scholarly journals A Review of Quantifying pCO2 in Inland Waters with a Global Perspective: Challenges and Prospects of Implementing Remote Sensing Technology

2021 ◽  
Vol 13 (23) ◽  
pp. 4916
Author(s):  
Zhidan Wen ◽  
Yingxin Shang ◽  
Lili Lyu ◽  
Sijia Li ◽  
Hui Tao ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Large Scale ◽  
Biological Activities ◽  
Spatial Scales ◽  
Measurement Data ◽  
Global Perspective ◽  
Published Data ◽  
Inland Waters ◽  
Rivers And Streams ◽  
Temporal And Spatial

The traditional field-based measurements of carbon dioxide (pCO2) for inland waters are a snapshot of the conditions on a particular site, which might not adequately represent the pCO2 variation of the entire lake. However, these field measurements can be used in the pCO2 remote sensing modeling and verification. By focusing on inland waters (including lakes, reservoirs, rivers, and streams), this paper reviews the temporal and spatial variability of pCO2 based on published data. The results indicate the significant daily and seasonal variations in pCO2 in lakes. Rivers and streams contain higher pCO2 than lakes and reservoirs in the same climatic zone, and tropical waters typically exhibit higher pCO2 than temperate, boreal, and arctic waters. Due to the temporal and spatial variations of pCO2, it can differ in different inland water types in the same space-time. The estimation of CO2 fluxes in global inland waters showed large uncertainties with a range of 1.40–3.28 Pg C y−1. This paper also reviews existing remote sensing models/algorithms used for estimating pCO2 in sea and coastal waters and presents some perspectives and challenges of pCO2 estimation in inland waters using remote sensing for future studies. To overcome the uncertainties of pCO2 and CO2 emissions from inland waters at the global scale, more reliable and universal pCO2 remote sensing models/algorithms will be needed for mapping the long-term and large-scale pCO2 variations for inland waters. The development of inverse models based on dissolved biogeochemical processes and the machine learning algorithm based on measurement data might be more applicable over longer periods and across larger spatial scales. In addition, it should be noted that the remote sensing-retrieved pCO2/the CO2 concentration values are the instantaneous values at the satellite transit time. A major technical challenge is in the methodology to transform the retrieved pCO2 values on time scales from instant to days/months, which will need further investigations. Understanding the interrelated control and influence processes closely related to pCO2 in the inland waters (including the biological activities, physical mixing, a thermodynamic process, and the air–water gas exchange) is the key to achieving remote sensing models/algorithms of pCO2 in inland waters. This review should be useful for a general understanding of the role of inland waters in the global carbon cycle.

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.

scholarly journals Multi-Scale Remote Sensing Semantic Analysis Based on a Global Perspective

2019 ◽  
Vol 8 (9) ◽  
pp. 417 ◽  
Author(s):  
Wei Cui ◽  
Dongyou Zhang ◽  
Xin He ◽  
Meng Yao ◽  
Ziwei Wang ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Receptive Field ◽  
Large Scale ◽  
Semantic Information ◽  
Short Term Memory ◽  
Global Perspective ◽  
Small Scale ◽  
Multi Scale ◽  
Memory Network ◽  
Long Short Term Memory

Remote sensing image captioning involves remote sensing objects and their spatial relationships. However, it is still difficult to determine the spatial extent of a remote sensing object and the size of a sample patch. If the patch size is too large, it will include too many remote sensing objects and their complex spatial relationships. This will increase the computational burden of the image captioning network and reduce its precision. If the patch size is too small, it often fails to provide enough environmental and contextual information, which makes the remote sensing object difficult to describe. To address this problem, we propose a multi-scale semantic long short-term memory network (MS-LSTM). The remote sensing images are paired into image patches with different spatial scales. First, the large-scale patches have larger sizes. We use a Visual Geometry Group (VGG) network to extract the features from the large-scale patches and input them into the improved MS-LSTM network as the semantic information, which provides a larger receptive field and more contextual semantic information for small-scale image caption so as to play the role of global perspective, thereby enabling the accurate identification of small-scale samples with the same features. Second, a small-scale patch is used to highlight remote sensing objects and simplify their spatial relations. In addition, the multi-receptive field provides perspectives from local to global. The experimental results demonstrated that compared with the original long short-term memory network (LSTM), the MS-LSTM’s Bilingual Evaluation Understudy (BLEU) has been increased by 5.6% to 0.859, thereby reflecting that the MS-LSTM has a more comprehensive receptive field, which provides more abundant semantic information and enhances the remote sensing image captions.

scholarly journals A coupled remote sensing and the Surface Energy Balance with Topography Algorithm (SEBTA) to estimate actual evapotranspiration under complex terrain

2010 ◽  
Vol 7 (4) ◽  
pp. 4875-4924 ◽  
Author(s):  
Z. Q. Gao ◽  
C. S. Liu ◽  
W. Gao ◽  
N. B. Chang
Keyword(s):  
Remote Sensing ◽  
Energy Balance ◽  
Surface Energy ◽  
Vegetation Cover ◽  
Complex Terrain ◽  
Surface Energy Balance ◽  
Spatial Scales ◽  
Ecological Indicator ◽  
Temporal And Spatial ◽  
Over Time

Abstract. Evapotranspiration (ET) may be used as an ecological indicator to address the ecosystem complexity. The accurate measurement of ET is of great significance for studying environmental sustainability, global climate changes, and biodiversity. Remote sensing technologies are capable of monitoring both energy and water fluxes on the surface of the Earth. With this advancement, existing models, such as SEBAL, S_SEBI and SEBS, enable us to estimate the regional ET with limited temporal and spatial scales. This paper extends the existing modeling efforts with the inclusion of new components for ET estimation at varying temporal and spatial scales under complex terrain. Following a coupled remote sensing and surface energy balance approach, this study emphasizes the structure and function of the Surface Energy Balance with Topography Algorithm (SEBTA). With the aid of the elevation and landscape information, such as slope and aspect parameters derived from the digital elevation model (DEM), and the vegetation cover derived from satellite images, the SEBTA can fully account for the dynamic impacts of complex terrain and changing land cover in concert with some varying kinetic parameters (i.e., roughness and zero-plane displacement) over time. Besides, the dry and wet pixels can be recognized automatically and dynamically in image processing thereby making the SEBTA more sensitive to derive the sensible heat flux for ET estimation. To prove the application potential, the SEBTA was carried out to present the robust estimates of 24 h solar radiation over time, which leads to the smooth simulation of the ET over seasons in northern China where the regional climate and vegetation cover in different seasons compound the ET calculations. The SEBTA was validated by the measured data at the ground level. During validation, it shows that the consistency index reached 0.92 and the correlation coefficient was 0.87.

scholarly journals Understanding the Distribution of Multimodel Ensembles

2020 ◽  
Vol 33 (21) ◽  
pp. 9447-9465
Author(s):  
Bo Christiansen
Keyword(s):  
Statistical Model ◽  
Large Scale ◽  
Climate Models ◽  
Spatial Scales ◽  
Model Errors ◽  
High Dimensions ◽  
Ensemble Mean ◽  
Temporal And Spatial ◽  
Multimodel Ensembles ◽  
Annual Means

AbstractWhen analyzing multimodel climate ensembles it is often assumed that the ensemble is either truth centered or that models and observations are drawn from the same distribution. Here we analyze CMIP5 ensembles focusing on three measures that separate the two interpretations: the error of the ensemble mean relative to the error of individual models, the decay of the ensemble mean error for increasing ensemble size, and the correlations of the model errors. The measures are analyzed using a simple statistical model that includes the two interpretations as different limits and for which analytical results for the three measures can be obtained in high dimensions. We find that the simple statistical model describes the behavior of the three measures in the CMIP5 ensembles remarkably well. Except for the large-scale means we find that the indistinguishable interpretation is a better assumption than the truth centered interpretation. Furthermore, the indistinguishable interpretation becomes an increasingly better assumption when the errors are based on smaller temporal and spatial scales. Building on this, we present a simple conceptual mechanism for the indistinguishable interpretation based on the assumption that the climate models are calibrated on large-scale features such as, e.g., annual means or global averages.

scholarly journals CLIMATIC VARIABILITY PREDICTION WITH SATELLITE REMOTE SENSING AND METEOROLOGICAL DATA IN THE SOUTH WESTERN NIGERIA

2013 ◽  
Vol 39 (2) ◽  
pp. 59-63
Author(s):  
Ebenezer Yemi Ogunbadewa
Keyword(s):  
Climate Change ◽  
Remote Sensing ◽  
Satellite Remote Sensing ◽  
Spatial Scales ◽  
Climatic Variability ◽  
Data Availability ◽  
The South ◽  
Suitable Alternative ◽  
Temporal And Spatial ◽  
Satellite Sensor

Climatic variability affects both seasonal phenological cycles of vegetation and monthly distribution of rainfall in the south western Nigeria. Variations in vegetation biophysical parameters have been known to be a good indicator of climate variability; hence they are used as key inputs into climate change models. However, understanding the response of vegetation to the influence of climate at both temporal and spatial scales have been a major challenge. This is because most climatic data available are derived from ground-based instruments, which are mainly point measurements and are characterized by sparse network of meteorological stations that lacks the spatial coverage required for climate change investigation. Satellite remote sensing instruments can provide a suitable alternative of time-reliable datasets in a more consistent manner at both temporal and spatial scales. The aim of this study is to test the suitability of one year time series datasets obtained from satellite sensor and meteorological stations as a starting point for the development of a climate change model that can be exploited in planning adaptation strategies. Taking into consideration that rainfall is the most variable element of climate in the study area, rainfall data acquired from five meteorological stations for the year 2006 were correlated with changes in Normalized Difference Vegetation Index (NDVI) derived from Moderate Resolution Imaging Spectroradiometer (MODIS)/Terra satellite sensor for the same period using a linear regression equation. The results shows that rainfall–NDVI relationship was stronger along the seasonal track with R2 ranging from 0.74 to 0.94, indicating that NDVI seasonal variations can be used as a surrogate data source for monitoring climate change for short and long term scales ranging from regional to global magnitude especially in areas where data availability from ground-based measurements are unreliable.

Developing statistically driven eco-engineering designs from LiDAR and structure from motion surveys for marine artificial structures.

2020 ◽  
Author(s):  
Peter Lawrence ◽  
Ally Evans ◽  
Paul Brooks ◽  
Tim D'Urban Jackson ◽  
Stuart Jenkins ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Habitat Loss ◽  
Structure From Motion ◽  
Laser Scanning ◽  
Large Scale ◽  
Spatial Scales ◽  
Coastal Development ◽  
Ecological Design ◽  
Artificial Structures ◽  
Engineering Research

<p>Coastal ecosystems are threatened by habitat loss and anthropogenic “smoothing” as hard engineering approaches to sea defence, such as sea-walls, rock armouring, and offshore reefs, become common place. These artificial structures use homogenous materials (e.g. concrete or quarried rock) and as a result, lack the surface heterogeneity of natural rocky shoreline known to play a key role in niche creation and higher species diversity. Despite significant investment and research into soft engineering and ecologically sensitive approaches to coastal development, there are still knowledge gaps, particularly in relation to how patterns that are observed in nature can be utilised to improve artificial shores.</p><p>Given the technical improvements and significant reductions in cost within the portable remote sensing field (structure from motion and laser scanning), we are now able to plug gaps in our understanding of how habitat heterogeneity can influence overall site diversity. These improvements represent an excellent opportunity to improve our understanding of the spatial scales and complexity of habitats that species occur within and ultimately improve the ecological design of engineered structures in areas experiencing “smoothing” and habitat loss.</p><p>In this talk, I will highlight how advances in remote sensing techniques can be applied to context-specific ecological problems, such as low diversity and loss of rare species within marine infrastructure. I will describe our approach to combining large-scale ecological, 3D geophysical and engineering research to design statistically-derived ecologically-inspired solutions to smooth artificial surfaces. We created experimental concrete enhancement units and deployed them at a number of coastal locations. I will present preliminary ecological results, provide a workflow of unit development and statistical approaches, and finally discuss how these advances may improve future ecological intervention and design options.</p>

scholarly journals Ocean–Atmosphere Coupling on Different Spatiotemporal Scales: A Mechanism for Intraseasonal Instabilities

2009 ◽  
Vol 66 (6) ◽  
pp. 1834-1844 ◽  
Author(s):  
Lei Zhou ◽  
Raghu Murtugudde
Keyword(s):  
Analytical Solutions ◽  
Large Scale ◽  
Spatial Scales ◽  
Coupled Model ◽  
Governing Equations ◽  
Resonance Mechanism ◽  
Ocean Atmosphere ◽  
Spatiotemporal Scales ◽  
Temporal And Spatial ◽  
Reasonable Choice

Abstract The possibility of interactions between oceanic and atmospheric oscillations with different temporal and spatial scales is examined with analytical solutions to idealized linear governing equations. With a reasonable choice for relevant parameters, the mesoscale oceanic features and the large-scale atmospheric oscillations can interact with each other and lead to unstable waves in the intraseasonal band in the specific coupled model presented in this study. This mechanism is different from the resonance mechanism, which requires similar temporal or spatial scales in the two media. Instead, this mechanism indicates that even in the cases in which the temporal and spatial scales are different but the dispersion relations (i.e., functions of frequency and wavenumber) of the oceanic and atmospheric oscillations are proximal, instabilities can still be generated due to the ocean–atmosphere coupling.

scholarly journals Editorial for the Special Issue “Remote Sensing in Coastal Zone Monitoring and Management—How Can Remote Sensing Challenge the Broad Spectrum of Temporal and Spatial Scales in Coastal Zone Dynamic?”

2019 ◽  
Vol 11 (9) ◽  
pp. 1028
Author(s):  
Doxaran ◽  
Bustamante ◽  
Dogliotti ◽  
Malthus ◽  
Senechal
Keyword(s):  
Remote Sensing ◽  
Coastal Zone ◽  
Spatial Scales ◽  
Coastal Zones ◽  
Special Issue ◽  
Physico Chemical ◽  
Long Term Trends ◽  
Morphological Processes ◽  
Temporal And Spatial

Coastal zones are sensitive areas responding at various scales (events to long-term trends) where the monitoring and management of physico-chemical, biological, morphological processes, and fluxes are highly challenging [...]

Continent-wide bimonthly mapping of Antarctic surface meltwater using Google Earth Engine

2021 ◽  
Author(s):  
Peter Tuckett ◽  
Jeremy Ely ◽  
Andrew Sole ◽  
Stephen Livingstone ◽  
James Lea
Keyword(s):  
Remote Sensing ◽  
Spatial Analysis ◽  
Mass Balance ◽  
Large Scale ◽  
Ice Sheet ◽  
Google Earth ◽  
Landsat 8 ◽  
Supraglacial Lakes ◽  
Google Earth Engine ◽  
Temporal And Spatial

<p>Surface meltwater is widespread around the margin of the Antarctic Ice Sheet during the austral summer. This meltwater, typically transported via surface streams and rivers and stored in supraglacial lakes, has the potential to influence ice-sheet mass balance through ice-dynamic and albedo feedbacks. To predict the impact that surface melt will have on mass balance over coming decades, it is important to understand spatial and temporal variability in surface meltwater extent. A variety of methods have been used to detect supraglacial lakes in Antarctica, yet a multi-annual, continent-wide study of Antarctic supraglacial meltwater has yet to be conducted. Cloud-based computational platforms, such as Google Earth Engine (GEE), enable large-scale temporal and spatial analysis of remote sensing datasets at minimal time expense. Here, we implement an automated method for meltwater detection in GEE to generate continent-wide, bimonthly repeat assessments of supraglacial lake extent between 2013 and 2020. We use a band-threshold based approach to delineate surface water from Landsat-8 imagery. Furthermore, our method incorporates a novel technique for quantifying meltwater extent that accounts for variability in optical image coverage and cloud cover, enabling an upper uncertainty bound to be attached to minimum mapped lake areas. We present results from continent-wide mapping, and highlight initial findings that indicate evolution of lakes in Antarctica over the past seven years. This work demonstrates how platforms such as GEE have revolutionized our ability to undertake large-scale projects from remote sensing datasets, allowing for greater temporal and spatial analysis of cryospheric processes than previously possible.</p>

scholarly journals Novel Imaging Modalities Shedding Light on Plant Biology: Start Small and Grow Big

2020 ◽  
Vol 71 (1) ◽  
pp. 789-816 ◽  
Author(s):  
Natalie M. Clark ◽  
Lisa Van den Broeck ◽  
Marjorie Guichard ◽  
Adam Stager ◽  
Herbert G. Tanner ◽  
...  
Keyword(s):  
Plant Growth ◽  
Large Scale ◽  
Spatial Scales ◽  
Quantitative Information ◽  
Imaging Modalities ◽  
Data Sets ◽  
Large Scale Data ◽  
Temporal And Spatial ◽  
Multiple Samples ◽  
Near Future

The acquisition of quantitative information on plant development across a range of temporal and spatial scales is essential to understand the mechanisms of plant growth. Recent years have shown the emergence of imaging methodologies that enable the capture and analysis of plant growth, from the dynamics of molecules within cells to the measurement of morphometricand physiological traits in field-grown plants. In some instances, these imaging methods can be parallelized across multiple samples to increase throughput. When high throughput is combined with high temporal and spatial resolution, the resulting image-derived data sets could be combined with molecular large-scale data sets to enable unprecedented systems-level computational modeling. Such image-driven functional genomics studies may be expected to appear at an accelerating rate in the near future given the early success of the foundational efforts reviewed here. We present new imaging modalities and review how they have enabled a better understanding of plant growth from the microscopic to the macroscopic scale.

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