scholarly journals Assessing Spatiotemporal Variations of Landsat Land Surface Temperature and Multispectral Indices in the Arctic Mackenzie Delta Region between 1985 and 2018

2019 ◽  
Vol 11 (19) ◽  
pp. 2329 ◽  
Author(s):  
Leon Nill ◽  
Tobias Ullmann ◽  
Christof Kneisel ◽  
Jennifer Sobiech-Wolf ◽  
Roland Baumhauer
Keyword(s):  
Land Surface ◽  
Large Scale ◽  
Vegetation Index ◽  
Google Earth ◽  
Influential Factors ◽  
The Arctic ◽  
Moisture Regime ◽  
Mackenzie Delta ◽  
Delta Region

Air temperatures in the Arctic have increased substantially over the last decades, which has extensively altered the properties of the land surface. Capturing the state and dynamics of Land Surface Temperatures (LSTs) at high spatial detail is of high interest as LST is dependent on a variety of surficial properties and characterizes the land–atmosphere exchange of energy. Accordingly, this study analyses the influence of different physical surface properties on the long-term mean of the summer LST in the Arctic Mackenzie Delta Region (MDR) using Landsat 30 m-resolution imagery between 1985 and 2018 by taking advantage of the cloud computing capabilities of the Google Earth Engine. Multispectral indices, including the Normalized Difference Vegetation Index (NDVI), Normalized Difference Water Index (NDWI) and Tasseled Cap greenness (TCG), brightness (TCB), and wetness (TCW) as well as topographic features derived from the TanDEM-X digital elevation model are used in correlation and multiple linear regression analyses to reveal their influence on the LST. Furthermore, surface alteration trends of the LST, NDVI, and NDWI are revealed using the Theil-Sen (T-S) regression method. The results indicate that the mean summer LST appears to be mostly influenced by the topographic exposition as well as the prevalent moisture regime where higher evapotranspiration rates increase the latent heat flux and cause a cooling of the surface, as the variance is best explained by the TCW and northness of the terrain. However, fairly diverse model outcomes for different regions of the MDR (R2 from 0.31 to 0.74 and RMSE from 0.51 °C to 1.73 °C) highlight the heterogeneity of the landscape in terms of influential factors and suggests accounting for a broad spectrum of different factors when modeling mean LSTs. The T-S analysis revealed large-scale wetting and greening trends with a mean decadal increase of the NDVI/NDWI of approximately +0.03 between 1985 and 2018, which was mostly accompanied by a cooling of the land surface given the inverse relationship between mean LSTs and vegetation and moisture conditions. Disturbance through wildfires intensifies the surface alterations locally and lead to significantly cooler LSTs in the long-term compared to the undisturbed surroundings.

scholarly journals A Bayesian Three-Cornered Hat (BTCH) Method: Improving the Terrestrial Evapotranspiration Estimation

2020 ◽  
Vol 12 (5) ◽  
pp. 878
Author(s):  
Xinlei He ◽  
Tongren Xu ◽  
Youlong Xia ◽  
Sayed M. Bateni ◽  
Zhixia Guo ◽  
...  
Keyword(s):  
Mississippi River ◽  
Land Surface ◽  
Vegetation Index ◽  
A Priori ◽  
Influential Factors ◽  
Surface Models ◽  
Evapotranspiration Estimation ◽  
The Individual ◽  
Gravity Recovery

In this study, a Bayesian-based three-cornered hat (BTCH) method is developed to improve the estimation of terrestrial evapotranspiration (ET) by integrating multisource ET products without using any a priori knowledge. Ten long-term (30 years) gridded ET datasets from statistical or empirical, remotely-sensed, and land surface models over contiguous United States (CONUS) are integrated by the BTCH and ensemble mean (EM) methods. ET observations from eddy covariance towers (ETEC) at AmeriFlux sites and ET values from the water balance method (ETWB) are used to evaluate the BTCH- and EM-integrated ET estimates. Results indicate that BTCH performs better than EM and all the individual parent products. Moreover, the trend of BTCH-integrated ET estimates, and their influential factors (e.g., air temperature, normalized differential vegetation index, and precipitation) from 1982 to 2011 are analyzed by the Mann–Kendall method. Finally, the 30-year (1982 to 2011) total water storage anomaly (TWSA) in the Mississippi River Basin (MRB) is retrieved based on the BTCH-integrated ET estimates. The TWSA retrievals in this study agree well with those from the Gravity Recovery and Climate Experiment (GRACE).

scholarly journals Improved Estimates of Arctic Land Surface Phenology Using Sentinel-2 Time Series

2020 ◽  
Vol 12 (22) ◽  
pp. 3738
Author(s):  
Adrià Descals ◽  
Aleixandre Verger ◽  
Gaofei Yin ◽  
Josep Peñuelas
Keyword(s):  
Time Series ◽  
High Resolution ◽  
Land Surface ◽  
Vegetation Index ◽  
Mean Squared Error ◽  
Google Earth ◽  
The Arctic ◽  
Threshold Method ◽  
Land Surface Phenology ◽  
Sentinel 2

The high spatial resolution and revisit time of Sentinel-2A/B tandem satellites allow a potentially improved retrieval of land surface phenology (LSP). The biome and regional characteristics, however, greatly constrain the design of the LSP algorithms. In the Arctic, such biome-specific characteristics include prolonged periods of snow cover, persistent cloud cover, and shortness of the growing season. Here, we evaluate the feasibility of Sentinel-2 for deriving high-resolution LSP maps of the Arctic. We extracted the timing of the start and end of season (SoS and EoS, respectively) for the years 2019 and 2020 with a simple implementation of the threshold method in Google Earth Engine (GEE). We found a high level of similarity between Sentinel-2 and PhenoCam metrics; the best results were observed with Sentinel-2 enhanced vegetation index (EVI) (root mean squared error (RMSE) and mean error (ME) of 3.0 d and −0.3 d for the SoS, and 6.5 d and −3.8 d for the EoS, respectively), although other vegetation indices presented similar performances. The phenological maps of Sentinel-2 EVI compared well with the same maps extracted from the Moderate Resolution Imaging Spectroradiometer (MODIS) in homogeneous landscapes (RMSE and ME of 9.2 d and 2.9 d for the SoS, and 6.4 and −0.9 d for the EoS, respectively). Unreliable LSP estimates were filtered and a quality flag indicator was activated when the Sentinel-2 time series presented a long period (>40 d) of missing data; discontinuities were lower in spring and early summer (9.2%) than in late summer and autumn (39.4%). The Sentinel-2 high-resolution LSP maps and the GEE phenological extraction method will support vegetation monitoring and contribute to improving the representation of Artic vegetation phenology in land surface models.

GPP and NEE estimation for global forests based on a deep convolutional neural network

2020 ◽  
Author(s):  
Wenjin Wu
Keyword(s):  
Neural Network ◽  
Surface Temperature ◽  
Land Surface Temperature ◽  
Land Surface ◽  
Large Scale ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Forest Type ◽  
Google Earth ◽  
New Model

<p>To generate FluxNet-consistent annual forest GPP and NEE, we have developed a deep neural network that can retrieve estimations globally. Seven parameters considering different aspects of forest ecological and climatic features which include the Normalized Difference Vegetation Index (NDVI), the Enhanced Vegetation Index (EVI), Evapotranspiration (ET), Land Surface Temperature during Daytime (LSTD), Land Surface Temperature at Night (LSTN), precipitation, and forest type were selected as the input. All these datasets can be acquired from the Google earth engine platform to ensure rapid large-scale analysis. The model has three favorable traits: (1) Based on a multidimensional convolutional block, this model arranges all temporal variables into a two-dimensional feature map to consider phenology and inter-parameter relationships. The model can thus obtain the estimation with encoded meaningful patterns instead of raw input variables. (2) In contrast to filling data gaps with historical values or smoothing methods, the new model is developed and trained to catch signals with certain levels of occlusions; therefore, it can tolerate a relativly large portion of missing data. (3) The model is data-driven and interpretable. Therefore, it can potentially discover unknown mechanisms of forest carbon absorption by showing us how these mechanisms work to make correct estimations. The model was compared to three traditional machine learning models and presented superior performances. With this new model, global forest GPP and NEE in 2003 and 2018 were obtained. Variations of the carbon flux during the 16 years in between were analyzed.</p>

scholarly journals Cities Exacerbate Climate Warming

Urban Science ◽  
2021 ◽  
Vol 5 (1) ◽  
pp. 27
Author(s):  
Lahouari Bounoua ◽  
Kurtis Thome ◽  
Joseph Nigro
Keyword(s):  
Surface Temperature ◽  
Land Surface ◽  
Large Scale ◽  
Climate Models ◽  
Warming Trend ◽  
Climate Mitigation ◽  
Temperature Changes ◽  
Surface Warming ◽  
The Us

Urbanization is a complex land transformation not explicitly resolved within large-scale climate models. Long-term timeseries of high-resolution satellite data are essential to characterize urbanization within land surface models and to assess its contribution to surface temperature changes. The potential for additional surface warming from urbanization-induced land use change is investigated and decoupled from that due to change in climate over the continental US using a decadal timescale. We show that, aggregated over the US, the summer mean urban-induced surface temperature increased by 0.15 °C, with a warming of 0.24 °C in cities built in vegetated areas and a cooling of 0.25 °C in cities built in non-vegetated arid areas. This temperature change is comparable in magnitude to the 0.13 °C/decade global warming trend observed over the last 50 years caused by increased CO2. We also show that the effect of urban-induced change on surface temperature is felt above and beyond that of the CO2 effect. Our results suggest that climate mitigation policies must consider urbanization feedback to put a limit on the worldwide mean temperature increase.

scholarly journals Changes to the chemical state of the northern hemisphere atmosphere during the second half of the twentieth century

2016 ◽  
Author(s):  
Mike J. Newland ◽  
Patricia Martinerie ◽  
Emmanuel Witrant ◽  
Detlev Helmig ◽  
David R. Worton ◽  
...  
Keyword(s):  
Northern Hemisphere ◽  
Large Scale ◽  
Atmospheric Composition ◽  
The Arctic ◽  
Major Influence ◽  
Secondary Products ◽  
Alkyl Nitrates ◽  
Mixing Ratios ◽  
Wide Range

Abstract. The NOX (NO and NO2) and HOX (OH and HO2) budgets of the atmosphere exert a major influence on atmospheric composition, controlling removal of primary pollutants and formation of a wide range of secondary products, including ozone, that can influence human health and climate. However, there remain large uncertainties in the changes to these budgets over recent decades. Due to their short atmospheric lifetimes, NOX and HOX are highly variable in space and time, and so the measurements of these species are of very limited value for examining long term, large scale changes to their budgets. Here, we take an alternative approach by examining long-term atmospheric trends of alkyl nitrates, the formation of which is dependent on the atmospheric NO / HO2 ratio. We derive long term trends in the alkyl nitrates from measurements in firn air from the NEEM site, Greenland. Their mixing ratios increased by a factor of 4–5 between the 1970s and 1990s. This was followed by a steep decline to the sampling date of 2008. Moreover, we examine how the trends in the alkyl nitrates compare to similarly derived trends in their parent alkanes (i.e. the alkanes which, when oxidised in the presence of NOX, lead to the formation of the alkyl nitrates). The ratios of the alkyl nitrates to their parent alkanes increase from around 1970 to the late 1990's consistent with large changes to the [NO] / [HO2] ratio in the northern hemisphere atmosphere during this period. These could represent historic changes to NOX sources and sinks. Alternatively, they could represent changes to concentrations of the hydroxyl radical, OH, or to the transport time of the air masses from source regions to the Arctic.

scholarly journals Climate Drivers Linked to Changing Seasonality of Alaska Coastal Tundra Vegetation Productivity

2015 ◽  
Vol 19 (19) ◽  
pp. 1-29 ◽  
Author(s):  
Peter A. Bieniek ◽  
Uma S. Bhatt ◽  
Donald A. Walker ◽  
Martha K. Raynolds ◽  
Josefino C. Comiso ◽  
...  
Keyword(s):  
Land Surface ◽  
Large Scale ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Sea Breeze ◽  
Tundra Vegetation ◽  
Land Surface Temperatures ◽  
Alaskan Tundra ◽  
Northern Alaska

Abstract The mechanisms driving trends and variability of the normalized difference vegetation index (NDVI) for tundra in Alaska along the Beaufort, east Chukchi, and east Bering Seas for 1982–2013 are evaluated in the context of remote sensing, reanalysis, and meteorological station data as well as regional modeling. Over the entire season the tundra vegetation continues to green; however, biweekly NDVI has declined during the early part of the growing season in all of the Alaskan tundra domains. These springtime declines coincide with increased snow depth in spring documented in northern Alaska. The tundra region generally has warmed over the summer but intraseasonal analysis shows a decline in midsummer land surface temperatures. The midsummer cooling is consistent with recent large-scale circulation changes characterized by lower sea level pressures, which favor increased cloud cover. In northern Alaska, the sea-breeze circulation is strengthened with an increase in atmospheric moisture/cloudiness inland when the land surface is warmed in a regional model, suggesting the potential for increased vegetation to feedback onto the atmospheric circulation that could reduce midsummer temperatures. This study shows that both large- and local-scale climate drivers likely play a role in the observed seasonality of NDVI trends.

scholarly journals Crop Mapping in the Sanjiang Plain Using an Improved Object-Oriented Method Based on Google Earth Engine and Combined Growth Period Attributes

2022 ◽  
Vol 14 (2) ◽  
pp. 273
Author(s):  
Mengyao Li ◽  
Rui Zhang ◽  
Hongxia Luo ◽  
Songwei Gu ◽  
Zili Qin
Keyword(s):  
Large Scale ◽  
Vegetation Index ◽  
Object Oriented ◽  
Growth Period ◽  
Sanjiang Plain ◽  
Google Earth ◽  
Utilization Rate ◽  
Growth Stages ◽  
The Sanjiang Plain ◽  
Growth Method

In recent years, the scale of rural land transfer has gradually expanded, and the phenomenon of non-grain-oriented cultivated land has emerged. Obtaining crop planting information is of the utmost importance to guaranteeing national food security; however, the acquisition of the spatial distribution of crops in large-scale areas often has the disadvantages of excessive calculation and low accuracy. Therefore, the IO-Growth method, which takes the growth stage every 10 days as the index and combines the spectral features of crops to refine the effective interval of conventional wavebands for object-oriented classification, was proposed. The results were as follows: (1) the IO-Growth method obtained classification results with an overall accuracy and F1 score of 0.92, and both values increased by 6.98% compared to the method applied without growth stages; (2) the IO-Growth method reduced 288 features to only 5 features, namely Sentinel-2: Red Edge1, normalized difference vegetation index, Red, short-wave infrared2, and Aerosols, on the 261st to 270th days, which greatly improved the utilization rate of the wavebands; (3) the rise of geographic data processing platforms makes it simple to complete computations with massive data in a short time. The results showed that the IO-Growth method is suitable for large-scale vegetation mapping.

scholarly journals Challenges in modelling isoprene and monoterpene emission dynamics of Arctic plants: a case study from a subarctic tundra heath

2016 ◽  
Vol 13 (24) ◽  
pp. 6651-6667 ◽  
Author(s):  
Jing Tang ◽  
Guy Schurgers ◽  
Hanna Valolahti ◽  
Patrick Faubert ◽  
Päivi Tiiva ◽  
...  
Keyword(s):  
Response Curve ◽  
Large Scale ◽  
Ecosystem Model ◽  
The Arctic ◽  
Short Term ◽  
Response Curves ◽  
Arctic Plants ◽  
Scale Modelling

Abstract. The Arctic is warming at twice the global average speed, and the warming-induced increases in biogenic volatile organic compounds (BVOCs) emissions from Arctic plants are expected to be drastic. The current global models' estimations of minimal BVOC emissions from the Arctic are based on very few observations and have been challenged increasingly by field data. This study applied a dynamic ecosystem model, LPJ-GUESS, as a platform to investigate short-term and long-term BVOC emission responses to Arctic climate warming. Field observations in a subarctic tundra heath with long-term (13-year) warming treatments were extensively used for parameterizing and evaluating BVOC-related processes (photosynthesis, emission responses to temperature and vegetation composition). We propose an adjusted temperature (T) response curve for Arctic plants with much stronger T sensitivity than the commonly used algorithms for large-scale modelling. The simulated emission responses to 2 °C warming between the adjusted and original T response curves were evaluated against the observed warming responses (WRs) at short-term scales. Moreover, the model responses to warming by 4 and 8 °C were also investigated as a sensitivity test. The model showed reasonable agreement to the observed vegetation CO2 fluxes in the main growing season as well as day-to-day variability of isoprene and monoterpene emissions. The observed relatively high WRs were better captured by the adjusted T response curve than by the common one. During 1999–2012, the modelled annual mean isoprene and monoterpene emissions were 20 and 8 mg C m−2 yr−1, with an increase by 55 and 57 % for 2 °C summertime warming, respectively. Warming by 4 and 8 °C for the same period further elevated isoprene emission for all years, but the impacts on monoterpene emissions levelled off during the last few years. At hour-day scale, the WRs seem to be strongly impacted by canopy air T, while at the day–year scale, the WRs are a combined effect of plant functional type (PFT) dynamics and instantaneous BVOC responses to warming. The identified challenges in estimating Arctic BVOC emissions are (1) correct leaf T estimation, (2) PFT parameterization accounting for plant emission features as well as physiological responses to warming, and (3) representation of long-term vegetation changes in the past and the future.

scholarly journals Synthesis of Global Actual Evapotranspiration from 1982 to 2019

2020 ◽  
Author(s):  
Abdelrazek Elnashar ◽  
Linjiang Wang ◽  
Bingfang Wu ◽  
Weiwei Zhu ◽  
Hongwei Zeng
Keyword(s):  
Land Surface ◽  
Water Resource Management ◽  
Large Scale ◽  
The United States ◽  
Google Earth ◽  
Actual Evapotranspiration ◽  
Digital Object Identifier ◽  
Carbon Cycles ◽  
Study Results ◽  
Moderate Resolution Imaging Spectroradiometer

Abstract. As a linkage among water, energy, and carbon cycles, global actual evapotranspiration (ET) plays an essential role in agriculture, water resource management, and climate change. Although it is difficult to estimate ET over a large scale and for a long time, there are several global ET datasets available with varied algorithms, parameters, and inputs, and they produce different levels of uncertainties. In this study, we propose a long-term synthesized ET product at a kilometer spatial resolution and monthly temporal resolution from 1982 to 2019. Through a site-pixel validation of certain global ET products over different land surface types and conditions, the high performing products were selected through a high-quality flux eddy covariance covering the entire globe. According to the study results, Penman-Monteith Leuning (PML), operational Simplified Surface Energy Balance (SSEBop), Moderate Resolution Imaging Spectroradiometer (MODIS, MOD16A2105) and the Numerical Terradynamic Simulation Group (NTSG) ET products were chosen to create the synthesized ET set. The proposed product agreed well with flux EC ET over most of the all comparison levels, with a maximum ME (RME) of 13.94 mm (17.13 %) and a maximum RMSE (RRMSE) of 38.61 mm (47.45 %). Furthermore, the product performed better than local ET products over China, the United States, and the African continent and presented an ET estimation across all land cover classes. While no product can perform best in all cases, the proposed ET can be used without looking at other datasets and performing further assessments. Data are available on the Harvard Dataverse public repository through the following Digital Object Identifier (DOI): https://doi.org/10.7910/DVN/ZGOUED (Elnashar et al., 2020) as well as it is available as Google Earth Engine (GEE) application through this link: https://elnashar.users.earthengine.app/view/synthesizedet.

scholarly journals Development of a global dataset of Wetland Area and Dynamics for Methane Modeling (WAD2M)

2020 ◽  
Author(s):  
Zhen Zhang ◽  
Etienne Fluet-Chouinard ◽  
Katherine Jensen ◽  
Kyle McDonald ◽  
Gustaf Hugelius ◽  
...  
Keyword(s):  
Land Surface ◽  
Large Scale ◽  
Southern Oscillation ◽  
Microwave Remote Sensing ◽  
Global Scale ◽  
Annual Maximum ◽  
Time Step ◽  
Wetland Area ◽  
Wetland Extent

Abstract. Seasonal and interannual variations in global wetland area is a strong driver of fluctuations in global methane (CH4) emissions. Current maps of global wetland extent vary with wetland definition, causing substantial disagreement and large uncertainty in estimates of wetland methane emissions. To reconcile these differences for large-scale wetland CH4 modeling, we developed a global Wetland Area and Dynamics for Methane Modeling (WAD2M) dataset at ~25 km resolution at equator (0.25 arc-degree) at monthly time-step for 2000–2018. WAD2M combines a time series of surface inundation based on active and passive microwave remote sensing at coarse resolution (~25 km) with six static datasets that discriminate inland waters, agriculture, shoreline, and non-inundated wetlands. We exclude all permanent water bodies (e.g. lakes, ponds, rivers, and reservoirs), coastal wetlands (e.g., mangroves and sea grasses), and rice paddies to only represent spatiotemporal patterns of inundated and non-inundated vegetated wetlands. Globally, WAD2M estimates the long-term maximum wetland area at 13.0 million km2 (Mkm2), which can be separated into three categories: mean annual minimum of inundated and non-inundated wetlands at 3.5 Mkm2, seasonally inundated wetlands at 4.0 Mkm2 (mean annual maximum minus mean annual minimum), and intermittently inundated wetlands at 5.5 Mkm2 (long-term maximum minus mean annual maximum). WAD2M has good spatial agreements with independent wetland inventories for major wetland complexes, i.e., the Amazon Lowland Basin and West Siberian Lowlands, with high Cohen's kappa coefficient of 0.54 and 0.70 respectively among multiple wetlands products. By evaluating the temporal variation of WAD2M against modeled prognostic inundation (i.e., TOPMODEL) and satellite observations of inundation and soil moisture, we show that it adequately represents interannual variation as well as the effect of El Niño-Southern Oscillation on global wetland extent. This wetland extent dataset will improve estimates of wetland CH4 fluxes for global-scale land surface modeling. The dataset can be found at http://doi.org/10.5281/zenodo.3998454 (Zhang et al., 2020).

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