fields across the contiguous United States. We compared the apparent thermal inertia (ATI) index, land surface water index (LSWI), normalized difference water index (NDWI), and a new index derived from red and shortwave infrared bands (soil moisture divergence index [SMDI]). Relationships were significant at the 95% confidence band. LSWI and SMDI explained 18–33% of variance in 8-day soil evaporation. This led to a 3–11% increase in explained ET variance. LSWI and SMDI tended to perform better at the irrigated sites than RH. LSWI and SMDI led to markedly better performance over other indices at a seasonal time step. L-band microwave backscatter can penetrate clouds and can distinguish soil from canopy moisture content. We are presently fusing red-SWIR-RADAR to improve soil evaporation estimation.Fil: Marshall, Michael. University of Twente. Faculty of Geo‐information Science and Earth Observation. Department of Natural Resources; PaĂ­ses BajosFil: Tu, Kevin. Corteva Agriscience; Estados UnidosFil: Andreo, VerĂłnica Carolina. Comision Nacional de Actividades Espaciales. Instituto de Altos Estudios Espaciales "Mario Gulich"; Argentina. Consejo Nacional de Investigaciones CientĂ­ficas y TĂ©cnicas. Centro CientĂ­fico TecnolĂłgico Conicet - CĂłrdoba; Argentin

    Fusing optical and SAR time series for LAI gap filling with multioutput Gaussian processes

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    The availability of satellite optical information is often hampered by the natural presence of clouds, which can be problematic for many applications. Persistent clouds over agricultural fields can mask key stages of crop growth, leading to unreliable yield predictions. Synthetic Aperture Radar (SAR) provides all-weather imagery which can potentially overcome this limitation, but given its high and distinct sensitivity to different surface properties, the fusion of SAR and optical data still remains an open challenge. In this work, we propose the use of Multi-Output Gaussian Process (MOGP) regression, a machine learning technique that learns automatically the statistical relationships among multisensor time series, to detect vegetated areas over which the synergy between SAR-optical imageries is profitable. For this purpose, we use the Sentinel-1 Radar Vegetation Index (RVI) and Sentinel-2 Leaf Area Index (LAI) time series over a study area in north west of the Iberian peninsula. Through a physical interpretation of MOGP trained models, we show its ability to provide estimations of LAI even over cloudy periods using the information shared with RVI, which guarantees the solution keeps always tied to real measurements. Results demonstrate the advantage of MOGP especially for long data gaps, where optical-based methods notoriously fail. The leave-one-image-out assessment technique applied to the whole vegetation cover shows MOGP predictions improve standard GP estimations over short-time gaps (R 2 of 74% vs 68%, RMSE of 0.4 vs 0.44 [m 2 m −2 ]) and especially over long-time gaps (R 2 of 33% vs 12%, RMSE of 0.5 vs 1.09 [m 2 m −2 ])

    Global evaluation of SMAP/Sentinel-1 soil moisture products

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    MAP/Sentinel-1 soil moisture is the latest SMAP (Soil Moisture Active Passive) product derived from synergistic utilization of the radiometry observations of SMAP and radar backscattering data of Sentinel-1. This product is the first and only global soil moisture (SM) map at 1 km and 3 km spatial resolutions. In this paper, we evaluated the SMAP/Sentinel-1 SM product from different viewpoints to better understand its quality, advantages, and likely limitations. A comparative analysis of this product and in situ measurements, for the time period March 2015 to January 2022, from 35 dense and sparse SM networks and 561 stations distributed around the world was carried out. We examined the effects of land cover, vegetation fraction, water bodies, urban areas, soil characteristics, and seasonal climatic conditions on the performance of active–passive SMAP/Sentinel-1 in estimating the SM. We also compared the performance metrics of enhanced SMAP (9 km) and SMAP/Sentinel-1 products (3 km) to analyze the effects of the active–passive disaggregation algorithm on various features of the SMAP SM maps. Results showed satisfactory agreement between SMAP/Sentinel-1 and in situ SM measurements for most sites (r values between 0.19 and 0.95 and ub-RMSE between 0.03 and 0.17), especially for dense sites without representativeness errors. Thanks to the vegetation effect correction applied in the active–passive algorithm, the SMAP/Sentinel-1 product had the highest correlation with the reference data in grasslands and croplands. Results also showed that the accuracy of the SMAP/Sentinel-1 SM product in different networks is independent of the presence of water bodies, urban areas, and soil types.Peer ReviewedPostprint (published version

    RITA: a 1U multi-sensor Earth observation payload for the AlainSat-1

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    The Remote sensing and Interference detector with radiomeTry and vegetation Analysis (RITA) is one of the Remote Sensing payloads selected as winners of the 2nd GRSS Student Grand Challenge in 2019, to fly on board of the 3U AlainSat-1. This CubeSat is being developed by the National Space Science and Technology Center (NSSTC), United Arab Emirates University. RITA has been designed as an academic mission, which brings together students from different backgrounds in a joint effort to apply very distinct sensors in an Earth Observation mission, fusing their results to obtain higher-accuracy measurements. The main payload used in RITA is a Total Power Radiometer such as the one on board the FSSCat mission. With these radiometric measurements, soil moisture and ice thickness will be obtained. To better characterize the extensive Radio-Frequency Interferences received by EO satellites in protected bands, several RFI Detection and Classification algorithms will be included to generate a worldwide map of RFI. As a novel addition to the 3Cat family of satellites and payloads, a hyper-spectral camera with 25 bands ranging from 600 to 975 nm will be used to obtain several indexes related to vegetation. By linking these measurements with the soil moisture obtained from the MWR, pixel downscaling can be attempted. Finally, a custom- developed LoRa transceiver will be included to provide a multi-level approach to in-situ sensors: On-demand executions of the other payloads will be able to be triggered from ground sensors if necessary, as well as simple reception of other measurements that will complement the ones obtained on the satellite. The antennas for both the MWR and the LoRa experiments have been developed in-house, and will span the entirety of one of the 3U sides of the satellite. In this work, the latest development advances will be presented, together with an updated system overview and information about the operations that will be conducted. Results obtained from the test campaign are also presented in the conference

    Perspective Chapter: Downscaling of Satellite Soil Moisture Estimates

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    Soil moisture is a key parameter in the hydrological cycle and plays a critical role in global climate. The capacity to forecast drought and floods, manage water resources, and make field-scale decisions depends on accurate and thorough information on soil moisture. In addition to the instrument-based field observation approaches, dynamic mapping of soil moisture has been made possible by satellite remote sensing technologies. Estimates of soil moisture at a global and regional scale from optical and thermal remote sensing have been explored, and considerable advancements have been made. However, these global soil moisture products have coarse spatial resolutions and are typically unsuitable for field-level hydrological and agricultural applications. In this regard, this chapter presents a comprehensive review of the latest downscaling methods to improve the coarse-spatial and temporal resolution of soil moisture products. The main approaches discussed in the chapter include active passive fusion, optical/thermal based, topography based, and data assimilation methods. The physical background, current status, advantages and limitations associated with each downscaling approach has been thoroughly examined. Each of these optical/thermal, microwave-based methods for soil moisture estimation involves intricate derivation at different spatiotemporal scales, which can be combined using recent advances in machine learning

    Désagrégation de l'humidité du sol issue des produits satellitaires micro-ondes passives et exploration de son utilisation pour l'amélioration de la modélisation et la prévision hydrologique

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    De plus en plus de produits satellitaires en micro-ondes passives sont disponibles. Cependant, leur large rĂ©solution spatiale (25-50 km) n’en font pas un outil adĂ©quat pour des applications hydrologiques Ă  une Ă©chelle locale telles que la modĂ©lisation et la prĂ©vision hydrologiques. Dans de nombreuses Ă©tudes, une dĂ©sagrĂ©gation d’échelle de l’humiditĂ© du sol des produits satellites micro-ondes est faite puis validĂ©e avec des mesures in-situ. Toutefois, l’utilisation de ces donnĂ©es issues d’une dĂ©sagrĂ©gation d’échelle n’a pas encore Ă©tĂ© pleinement Ă©tudiĂ©e pour des applications en hydrologie. Ainsi, l’objectif de cette thĂšse est de proposer une mĂ©thode de dĂ©sagrĂ©gation d’échelle de l’humiditĂ© du sol issue de donnĂ©es satellitaires en micro-ondes passives (Satellite Passive Microwave Active and Passive - SMAP) Ă  diffĂ©rentes rĂ©solutions spatiales afin d’évaluer leur apport sur l’amĂ©lioration potentielle des modĂ©lisations et prĂ©visions hydrologiques. À partir d’un modĂšle de forĂȘt alĂ©atoire, une dĂ©sagrĂ©gation d’échelle de l’humiditĂ© du sol de SMAP l’amĂšne de 36-km de rĂ©solution initialement Ă  des produits finaux Ă  9-, 3- et 1-km de rĂ©solution. Les prĂ©dicteurs utilisĂ©s sont Ă  haute rĂ©solution spatiale et de sources diffĂ©rentes telles que Sentinel-1A, MODIS et SRTM. L'humiditĂ© du sol issue de cette dĂ©sagrĂ©gation d’échelle est ensuite assimilĂ©e dans un modĂšle hydrologique distribuĂ© Ă  base physique pour tenter d’amĂ©liorer les sorties de dĂ©bit. Ces expĂ©riences sont menĂ©es sur les bassins versants des riviĂšres Susquehanna (de grande taille) et Upper-Susquehanna (en comparaison de petite taille), tous deux situĂ©s aux États-Unis. De plus, le modĂšle assimile aussi des donnĂ©es d’humiditĂ© du sol en profondeur issue d’une extrapolation verticale des donnĂ©es SMAP. Par ailleurs, les donnĂ©es d’humiditĂ© du sol SMAP et les mesures in-situ sont combinĂ©es par la technique de fusion conditionnelle. Ce produit de fusion SMAP/in-situ est assimilĂ© dans le modĂšle hydrologique pour tenter d’amĂ©liorer la prĂ©vision hydrologique sur le bassin versant Au Saumon situĂ© au QuĂ©bec. Les rĂ©sultats montrent que l'utilisation de l’humiditĂ© du sol Ă  fine rĂ©solution spatiale issue de la dĂ©sagrĂ©gation d’échelle amĂ©liore la reprĂ©sentation de la variabilitĂ© spatiale de l’humiditĂ© du sol. En effet, le produit Ă  1- km de rĂ©solution fournit plus de dĂ©tails que les produits Ă  3- et 9-km ou que le produit SMAP de base Ă  36-km de rĂ©solution. De mĂȘme, l’utilisation du produit de fusion SMAP/ in-situ amĂ©liore la qualitĂ© et la reprĂ©sentation spatiale de l’humiditĂ© du sol. Sur le bassin versant Susquehanna, la modĂ©lisation hydrologique s’amĂ©liore avec l’assimilation du produit de dĂ©sagrĂ©gation d’échelle Ă  9-km, sans avoir recours Ă  des rĂ©solutions plus fines. En revanche, sur le bassin versant Upper-Susquehanna, c’est le produit avec la rĂ©solution spatiale la plus fine Ă  1- km qui offre les meilleurs rĂ©sultats de modĂ©lisation hydrologique. L’assimilation de l’humiditĂ© du sol en profondeur issue de l’extrapolation verticale des donnĂ©es SMAP n’amĂ©liore que peu la qualitĂ© du modĂšle hydrologique. Par contre, l’assimilation du produit de fusion SMAP/in-situ sur le bassin versant Au Saumon amĂ©liore la qualitĂ© de la prĂ©vision du dĂ©bit, mĂȘme si celle-ci n’est pas trĂšs significative.Abstract: The availability of satellite passive microwave soil moisture is increasing, yet its spatial resolution (i.e., 25-50 km) is too coarse to use for local scale hydrological applications such as streamflow simulation and forecasting. Many studies have attempted to downscale satellite passive microwave soil moisture products for their validation with in-situ soil moisture measurements. However, their use for hydrological applications has not yet been fully explored. Thus, the objective of this thesis is to downscale the satellite passive microwave soil moisture (i.e., Satellite Microwave Active and Passive - SMAP) to a range of spatial resolutions and explore its value in improving streamflow simulation and forecasting. The random forest machine learning technique was used to downscale the SMAP soil moisture from 36-km to 9-, 3- and 1-km spatial resolutions. A combination of host of high-resolution predictors derived from different sources including Sentinel-1A, MODIS and SRTM were used for downscaling. The downscaled SMAP soil moisture was then assimilated into a physically-based distributed hydrological model for improving streamflow simulation for Susquehanna (larger in size) and Upper Susquehanna (relatively smaller in size) watersheds, located in the United States. In addition, the vertically extrapolated SMAP soil moisture was assimilated into the model. On the other hand, the SMAP and in-situ soil moisture were merged using the conditional merging technique and the merged SMAP/in-situ soil moisture was then assimilated into the model to improve streamflow forecast over the au Saumon watershed. The results show that the downscaling improved the spatial variability of soil moisture. Indeed, the 1-km downscaled SMAP soil moisture presented a higher spatial detail of soil moisture than the 3-, 9- or original resolution (36-km) SMAP product. Similarly, the merging of SMAP and in-situ soil moisture improved the accuracy as well as spatial representation soil moisture. Interestingly, the assimilation of the 9-km downscaled SMAP soil moisture significantly improved the accuracy of streamflow simulation for the Susquehanna watershed without the need of going to higher spatial resolution, whereas for the Upper Susquehanna watershed the 1-km downscaled SMAP showed better results than the coarser resolutions. The assimilation of vertically extrapolated SMAP soil moisture only slightly further improved the accuracy of the streamflow simulation. On the other hand, the assimilation of merged SMAP/in-situ soil moisture for the au Saumon watershed improved the accuracy of streamflow forecast, yet the improvement was not that significant. Overall, this study demonstrated the potential of satellite passive microwave soil moisture for streamflow simulation and forecasting
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