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Special Issue "Remote Sensing of Land-Atmosphere Interactions"

A special issue of Remote Sensing (ISSN 2072-4292). This special issue belongs to the section "Atmosphere Remote Sensing".

Deadline for manuscript submissions: closed (31 March 2018)

Special Issue Editors

Guest Editor
Prof. Liming Zhou

Department of Atmospheric and Environmental Sciences, University at Albany, State University of New York, Albany, NY 12222, USA
Website | E-Mail
Phone: +1-(518) 442-4446
Interests: land-atmosphere/climate interaction, land-surface remote sensing, remote sensing of vegetation dynamics; applications of various remote sensed products in weather, climate, and environmental sciences
Guest Editor
Dr. Joseph A. Santanello

NASA/GSFC, Greenbelt, MD 20771, USA
Website | E-Mail
Interests: land-atmosphere coupling of water and energy cycles, soil moisture-PBL interactions, satellite remote sensing of surface and PBL properties, land surface, PBL, and mesoscale modeling, land data assimilation and calibration
Guest Editor
Prof. Dev Niyogi

Department of Earth, Atmospheric, and Planetary Sciences, Purdue University
Website | E-Mail
Interests: representing and detecting the effect of land surface processes including the effects of land-atmosphere interactions on micro-,regional- and large scale terrestrial hydrological and earth system processes
Guest Editor
Prof. Huilin Gao

Zachry Department of Civil Engineering, Texas A&M University
Website | E-Mail
Interests: hydrological modeling across scales, water resources management, environmental changes and sustainability, biodiversity, remote sensing of precipitation, soil moisture, water storage, and water quality

Special Issue Information

Dear Colleagues,

As a key component of the Earth system, land interacts with the overlying atmosphere via various biophysical and biogeochemical processes and feedbacks. Key land surface parameters (e.g., vegetation type/amount, soil type/moisture, snow, surface roughness, albedo, and emissivity) determine the transfers of energy, moisture and trace gases between the land surface and planetary boundary layer (PBL). These transfers in turn, influence turbulence, atmospheric stability, and convection, and impact weather and climate variability, predictability, and extreme events such as drought and flooding. Such land-atmosphere interactions connect PBL and meteorological processes with radiative, hydrological, physical, geochemical, biological processes and feedbacks in various highly complex and mutually interactive ways. Despite much progress made in recent years, it remains a huge challenge to observe, understand, and model the land-atmosphere interactions because of their complexity and the paucity of observations. Furthermore, such interactions operate on a wide range of temporal and spatial scales and feedback loops among land, the atmosphere, and ecosystems, require multidisciplinary approaches and knowledge.

This Special Issue on Remote Sensing of Land-Atmosphere Interaction aims at improving our understanding of the processes, coupling, interactions, feedbacks and teleconnections in the land-atmosphere interface from the perspectives of remote sensing. We invite manuscripts from original research to review articles on any topics pertinent to land-atmosphere interactions across all spatial and temporal scales, which can include anything from satellite to ground/airborne/UAV -based instruments and datasets. While remote sensing is the foci of this special issue, combining remote sensed data with observations, reanalysis products, model output and simulations is strongly encouraged as well. In such cases, however, the remote sensed data should at least play an important role in understanding the land-atmosphere interactions.

Potential topics include, but are not limited to:

  • Metrics in land-atmosphere interactions
  • Scale and heterogeneities in land-atmosphere interactions
  • Budget of surface energy, moisture and trace gases
  • Biogeophyscial and biogeochemical land surface processes
  • Land-atmosphere coupling and hotspots
  • Vegetation dynamics and drivers
  • Land cover/use change such as fires, urbanization, reenewable energy, afforestation and deforestation
  • Extreme evenets such as drought, heat wave, and flooding
  • Coupling between land surface and boundary layer turbulence and convection
  • Land surface sensitivity and feedbacks
  • Seasonal and intraseasonal variability and predicitibility
  • Land-atmosphere teleconnections
  • Climate change studies on the land-atmosphere interface

Dr. Liming Zhou
Dr. Joseph A. Santanello
Dr. Dev Niyogi
Dr. Huilin Gao
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All papers will be peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Remote Sensing is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 1800 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • Land-atmosphere interactions
  • Land-atmosphere coupling
  • Land-atmosphere hotspots
  • Land-climate interactions
  • Land cover/use change
  • Land surface processes
  • Boundary layer processes
  • Remote sensing
  • Hydrometeorology
  • Vegetation dynamics

Published Papers (24 papers)

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Open AccessArticle
Combining a Two Source Energy Balance Model Driven by MODIS and MSG-SEVIRI Products with an Aggregation Approach to Estimate Turbulent Fluxes over Sparse and Heterogeneous Vegetation in Sahel Region (Niger)
Remote Sens. 2018, 10(6), 974; https://doi.org/10.3390/rs10060974
Received: 16 May 2018 / Revised: 12 June 2018 / Accepted: 12 June 2018 / Published: 19 June 2018
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Abstract
Estimates of turbulent fluxes (i.e., sensible and latent heat fluxes H and LE) over heterogeneous surfaces is not an easy task. The heterogeneity caused by the contrast in vegetation, hydric and soil conditions can generate a large spatial variability in terms of surface–atmosphere [...] Read more.
Estimates of turbulent fluxes (i.e., sensible and latent heat fluxes H and LE) over heterogeneous surfaces is not an easy task. The heterogeneity caused by the contrast in vegetation, hydric and soil conditions can generate a large spatial variability in terms of surface–atmosphere interactions. This study considered the issue of using a thermal-based two-source energy model (TSEB) driven by MODIS (Moderate resolution Imaging Spectroradiometer) and MSG (Meteosat Second Generation) observations in conjunction with an aggregation scheme to derive area-averaged H and LE over a heterogeneous watershed in Niamey, Niger (Wankama catchment). Data collected in the context of the African Monsoon Multidisciplinary Analysis (AMMA) program, including a scintillometry campaign, were used to test the proposed approach. The model predictions of area-averaged turbulent fluxes were compared to data acquired by a Large Aperture Scintillometer (LAS) set up over a transect about 3.2 km-long and spanning three vegetation types (millet, fallow and degraded shrubs). First, H and LE fluxes were estimated at the MSG-SEVIRI grid scale by neglecting explicitly the subpixel heterogeneity. Moreover, the impact of upscaling the model’s inputs was investigated using in-situ input data and three aggregation schemes of increasing complexity based on MODIS products: a simple averaging of inputs at the MODIS resolution scale, another simple averaging scheme that considers scintillometer footprint extent, and the weighted average of inputs based on the footprint weighting function. The H and LE simulated using the footprint weighted method were more accurate than for the two other aggregation rules despite the heterogeneity of the landscape. The statistical values are: correlation coefficient (R) = 0.71, root mean square error (RMSE) = 63 W/m2 and mean bias error (MBE) = −23 W/m2 for H and an R = 0.82, RMSE = 88 W/m2 and MBE = 45 W/m2 for LE. This study opens perspectives for the monitoring of convective and evaporative fluxes over heterogeneous landscape based on medium resolution satellite products. Full article
(This article belongs to the Special Issue Remote Sensing of Land-Atmosphere Interactions)
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Open AccessArticle
Relation between Convective Rainfall Properties and Antecedent Soil Moisture Heterogeneity Conditions in North Africa
Remote Sens. 2018, 10(6), 969; https://doi.org/10.3390/rs10060969
Received: 5 May 2018 / Revised: 10 June 2018 / Accepted: 14 June 2018 / Published: 17 June 2018
Cited by 1 | PDF Full-text (4716 KB) | HTML Full-text | XML Full-text
Abstract
Recent observational studies have demonstrated the relevance of soil moisture heterogeneity and the associated thermally-induced circulation on deep convection and rainfall triggering. However, whether this dynamical mechanism further influences rainfall properties—such as rain volume or timing—has yet to be confirmed by observational data. [...] Read more.
Recent observational studies have demonstrated the relevance of soil moisture heterogeneity and the associated thermally-induced circulation on deep convection and rainfall triggering. However, whether this dynamical mechanism further influences rainfall properties—such as rain volume or timing—has yet to be confirmed by observational data. Here, we analyze 10 years of satellite-based sub-daily soil moisture and precipitation records and explore the potential of strong spatial gradients in morning soil moisture to influence the properties of afternoon rainfall in the North African region, at the 100-km scale. We find that the convective rain systems that form over locally drier soils and anomalously strong soil moisture gradients have a tendency to initiate earlier in the afternoon; they also yield lower volumes of rain, weaker intensity and lower spatial variability. The strongest sensitivity to antecedent soil conditions is identified for the timing of the rain onset; it is found to be correlated with the magnitude of the soil moisture gradient. Further analysis shows that the early initiation of rainfall over dry soils and strong surface gradients yet requires the presence of a very moist boundary layer on that day. Our findings agree well with the expected effects of thermally-induced circulation on rainfall properties suggested by theoretical studies and point to the potential of locally drier and heterogeneous soils to influence convective rainfall development. The systematic nature of the identified effect of soil moisture state on the onset time of rainstorms in the region is of particular relevance and may help foster research on rainfall predictability. Full article
(This article belongs to the Special Issue Remote Sensing of Land-Atmosphere Interactions)
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Open AccessArticle
High Temporal Resolution Refractivity Retrieval from Radar Phase Measurements
Remote Sens. 2018, 10(6), 896; https://doi.org/10.3390/rs10060896
Received: 14 March 2018 / Revised: 4 June 2018 / Accepted: 5 June 2018 / Published: 7 June 2018
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Abstract
Knowledge of the spatial and temporal variability of near-surface water vapor is of great importance to successfully model reliable radio communications systems and forecast atmospheric phenomena such as convective initiation and boundary layer processes. However, most current methods to measure atmospheric moisture variations [...] Read more.
Knowledge of the spatial and temporal variability of near-surface water vapor is of great importance to successfully model reliable radio communications systems and forecast atmospheric phenomena such as convective initiation and boundary layer processes. However, most current methods to measure atmospheric moisture variations hardly provide the temporal and spatial resolutions required for detection of such atmospheric processes. Recently, considering the high correlation between refractivity variations and water vapor pressure variations at warm temperatures, and the good temporal and spatial resolution that weather radars provide, the measurement of the refractivity with radar became of interest. Firstly, it was proposed to estimate refractivity variations from radar phase measurements of ground-based stationary targets returns. For that, it was considered that the backscattering from ground targets is stationary and the vertical gradient of the refractivity could be neglected. Initial experiments showed good results over flat terrain when the radar and target heights are similar. However, the need to consider the non-zero vertical gradient of the refractivity over hilly terrain is clear. Up to date, the methods proposed consider previous estimation of the refractivity gradient in order to correct the measured phases before the refractivity estimation. In this paper, joint estimation of the refractivity variations at the radar height and the refractivity vertical gradient variations using scan-to-scan phase measurement variations is proposed. To reduce the noisiness of the estimates, a least squares method is used. Importantly, to apply this algorithm, it is not necessary to modify the radar scanning mode. For the purpose of this study, radar data obtained during the Refractivity Experiment for H 2 O Research and Collaborative Operational Technology Transfer (REFRACTT_2006), held in northeastern Colorado (USA), are used. The refractivity estimates obtained show a good performance of the algorithm proposed compared to the refractivity derived from two automatic weather stations located close to the radar, demonstrating the possibility of radar based refractivity estimation in hilly terrain and non-homogeneous atmosphere with high spatial resolution. Full article
(This article belongs to the Special Issue Remote Sensing of Land-Atmosphere Interactions)
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Open AccessFeature PaperArticle
Evaluation of Climate Change Impacts on Wetland Vegetation in the Dunhuang Yangguan National Nature Reserve in Northwest China Using Landsat Derived NDVI
Remote Sens. 2018, 10(5), 735; https://doi.org/10.3390/rs10050735
Received: 27 March 2018 / Revised: 26 April 2018 / Accepted: 7 May 2018 / Published: 10 May 2018
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Abstract
Based on 541 Landsat images between 1988 and 2016, the normalized difference vegetation indices (NDVIs) of the wetland vegetation at Xitugou (XTG) and Wowachi (WWC) inside the Dunhuang Yangguan National Nature Reserve (YNNR) in northwest China were calculated for assessing the impacts of [...] Read more.
Based on 541 Landsat images between 1988 and 2016, the normalized difference vegetation indices (NDVIs) of the wetland vegetation at Xitugou (XTG) and Wowachi (WWC) inside the Dunhuang Yangguan National Nature Reserve (YNNR) in northwest China were calculated for assessing the impacts of climate change on wetland vegetation in the YNNR. It was found that the wetland vegetation at the XTG and WWC had both shown a significant increasing trend in the past 20–30 years and the increase in both the annual mean temperature and annual peak snow depth over the Altun Mountains led to the increase of the wetland vegetation. The influence of the local precipitation on the XTG wetland vegetation was greater than on the WWC wetland vegetation, which demonstrates that in extremely arid regions, the major constraint to the wetland vegetation is the availability of water in soils, which is greatly related to the surface water detention and discharge of groundwater. At both XTG and WWC, the snowmelt from the Altun Mountains is the main contributor to the groundwater discharge, while the local precipitation plays a lesser role in influencing the wetland vegetation at the WWC than at the XTG, because the wetland vegetation grows on a relatively flat terrain at the WWC, while it grows on a stream channel at the XTG. Full article
(This article belongs to the Special Issue Remote Sensing of Land-Atmosphere Interactions)
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Open AccessFeature PaperArticle
Rainfall Variability, Wetland Persistence, and Water–Carbon Cycle Coupling in the Upper Zambezi River Basin in Southern Africa
Remote Sens. 2018, 10(5), 692; https://doi.org/10.3390/rs10050692
Received: 3 April 2018 / Revised: 25 April 2018 / Accepted: 27 April 2018 / Published: 1 May 2018
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Abstract
The Upper Zambezi River Basin (UZRB) delineates a complex region of topographic, soil and rainfall gradients between the Congo rainforest and the Kalahari Desert. Satellite imagery shows permanent wetlands in low-lying convergence zones where surface–groundwater interactions are vigorous. A dynamic wetland classification based [...] Read more.
The Upper Zambezi River Basin (UZRB) delineates a complex region of topographic, soil and rainfall gradients between the Congo rainforest and the Kalahari Desert. Satellite imagery shows permanent wetlands in low-lying convergence zones where surface–groundwater interactions are vigorous. A dynamic wetland classification based on MODIS Nadir BRDF-Adjusted Reflectance is developed to capture the inter-annual and seasonal changes in areal extent due to groundwater redistribution and rainfall variability. Simulations of the coupled water–carbon cycles of seasonal wetlands show nearly double rates of carbon uptake as compared to dry areas, at increasingly lower water-use efficiencies as the dry season progresses. Thus, wetland extent and persistence into the dry season is key to the UZRB’s carbon sink and water budget. Whereas groundwater recharge governs the expansion of wetlands in the rainy season under large-scale forcing, wetland persistence in April–June (wet–dry transition months) is tied to daily morning fog and clouds, and by afternoon land–atmosphere interactions (isolated convection). Rainfall suppression in July–September results from colder temperatures, weaker regional circulations, and reduced instability in the lower troposphere, shutting off moisture recycling in the dry season despite high evapotranspiration rates. The co-organization of precipitation and wetlands reflects land–atmosphere interactions that determine wetland seasonal persistence, and the coupled water and carbon cycles. Full article
(This article belongs to the Special Issue Remote Sensing of Land-Atmosphere Interactions)
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Open AccessArticle
Effects of Warming Hiatuses on Vegetation Growth in the Northern Hemisphere
Remote Sens. 2018, 10(5), 683; https://doi.org/10.3390/rs10050683
Received: 21 March 2018 / Revised: 16 April 2018 / Accepted: 23 April 2018 / Published: 27 April 2018
Cited by 1 | PDF Full-text (6921 KB) | HTML Full-text | XML Full-text | Supplementary Files
Abstract
There have been hiatuses in global warming since the 1990s, and their potential impacts have attracted extensive attention and discussion. Changes in temperature not only directly affect the greening of vegetation but can also indirectly alter both the growth state and the growth [...] Read more.
There have been hiatuses in global warming since the 1990s, and their potential impacts have attracted extensive attention and discussion. Changes in temperature not only directly affect the greening of vegetation but can also indirectly alter both the growth state and the growth tendency of vegetation by altering other climatic elements. The middle-high latitudes of the Northern Hemisphere (NH) constitute the region that has experienced the most warming in recent decades; therefore, identifying the effects of warming hiatuses on the vegetation greening in that region is of great importance. Using satellite-derived Normalized Difference Vegetation Index (NDVI) data and climatological observation data from 1982–2013, we investigated hiatuses in warming trends and their impact on vegetation greenness in the NH. Our results show that the regions with warming hiatuses in the NH accounted for 50.1% of the total area and were concentrated in Mongolia, central China, and other areas. Among these regions, 18.8% of the vegetation greenness was inhibited in the warming hiatus areas, but 31.3% of the vegetation grew faster. Because temperature was the main positive climatic factor in central China, the warming hiatuses caused the slow vegetation greening rate. However, precipitation was the main positive climatic factor affecting vegetation greenness in Mongolia; an increase in precipitation accelerated vegetation greening. The regions without a warming hiatus, which were mainly distributed in northern Russia, northern central Asia, and other areas, accounted for 49.9% of the total area. Among these regions, 21.4% of the vegetation grew faster over time, but 28.5% of the vegetation was inhibited. Temperature was the main positive factor affecting vegetation greenness in northern Russia; an increase in temperature promoted vegetation greening. However, radiation was the main positive climatic factor in northern central Asia; reductions in radiation inhibited the greenness of vegetation. Our findings suggest that warming hiatuses differentially affect vegetation greening and depend on meteorological factors, especially the main meteorological factors. Full article
(This article belongs to the Special Issue Remote Sensing of Land-Atmosphere Interactions)
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Open AccessArticle
Optimizing Window Length for Turbulent Heat Flux Calculations from Airborne Eddy Covariance Measurements under Near Neutral to Unstable Atmospheric Stability Conditions
Remote Sens. 2018, 10(5), 670; https://doi.org/10.3390/rs10050670
Received: 7 March 2018 / Revised: 9 April 2018 / Accepted: 20 April 2018 / Published: 25 April 2018
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Abstract
Airborne eddy covariance (EC) is one of the most effective ways to directly measure turbulent flux at a regional scale. This study aims to find the optimum spatial window length for turbulent heat fluxes calculation from airborne eddy covariance measurements under near neutral [...] Read more.
Airborne eddy covariance (EC) is one of the most effective ways to directly measure turbulent flux at a regional scale. This study aims to find the optimum spatial window length for turbulent heat fluxes calculation from airborne eddy covariance measurements under near neutral to unstable atmospheric stability conditions, to reduce the negative influences from mesoscale turbulence, and to estimate local meaningful turbulent heat fluxes accurately. The airborne flux measurements collected in 2008 in the Netherlands were used in this study. Firstly, the raw data was preprocessed, including de-spike, segmentation, and stationarity test. The atmospheric stability conditions were classified as near neutral, moderately unstable, or very unstable; the stable condition was excluded. Secondly, Ogive analysis for turbulent heat fluxes from all available segmentations of the airborne measurements was used to determine the possible window length range. After that, the optimum window length for turbulent heat flux calculations was defined based on the analysis of all possible window lengths and their uncertainties. The results show that the choice of the optimum window length strongly depends on the atmospheric stability conditions. Under near neutral conditions, local turbulence is mixed insufficiently and vulnerable to heterogeneous turbulence. A relatively short window length is needed to exclude the influence of mesoscale turbulence, and we found the optimum window length ranges from 2000 m to 2500 m. Under moderately unstable conditions, the typical scale of local turbulence is relative large, and the influence of mesoscale turbulence is relatively small. We found the optimum window length ranges from 3900 m to 5000 m. Under very unstable conditions, large convective eddies dominate the transmission of energy so that the window length needs to cover the large eddies with large energy transmission. We found the optimum window length ranges from 4500 m to 5000 m. This study gives a comprehensive methodology to determine the optimizing window length in order to compromise a balance between the accuracy and the surface representativeness of turbulent heat fluxes from airborne EC measurements. Full article
(This article belongs to the Special Issue Remote Sensing of Land-Atmosphere Interactions)
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Open AccessArticle
An Assessment of the Impact of Land Thermal Infrared Observation on Regional Weather Forecasts Using Two Different Data Assimilation Approaches
Remote Sens. 2018, 10(4), 625; https://doi.org/10.3390/rs10040625
Received: 12 March 2018 / Revised: 13 April 2018 / Accepted: 14 April 2018 / Published: 18 April 2018
Cited by 1 | PDF Full-text (22984 KB) | HTML Full-text | XML Full-text
Abstract
Recent studies have shown the unique value of satellite-observed land surface thermal infrared (TIR) information (e.g., skin temperature) and the feasibility of assimilating land surface temperature (LST) into land surface models (LSMs) to improve the simulation of land-atmosphere water and energy exchanges. In [...] Read more.
Recent studies have shown the unique value of satellite-observed land surface thermal infrared (TIR) information (e.g., skin temperature) and the feasibility of assimilating land surface temperature (LST) into land surface models (LSMs) to improve the simulation of land-atmosphere water and energy exchanges. In this study, two different types of LST assimilation techniques are implemented and the benefits from the techniques are compared. One of the techniques is to directly assimilate LST using ensemble Kalman filter (EnKF) data assimilation (DA) utilities. The other is to use the Atmosphere-Land Exchange Inversion model (ALEXI) as an “observation operator” that converts LST retrievals into the soil moisture (SM) proxy based on the ratio of actual to potential evapotranspiration (fPET), which is then assimilated into an LSM. While most current studies have shown some success in both directly the assimilating LST and assimilating ALEXI SM proxy into offline LSMs, the potential impact of the assimilation of TIR information through coupled numerical weather prediction (NWP) models is unclear. In this study, a semi-coupled Land Information System (LIS) and Weather Research and Forecast (WRF) system is employed to assess the impact of the two different techniques for assimilating the TIR observations from NOAA GOES satellites on WRF model forecasts. The NASA LIS, equipped with a variety of LSMs and advanced data assimilation tools (e.g., the ensemble Kalman Filter (EnKF)), takes atmospheric forcing data from the WRF model run, generates updated initial land surface conditions with the assimilation of either LST- or TIR-based SM and returns them to WRF for initializing the forecasts. The WRF forecasts using the daily updated initializations with the TIR data assimilation are evaluated against ground weather observations and re-analysis products. It is found that WRF forecasts with the LST-based SM assimilation have better agreement with the ground weather observations than those with the direct LST assimilation or without the land TIR data assimilation. Full article
(This article belongs to the Special Issue Remote Sensing of Land-Atmosphere Interactions)
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Open AccessArticle
Projecting Climate and Land Use Change Impacts on Actual Evapotranspiration for the Narmada River Basin in Central India in the Future
Remote Sens. 2018, 10(4), 578; https://doi.org/10.3390/rs10040578
Received: 20 February 2018 / Revised: 3 April 2018 / Accepted: 5 April 2018 / Published: 9 April 2018
Cited by 1 | PDF Full-text (61706 KB) | HTML Full-text | XML Full-text | Supplementary Files
Abstract
Assessment of actual evapotranspiration (ET) is essential as it controls the exchange of water and heat energy between the atmosphere and land surface. ET also influences the available water resources and assists in the crop water assessment in agricultural areas. This study involves [...] Read more.
Assessment of actual evapotranspiration (ET) is essential as it controls the exchange of water and heat energy between the atmosphere and land surface. ET also influences the available water resources and assists in the crop water assessment in agricultural areas. This study involves the assessment of spatial distribution of seasonal and annual ET using Surface Energy Balance Algorithm for Land (SEBAL) and provides an estimation of future changes in ET due to land use and climate change for a portion of the Narmada river basin in Central India. Climate change effects on future ET are assessed using the ACCESS1-0 model of CMIP5. A Markov Chain model estimated future land use based on the probability of changes in the past. The ET analysis is carried out for the years 2009–2011. The results indicate variation in the seasonal ET with the changed land use. High ET is observed over forest areas and crop lands, but ET decreases over crop lands after harvest. The overall annual ET is high over water bodies and forest areas. ET is high in the premonsoon season over the water bodies and decreases in the winter. Future ET in the 2020s, 2030s, 2040s, and 2050s is shown with respect to land use and climate changes that project a gradual decrease due to the constant removal of the forest areas. The lowest ET is projected in 2050. Individual impact of land use change projects decreases in ET from 1990 to 2050, while climate change effect projects increases in ET in the future due to rises in temperature. However, the combined impacts of land use and climate changes indicate a decrease in ET in the future. Full article
(This article belongs to the Special Issue Remote Sensing of Land-Atmosphere Interactions)
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Open AccessArticle
Regional Daily ET Estimates Based on the Gap-Filling Method of Surface Conductance
Remote Sens. 2018, 10(4), 554; https://doi.org/10.3390/rs10040554
Received: 9 March 2018 / Revised: 22 March 2018 / Accepted: 2 April 2018 / Published: 4 April 2018
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Abstract
Remote sensing allows regional evapotranspiration (ET) values to be obtained. Surface conductance is a key variable in estimating ET and controls surface flux interactions between the underlying surface and atmosphere. Limited by the influence of clouds, ET can only be estimated on cloud-free [...] Read more.
Remote sensing allows regional evapotranspiration (ET) values to be obtained. Surface conductance is a key variable in estimating ET and controls surface flux interactions between the underlying surface and atmosphere. Limited by the influence of clouds, ET can only be estimated on cloud-free days. In this study, a gap-filling method is proposed to acquire daily surface conductance, which was coupled into a Penman-Monteith (P-M) equation, to estimate the regional daily ET over the Hai River Basin. The gap-filling method is coupled with the canopy conductance, surface conductance and a simple time extension method, which provides more mechanisms and is more comprehensive. Field observations, including eddy covariance (EC) fluxes and meteorological elements from automatic weather station (AWS), were collected from two sites for calibration and validation. One site is located in Guantao County, which is cropped in a circular pattern with winter wheat and summer maize. The other site is located in Miyun County, which has orchard and summer maize crops. The P-M equation was inverted to the computed surface conductance at the field scale, and latent heat fluxes from EC were processed and converted to daily ET. The results show that the surface conductance model used in the gap-filling method performs well compared with the inverted surface conductance, which suggests that the model used here is reasonable. In addition, the relationship between the results estimated from the gap-filling method and EC measurements is more pronounced than that between the other method and the EC measurements. The R 2 values improve from 0.68 to 0.75 at the Guantao site and from 0.79 to 0.88 at the Miyun site. The improvement mainly occurs during the growing crop season, according to the temporal variations in the results. Full article
(This article belongs to the Special Issue Remote Sensing of Land-Atmosphere Interactions)
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Open AccessArticle
Reducing Uncertainties in Applying Remotely Sensed Land Use and Land Cover Maps in Land-Atmosphere Interaction: Identifying Change in Space and Time
Remote Sens. 2018, 10(4), 506; https://doi.org/10.3390/rs10040506
Received: 15 February 2018 / Revised: 19 March 2018 / Accepted: 19 March 2018 / Published: 23 March 2018
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Abstract
Land use and land cover (LULC) data are a central component of most land-atmosphere interaction studies, but there are two common and highly problematic scale mismatches between LULC and climate data. First, in the spatial domain, researchers rarely consider the impact of scaling [...] Read more.
Land use and land cover (LULC) data are a central component of most land-atmosphere interaction studies, but there are two common and highly problematic scale mismatches between LULC and climate data. First, in the spatial domain, researchers rarely consider the impact of scaling up fine-scale LULC data to match coarse-scale climate datasets. Second, in the temporal domain, climate data typically have sub-daily, daily, monthly, or annual resolution, but LULC datasets often have much coarser (e.g., decadal) resolution. We first explored the effect of three spatial scaling methods on correlations among LULC data and a land surface climatic variable, latent heat flux in China. Scaling by a fractional method preserved significant correlations among LULC data and latent heat flux at all three studied scales (0.5°, 1.0°, and 2.5°), whereas nearest-neighbor and majority-aggregation methods caused these correlations to diminish and even become statistically non-significant at coarser spatial scales (i.e., 2.5°). In the temporal domain, we identified fractional changes in croplands, forests, and grasslands in China using a recently developed and annually resolved time series of LULC maps from 1982 to 2012. Relative to common LULC change (LULCC) analyses conducted over two-time steps or several time periods, this annually resolved, 31-year time series of LULC maps enables robust interpretation of LULCC. Specifically, the annual resolution of these data enabled us to more precisely observe three key and statistically significant LULCC trends and transitions that could have consequential effects on land-atmosphere interaction: (1) decreasing grasslands to increasing croplands in the Northeast China plain and the Yellow river basin, (2) decreasing croplands to increasing forests in the Yangtze river basin, and (3) decreasing grasslands to increasing forests in Southwest China. Our study not only demonstrates the importance of using a fractional spatial rescaling method, but also illustrates the value of annually resolved LULC time series for detecting significant trends and transitions in LULCC, thus potentially facilitating a more robust use of remotely sensed data in land-atmosphere interaction studies. Full article
(This article belongs to the Special Issue Remote Sensing of Land-Atmosphere Interactions)
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Open AccessArticle
Merging Satellite Retrievals and Reanalyses to Produce Global Long-Term and Consistent Surface Incident Solar Radiation Datasets
Remote Sens. 2018, 10(1), 115; https://doi.org/10.3390/rs10010115
Received: 11 November 2017 / Revised: 12 January 2018 / Accepted: 13 January 2018 / Published: 16 January 2018
Cited by 4 | PDF Full-text (2936 KB) | HTML Full-text | XML Full-text
Abstract
Surface incident solar radiation (Rs) is a key parameter in many climatic and ecological processes. The data from satellites and reanalysis have been widely used. However, for reanalysis, Rs data has been shown to have substantial spatial bias, and [...] Read more.
Surface incident solar radiation (Rs) is a key parameter in many climatic and ecological processes. The data from satellites and reanalysis have been widely used. However, for reanalysis, Rs data has been shown to have substantial spatial bias, and the time span of reliable satellite Rs is too short for climatic and ecological studies. Combining reanalysis and satellite data would be an effective method for generating long-term and consistent Rs datasets. Here, we apply a cumulative probability density function-based (CPDF) method to merge eight reanalyses with the latest available satellite Rs data from Clouds and Earth’s Radiant Energy System Energy Balanced and Filled (CERES EBAF) surface retrievals. The CPDF method not only reduces the spatial bias of the reanalysis Rs data, but also makes the Rs datasets in a global, long-term and consistent way. The observed Rs data collected at 54 Baseline Surface Radiation Network (BSRN) stations from 1992 to 2016 are used to evaluate the method. Results show that the CPDF method could reduce the mean absolute biases (MAB) of the reanalysis Rs effectively by 21.24–64.36%. The European Centre for Medium-Range Weather Forecasts Re-Analysis interim (ERA-interim) reanalysis Rs data, which are available for 1979 onward, perform the best before MAB = 13.20 W·m−2 and after MAB = 10.40 W·m−2 merging. This small post-merging MAB of the ERA-interim reanalysis is caused by the MAB of 9.90 W·m−2 in the satellite Rs retrievals. The Japanese 55-year reanalysis provides Rs values back to 1958, and CPDF can reduce its MAB by 32.87%, to 11.17 W·m−2. The National Oceanic and Atmospheric Administration (NOAA)-CIRES twentieth-century reanalysis (CIRES) and the ECMWF twentieth-century reanalysis (ERA20CM) provide century-long Rs estimates. CIRES performs better after merging. The MAB of CIRES can be reduced by 32.10%, to 12.99 W·m−2, while ERA20CM’s can be reduced by 12.51%, to 16.40 W·m−2. Full article
(This article belongs to the Special Issue Remote Sensing of Land-Atmosphere Interactions)
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Open AccessArticle
Comparison of Spatial Interpolation and Regression Analysis Models for an Estimation of Monthly Near Surface Air Temperature in China
Remote Sens. 2017, 9(12), 1278; https://doi.org/10.3390/rs9121278
Received: 24 October 2017 / Revised: 5 December 2017 / Accepted: 7 December 2017 / Published: 8 December 2017
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Abstract
Near surface air temperature (NSAT) is a primary descriptor of terrestrial environmental conditions. In recent decades, many efforts have been made to develop various methods for obtaining spatially continuous NSAT from gauge or station observations. This study compared three spatial interpolation (i.e., Kriging, [...] Read more.
Near surface air temperature (NSAT) is a primary descriptor of terrestrial environmental conditions. In recent decades, many efforts have been made to develop various methods for obtaining spatially continuous NSAT from gauge or station observations. This study compared three spatial interpolation (i.e., Kriging, Spline, and Inversion Distance Weighting (IDW)) and two regression analysis (i.e., Multiple Linear Regression (MLR) and Geographically Weighted Regression (GWR)) models for predicting monthly minimum, mean, and maximum NSAT in China, a domain with a large area, complex topography, and highly variable station density. This was conducted for a period of 12 months of 2010. The accuracy of the GWR model is better than the MLR model with an improvement of about 3 °C in the Root Mean Squared Error (RMSE), which indicates that the GWR model is more suitable for predicting monthly NSAT than the MLR model over a large scale. For three spatial interpolation models, the RMSEs of the predicted monthly NSAT are greater in the warmer months, and the mean RMSEs of the predicted monthly mean NSAT for 12 months in 2010 are 1.56 °C for the Kriging model, 1.74 °C for the IDW model, and 2.39 °C for the Spline model, respectively. The GWR model is better than the Kriging model in the warmer months, while the Kriging model is superior to the GWR model in the colder months. The total precision of the GWR model is slightly higher than the Kriging model. The assessment result indicated that the higher standard deviation and the lower mean of NSAT from sample data would be associated with a better performance of predicting monthly NSAT using spatial interpolation models. Full article
(This article belongs to the Special Issue Remote Sensing of Land-Atmosphere Interactions)
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Open AccessArticle
Chlorophyll Fluorescence Data Reveals Climate-Related Photosynthesis Seasonality in Amazonian Forests
Remote Sens. 2017, 9(12), 1275; https://doi.org/10.3390/rs9121275
Received: 1 October 2017 / Revised: 29 November 2017 / Accepted: 6 December 2017 / Published: 8 December 2017
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Abstract
Amazonia is the world largest tropical forest, playing a key role in the global carbon cycle. Thus, understanding climate controls of photosynthetic activity in this region is critical. The establishment of the relationship between photosynthetic activity and climate has been controversial when based [...] Read more.
Amazonia is the world largest tropical forest, playing a key role in the global carbon cycle. Thus, understanding climate controls of photosynthetic activity in this region is critical. The establishment of the relationship between photosynthetic activity and climate has been controversial when based on conventional remote sensing-derived indices. Here, we use nine years of solar-induced chlorophyll fluorescence (ChlF) data from the Global Ozone Monitoring Experiment (GOME-2) sensor, as a direct proxy for photosynthesis, to assess the seasonal response of photosynthetic activity to solar radiation and precipitation in Amazonia. Our results suggest that 76% of photosynthesis seasonality in Amazonia is explained by seasonal variations of solar radiation. However, 13% of these forests are limited by precipitation. The combination of both radiation and precipitation drives photosynthesis in the remaining 11% of the area. Photosynthesis tends to rise only after radiation increases in 61% of the forests. Furthermore, photosynthesis peaks in the wet season in about 58% of the Amazon forest. We found that a threshold of ≈1943 mm per year can be defined as a limit for precipitation phenological dependence. With the potential increase in the frequency and intensity of extreme droughts, forests that have the photosynthetic process currently associated with radiation seasonality may shift towards a more water-limited system. Full article
(This article belongs to the Special Issue Remote Sensing of Land-Atmosphere Interactions)
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Open AccessArticle
Improved Atmospheric Modelling of the Oasis-Desert System in Central Asia Using WRF with Actual Satellite Products
Remote Sens. 2017, 9(12), 1273; https://doi.org/10.3390/rs9121273
Received: 25 September 2017 / Revised: 30 November 2017 / Accepted: 4 December 2017 / Published: 7 December 2017
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Abstract
Because of the use of outdated terrestrial datasets, regional climate models (RCMs) have a limited ability to accurately simulate weather and climate conditions over heterogeneous oasis-desert systems, especially near large mountains. Using actual terrestrial datasets from satellite products for RCMs is the only [...] Read more.
Because of the use of outdated terrestrial datasets, regional climate models (RCMs) have a limited ability to accurately simulate weather and climate conditions over heterogeneous oasis-desert systems, especially near large mountains. Using actual terrestrial datasets from satellite products for RCMs is the only possible solution to the limitation; however, it is impractical for long-period simulations due to the limited satellite products available before 2000 and the extremely time- and labor-consuming processes involved. In this study, we used the Weather Research and Forecasting (WRF) model with observed estimates of land use (LU), albedo, Leaf Area Index (LAI), and green Vegetation Fraction (VF) datasets from satellite products to examine which terrestrial datasets have a great impact on simulating water and heat conditions over heterogeneous oasis-desert systems in the northern Tianshan Mountains. Five simulations were conducted for 1–31 July in both 2010 and 2012. The decrease in the root mean squared error and increase in the coefficient of determination for the 2 m temperature (T2), humidity (RH), latent heat flux (LE), and wind speed (WS) suggest that these datasets improve the performance of WRF in both years; in particular, oasis effects are more realistically simulated. Using actual satellite-derived fractional vegetation coverage data has a much greater effect on the simulation of T2, RH, and LE than the other parameters, resulting in mean error correction values of 62%, 87%, and 92%, respectively. LU data is the primary parameter because it strongly influences other secondary land surface parameters, such as LAI and albedo. We conclude that actual LU and VF data should be used in the WRF for both weather and climate simulations. Full article
(This article belongs to the Special Issue Remote Sensing of Land-Atmosphere Interactions)
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Open AccessArticle
The Spatial and Temporal Distributions of Absorbing Aerosols over East Asia
Remote Sens. 2017, 9(10), 1050; https://doi.org/10.3390/rs9101050
Received: 23 August 2017 / Revised: 30 September 2017 / Accepted: 12 October 2017 / Published: 16 October 2017
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Abstract
Absorbing aerosols can strongly absorb solar radiation and have a profound impact on the global and regional climate. Black carbon (BC), organic carbon (OC) and dust are three major types of absorbing aerosols. In order to deepen the overall understanding of absorbing aerosols [...] Read more.
Absorbing aerosols can strongly absorb solar radiation and have a profound impact on the global and regional climate. Black carbon (BC), organic carbon (OC) and dust are three major types of absorbing aerosols. In order to deepen the overall understanding of absorbing aerosols over East Asia and provide a basis for further investigation of its role in enhanced warming in drylands, the spatial-temporal distribution of absorbing aerosols over East Asia for the period of 2005–2016 was investigated based on the Ozone Monitoring Instrument (OMI) satellite retrievals. Overall, high values of Aerosol Absorption Optical Depth (AAOD) mainly distribute near dust sources as well as BC and OC sources. AAOD reaches its maximum during spring over East Asia as a result of dust activity and biomass burning. Single-scattering albedo (SSA) is comparatively high (>0.96) in the most part of East Asia in the summer, indicating the dominance of aerosol scattering. Hyper-arid regions have the highest Aerosol Optical Depth (AOD) and AAOD among the five climatic regions, with springtime values up to 0.72 and 0.04, respectively. Humid and sub-humid regions have relatively high AOD and AAOD during the spring and winter and the highest SSA during the summer. AAOD in some areas shows significant upward trends, which is likely due to the increase of BC and OC emission. SSA shows overall downward trends, indicating the enhancement of the aerosol absorption. Analysis of emission inventory and dust index data shows that BC and OC emissions mainly come from the humid regions, while dust sources mainly distribute in drylands. Full article
(This article belongs to the Special Issue Remote Sensing of Land-Atmosphere Interactions)
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Open AccessArticle
Estimating High Resolution Daily Air Temperature Based on Remote Sensing Products and Climate Reanalysis Datasets over Glacierized Basins: A Case Study in the Langtang Valley, Nepal
Remote Sens. 2017, 9(9), 959; https://doi.org/10.3390/rs9090959
Received: 28 July 2017 / Revised: 12 September 2017 / Accepted: 13 September 2017 / Published: 15 September 2017
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Abstract
Near surface air temperature (Ta) is one of the key input parameters in land surface models and hydrological models as it affects most biogeophysical and biogeochemical processes of the earth surface system. For distributed hydrological modeling over glacierized basins, obtaining high resolution Ta [...] Read more.
Near surface air temperature (Ta) is one of the key input parameters in land surface models and hydrological models as it affects most biogeophysical and biogeochemical processes of the earth surface system. For distributed hydrological modeling over glacierized basins, obtaining high resolution Ta forcing is one of the major challenges. In this study, we proposed a new high resolution daily Ta estimation scheme under both clear and cloudy sky conditions through integrating the moderate-resolution imaging spectroradiometer (MODIS) land surface temperature (LST) and China Meteorological Administration (CMA) land data assimilation system (CLDAS) reanalyzed daily Ta. Spatio-temporal continuous MODIS LST was reconstructed through the data interpolating empirical orthogonal functions (DINEOF) method. Multi-variable regression models were developed at CLDAS scale and then used to estimate Ta at MODIS scale. The new Ta estimation scheme was tested over the Langtang Valley, Nepal as a demonstrating case study. Observations from two automatic weather stations at Kyanging and Yala located in the Langtang Valley from 2012 to 2014 were used to validate the accuracy of Ta estimation. The RMSEs are 2.05, 1.88, and 3.63 K, and the biases are 0.42, −0.68 and −2.86 K for daily maximum, mean and minimum Ta, respectively, at the Kyanging station. At the Yala station, the RMSE values are 4.53, 2.68 and 2.36 K, and biases are 4.03, 1.96 and −0.35 K for the estimated daily maximum, mean and minimum Ta, respectively. Moreover, the proposed scheme can produce reasonable spatial distribution pattern of Ta at the Langtang Valley. Our results show the proposed Ta estimation scheme is promising for integration with distributed hydrological model for glacier melting simulation over glacierized basins. Full article
(This article belongs to the Special Issue Remote Sensing of Land-Atmosphere Interactions)
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Open AccessFeature PaperArticle
Diurnal Air Temperature Modeling Based on the Land Surface Temperature
Remote Sens. 2017, 9(9), 915; https://doi.org/10.3390/rs9090915
Received: 27 July 2017 / Revised: 29 August 2017 / Accepted: 30 August 2017 / Published: 1 September 2017
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Abstract
The air temperature is an essential variable in many applications related to Earth science. Sporadic spatial distribution of weather stations causes a low spatial resolution of measured air temperatures. This study focused on modeling the air diurnal temperature cycle (DTC) based on the [...] Read more.
The air temperature is an essential variable in many applications related to Earth science. Sporadic spatial distribution of weather stations causes a low spatial resolution of measured air temperatures. This study focused on modeling the air diurnal temperature cycle (DTC) based on the land surface temperature (LST) DTC. The air DTC model parameters were estimated from LST DTC model parameters by a regression analysis. Here, the LST obtained from the INSAT-3D geostationary satellite and the air temperature extracted from weather stations were used within the time frame of 4 March 2015 to 22 May 2017 across Iran. Constant parameters of the air DTC model for each weather station were estimated based on an experimental approach over the time period. Results showed these parameters decrease as elevation increases. The mean absolute error (MAE) and the root mean square error (RMSE) for three hours sampling were calculated. The MAE and RMSE ranges were between [0.1, 4] °C and [0.1, 3.3] °C, respectively. Additionally, 95% of MAEs and RMSEs were less than 2.9 °C and 2.4 °C values, correspondingly. The range of the mean values of MAEs and RMSEs for a three-hour sampling time were [−0.29, 0.6] °C and [2, 2.11] °C. The DTC model results showed a meaningful statistical fitting in both air DTCs modeled from LST and weather station-based DTCs. The variability of mean error and RMSE in different land covers and elevation classes were also investigated. In spite of the complex behavior of the environmental variables in the study area, the model error bar did not show significantly biased estimations for various classes. Therefore, the developed model was less sensitive to variations of land covers and elevation changes. It can be conclude that the coefficients of regression between LST and air DTC could model properly the environmental factors. Full article
(This article belongs to the Special Issue Remote Sensing of Land-Atmosphere Interactions)
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Open AccessEditor’s ChoiceArticle
Gauging the Severity of the 2012 Midwestern U.S. Drought for Agriculture
Remote Sens. 2017, 9(8), 767; https://doi.org/10.3390/rs9080767
Received: 7 June 2017 / Revised: 21 July 2017 / Accepted: 22 July 2017 / Published: 26 July 2017
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Abstract
Different drought indices often provide different diagnoses of drought severity, making it difficult to determine the best way to evaluate these different drought monitoring results. Additionally, the ability of a newly proposed drought index, the Process-based Accumulated Drought Index (PADI) has not yet [...] Read more.
Different drought indices often provide different diagnoses of drought severity, making it difficult to determine the best way to evaluate these different drought monitoring results. Additionally, the ability of a newly proposed drought index, the Process-based Accumulated Drought Index (PADI) has not yet been tested in United States. In this study, we quantified the severity of 2012 drought which affected the agricultural output for much of the Midwestern US. We used several popular drought indices, including the Standardized Precipitation Index and Standardized Precipitation Evapotranspiration Index with multiple time scales, Palmer Drought Severity Index, Palmer Z-index, VegDRI, and PADI by comparing the spatial distribution, temporal evolution, and crop impacts produced by each of these indices with the United States Drought Monitor. Results suggested this drought incubated around June 2011 and ended in May 2013. While different drought indices depicted drought severity variously. SPI outperformed SPEI and has decent correlation with yield loss especially at a 6 months scale and in the middle growth season, while VegDRI and PADI demonstrated the highest correlation especially in late growth season, indicating they are complementary and should be used together. These results are valuable for comparing and understanding the different performances of drought indices in the Midwestern US. Full article
(This article belongs to the Special Issue Remote Sensing of Land-Atmosphere Interactions)
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Open AccessArticle
Detecting Wind Farm Impacts on Local Vegetation Growth in Texas and Illinois Using MODIS Vegetation Greenness Measurements
Remote Sens. 2017, 9(7), 698; https://doi.org/10.3390/rs9070698
Received: 19 May 2017 / Revised: 3 July 2017 / Accepted: 4 July 2017 / Published: 6 July 2017
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Abstract
This study examines the possible impacts of real-world wind farms (WFs) on vegetation growth using two vegetation indices (VIs), the Normalized Difference Vegetation Index (NDVI) and Enhanced Vegetation Index (EVI), at a ~250 m resolution from the MODerate resolution Imaging Spectroradimeter (MODIS) for [...] Read more.
This study examines the possible impacts of real-world wind farms (WFs) on vegetation growth using two vegetation indices (VIs), the Normalized Difference Vegetation Index (NDVI) and Enhanced Vegetation Index (EVI), at a ~250 m resolution from the MODerate resolution Imaging Spectroradimeter (MODIS) for the period 2003–2014. We focus on two well-studied large WF regions, one in western Texas and the other in northern Illinois. These two regions differ distinctively in terms of land cover, topography, and background climate, allowing us to examine whether the WF impacts on vegetation, if any, vary due to the differences in atmospheric and boundary conditions. We use three methods (spatial coupling analysis, time series analysis, and seasonal cycle analysis) and consider two groups of pixels, wind farm pixels (WFPs) and non-wind-farm pixels (NWFPs), to quantify and attribute such impacts during the pre- and post-turbine periods. Our results indicate that the WFs have insignificant or no detectible impacts on local vegetation growth. At the pixel level, the VI changes demonstrate a random nature and have no spatial coupling with the WF layout. At the regional level, there is no systematic shift in vegetation greenness between the pre- and post-turbine periods. At interannual and seasonal time scales, there are no confident vegetation changes over WFPs relative to NWFPs. These results remain robust when the pre- and post-turbine periods and NWFPs are defined differently. Most importantly, the majority of the VI changes are within the MODIS data uncertainty, suggesting that the WF impacts on vegetation, if any, cannot be separated confidently from the data uncertainty and noise. Overall, there are some small decreases in vegetation greenness over WF regions, but no convincing observational evidence is found for the impacts of operating WFs on vegetation growth. Full article
(This article belongs to the Special Issue Remote Sensing of Land-Atmosphere Interactions)
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Open AccessArticle
Satellite Observations of El Niño Impacts on Eurasian Spring Vegetation Greenness during the Period 1982–2015
Remote Sens. 2017, 9(7), 628; https://doi.org/10.3390/rs9070628
Received: 25 April 2017 / Revised: 8 June 2017 / Accepted: 14 June 2017 / Published: 22 June 2017
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Abstract
As Earth’s most influential naturally-recurring sea and atmospheric oscillation, ENSO results in widespread changes in the climate system not only over much of the tropics and subtropics, but also in high latitudes via atmospheric teleconnections. In the present study, the linkages between springtime [...] Read more.
As Earth’s most influential naturally-recurring sea and atmospheric oscillation, ENSO results in widespread changes in the climate system not only over much of the tropics and subtropics, but also in high latitudes via atmospheric teleconnections. In the present study, the linkages between springtime vegetation greenness over Eurasia and El Niño are investigated based on two long-term normalized difference vegetation index (NDVI) datasets from 1982 to 2015, and possible physical mechanisms for the teleconnections are explored. Results from the Empirical Orthogonal Function (EOF) and Singular Value Decomposition (SVD) analyses consistently suggest that the spatial patterns of NDVI, with “negative-positive-negative” values, have closer connections to El Niño. In particular, East Russia is identified as the key region with the strongest negative influences from Eastern Pacific (EP) El Niño on spring vegetation growth. During EP El Niño years, suppressed convection over the Bay of Bengal (BoB) may excite a Rossby wave from the BoB to the Far East. East Russia is located in the west of a large cyclone anomaly accompanied by the strong North and Northwesterly wind anomalies and the transport of cold air from Siberia. As a result, surface air temperature decreases significantly over East Russia and thus inhibits the vegetation growth during spring in the EP El Niño years. Full article
(This article belongs to the Special Issue Remote Sensing of Land-Atmosphere Interactions)
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Open AccessArticle
Observational Quantification of Climatic and Human Influences on Vegetation Greening in China
Remote Sens. 2017, 9(5), 425; https://doi.org/10.3390/rs9050425
Received: 21 March 2017 / Revised: 23 April 2017 / Accepted: 27 April 2017 / Published: 30 April 2017
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Abstract
This study attempts to quantify the relative contributions of vegetation greening in China due to climatic and human influences from multiple observational datasets. Satellite measured vegetation greenness, Normalized Difference Vegetation Index (NDVI), and relevant climate, land cover, and socioeconomic data since 1982 are [...] Read more.
This study attempts to quantify the relative contributions of vegetation greening in China due to climatic and human influences from multiple observational datasets. Satellite measured vegetation greenness, Normalized Difference Vegetation Index (NDVI), and relevant climate, land cover, and socioeconomic data since 1982 are analyzed using a multiple linear regression (MLR) method. A statistically significant positive trend of average growing-season (April–October) NDVI is found over more than 34% of the vegetated areas, mainly in North China, while significant decreases in NDVI are only seen in less than 5% of the areas. The relationships between vegetation and climate (temperature, precipitation, and radiation) vary by geographical location and vegetation type. We estimate the NDVI changes in association with the non-climatic effects by removing the climatic effects from the original NDVI time series using the MLR analysis. Our results indicate that land use change is the dominant factor driving the long-term changes in vegetation greenness. The significant greening in North China is due to the increase in crops, grasslands, and forests. The socioeconomic datasets provide consistent and supportive results for the non-climatic effects at the provincial level that afforestation and reduced fire events generally have a major contribution. This study provides a basis for quantifying the non-climatic effects due to possible human influences on the vegetation greening in China. Full article
(This article belongs to the Special Issue Remote Sensing of Land-Atmosphere Interactions)
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Open AccessTechnical Note
Land-Air Interactions over Urban-Rural Transects Using Satellite Observations: Analysis over Delhi, India from 1991–2016
Remote Sens. 2017, 9(12), 1283; https://doi.org/10.3390/rs9121283
Received: 28 September 2017 / Revised: 23 November 2017 / Accepted: 7 December 2017 / Published: 20 December 2017
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Abstract
Over the past four decades Delhi, India, has witnessed rapid urbanization and change in land use land cover (LULC) pattern, with most of the cultivable areas and wasteland being converted into built-up areas. Presently around 40% land is under built-up area, a drastic [...] Read more.
Over the past four decades Delhi, India, has witnessed rapid urbanization and change in land use land cover (LULC) pattern, with most of the cultivable areas and wasteland being converted into built-up areas. Presently around 40% land is under built-up area, a drastic rise of 30% from 1977. The effect of changing LULC, at a local scale, on various variables-land surface temperature (LST), normalized difference vegetation index (NDVI), emissivity, albedo, evaporation, Bowen ratio, and planetary boundary layer (PBL) height, from 1991–2016, is investigated. To assess the spatio-temporal dynamics of land-air interactions, we select two different 100 km transects covering the NE-SW and NW-SE expanse of Delhi and its adjoining areas. High NDVI and emissivity is found for regions with green cover and drastic reduction is noted in built-up area clusters. In both of the transects, land surface variations manifest itself in patterns of LST variation. Parametric and non-parametric correlations are able to statistically establish the land-air interactions in the city. NDVI, an indirect indicator for LULC classes, significantly helps in understanding the modifications in LST and ultimately air temperature. Significant, strong positive relationships exist between skin temperature and evaporation, skin temperature and PBL height, and PBL height and evaporation, providing insights into the meteorological changes that are associated with urbanization. Full article
(This article belongs to the Special Issue Remote Sensing of Land-Atmosphere Interactions)
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Open AccessLetter
Irrigation-Induced Environmental Changes around the Aral Sea: An Integrated View from Multiple Satellite Observations
Remote Sens. 2017, 9(9), 900; https://doi.org/10.3390/rs9090900
Received: 15 August 2017 / Revised: 26 August 2017 / Accepted: 29 August 2017 / Published: 31 August 2017
Cited by 5 | PDF Full-text (4000 KB) | HTML Full-text | XML Full-text | Supplementary Files
Abstract
The Aral Sea basin (ASB) is one of the most environmentally vulnerable regions to climate change and human activities. During the past 60 years, irrigation has greatly changed the water distribution and caused severe environmental issues in the ASB. Using remote sensing data, [...] Read more.
The Aral Sea basin (ASB) is one of the most environmentally vulnerable regions to climate change and human activities. During the past 60 years, irrigation has greatly changed the water distribution and caused severe environmental issues in the ASB. Using remote sensing data, this study investigated the environmental changes induced by irrigation activities in this region. The results show that, in the past decade, land water storage has significantly increased in the irrigated upstream regions (13 km3 year−1) but decreased in the downstream regions (−27 km3 year−1) of the Amu Darya River basin, causing a water storage decrease in the whole basin (−20 km3 year−1). As a result, the water surface area of the Aral Sea has decreased from 32,000 in 2000 to 10,000 km2 in 2015. The shrinking Aral Sea exposed a large portion of the lake bottom to the air, increasing (decreasing) the daytime (nighttime) temperatures by about 1 °C year−1 (0.5 °C year−1). Moreover, there were other potential environmental changes, including drier soil, less vegetation, decreasing cloud and precipitation, and more severe and frequent dust storms. Possible biases in the remote sensing data due to the neglect of the shrinking water surface area of the Aral Sea were identified. These findings highlight the severe environmental threats caused by irrigation in Central Asia and call attention to sustainable water use in such dryland regions. Full article
(This article belongs to the Special Issue Remote Sensing of Land-Atmosphere Interactions)
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