Special Issue on Remote Sensing of Snow and Its Applications

Snow cover is an essential climate variable directly affecting the Earth’s energy balance. Snow cover has a number of important physical properties that exert an influence on global and regional energy, water, and carbon cycles. Remote sensing provides a good understanding of snow cover and enable snow cover information to be assimilated into hydrological, land surface, meteorological, and climate models for predicting snowmelt runoff, snow water resources, and to warn about snow-related natural hazards. The main objectives of this Special Issue, “Remote Sensing of Snow and Its Applications” in Geosciences are to present a wide range of topics such as (1) remote sensing techniques and methods for snow, (2) modeling, retrieval algorithms, and in-situ measurements of snow parameters, (3) multi-source and multi-sensor remote sensing of snow, (4) remote sensing and model integrated approaches of snow, and (5) applications where remotely sensed snow information is used for weather forecasting, flooding, avalanche, water management, traffic, health and sport, agriculture and forestry, climate scenarios, etc. It is very important to understand (a) differences and similarities, (b) representativeness and applicability, (c) accuracy and sources of error in measuring of snow both in-situ and remote sensing and assimilating snow into hydrological, land surface, meteorological, and climate models. This Special Issue contains nine articles and covers some of the topics we listed above.

Download Full-text

Ground Heat Flux Determination according to Land Skin Temperature Observations from In Situ Stations and Satellites

Journal of Hydrometeorology ◽

10.1175/jhm425.1 ◽

2005 ◽

Vol 6 (4) ◽

pp. 371-390 ◽

Cited By ~ 19

Author(s):

Ben-Jei Tsuang

Keyword(s):

Remote Sensing ◽

Heat Flux ◽

Skin Temperature ◽

Land Surface ◽

Rapid Progress ◽

Ground Heat Flux ◽

Global Coverage ◽

Temperature Observations ◽

Remote Sensing Techniques

Abstract Due to rapid progress in the development of remote sensing techniques, skin temperature can now be observed with global coverage from satellites. This study derives an equation for utilizing skin temperature measurements to determine ground heat flux. This equation is verified at the First International Satellite Land Surface Climatology Project (ISLSCP) Field Experiment (FIFE) campaign site and four Fluxnet sites. A preliminary monthly global dataset of ground heat flux with 1° resolution, covering the years 1984–95, is derived based on skin temperature observations obtained via satellite. It shows that the seasonal variation of ground heat flux over land can be determined at 1.5 months ahead by observing the increasing rate of skin temperature, and that the variation increases from ±2 W m−2 at the equator to ±20 W m−2 at the poles. Over land, the resulting ground heat flux can be compared with that of the ERA-15 reanalysis, showing rms differences of about 4 W m−2.

Download Full-text

Validation of the SMOS Level 1C Brightness Temperature and Level 2 Soil Moisture Data over the West and Southwest of Iran

Remote Sensing ◽

10.3390/rs12172819 ◽

2020 ◽

Vol 12 (17) ◽

pp. 2819

Author(s):

Mozhdeh Jamei ◽

Mohammad Mousavi Baygi ◽

Ebrahim Asadi Oskouei ◽

Ernesto Lopez-Baeza

Keyword(s):

Soil Moisture ◽

Brightness Temperature ◽

Land Surface ◽

Climate Models ◽

Weather Forecasting ◽

European Space Agency ◽

In Situ Measurements ◽

Satellite Measurements ◽

Level 2

The European Space Agency (ESA) Soil Moisture and Ocean Salinity (SMOS) mission with the MIRAS (Microwave Imaging Radiometer using Aperture Synthesis) L-band radiometer provides global soil moisture (SM) data. SM data and products from remote sensing are relatively new, but they are providing significant observations for weather forecasting, water resources management, agriculture, land surface, and climate models assessment, etc. However, the accuracy of satellite measurements is still subject to error from the retrieval algorithms and vegetation cover. Therefore, the validation of satellite measurements is crucial to understand the quality of retrieval products. The objectives of this study, precisely framed within this mission, are (i) validation of the SMOS Level 1C Brightness Temperature (TBSMOS) products in comparison with simulated products from the L-MEB model (TBL-MEB) and (ii) validation of the SMOS Level 2 SM (SMSMOS) products against ground-based measurements at 10 significant Iranian agrometeorological stations. The validations were performed for the period of January 2012 to May 2015 over the Southwest and West of Iran. The results of the validation analysis showed an RMSE ranging between 9 to 13 K and a strong correlation (R = 0.61–0.84) between TBSMOS and TBL-MEB at all stations. The bias values (0.1 to 7.5 K) showed a slight overestimation for TBSMOS at most of the stations. The results of SMSMOS validation indicated a high agreement (RMSE = 0.046–0.079 m3 m−3 and R = 0.65–0.84) between the satellite SM and in situ measurements over all the stations. The findings of this research indicated that SMSMOS shows high accuracy and agreement with in situ measurements which validate its potential. Due to the limitation of SM measurements in Iran, the SMOS products can be used in different scientific and practical applications at different Iranian study areas.

Download Full-text

Site specific agricultural soil management with the use of new technologies

Global NEST Journal ◽

10.30955/gnj.001203 ◽

2013 ◽

Vol 16 (1) ◽

pp. 59-67

Keyword(s):

New Technologies ◽

Digital Interface ◽

Appropriate Treatment ◽

Wide Range ◽

Soil Variation ◽

Soil Information ◽

Remote Sensing Techniques ◽

Agricultural Areas

<p>The Soil Science Institute of Thessaloniki produces new digitized Soil Maps that provide a useful electronic database for the spatial representation of the soil variation within a region, based on in situ soil sampling, laboratory analyses, GIS techniques and plant nutrition mathematical models, coupled with the local land cadastre. The novelty of these studies is that local agronomists have immediate access to a wide range of soil information by clicking on a field parcel shown in this digital interface and, therefore, can suggest an appropriate treatment (e.g. liming, manure incorporation, desalination, application of proper type and quantity of fertilizer) depending on the field conditions and cultivated crops. A specific case study is presented in the current work with regards to the construction of the digitized Soil Map of the regional unit of Kastoria. The potential of this map can easily be realized by the fact that the mapping of the physicochemical properties of the soils in this region provided delineation zones for differential fertilization management. An experiment was also conducted using remote sensing techniques for the enhancement of the fertilization advisory software database, which is a component of the digitized map, and the optimization of nitrogen management in agricultural areas.</p>

Download Full-text

Quantification of the Environmental Impacts of Highway Construction Using Remote Sensing Approach

Remote Sensing ◽

10.3390/rs13071340 ◽

2021 ◽

Vol 13 (7) ◽

pp. 1340

Author(s):

Shuailong Feng ◽

Shuguang Liu ◽

Lei Jing ◽

Yu Zhu ◽

Wende Yan ◽

...

Keyword(s):

Remote Sensing ◽

Environmental Impacts ◽

Land Surface ◽

Vegetation Index ◽

Highway Construction ◽

Temporal Changes ◽

Moisture Index ◽

Buffer Zones ◽

Wide Range ◽

Spatial And Temporal Changes

Highways provide key social and economic functions but generate a wide range of environmental consequences that are poorly quantified and understood. Here, we developed a before–during–after control-impact remote sensing (BDACI-RS) approach to quantify the spatial and temporal changes of environmental impacts during and after the construction of the Wujing Highway in China using three buffer zones (0–100 m, 100–500 m, and 500–1000 m). Results showed that land cover composition experienced large changes in the 0–100 m and 100–500 m buffers while that in the 500–1000 m buffer was relatively stable. Vegetation and moisture conditions, indicated by the normalized difference vegetation index (NDVI) and the normalized difference moisture index (NDMI), respectively, demonstrated obvious degradation–recovery trends in the 0–100 m and 100–500 m buffers, while land surface temperature (LST) experienced a progressive increase. The maximal relative changes as annual means of NDVI, NDMI, and LST were about −40%, −60%, and 12%, respectively, in the 0–100m buffer. Although the mean values of NDVI, NDMI, and LST in the 500–1000 m buffer remained relatively stable during the study period, their spatial variabilities increased significantly after highway construction. An integrated environment quality index (EQI) showed that the environmental impact of the highway manifested the most in its close proximity and faded away with distance. Our results showed that the effect distance of the highway was at least 1000 m, demonstrated from the spatial changes of the indicators (both mean and spatial variability). The approach proposed in this study can be readily applied to other regions to quantify the spatial and temporal changes of disturbances of highway systems and subsequent recovery.

Download Full-text

Coupling between the JULES land-surface scheme and the CCATT-BRAMS atmospheric chemistry model (JULES-CCATT-BRAMS1.0): applications to numerical weather forecasting and the CO<sub>2</sub> budget in South America

Geoscientific Model Development Discussions ◽

10.5194/gmdd-6-453-2013 ◽

2013 ◽

Vol 6 (1) ◽

pp. 453-494 ◽

Cited By ~ 4

Author(s):

D. S. Moreira ◽

S. R. Freitas ◽

J. P. Bonatti ◽

L. M. Mercado ◽

N. M. É. Rosário ◽

...

Keyword(s):

South America ◽

Atmospheric Chemistry ◽

Land Surface ◽

Amazon Basin ◽

Mean Squared Error ◽

Weather Forecasting ◽

Dew Point ◽

Surface Model ◽

Co2 Concentrations ◽

Wide Range

Abstract. This article presents the development of a new numerical system denominated JULES-CCATT-BRAMS, which resulted from the coupling of the JULES surface model to the CCATT-BRAMS atmospheric chemistry model. The performance of this system in relation to several meteorological variables (wind speed at 10 m, air temperature at 2 m, dew point temperature at 2 m, pressure reduced to mean sea level and 6 h accumulated precipitation) and the CO2 concentration above an extensive area of South America is also presented, focusing on the Amazon basin. The evaluations were conducted for two periods, the wet (March) and dry (September) seasons of 2010. The statistics used to perform the evaluation included bias (BIAS) and root mean squared error (RMSE). The errors were calculated in relation to observations at conventional stations in airports and automatic stations. In addition, CO2 concentrations in the first model level were compared with meteorological tower measurements and vertical CO2 profiles were compared with aircraft data. The results of this study show that the JULES model coupled to CCATT-BRAMS provided a significant gain in performance in the evaluated atmospheric fields relative to those simulated by the LEAF (version 3) surface model originally utilized by CCATT-BRAMS. Simulations of CO2 concentrations in Amazonia and a comparison with observations are also discussed and show that the system presents a gain in performance relative to previous studies. Finally, we discuss a wide range of numerical studies integrating coupled atmospheric, land surface and chemistry processes that could be produced with the system described here. Therefore, this work presents to the scientific community a free tool, with good performance in relation to the observed data and re-analyses, able to produce atmospheric simulations/forecasts at different resolutions, for any period of time and in any region of the globe.

Download Full-text

ARIMA modeling for forecasting land surface temperature and determination of urban heat island using remote sensing techniques for Chennai city, India

Arabian Journal of Geosciences ◽

10.1007/s12517-021-07351-5 ◽

2021 ◽

Vol 14 (11) ◽

Author(s):

Ramesh Kesavan ◽

Misba Muthian ◽

Karuppasamy Sudalaimuthu ◽

Shirly Sundarsingh ◽

Saravanan Krishnan

Keyword(s):

Remote Sensing ◽

Surface Temperature ◽

Land Surface Temperature ◽

Land Surface ◽

Heat Island ◽

Urban Heat ◽

Arima Modeling ◽

Remote Sensing Techniques ◽

Chennai City

Download Full-text

Image Segmentation and Object-Based Image Analysis for Environmental Monitoring: Recent Areas of Interest, Researchers’ Views on the Future Priorities

Remote Sensing ◽

10.3390/rs12111772 ◽

2020 ◽

Vol 12 (11) ◽

pp. 1772

Author(s):

Brian Alan Johnson ◽

Lei Ma

Keyword(s):

Remote Sensing ◽

Image Analysis ◽

Image Segmentation ◽

Online Survey ◽

Accuracy Assessment ◽

Learning Approaches ◽

Special Issue ◽

Object Based Image Analysis ◽

Object Based ◽

Wide Range

Image segmentation and geographic object-based image analysis (GEOBIA) were proposed around the turn of the century as a means to analyze high-spatial-resolution remote sensing images. Since then, object-based approaches have been used to analyze a wide range of images for numerous applications. In this Editorial, we present some highlights of image segmentation and GEOBIA research from the last two years (2018–2019), including a Special Issue published in the journal Remote Sensing. As a final contribution of this special issue, we have shared the views of 45 other researchers (corresponding authors of published papers on GEOBIA in 2018–2019) on the current state and future priorities of this field, gathered through an online survey. Most researchers surveyed acknowledged that image segmentation/GEOBIA approaches have achieved a high level of maturity, although the need for more free user-friendly software and tools, further automation, better integration with new machine-learning approaches (including deep learning), and more suitable accuracy assessment methods was frequently pointed out.

Download Full-text

Estimating Surface Soil Heat Flux in Permafrost Regions Using Remote Sensing-Based Models on the Northern Qinghai-Tibetan Plateau under Clear-Sky Conditions

Remote Sensing ◽

10.3390/rs11040416 ◽

2019 ◽

Vol 11 (4) ◽

pp. 416 ◽

Cited By ~ 1

Author(s):

Cheng Yang ◽

Tonghua Wu ◽

Jiemin Wang ◽

Jimin Yao ◽

Ren Li ◽

...

Keyword(s):

Remote Sensing ◽

Land Surface ◽

Surface Soil ◽

Ground Surface ◽

Soil Heat Flux ◽

Parameterization Scheme ◽

Clear Sky ◽

Sky Conditions ◽

Permafrost Regions

The ground surface soil heat flux (G0) quantifies the energy transfer between the atmosphere and the ground through the land surface. However; it is difficult to obtain the spatial distribution of G0 in permafrost regions because of the limitation of in situ observation and complication of ground surface conditions. This study aims at developing an improved G0 parameterization scheme applicable to permafrost regions of the Qinghai-Tibet Plateau under clear-sky conditions. We validated several existing remote sensing-based models to estimate G0 by analyzing in situ measurement data. Based on the validation of previous models on G0; we added the solar time angle to the G0 parameterization scheme; which considered the phase difference problem. The maximum values of RMSE and MAE between “measured G0” and simulated G0 using the improved parameterization scheme and in situ data were calculated to be 6.102 W/m2 and 5.382 W/m2; respectively. When the error of the remotely sensed land surface temperature is less than 1 K and the surface albedo measured is less than 0.02; the accuracy of estimates based on remote sensing data for G0 will be less than 5%. MODIS data (surface reflectance; land surface temperature; and emissivity) were used to calculate G0 in a 10 x 10 km region around Tanggula site; which is located in the continuous permafrost region with long-term records of meteorological and permafrost parameters. The results obtained by the improved scheme and MODIS data were consistent with the observation. This study enhances our understanding of the impacts of climate change on the ground thermal regime of permafrost and the land surface processes between atmosphere and ground surface in cold regions.

Download Full-text