scholarly journals Ptychographic sensor for large-scale lensless microbial monitoring with high spatiotemporal resolution

2021 ◽  
pp. 113699
Author(s):  
Shaowei Jiang ◽  
Chengfei Guo ◽  
Zichao Bian ◽  
Ruihai Wang ◽  
Jiakai Zhu ◽  
...  
Keyword(s):  
Large Scale ◽  
Spatiotemporal Resolution ◽  
Microbial Monitoring ◽  
High Spatiotemporal Resolution

scholarly journals Sentinel-1 time series for mapping snow cover and timing of snowmelt in Arctic periglacial environments: Case study from the Zackenberg Valley, Greenland

2021 ◽  
Author(s):  
Sebastian Buchelt ◽  
Kirstine Skov ◽  
Tobias Ullmann
Keyword(s):  
Time Series ◽  
Snow Cover ◽  
Large Scale ◽  
Environmental Changes ◽  
Time Lapse ◽  
Ecosystem Functions ◽  
Small Scale ◽  
Spatiotemporal Resolution ◽  
High Spatiotemporal Resolution ◽  
Wide Range

Abstract. Snow cover (SC) and timing of snowmelt are key regulators of a wide range of Arctic ecosystem functions. Both are strongly influenced by the amplified arctic warming and essential variables to understand environmental changes and their dynamics. This study evaluates the potential of Sentinel-1 (S-1) synthetic aperture radar (SAR) time series for monitoring SC and snowmelt with high spatiotemporal resolution to capture their understudied small-scale heterogeneity. We use 97 dual-polarized S-1 SAR images acquired over north-eastern Greenland in the interferometric wide swath mode from the years 2017 and 2018. Comparison of S-1 intensity against SC fraction maps derived from orthorectified terrestrial time lapse imagery indicates an increase of the SAR intensity before a decrease of SC fraction is observed. Hence, increase of backscatter is related to changing snowpack properties during the runoff phase as well as decreasing SC fraction. We here present a novel approach using backscatter intensity thresholds to identify start and end of snowmelt (SOS and EOS), perennial snow and wet/dry SC based on the temporal evolution of the SAR signal. Comparison of SC with orthorectified time lapse imagery indicate that HV polarization outperforms HH when using a global threshold. With a global configuration (Threshold: 4 dB; polarization: HV), the overall accuracy of SC maps was in all cases above 75 % and in more than half cases above 90 % enabling a large-scale SC monitoring at high spatiotemporal resolution (20 m, 6 days) with high accuracy.

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.

Imaging collagen type I fibrillogenesis with high spatiotemporal resolution

2015 ◽  
Vol 149 ◽  
pp. 86-94 ◽  
Author(s):  
Dimitar R Stamov ◽  
Erik Stock ◽  
Clemens M Franz ◽  
Torsten Jähnke ◽  
Heiko Haschke
Keyword(s):  
Collagen Type ◽  
Collagen Type I ◽  
Type I ◽  
Spatiotemporal Resolution ◽  
High Spatiotemporal Resolution

scholarly journals Mechanistic evidence for tracking the seasonality of photosynthesis with solar-induced fluorescence

2019 ◽  
pp. 201900278 ◽  
Author(s):  
Troy S. Magney ◽  
David R. Bowling ◽  
Barry A. Logan ◽  
Katja Grossmann ◽  
Jochen Stutz ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Large Scale ◽  
Spatial Scales ◽  
Gross Primary Production ◽  
Canopy Structure ◽  
Operating Efficiency ◽  
Conifer Forest ◽  
Spatiotemporal Resolution ◽  
Evergreen Forests ◽  
Spectrometer System

Northern hemisphere evergreen forests assimilate a significant fraction of global atmospheric CO2 but monitoring large-scale changes in gross primary production (GPP) in these systems is challenging. Recent advances in remote sensing allow the detection of solar-induced chlorophyll fluorescence (SIF) emission from vegetation, which has been empirically linked to GPP at large spatial scales. This is particularly important in evergreen forests, where traditional remote-sensing techniques and terrestrial biosphere models fail to reproduce the seasonality of GPP. Here, we examined the mechanistic relationship between SIF retrieved from a canopy spectrometer system and GPP at a winter-dormant conifer forest, which has little seasonal variation in canopy structure, needle chlorophyll content, and absorbed light. Both SIF and GPP track each other in a consistent, dynamic fashion in response to environmental conditions. SIF and GPP are well correlated (R2 = 0.62–0.92) with an invariant slope over hourly to weekly timescales. Large seasonal variations in SIF yield capture changes in photoprotective pigments and photosystem II operating efficiency associated with winter acclimation, highlighting its unique ability to precisely track the seasonality of photosynthesis. Our results underscore the potential of new satellite-based SIF products (TROPOMI, OCO-2) as proxies for the timing and magnitude of GPP in evergreen forests at an unprecedented spatiotemporal resolution.

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