Multifrequency ground-based radiometer and in-situ measurements of soil moisture at high temporal resolution

2003 ◽  
Author(s):  
Katrin Schneeberger ◽  
Christian Stamm ◽  
Christian Maetzler ◽  
Hannes Fluehler ◽  
Eberhard Lehmann ◽  
...  
Keyword(s):  
Soil Moisture ◽  
Temporal Resolution ◽  
In Situ Measurements ◽  
High Temporal Resolution

scholarly journals Mapping High Spatiotemporal-Resolution Soil Moisture by Upscaling Sparse Ground-Based Observations Using a Bayesian Linear Regression Method for Comparison with Microwave Remotely Sensed Soil Moisture Products

2021 ◽  
Vol 13 (2) ◽  
pp. 228
Author(s):  
Jian Kang ◽  
Rui Jin ◽  
Xin Li ◽  
Yang Zhang
Keyword(s):  
Soil Moisture ◽  
Large Scale ◽  
Spatial Scales ◽  
Microwave Remote Sensing ◽  
In Situ Measurements ◽  
Spatial Density ◽  
Linear Regression Method ◽  
Spatiotemporal Resolution ◽  
Pixel Scale

In recent decades, microwave remote sensing (RS) has been used to measure soil moisture (SM). Long-term and large-scale RS SM datasets derived from various microwave sensors have been used in environmental fields. Understanding the accuracies of RS SM products is essential for their proper applications. However, due to the mismatched spatial scale between the ground-based and RS observations, the truth at the pixel scale may not be accurately represented by ground-based observations, especially when the spatial density of in situ measurements is low. Because ground-based observations are often sparsely distributed, temporal upscaling was adopted to transform a few in situ measurements into SM values at a pixel scale of 1 km by introducing the temperature vegetation dryness index (TVDI) related to SM. The upscaled SM showed high consistency with in situ SM observations and could accurately capture rainfall events. The upscaled SM was considered as the reference data to evaluate RS SM products at different spatial scales. In regard to the validation results, in addition to the correlation coefficient (R) of the Soil Moisture Active Passive (SMAP) SM being slightly lower than that of the Climate Change Initiative (CCI) SM, SMAP had the best performance in terms of the root-mean-square error (RMSE), unbiased RMSE and bias, followed by the CCI. The Soil Moisture and Ocean Salinity (SMOS) products were in worse agreement with the upscaled SM and were inferior to the R value of the X-band SM of the Advanced Microwave Scanning Radiometer 2 (AMSR2). In conclusion, in the study area, the SMAP and CCI SM are more reliable, although both products were underestimated by 0.060 cm3 cm−3 and 0.077 cm3 cm−3, respectively. If the biases are corrected, then the improved SMAP with an RMSE of 0.043 cm3 cm−3 and the CCI with an RMSE of 0.039 cm3 cm−3 will hopefully reach the application requirement for an accuracy with an RMSE less than 0.040 cm3 cm−3.

Towards an operationally capable satellite-based 0-100 cm soil moisture dataset from C3S

2021 ◽  
Author(s):  
Adam Pasik ◽  
Wolfgang Preimesberger ◽  
Bernhard Bauer-Marschallinger ◽  
Wouter Dorigo
Keyword(s):  
Soil Moisture ◽  
Soil Profile ◽  
Land Surface ◽  
Root Zone ◽  
Soil Layer ◽  
Land Surface Model ◽  
In Situ Measurements ◽  
Surface Model ◽  
T Values

<p>Multiple satellite-based global surface soil moisture (SSM) datasets are presently available, these however, address exclusively the top layer of the soil (0-5cm). Meanwhile, root-zone soil moisture cannot be directly quantified with remote sensing but can be estimated from SSM using a land surface model. Alternatively, soil water index (SWI; calculated from SSM as a function of time needed for infiltration) can be used as a simple approximation of root-zone conditions. SWI is a proxy for deeper layers of the soil profile which control evapotranspiration, and is hence especially important for studying hydrological processes over vegetation-covered areas and meteorological modelling.</p><p>Here we introduce the advances in our work on the first operationally capable SWI-based root-zone soil moisture dataset from C3S Soil Moisture v201912 COMBINED product, spanning the period 2002-2020. The uniqueness of this dataset lies in the fact that T-values (temporal lengths ruling the infiltration) characteristic of SWI were translated into particular soil depths making it much more intuitive, user-friendly and easily applicable. Available are volumetric soil moisture values for the top 1 m of the soil profile at 10 cm intervals, where the optimal T-value (T-best) for each soil layer is selected based on a range of correlation metrics with in situ measurements from the International Soil Moisture Network (ISMN) and the relevant soil and climatic parameters.<br>Additionally we present the results of an extensive global validation against in situ measurements (ISMN) as well as the results of investigations into the relationship between a range of soil and climate characteristics and the optimal T-values for particular soil depths.</p>

scholarly journals In-situ measurements of the ice flow motion at Eqip Sermia Glacier using a remotely controlled UAV

2019 ◽  
Author(s):  
Guillaume Jouvet ◽  
Eef van Dongen ◽  
Martin P. Lüthi ◽  
Andreas Vieli
Keyword(s):  
Remote Sensing ◽  
Template Matching ◽  
In Situ Measurements ◽  
In Situ Monitoring ◽  
High Temporal Resolution ◽  
Ice Flow ◽  
Flow Motion ◽  
Remote Sensing Techniques ◽  
Matching Techniques

Abstract. Measuring the ice flow motion accurately is essential to better understand the time evolution of glaciers and ice sheets, and therefore to better anticipate the future consequence of climate change in terms of sea-level rise. Although there exist a variety of remote sensing methods to fill this task, in-situ measurements are always needed for validation or to capture high temporal resolution movements. Yet glaciers are in general hostile environments where the installation of instruments might be tedious and risky when not impossible. Here we report the first-ever in-situ measurements of ice flow motion using a remotely controlled Unmanned Aerial Vehicle (UAV). We used a multicopter UAV to land on a highly crevassed area of Eqip Sermia Glacier, West Greenland, to measure the displacement of the glacial surface with the aid of an on-board differential GNSS receiver. Despite the unfortunate loss of the UAV, we measured approximately 70 cm of displacement over 4.36 hours without setting foot onto the glacier – a result validated by applying UAV photogrammetry and template matching techniques. Our study demonstrates that UAVs are promising instruments for in-situ monitoring, and have a great potential for capturing short-term ice flow variations in inaccessible glaciers – a task that remote sensing techniques can hardly achieve.

scholarly journals In Situ Measurements of Polymer Micellization Kinetics with Millisecond Temporal Resolution

2019 ◽  
Vol 52 (9) ◽  
pp. 3151-3157 ◽  
Author(s):  
Joseph Kalkowski ◽  
Chang Liu ◽  
Paola Leon-Plata ◽  
Magdalena Szymusiak ◽  
Pin Zhang ◽  
...  
Keyword(s):  
Temporal Resolution ◽  
In Situ Measurements

Comparing Assimilation of Soil Moisture and C-band Backscatter in High Resolution Land Surface Model

2020 ◽  
Author(s):  
Leqiang Sun ◽  
Stéphane Belair ◽  
Marco Carrera ◽  
Bernard Bilodeau
Keyword(s):  
Soil Moisture ◽  
Land Surface ◽  
Land Surface Model ◽  
Open Loop ◽  
In Situ Measurements ◽  
Lookup Table ◽  
Surface Model ◽  
Loop Model ◽  
The Impact

<p>Canadian Space Agency (CSA) has recently started receiving and processing the images from the recently launched C-band RADARSAT Constellation Mission (RCM). The backscatter and soil moisture retrievals products from the previously launched RADARSAT-2 agree well with both in-situ measurements and surface soil moisture modeled with land surface model Soil, Vegetation, and Snow (SVS). RCM will provide those products at an even better spatial coverage and temporal resolution. In preparation of the potential operational application of RCM products in Canadian Meteorological Center (CMC), this paper presents the scenarios of assimilating either soil moisture retrieval or outright backscatter signal in a 100-meter resolution version of the Canadian Land Data Assimilation System (CaLDAS) on field scale with time interval of three hours. The soil moisture retrieval map was synthesized by extrapolating the regression relationship between in-situ measurements and open loop model output based on soil texture lookup table. Based on this, the backscatter map was then generated with the surface roughness retrieved from RADARSAT-2 images using a modified Integral Equation Model (IEM) model. Bias correction was applied to the Ensemble Kalman filter (EnKF) to mitigate the impact of nonlinear errors introduced by multi-sourced perturbations. Initial results show that the assimilation of backscatter is as effective as assimilating soil moisture retrievals. Compared to open loop, both can improve the analysis of surface moisture, particularly in terms of reducing bias.  </p>

Validation and correction of sea surface salinity retrieval from SMAP

2020 ◽  
Author(s):  
Sisi Qin
Keyword(s):  
Soil Moisture ◽  
In Situ Measurements ◽  
Sea Surface ◽  
Sea Surface Salinity ◽  
Surface Salinity ◽  
Tropical Regions ◽  
Ocean Salinity ◽  
Forecast Models ◽  
Level 3

<p>In this study, Sea Surface Salinity (SSS) Level 3 (L3) daily product derived from Soil Moisture Active Passive (SMAP) during the year 2016, was validated and compared with SSS daily products derived from Soil Moisture and Ocean Salinity (SMOS) and in-situ measurements. Generally, the Root Mean Square Error (RMSE) of the daily SSS products is larger along the coastal areas and at high latitudes and is smaller in the tropical regions and open oceans. Comparisons between the two types of daily satellite SSS product revealed that the RMSE was higher in the daily SMOS product than in the SMAP, whereas the bias of the daily SMOS was observed to be less than that of the SMAP when compared with Argo floats data. In addition, the latitude-dependent bias and RMSE of the SMAP SSS were found to be primarily influenced by the precipitation and the Sea Surface Temperature (SST).Then, aregression analysis method which has adopted the precipitation and SST data was used to correct the larger bias of the daily SMAP product. It was confirmed that the corrected daily SMAP product could be used for assimilation in high-resolution forecast models, due to the fact that it was demonstrated to be unbiased and much closer to the in-situ measurements than the original uncorrected SMAP product.</p>

scholarly journals Automated Online Flow Cytometry Advances Microalgal Ecosystem Management as in situ, High-Temporal Resolution Monitoring Tool

2021 ◽  
Vol 9 ◽  
Author(s):  
Iris Haberkorn ◽  
Cosima L. Off ◽  
Michael D. Besmer ◽  
Leandro Buchmann ◽  
Alexander Mathys
Keyword(s):  
Flow Cytometry ◽  
Real Time ◽  
Temporal Resolution ◽  
High Temporal Resolution ◽  
Natural Ecosystems ◽  
Biotechnological Production ◽  
Microalgal Biomass ◽  
Bioprocess Optimization ◽  
Related Population

Microalgae are emerging as a next-generation biotechnological production system in the pharmaceutical, biofuel, and food domain. The economization of microalgal biorefineries remains a main target, where culture contamination and prokaryotic upsurge are main bottlenecks to impair culture stability, reproducibility, and consequently productivity. Automated online flow cytometry (FCM) is gaining momentum as bioprocess optimization tool, as it allows for spatial and temporal landscaping, real-time investigations of rapid microbial processes, and the assessment of intrinsic cell features. So far, automated online FCM has not been applied to microalgal ecosystems but poses a powerful technology for improving the feasibility of microalgal feedstock production through in situ, real-time, high-temporal resolution monitoring. The study lays the foundations for an application of automated online FCM implying far-reaching applications to impel and facilitate the implementation of innovations targeting at microalgal bioprocesses optimization. It shows that emissions collected on the FL1/FL3 fluorescent channels, harnessing nucleic acid staining and chlorophyll autofluorescence, enable a simultaneous assessment (quantitative and diversity-related) of prokaryotes and industrially relevant phototrophic Chlorella vulgaris in mixed ecosystems of different complexity over a broad concentration range (2.2–1,002.4 cells ⋅μL–1). Automated online FCM combined with data analysis relying on phenotypic fingerprinting poses a powerful tool for quantitative and diversity-related population dynamics monitoring. Quantitative data assessment showed that prokaryotic growth phases in engineered and natural ecosystems were characterized by different growth speeds and distinct peaks. Diversity-related population monitoring based on phenotypic fingerprinting indicated that prokaryotic upsurge in mixed cultures was governed by the dominance of single prokaryotic species. Automated online FCM is a powerful tool for microalgal bioprocess optimization owing to its adaptability to myriad phenotypic assays and its compatibility with various cultivation systems. This allows advancing bioprocesses associated with both microalgal biomass and compound production. Hence, automated online FCM poses a viable tool with applications across multiple domains within the biobased sector relying on single cell–based value chains.

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