scholarly journals Near-real-time gradually expanding 3D land surface reconstruction in disaster areas by sequential drone imagery

2022 ◽  
Vol 135 ◽  
pp. 104105
Author(s):  
Min-Lung Cheng ◽  
Masashi Matsuoka ◽  
Wen Liu ◽  
Fumio Yamazaki
Keyword(s):  
Real Time ◽  
Surface Reconstruction ◽  
Land Surface

scholarly journals Unsupervised Sub-Pixel Water Body Mapping with Sentinel-3 OLCI Image

2019 ◽  
Vol 11 (3) ◽  
pp. 327 ◽  
Author(s):  
Xia Wang ◽  
Feng Ling ◽  
Huaiying Yao ◽  
Yaolin Liu ◽  
Shuna Xu
Keyword(s):  
Surface Water ◽  
Real Time ◽  
Spatial Resolution ◽  
Water Body ◽  
Land Surface ◽  
Clustering Algorithm ◽  
Body Mapping ◽  
Water Index ◽  
Land Surface Water ◽  
Body Map

Mapping land surface water bodies from satellite images is superior to conventional in situ measurements. With the mission of long-term and high-frequency water quality monitoring, the launch of the Ocean and Land Colour Instrument (OLCI) onboard Sentinel-3A and Sentinel-3B provides the best possible approach for near real-time land surface water body mapping. Sentinel-3 OLCI contains 21 bands ranging from visible to near-infrared, but the spatial resolution is limited to 300 m, which may include lots of mixed pixels around the boundaries. Sub-pixel mapping (SPM) provides a good solution for the mixed pixel problem in water body mapping. In this paper, an unsupervised sub-pixel water body mapping (USWBM) method was proposed particularly for the Sentinel-3 OLCI image, and it aims to produce a finer spatial resolution (e.g., 30 m) water body map from the multispectral image. Instead of using the fraction maps of water/non-water or multispectral images combined with endmembers of water/non-water classes as input, USWBM directly uses the spectral water index images of the Normalized Difference Water Index (NDWI) extracted from the Sentinel-3 OLCI image as input and produces a water body map at the target finer spatial resolution. Without the collection of endmembers, USWBM accomplished the unsupervised process by developing a multi-scale spatial dependence based on an unsupervised sub-pixel Fuzzy C-means (FCM) clustering algorithm. In both validations in the Tibet Plate lake and Poyang lake, USWBM produced more accurate water body maps than the other pixel and sub-pixel based water body mapping methods. The proposed USWBM, therefore, has great potential to support near real-time sub-pixel water body mapping with the Sentinel-3 OLCI image.

scholarly journals NOAA Satellite Soil Moisture Operational Product System (SMOPS) Version 3.0 Generates Higher Accuracy Blended Satellite Soil Moisture

2020 ◽  
Vol 12 (17) ◽  
pp. 2861
Author(s):  
Jifu Yin ◽  
Xiwu Zhan ◽  
Jicheng Liu
Keyword(s):  
Soil Moisture ◽  
Real Time ◽  
Land Surface ◽  
Vital Role ◽  
Product System ◽  
Global Soil ◽  
One Stop ◽  
Satellite Sensors ◽  
Blended Data

Soil moisture plays a vital role for the understanding of hydrological, meteorological, and climatological land surface processes. To meet the need of real time global soil moisture datasets, a Soil Moisture Operational Product System (SMOPS) has been developed at National Oceanic and Atmospheric Administration to produce a one-stop shop for soil moisture observations from all available satellite sensors. What makes the SMOPS unique is its near real time global blended soil moisture product. Since the first version SMOPS publicly released in 2010, the SMOPS has been updated twice based on the users’ feedbacks through improving retrieval algorithms and including observations from new satellite sensors. The version 3.0 SMOPS has been operationally released since 2017. Significant differences in climatological averages lead to remarkable distinctions in data quality between the newest and the older versions of SMOPS blended soil moisture products. This study reveals that the SMOPS version 3.0 has overwhelming advantages of reduced data uncertainties and increased correlations with respect to the quality controlled in situ measurements. The new version SMOPS also presents more robust agreements with the European Space Agency’s Climate Change Initiative (ESA_CCI) soil moisture datasets. With the higher accuracy, the blended data product from the new version SMOPS is expected to benefit the hydrological, meteorological, and climatological researches, as well as numerical weather, climate, and water prediction operations.

scholarly journals Real-time monitoring of nitrate transport in the deep vadose zone under a crop field – implications for groundwater protection

2016 ◽  
Vol 20 (8) ◽  
pp. 3099-3108 ◽  
Author(s):  
Tuvia Turkeltaub ◽  
Daniel Kurtzman ◽  
Ofer Dahan
Keyword(s):  
Real Time ◽  
Water Table ◽  
Vadose Zone ◽  
Land Surface ◽  
Agricultural Land ◽  
Root Zone ◽  
Isotopic Signature ◽  
Nitrate Transport ◽  
Mass Migration ◽  
Crop Field

Abstract. Nitrate is considered the most common non-point pollutant in groundwater. It is often attributed to agricultural management, when excess application of nitrogen fertilizer leaches below the root zone and is eventually transported as nitrate through the unsaturated zone to the water table. A lag time of years to decades between processes occurring in the root zone and their final imprint on groundwater quality prevents proper decision-making on land use and groundwater-resource management. This study implemented the vadose-zone monitoring system (VMS) under a commercial crop field. Data obtained by the VMS for 6 years allowed, for the first time known to us, a unique detailed tracking of water percolation and nitrate migration from the surface through the entire vadose zone to the water table at 18.5 m depth. A nitrate concentration time series, which varied with time and depth, revealed – in real time – a major pulse of nitrate mass propagating down through the vadose zone from the root zone toward the water table. Analysis of stable nitrate isotopes indicated that manure is the prevalent source of nitrate in the deep vadose zone and that nitrogen transformation processes have little effect on nitrate isotopic signature. The total nitrogen mass calculations emphasized the nitrate mass migration towards the water table. Furthermore, the simulated pore-water velocity through analytical solution of the convection–dispersion equation shows that nitrate migration time from land surface to groundwater is relatively rapid, approximately 5.9 years. Ultimately, agricultural land uses, which are constrained to high nitrogen application rates and coarse soil texture, are prone to inducing substantial nitrate leaching.

Updated gridded datasets version 2020 provided by the Global Precipitation Climatology Centre (GPCC)

2021 ◽  
Author(s):  
Elke Rustemeier ◽  
Udo Schneider ◽  
Markus Ziese ◽  
Peter Finger ◽  
Andreas Becker
Keyword(s):  
Quality Control ◽  
Real Time ◽  
Land Surface ◽  
Quality Data ◽  
Digital Object Identifier ◽  
Full Data ◽  
High Quality ◽  
Precipitation Climatology ◽  
Global Precipitation Climatology Centre ◽  
Global Precipitation

<p><span>Since its founding in 1989, the Global Precipitation Climatology Centre (GPCC) has been producing global precipitation analyses based on land surface in-situ measurements. </span><span>In the now over 30 years the underlying database has been continuously expanded and includes a high station density and large temporal coverage. Due to the semi-automatic quality control routinely performed on the incoming station data, the GPCC database has a very high quality.</span> <span>Today, the GPCC holds data from </span><span>123,000 stations, about three quarters of them having long time series.</span></p><p><span>The core of the analyses is formed by data from the global meteorological and hydrological services, which provided their records to the GPCC, as well as global and regional data collections.  </span><span>In addition, the GPCC receives SYNOP and CLIMAT reports via the WMO-GTS. These form a supplement for the high quality precipitation analyses and the basis for the near real-time evaluations.</span></p><p><span>Quality control activities include cross-referencing stations from different sources, flagging of data errors, and correcting temporally or spatially offset data. This data then forms the basis for the following interpolation and product generation.</span></p><p><span>In near real time, the 'First Guess Monthly', 'First Guess Daily', 'Monitoring Product', ‘Provisional Daily Precipitation Analysis’ and the 'GPCC Drought Index' are generated. These are based on WMO-GTS data and monthly data generated by the CPC (NOAA). </span></p><p><span>With a 2-3 year update cycle, the high quality data products are generated with intensive quality control and built on the entire GPCC data base. These non-real time products consist of the 'Full Data Monthly', 'Full Data Daily', 'Climatology', and 'HOMPRA-Europe' and are now available in the 2020 version. </span></p><p><span>A</span><span>ll gridded datasets presented in this paper are freely available in netcdf format on the GPCC website https://gpcc.dwd.de and referenced by a digital object identifier (DOI). The site also provides an overview of all datasets, as well as a detailed description and further references for each dataset.</span></p>

scholarly journals Impact of Insolation Data Source on Remote Sensing Retrievals of Evapotranspiration over the California Delta

2019 ◽  
Vol 11 (3) ◽  
pp. 216 ◽  
Author(s):  
Martha Anderson ◽  
George Diak ◽  
Feng Gao ◽  
Kyle Knipper ◽  
Christopher Hain ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Real Time ◽  
Land Surface ◽  
Water Resource Management ◽  
Radiant Energy ◽  
Data Sources ◽  
Delta Region ◽  
Spatiotemporal Resolution ◽  
Sky Conditions ◽  
Mean Square Errors

The energy delivered to the land surface via insolation is a primary driver of evapotranspiration (ET)—the exchange of water vapor between the land and atmosphere. Spatially distributed ET products are in great demand in the water resource management community for real-time operations and sustainable water use planning. The accuracy and deliverability of these products are determined in part by the characteristics and quality of the insolation data sources used as input to the ET models. This paper investigates the practical utility of three different insolation datasets within the context of a satellite-based remote sensing framework for mapping ET at high spatiotemporal resolution, in an application over the Sacramento–San Joaquin Delta region in California. The datasets tested included one reanalysis product: The Climate System Forecast Reanalysis (CFSR) at 0.25° spatial resolution, and two remote sensing insolation products generated with geostationary satellite imagery: a product for the continental United States at 0.2°, developed by the University of Wisconsin Space Sciences and Engineering Center (SSEC) and a coarser resolution (1°) global Clouds and the Earth’s Radiant Energy System (CERES) product. The three insolation data sources were compared to pyranometer data collected at flux towers within the Delta region to establish relative accuracy. The satellite products significantly outperformed CFSR, with root-mean square errors (RMSE) of 2.7, 1.5, and 1.4 MJ·m−2·d−1 for CFSR, CERES, and SSEC, respectively, at daily timesteps. The satellite-based products provided more accurate estimates of cloud occurrence and radiation transmission, while the reanalysis tended to underestimate solar radiation under cloudy-sky conditions. However, this difference in insolation performance did not translate into comparable improvement in the ET retrieval accuracy, where the RMSE in daily ET was 0.98 and 0.94 mm d−1 using the CFSR and SSEC insolation data sources, respectively, for all the flux sites combined. The lack of a notable impact on the aggregate ET performance may be due in part to the predominantly clear-sky conditions prevalent in central California, under which the reanalysis and satellite-based insolation data sources have comparable accuracy. While satellite-based insolation data could improve ET retrieval in more humid regions with greater cloud-cover frequency, over the California Delta and climatologically similar regions in the western U.S., the CFSR data may suffice for real-time ET modeling efforts.

scholarly journals Improving streamflow predictions at ungauged locations with real-time updating: application of an EnKF-based state-parameter estimation strategy

2014 ◽  
Vol 18 (10) ◽  
pp. 3923-3936 ◽  
Author(s):  
X. Xie ◽  
S. Meng ◽  
S. Liang ◽  
Y. Yao
Keyword(s):  
Real Time ◽  
Land Surface ◽  
Hydrological Model ◽  
Similarity Measures ◽  
State Parameter ◽  
Model Parameters ◽  
Prediction Errors ◽  
Distributed Hydrological Model ◽  
Streamflow Prediction ◽  
Short Period

Abstract. The challenge of streamflow predictions at ungauged locations is primarily attributed to various uncertainties in hydrological modelling. Many studies have been devoted to addressing this issue. The similarity regionalization approach, a commonly used strategy, is usually limited by subjective selection of similarity measures. This paper presents an application of a partitioned update scheme based on the ensemble Kalman filter (EnKF) to reduce the prediction uncertainties. This scheme performs real-time updating for states and parameters of a distributed hydrological model by assimilating gauged streamflow. The streamflow predictions are constrained by the physical rainfall-runoff processes defined in the distributed hydrological model and by the correlation information transferred from gauged to ungauged basins. This scheme is successfully demonstrated in a nested basin with real-world hydrological data where the subbasins have immediate upstream and downstream neighbours. The results suggest that the assimilated observed data from downstream neighbours have more important roles in reducing the streamflow prediction errors at ungauged locations. The real-time updated model parameters remain stable with reasonable spreads after short-period assimilation, while their estimation trajectories have slow variations, which may be attributable to climate and land surface changes. Although this real-time updating scheme is intended for streamflow predictions in nested basins, it can be a valuable tool in separate basins to improve hydrological predictions by assimilating multi-source data sets, including ground-based and remote-sensing observations.

scholarly journals A real-time interactive system of surface reconstruction and dynamic projection mapping with RGB-depth sensor and projector

2018 ◽  
Vol 14 (7) ◽  
pp. 155014771879085 ◽  
Author(s):  
Yundong Guo ◽  
Shu-Chuan Chu ◽  
Zhenyu Liu ◽  
Chan Qiu ◽  
Hao Luo ◽  
...  
Keyword(s):  
Real Time ◽  
Surface Reconstruction ◽  
Virtual Worlds ◽  
Target Surface ◽  
Calibration Procedure ◽  
Interactive System ◽  
Depth Sensor ◽  
Projection Mapping ◽  
Optimization Framework ◽  
Depth Data

Reconstruction and projection mapping enable us to bring virtual worlds into real spaces, which can give spectators an immersive augmented reality experience. Based on an interactive system with RGB-depth sensor and projector, we present a combined hardware and software solution for surface reconstruction and dynamic projection mapping in real time. In this article, a novel and adaptable calibration scheme is proposed, which is used to estimate approximate models to correct and transform raw depth data. Besides, our system allows for smooth real-time performance using an optimization framework, including denoising and stabilizing. In the entire pipeline, markers are only used in the calibration procedure, and any priors are not needed. Our approach enables us to interact with the target surface in real time, while maintaining correct illumination. It is easy and fast to develop different applications for our system, and some interesting cases are demonstrated at last.

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