Evaluation of Satellite-Based Optical and Thermal Trapezoid Methods for Groundwater Table Depth Monitoring in Estonian Bogs

2020 ◽  
Author(s):  
Iuliia Burdun ◽  
Valentina Sagris ◽  
Michel Bechtold ◽  
Viacheslav Komisarenko ◽  
Ülo Mander ◽  
...  
Keyword(s):  
Land Surface ◽  
Large Scale ◽  
Bare Soil ◽  
Groundwater Table ◽  
Soil Conditions ◽  
Landsat 8 ◽  
Raised Bogs ◽  
Groundwater Table Depth ◽  
Dryness Index ◽  
Moisture Conditions

<p>Groundwater table depth and peat moisture content are of crucial importance for many peatland processes, like for example their greenhouse gas budget. Thus, there is a strong need for remote sensing techniques that allow to spatially monitor these critical moisture conditions to quantify the hydrological responses to climate change and other anthropogenic disturbances. Previous studies have demonstrated the usefulness but also limitations of microwave observations for peatland moisture monitoring at the large scale. Here, we explore the potential of techniques based on optical and thermal imagery for smaller scale applications.</p><p>Satellite-derived land surface temperature (LST) as well as shortwave infrared transformed reflectance (STR) are sensitive to soil moisture conditions in mineral soils. Both data form, together with remotely sensed vegetation indices (VIs), trapezoids in the LST-VI and STR-VI space with the highest range of possible LST and STR for bare soil conditions. The lowest and highest LST and STR along the vegetation cover gradient define the wet and dry edge, respectively. In this study, we used Landsat 7 and Landsat 8 satellite data for the vegetation periods from 2008 through 2019 to calculate various VIs, LST and STR for hemiboreal raised bogs in Estonia. Two common approaches for the determination of wet and dry edges for the LST-based method were applied and compared. The first approach estimates the edges directly from the observed values of VIs and LST for each scene; while the second one relies on modelled theoretical edges for each scene. In contrast, the STR-VI trapezoid is derived from observed values from all scenes as proposed in literature. The trapezoids are used to calculate the dryness index of each Landsat pixel by linearly scaling between the wet and dry edge. These indices are evaluated with measured groundwater table depth time series. Preliminary results indicate that, for our study area, suitable LST-based trapezoids cannot be derived from satellite observations alone, indicated by the low dependency of the resulting dryness index on groundwater table depth. Evaluation of the theoretically-derived trapezoids and the STR-VI is ongoing and will be discussed.</p>

scholarly journals Shallow groundwater effect on land surface temperature and surface energy balance under bare soil conditions: modeling and description

2012 ◽  
Vol 16 (7) ◽  
pp. 1817-1831 ◽  
Author(s):  
F. Alkhaier ◽  
G. N. Flerchinger ◽  
Z. Su
Keyword(s):  
Energy Balance ◽  
Surface Temperature ◽  
Land Surface ◽  
Shallow Groundwater ◽  
Bare Soil ◽  
Heat Fluxes ◽  
Shortwave Radiation ◽  
Soil Conditions ◽  
Potential Evaporation ◽  
Available Energy

Abstract. Understanding when and how groundwater affects surface temperature and energy fluxes is significant for utilizing remote sensing in groundwater studies and for integrating aquifers within land surface models. To investigate the shallow groundwater effect under bare soil conditions, we numerically exposed two soil profiles to identical metrological forcing. One of the profiles had shallow groundwater. The different responses that the two profiles manifested were inspected regarding soil moisture, temperature and energy balance at the land surface. The findings showed that the two profiles differed in three aspects: the absorbed and emitted amounts of energy, the portioning out of the available energy and the heat fluency in the soil. We concluded that due to their lower albedo, shallow groundwater areas reflect less shortwave radiation and consequently get a higher magnitude of net radiation. When potential evaporation demand is sufficiently high, a large portion of the energy received by these areas is consumed for evaporation. This increases the latent heat flux and reduces the energy that could have heated the soil. Consequently, lower magnitudes of both sensible and ground heat fluxes are caused to occur. The higher soil thermal conductivity in shallow groundwater areas facilitates heat transfer between the top soil and the subsurface, i.e. soil subsurface is more thermally connected to the atmosphere. For the reliability of remote sensors in detecting shallow groundwater effect, it was concluded that this effect can be sufficiently clear to be detected if at least one of the following conditions occurs: high potential evaporation and high contrast between day and night temperatures. Under these conditions, most day and night hours are suitable for shallow groundwater depth detection.

scholarly journals Google Earth Engine for the Detection of Soiling on Photovoltaic Solar Panels in Arid Environments

2020 ◽  
Vol 12 (9) ◽  
pp. 1466 ◽  
Author(s):  
Hitesh Supe ◽  
Ram Avtar ◽  
Deepak Singh ◽  
Ankita Gupta ◽  
Ali P. Yunus ◽  
...  
Keyword(s):  
Land Surface ◽  
Power Plants ◽  
Remote Sensing Data ◽  
Bare Soil ◽  
Google Earth ◽  
Landsat 8 ◽  
Arid Environments ◽  
Solar Panels ◽  
Google Earth Engine ◽  
Sand Deposition

The soiling of solar panels from dry deposition affects the overall efficiency of power output from solar power plants. This study focuses on the detection and monitoring of sand deposition (wind-blown dust) on photovoltaic (PV) solar panels in arid regions using multitemporal remote sensing data. The study area is located in Bhadla solar park of Rajasthan, India which receives numerous sandstorms every year, carried by westerly and north-westerly winds. This study aims to use Google Earth Engine (GEE) in monitoring the soiling phenomenon on PV panels. Optical imageries archived in the GEE platform were processed for the generation of various sand indices such as the normalized differential sand index (NDSI), the ratio normalized differential soil index (RNDSI), and the dry bare soil index (DBSI). Land surface temperature (LST) derived from Landsat 8 thermal bands were also used to correlate with sand indices and to observe the pattern of sand accumulation in the target region. Additionally, high-resolution PlanetScope images were used to quantitatively validate the sand indices. Our study suggests that the use of freely available satellite data with semiautomated processing on GEE can be a useful alternative to manual methods. The developed method can provide near real-time monitoring of soiling on PV panels cost-effectively. This study concludes that the DBSI method has a comparatively higher potential (89.6% Accuracy, 0.77 Kappa) in the detection of sand deposition on PV panels as compared to other indices. The findings of this study can be useful to solar energy companies in the development of an operational plan for the cleaning of PV panels regularly.

10 yrs of Improved Groundwater Table Estimates in Northern Peatlands Through Assimilation of Passive Microwave Observations into PEATCLSM

2020 ◽  
Author(s):  
Michel Bechtold ◽  
Gabrielle De Lannoy ◽  
Rolf H Reichle ◽  
Dirk Roose ◽  
Nicole Balliston ◽  
...  
Keyword(s):  
Data Assimilation ◽  
Land Surface ◽  
Land Surface Model ◽  
Open Loop ◽  
Groundwater Table ◽  
Surface Model ◽  
Simulation Experiments ◽  
Peatland Hydrology ◽  
Groundwater Table Depth ◽  
L Band

<p>Groundwater table depth and peat moisture, exert a first order control on a range of biogeochemical and -physical peatland processes, and the susceptibility to peat fires. Therefore, one of the first critical measures to identify “peatlands under pressure” is the change of hydrological conditions, e.g. due to changing climatic conditions or direct “hydraulic” human influence. In this presentation, we introduce a new opportunity for the global-scale monitoring of moisture conditions in peatlands. We assimilate L-band brightness temperature (Tb) data from the Soil Moisture Ocean Salinity (SMOS) into the Catchment land surface model (CLSM) to improve the simulation of Northern peatland hydrology from 2010 through 2019. We compare four simulation experiments: two open loop and two data assimilation simulations, either using the default CLSM or a recently-developed peatland-specific adaptation of it (PEATCLSM, Bechtold et al. 2019). The assimilation system uses a spatially distributed ensemble Kalman filter to update soil moisture and groundwater table depth. The simulation experiments are evaluated against an in-situ dataset of groundwater table depth in about 20 natural and semi-natural peatlands that are large enough to be dominant in the corresponding 81-km<sup>2</sup> model grid cells. For PEATCLSM, Tb data assimilation increases the temporal Pearson correlation (R) and anomaly correlation (aR) between simulated and measured groundwater table from 0.53 and 0.38 (open-loop) to 0.58 and 0.45 (analysis), respectively. Time series comparison at monitoring sites demonstrates how the assimilation effectively corrects for remaining deficiencies in model physics and/or errors of the global meteorological data forcing the model. The generally lower coefficients of 0.30 (R) and 0.09 (aR) for the default CLSM also improve after Tb data assimilation to values of 0.39 (R) and 0.28 (aR). However, even with Tb data assimilation, the skill of CLSM remains inferior to that of PEATCLSM. The more realistic model physics of PEATCLSM are also supported by a reduction of the Tb misfits (observed Tb – forecasted Tb) over 94 % of the Northern peatland area. The temporal variance of Tb misfits is reduced by 20 % on average and is largest over the large peatland areas of the Western Siberian (25 %) and Hudson Bay Lowlands (40 %). This study demonstrates, for the first time, an improved estimation of the peatland hydrological dynamics by the assimilation of SMOS L-band brightness data into a global land surface model and suggests a new route of research focusing on the incorporation of additional satellite observations into peatland-specific modeling schemes.</p><p>Bechtold, M., De Lannoy, G.J M., Koster, R.D., Reichle, R.H., et al. (2019). PEAT-CLSM: A Specific Treatment of Peatland Hydrology in the NASA Catchment Land Surface Model. JOURNAL OF ADVANCES IN MODELING EARTH SYSTEMS, 11 (7), 2130-2162. doi: 10.1029/2018MS001574.</p>

scholarly journals Spatio-Temporal Assessment of Surface Moisture and Evapotranspiration Variability Using Remote Sensing Techniques

2021 ◽  
Vol 13 (9) ◽  
pp. 1667
Author(s):  
Mai Son Le ◽  
Yuei-An Liou
Keyword(s):  
Soil Moisture ◽  
Land Surface ◽  
Meteorological Data ◽  
Growing Season ◽  
Heat Index ◽  
Landsat 8 ◽  
Surface Moisture ◽  
Dryness Index ◽  
Spatio Temporal ◽  
Remote Sensing Techniques

The relationship between the physic features of the Earth’s surface and its temperature has been significantly investigated for further soil moisture assessment. In this study, the spatiotemporal impacts of surface properties on land surface temperature (LST) were examined by using Landsat-8 Operational Land Imager (OLI) and Thermal Infrared Sensor (TIRS) and meteorological data. The significant distinctions were observed during a crop growing season through the contrast in the correlation between different multi-spectral satellite indices and LST, in which the highest correlation of −0.65 was found when the Normalized Difference Latent heat Index (NDLI) was used. A new index, named as Temperature-soil Moisture Dryness Index (TMDI), is accordingly proposed to assess surface moisture and evapotranspiration (ET) variability. It is based on a triangle space where NDLI is set as a reference basis for examining surface water availability and the variation of LST is an indicator as a consequence of the cooling effect by ET. TMDI was evaluated against ET derived from the commonly-used model, namely Surface Energy Balance Algorithm for Land (SEBAL), as well as compared to the performance of Temperature Vegetation Dryness Index (TVDI). This study was conducted over five-time points for the 2014 winter crop growing season in southern Taiwan. Results indicated that TMDI exhibits significant sensitivity to surface moisture fluctuation by showing a strong correlation with SEBAL-derived ET with the highest correlation of −0.89 was found on 19 October. Moreover, TMDI revealed its superiority over TVDI in the response to a rapidly changing surface moisture due to water supply before the investigated time. It is suggested that TMDI is a proper and sensitive indicator to characterize the surface moisture and ET rate. Further exploitation of the usefulness of the TMDI in a variety of applications would be interesting.

scholarly journals The Social Restrictions Impact on Urban Heat Island Phenomena (Case Study: Cities in Java Island)

2021 ◽  
Vol 887 (1) ◽  
pp. 012017
Author(s):  
A. M. Agni ◽  
P. Pangi ◽  
B. Septiarani ◽  
K. D. Astuti
Keyword(s):  
Urban Heat Island ◽  
Land Surface ◽  
Local Governments ◽  
Large Scale ◽  
Landsat 8 ◽  
Heat Island ◽  
Capital Region ◽  
The Social ◽  
Urban Heat ◽  
The Impact

Abstract The Coronavirus Disease 2019 (Covid-19) pandemic has hit Indonesia since March 14, 2020. The rapid spread of the virus has caused the central and regional governments to implement community activity policies. Some terms and methods used by local governments such as PSBB (the Large-Scale Social Restrictions) are applied in Special Capital Region of Jakarta and Surabaya City, in Semarang City has PKM (Restrictions on Community Activities). This study aims to analyze the impact of the social restrictions on Urban Heat Island (UHI) in the Java Island big city. This research was conducted in big cities on Java Island that apply social restrictions, namely Special Capital Region of Jakarta, Bandung, Semarang, Yogyakarta, Surakarta, Surabaya, and Malang. The data used are Landsat 8 satellite imagery in 2019 and 2020. The method used is to compare the magnitude of the Land Surface Temperature (LST) and UHI before and after social restrictions. The results of the analysis explain that there is a decrease in LST and changes in UHI in the cities of Special Capital Region of Jakarta, Bandung, Semarang, Surakarta, and Yogyakarta. However, in Surabaya and Malang, there was an increase in LST. This study concludes that the implementation of social restrictions affects changes in UHI and decreases LST.

scholarly journals Description and Validation of the Simple, Efficient, Dynamic, Global, Ecological Simulator (SEDGES v.1.0)

2017 ◽  
Author(s):  
Pablo Paiewonsky ◽  
Oliver Elison Timm
Keyword(s):  
Primary Productivity ◽  
Land Surface ◽  
Large Scale ◽  
Net Primary Productivity ◽  
Climate Model ◽  
Hydrological Cycle ◽  
Bare Soil ◽  
Relative Advantage ◽  
Coupling Scheme ◽  
Area Index

Abstract. In this paper, we present a simple vegetation model whose primary intended use is auxiliary to the land-atmosphere coupling scheme of a climate model, particularly one of intermediate complexity. The model formulations and their derivations are presented here, in detail. The model includes some realistic and useful features for its level of complexity, including a photosynthetic dependency on light, full coupling of photosynthesis and transpiration through an interactive canopy resistance, and a soil organic carbon dependence for bare soil albedo. We evaluate the model's performance by running it using a simple land surface scheme that is driven by reanalysis data. The evaluation against observational data includes net primary productivity, leaf area index, surface albedo, and diagnosed variables relevant for the closure of the hydrological cycle. In this set up, we find that the model gives an adequate to good simulation of basic large-scale ecological and hydrological variables. Of the variables analyzed in this paper, gross primary productivity is particularly well simulated. The results also reveal the current limitations of the model. The most significant deficiency is the excessive simulation of evapotranspiration in mid- to high northern latitudes during their winter to spring transition. The model has relative advantage in situations that require some combination of computational efficiency, model transparency and tractability, and the simulation of the large scale vegetation and land surface characteristics under non-present day conditions.

scholarly journals An Idealized Prototype for Large-Scale Land–Atmosphere Coupling

2013 ◽  
Vol 26 (7) ◽  
pp. 2379-2389 ◽  
Author(s):  
Benjamin R. Lintner ◽  
Pierre Gentine ◽  
Kirsten L. Findell ◽  
Fabio D’Andrea ◽  
Adam H. Sobel ◽  
...  
Keyword(s):  
Soil Moisture ◽  
Land Surface ◽  
Large Scale ◽  
Deep Convection ◽  
Prototype Model ◽  
Surface Conditions ◽  
High Soil Moisture ◽  
Convective Adjustment ◽  
Large Scale Land ◽  
Moisture Conditions

Abstract A process-based, semianalytic prototype model for understanding large-scale land–atmosphere coupling is developed here. The metric for quantifying the coupling is the sensitivity of precipitation P to soil moisture W, . For a range of prototype parameters typical of conditions found over tropical or summertime continents, the sensitivity measure exhibits a broad minimum at intermediate soil moisture values. This minimum is attributed to a trade-off between evaporation (or evapotranspiration) E and large-scale moisture convergence across the range of soil moisture states. For water-limited, low soil moisture conditions, is dominated by evaporative sensitivity , reflecting high potential evaporation Ep arising from relatively warm surface conditions and a moisture-deficient atmospheric column under dry surface conditions. By contrast, under high soil moisture (or energy limited) conditions, becomes slightly negative as Ep decreases. However, because convergence and precipitation increase strongly with decreasing (drying) moisture advection, while soil moisture slowly saturates, is large. Variation of key parameters is shown to impact the magnitude of , for example, increasing the time scale for deep convective adjustment lowers at a given W, especially on the moist side of the profile where convergence dominates. While the prototype’s applicability for direct quantitative comparison with either observations or models is clearly limited, it nonetheless demonstrates how the complex interplay of surface turbulent and column radiative fluxes, deep convection, and horizontal and vertical moisture transport influences the coupling of the land surface and atmosphere that may be expected to occur in either more realistic models or observations.

scholarly journals Combining the trapezoidal relationship between land surface temperature and vegetation index with the Priestley-Taylor equation to estimate evapotranspiration

2015 ◽  
Vol 368 ◽  
pp. 379-384
Author(s):  
X. Wang ◽  
W. Wang ◽  
Y. Jiang
Keyword(s):  
Surface Temperature ◽  
Land Surface Temperature ◽  
Land Surface ◽  
Vegetation Index ◽  
Hydrological Cycle ◽  
Bare Soil ◽  
Landsat 8 ◽  
Extreme Conditions ◽  
Enhanced Vegetation Index ◽  
Vegetation Water

Abstract. Evapotranspiration (ET) plays an important role in the hydrological cycle. A method of combining the Priestley-Taylor (P-T) equation with a trapezoidal space between land surface temperature (Ts) and enhanced vegetation index (EVI) is proposed based on the principle of energy balance. Generally, this method is divided into three major parts: (1) construct the Ts versus EVI (Ts-VI) trapezoidal space for calculating the Ts at four extreme conditions (i.e. well-watered vegetation, water-stressed vegetation, saturated bare soil and dry bare soil); (2) calculate the P-T coefficient for each pixel according to the position of the observed (EVI, Ts) point in the trapezoid space; (3) calculate actual ET of the pixel using the P-T equation. The method is validated using Landsat-8 images and ground-observed data for a semi-humid area in China. The result shows that the ET estimates match the observations well, which indicates the effectiveness the proposed method here.

Hyperresolution Land Surface Modeling in the Context of SMAP Cal–Val

2015 ◽  
Vol 17 (1) ◽  
pp. 345-352 ◽  
Author(s):  
Camille Garnaud ◽  
Stéphane Bélair ◽  
Aaron Berg ◽  
Tracy Rowlandson
Keyword(s):  
Soil Moisture ◽  
Soil Temperature ◽  
Land Surface ◽  
Large Scale ◽  
Initial Conditions ◽  
Surface Condition ◽  
Soil Conditions ◽  
Small Scale ◽  
In Situ Observations

Abstract This study explores the performance of Environment Canada’s Surface Prediction System (SPS) in comparison to in situ observations from the Brightwater Creek soil moisture observation network with respect to soil moisture and soil temperature. To do so, SPS is run at hyperresolution (100 m) over a small domain in southern Saskatchewan (Canada) during the summer of 2014. It is shown that with initial conditions and surface condition forcings based on observations, SPS can simulate soil moisture and soil temperature evolution over time with high accuracy (mean bias of 0.01 m3 m−3 and −0.52°C, respectively). However, the modeled spatial variability is generally much weaker than observed. This is likely related to the model’s use of uniform soil texture, the lack of small-scale orography, as well as a predefined crop growth cycle in SPS. Nonetheless, the spatial averages of simulated soil conditions over the domain are very similar to those observed, suggesting that both are representative of large-scale conditions. Thus, in the context of the National Aeronautics and Space Administration’s (NASA) Soil Moisture Active Passive (SMAP) project, this study shows that both simulated and in situ observations can be upscaled to allow future comparison with upcoming satellite data.

scholarly journals Simulating the Impact of Urban Surface Evapotranspiration on the Urban Heat Island Effect Using the Modified RS-PM Model: A Case Study of Xuzhou, China

2020 ◽  
Vol 12 (3) ◽  
pp. 578
Author(s):  
Yuchen Wang ◽  
Yu Zhang ◽  
Nan Ding ◽  
Kai Qin ◽  
Xiaoyan Yang
Keyword(s):  
Remote Sensing ◽  
Urban Heat Island ◽  
Land Surface ◽  
Estimation Method ◽  
Bare Soil ◽  
Urban Surface ◽  
Landsat 8 ◽  
Heat Island ◽  
Urban Heat ◽  
The Impact

As an important energy absorption process in the Earth’s surface energy balance, evapotranspiration (ET) from vegetation and bare soil plays an important role in regulating the environmental temperatures. However, little research has been done to explore the cooling effect of ET on the urban heat island (UHI) due to the lack of appropriate remote-sensing-based estimation models for complex urban surface. Here, we apply the modified remote sensing Penman–Monteith (RS-PM) model (also known as the urban RS-PM model), which has provided a new regional ET estimation method with the better accuracy for the urban complex underlying surface. Focusing on the city of Xuzhou in China, ET and land surface temperature (LST) were inversed by using 10 Landsat 8 images during 2014–2018. The impact of ET on LST was then analyzed and quantified through statistical and spatial analyses. The results indicate that: (1) The alleviating effect of ET on the UHI was stronger during the warmest months of the year (May–October) but not during the colder months (November–March); (2) ET had the most significant alleviating effect on the UHI effect in those regions with the highest ET intensities; and (3) in regions with high ET intensities and their surrounding areas (within a radius of 150 m), variation in ET was a key factor for UHI regulation; a 10 W·m−2 increase in ET equated to 0.56 K decrease in LST. These findings provide a new perspective for the improvement of urban thermal comfort, which can be applied to urban management, planning, and natural design.

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