Surface Brightness Temperature Research Articles

Advances in a Two-Source Energy Balance Model: Partitioning of Evaporation and Transpiration for Cotton

Abstract. Accurate partitioning of the evaporation (E) and transpiration (T) components of evapotranspiration (ET) in remote sensing models is important for evaluating strategies aimed at increasing crop water productivity. A two-source energy balance (TSEB) model designed for row crops solves the energy balance of the soil-canopy-atmosphere continuum using surface brightness temperature. By solving the energy balance of the soil and plant canopy separately, the TSEB model can calculate E and T, which cannot be done with single-source models. However, few studies have tested the TSEB model where E or T measurements were available, which until recently has impeded its advance. This article reviews recent physically based advances of the TSEB model. The advances were tested using measurements of E, T, and ET by microlysimeters, sap flow gauges, and weighing lysimeters, respectively, at Bushland, Texas, for irrigated cotton having a wide range of canopy cover. Root mean square error (RMSE) and mean bias error (MBE) were 0.54 and -0.19 mm d-1, respectively, between measured and calculated E. RMSE and MBE were 0.87 and 0.31 mm d-1, respectively, between measured and calculated T. This was deemed an improvement over previous TSEB model versions, which overestimated E and underestimate T, resulting in RMSE and MBE of up to 3.8 and -3.5 mm d-1, respectively. Ongoing research includes testing the TSEB model using different remote sensing platforms, from ground-based to satellite scales.

A time series analysis of urbanization induced land use and land cover change and its impact on land surface temperature with Landsat imagery

Abstract The relationship between land use and land cover (LULC) patterns and thermal characteristic has long been studied for examining the impact of urbanization on urban thermal environment. Previous studies were inclined to the use of one or a few images without fully considering the temporal domain of the reflective and thermal infrared data on board Landsat sensors. This paper took the Atlanta metropolitan area as a case study to illustrate LULC change and its impact on land surface temperature (LST) variations. The Landsat L1T (Standard terrain correction) images from TM/ETM + from 1984 to 2011 (507 in total) were downloaded through the USGS online portal and consistently calibrated to surface reflectance and brightness temperature (BT). The cloud-, cloud shadow-, and snow-contaminated pixels were excluded in the analysis according to the metadata, and a further screening procedure based on the RIRLS (Robust Iteratively Reweighted Least Squares) technique was implemented. The time series LSTs (TSLSTs) was derived using the single channel algorithm because it required only the parameters of water vapor and land surface emissivity and had a reported error close to 1 K. The LULC classification and change detection was accomplished by using the Continuous Change Detection and Classification (CCDC) algorithm. The TSLSTs were further decomposed into the seasonal and trend components by an additive model. Results showed that the overall LULC classification and change detection accuracies were 89% and 92%, respectively. Urban growth was mainly observed in Fulton County and Gwinnett County. High-intensity urban land had the largest mean LST value of 294.9 K and yearly amplitude of 17.4 K. A comparison of the trend component between urban and non-urban land covers showed a difference of 1.8 K per decade. Next, temporal thermal signatures were created to characterize and quantify the impact of urban LULC changes. Decomposition analysis showed that the conversion of evergreen forest to medium-intensity urban land generated the largest difference in annual LST variation (5.7 K) and the largest trend difference (0.0004 K/day).

Spatio-temporal variation characteristics of the Mars surface brightness temperature

Spatio-temporal variation characteristics of the Mars surface brightness temperature

Optimum Image Formation for Spaceborne Microwave Radiometer Products.

This paper considers some of the issues of radiometer brightness image formation and reconstruction for use in the NASA-sponsored Calibrated Passive Microwave Daily Equal-Area Scalable Earth Grid 2.0 Brightness Temperature Earth System Data Record project, which generates a multisensor multidecadal time series of high-resolution radiometer products designed to support climate studies. Two primary reconstruction algorithms are considered: the Backus-Gilbert approach and the radiometer form of the scatterometer image reconstruction (SIR) algorithm. These are compared with the conventional drop-in-the-bucket (DIB) gridded image formation approach. Tradeoff study results for the various algorithm options are presented to select optimum values for the grid resolution, the number of SIR iterations, and the BG gamma parameter. We find that although both approaches are effective in improving the spatial resolution of the surface brightness temperature estimates compared to DIB, SIR requires significantly less computation. The sensitivity of the reconstruction to the accuracy of the measurement spatial response function (MRF) is explored. The partial reconstruction of the methods can tolerate errors in the description of the sensor measurement response function, which simplifies the processing of historic sensor data for which the MRF is not known as well as modern sensors. Simulation tradeoff results are confirmed using actual data.

An Algorithm for Sea Surface Wind Speed from SAC-D/Aquarius Microwave Radiometer

This paper provides the description of an ocean wind speed retrieval algorithm developed for the SAC-D/Aquarius microwave radiometer’s (MWR). Empirical model coefficients are derived using MWR brightness temperature at 36.5 GHz H- and V-pol with collocated numerical weather prediction (NWP) model estimates of oceanic and atmospheric environmental parameters. A unique feature of this algorithm is that it incorporates NWP estimates of wind direction to correct the MWR surface brightness temperatures before simultaneously retrieving atmospheric transmissivity, an isotropic ocean surface wind speed. Comprehensive on-orbit validation was conducted using 6 months of MWR wind speed measurements with collocated satellite ocean wind speed retrievals, provided by Remote Sensing Systems, Inc. These results indicate that MWR surface wind retrieval data are quite acceptable for scientific analysis.

THE CONNECTING MOLECULAR RIDGE IN THE GALACTIC CENTER

We report new observations of multiple transitions of the CS molecular lines in the SgrA region of the Galactic center, at an angular resolution of 40" (=1.5 pc). The objective of this paper is to study the polar arc, which is a molecular ridge near the SgrA region, with apparent non-coplanar motions, and a velocity gradient perpendicular to the Galactic plane. With our high resolution dense-gas maps, we search for the base and the origin of the polar arc, which is expected to be embedded in the Galactic disk. We find that the polar arc is connected to a continuous structure from one of the disk ring/arm in both the spatial and velocity domains. This structure near SgrA* has high CS(J=4-3)/CS(J=2-1) ratios >1. That this structure has eluded detection in previous observations, is likely due to the combination of high excitation and low surface brightness temperature. We call this new structure the connecting ridge. We discuss the possible mechanism to form this structure and to lift the gas above the Galactic plane.

A Comparison of Land Surface Temperature, Derived from AMSR-2, Landsat and ASTER Satellite Data

Land surface parameters are highly integrated and have a direct effect on water and energy balance and weather predictions. Due to the difficulties in correcting the influences of the atmosphere absorbability and the earth surface emissivity diversification, the retrieval of land surface temperature (LST) from satellite data is a challenging task. To retrieve microwave land emissivity, infrared surface skin temperatures have been used as surface physical temperature. However, passive microwave emissions originate from deeper layers with respect to the skin temperature. So, this inconsistency in sensitivity depths between skin temperatures and microwave temperatures may introduce a discrepancy in the determined emissivity. In this research work, 6 sample sites were chosen on the globe for 2013 and 2014 and then derive land surface temperature from AMSR-2, Landsat and ASTER brightness temperature values. The algorithm is developed from a surface brightness temperature dataset, which uses a range of surface parameters and atmospheric quantities as inputs. The retrieved LST is compared within AMSR-2, Landsat and ASTER for the same period and area. Maximum time ASTER shows highest temperature than other data and AMSR-2 has lowest temperature on same area. Landsat and ASTER is closer to ground measured temperature than AMSR-2 data. It will be interesting to see how the satellite-derived surface temperature will behave in an assimilation scheme in a follow-up study.

Effective surface areas for optimal correlations between surface brightness and air temperatures in an urban environment

We perform correlation analysis between air temperature and surface brightness temperature over the Adelaide Central Business District and the surrounding parklands. The results indicate that three effective surface areas associated with three different mechanisms exist. They are the effective surface area with upward sensible heat transfer to heat up and maintain the air temperature, the effective surface area with downward sensible heat transfer to cool down the air, and the effective surface area related to very localized conditions (e.g., sky-view factors). The three mechanisms occur at different times of the day and result in different air temperature and surface temperature correlations. The first effective surface area exists in the daytime after the surface is heated up by solar radiation and can persist into the night, particularly in urban environments. The second effective area occurs in night-time when the surface has been cooled down sufficiently. The third effective area coexists with the second one and has a weaker correlation between the surface and air temperatures. It was also found that in an urban area, exclusion of roof pixels in the calculation of surface brightness temperature can increase the correlation between the surface and air temperatures in the middle of the night.

Evaluation of multi-frequency bare soil microwave reflectivity models

Passive microwave remote sensing is commonly used to monitor the hydrological processes of the earth's surface (soil moisture) as well as key surface environmental processes (vegetation dynamics, snow cover, etc.). For many applications, it is important to model the effects of surface roughness. This study focuses on the unique PORTOS-93 measurement campaign that covered a wide range of bare soil conditions in terms of moisture, temperature and surface roughness. The PORTOS-93 campaign covers a frequency range from 1.41 to 90GHz. In this study, based on the PORTOS-93 dataset, we compare the Wegmüller & Mätzler (1999) model (referred to as WM99) with the Wang & Choudhury (1981) model (referred to as QHN) and evaluate their abilities to simulate the soil surface brightness temperature (TB). We show that improved results were obtained by tuning the parameters of the two models to the entire PORTOS-93 dataset for each frequency separately compared to the model tuned independently of frequency. In addition we found that the Mironov et al. (2009) soil permittivity model is slightly more accurate at lower frequencies (<11GHz) for simulating soil permittivity than the Dobson et al. (1985) soil permittivity model for the PORTOS-93 site. We also found that both of these permittivity models need to be tuned at higher frequencies (>20GHz) and that using the Wang & Schmugge (1980) model tuned using parameters derived by Calvet et al. (1995a) at higher frequencies for the PORTOS-93 dataset gives best results. Using the proposed tuned reflectivity models for each frequency separately, we obtained an overall bias of −0.20K and 0.21K for the WM99 and QHN models respectively between the measured and modelled brightness temperatures. This corresponds to a significant improvement in comparison to the use of the tuned WM99 model independently of the frequency (bias=−38.08K).

Temperatures, thermal structure, and behavior of eruptions at Kilauea and Erta Ale volcanoes using a consumer digital camcorder

Remote thermal monitoring of active volcanoes has many important applications for terrestrial and planetary volcanic systems. In this study, we describe observations of active eruptions on Kilauea and Erta Ale volcanoes using a short-wavelength, high-resolution, consumer digital camcorder and other non-imaging thermal detectors. These systems revealed brightness temperatures close to the eruption temperatures and temperature distributions, morphologies and thermal structures of flow features, tube systems and lava fountains. Lava flows observed by the camcorder through a skylight on Kilauea had a peak in maximum brightness temperatures at 1230°C and showed brightness temperature distributions consistent with most rapid flow at the center. Surface brightness temperatures of cooling lava flows on Kilauea were close to 850°C. Centimeter-scale thermal features are evident around pahoehoe ropes and inflated flows and stalactites in lava tubes. Observations of the fountaining Erta Ale lava lake in February 2011 extend the baseline of observations of the eruptive episode begun in late 2010. We observed a fountain using the camcorder and found a peak in maximum brightness temperatures at 1164°C, consistent with previous studies. Steep temperature gradients were observed across centimeter-scale distances between the highly exposed fountain and cracks and the much cooler lava lake surface and crater walls. The instrument and methods described here lead to robust pictures of the temperatures and temperature distributions at these volcanoes and reveal desired characteristics of planetary remote sensing platforms for the study of volcanically active bodies such as Io.

Prediction And Analysis Of Urban Heat Island Effect In Dangshan By Remote Sensing

Abstract Vegetation index (NDVI) was extracted from bi-temporal multispectral images based on the data obtained from Landsat ETM+on 14th, September, 2000, Landsat ETM+on 9th, September, 2004, Landsat ETM+on 15th, May, 2008 and Landsat-8 on 21st, May 2013 for Dangshan County in Anhui Province. Analysis and data extraction was carried out using the ENVI 5.0 software. Normalized values of thermal radiation brightness temperature and surface brightness temperature were inverted from the bi-temporal thermal infrared band images using the mono-window algorithm. Urban heat island effect in Dangshan County was divided into strong green island zone, green island zone, normal zone, heat island zone and strong heat island zone according based on arithmetic progression. Using regression analysis, quantitative relationship between NDVI and the heat island effect was determined. Results showed an acceleration in urbanization of Dangshan County over the years resulted in a gradual increase in the heat island effect from 2000 to 2013. In addition, area of the heat island and strong heat island increased was observed to increase rapidly, while the area of the green island and strong green island reduced by 46%. Also, using the Markov model, urban heat island effect in Dangshan County was predicted over the next 40 years. This model was feasible in predicting the urban heat island effect with small errors. Finally, it was determine that heat island effect was in negative correlation to the vegetation index (NDVI), and increasing green land appropriately would have a positive effect in alleviating the urban heat island effect

Land Surface Temperature and Scaling Factors for Different Satellites Datasets

Land surface parameters are highly integrated and have a direct effect on water and energy balance and weather predictions. Due to the difficulties in correcting the influences of the atmosphere absorbability and the earth surface emissivity diversification, the retrieval of land surface temperature (LST) from satellite data is a challenging task. To retrieve microwave land emissivity, infrared surface skin temperatures have been used as surface physical temperature. However, passive microwave emissions originate from deeper layers with respect to the skin temperature. So, this inconsistency in sensitivity depths between skin temperatures and microwave temperatures may introduce a discrepancy in the determined emissivity. In this research, six sample sites were chosen on the earth for 2013 and 2014 and then land surface temperature from AMSR-2, Landsat and ASTER brightness temperature values have been derived. The algorithm has been developed from a surface brightness temperature dataset, which has used as inputs surface parameters and atmospheric quantities. The retrieved LST has been compared within AMSR-2, Landsat and ASTER for the same period and area. Maximum time ASTER has shown higher temperature than other data and AMSR-2 has lower temperature on same area. Landsat and ASTER is closer to ground measured temperature than AMSR-2 data. It will be interesting to see how the satellite-derived surface temperature will behave in an assimilation scheme in a follow-up study.

On direct passive microwave remote sensing of sea spray aerosol production

Abstract. This study addresses and attempts to mitigate persistent uncertainty and scatter among existing approaches for determining the rate of sea spray aerosol production by breaking waves in the open ocean. The new approach proposed here utilizes passive microwave emissions from the ocean surface, which are known to be sensitive to surface roughness and foam. Direct, simultaneous, and collocated measurements of the aerosol production and microwave emissions were collected aboard the FLoating Instrument Platform (FLIP) in deep water ~ 150 km off the coast of California over a period of ~ 4 days. Vertical profiles of coarse-mode aerosol (0.25–23.5 μm) concentrations were measured with a forward-scattering spectrometer and converted to surface flux using dry deposition and vertical gradient methods. Back-trajectory analysis of eastern North Pacific meteorology verified the clean marine origin of the sampled air mass over at least 5 days prior to measurements. Vertical and horizontal polarization surface brightness temperature were measured with a microwave radiometer at 10.7 GHz frequency. Data analysis revealed a strong sensitivity of the brightness temperature polarization difference to the rate of aerosol production. An existing model of microwave emission from the ocean surface was used to determine the empirical relationship and to attribute its underlying physical basis to microwave emissions from surface roughness and foam within active and passive phases of breaking waves. A possibility of and initial steps towards satellite retrievals of the sea spray aerosol production are briefly discussed in concluding remarks.

Vegetation, land surface brightness, and temperature dynamics after aspen forest die‐off

AbstractForest dynamics following drought‐induced tree mortality can affect regional climate through biophysical surface properties. These dynamics have not been well quantified, particularly at the regional scale, and are a large uncertainty in ecosystem‐climate feedback. We investigated regional biophysical characteristics through time (1995–2011) in drought‐impacted (2001–2003), trembling aspen (Populus tremuloides Michx.) forests by utilizing Landsat time series green and brown vegetation cover, surface brightness (total shortwave albedo), and daytime land surface temperature. We quantified the temporal dynamics and postdrought recovery of these characteristics for aspen forests experiencing severe drought‐induced mortality in the San Juan National Forest in southwestern Colorado, USA. We partitioned forests into three categories from healthy to severe mortality (Healthy, Intermediate, and Die‐off) by referring to field observations of aspen canopy mortality and live aboveground biomass losses. The vegetation cover of die‐off areas in 2011 (26.9% of the aspen forest) was significantly different compared to predrought conditions (decrease of 7.4% of the green vegetation cover and increase of 12.1% of the brown vegetation cover compared to 1999). The surface brightness of the study region 9 years after drought however was comparable to predrought estimates (12.7–13.7%). Postdrought brightness was potentially influenced by understory shrubs, since they became the top layer green canopies in disturbed sites from a satellite's point of view. Satellite evidence also showed that the differences of land surface temperature among the three groups increased substantially (≥45%) after drought, possibly due to the reduction of plant evapotranspiration in the Intermediate and Die‐off sites. Our results suggest that the mortality‐affected systems have not recovered in terms of the surface biophysical properties. We also find that the temporal dynamics of vegetation cover holds great potential for assessing propensity of subsequent mortality during drought itself, which could provide effective monitoring and potentially a much needed “early warning” of drought‐induced tree mortality.

Urban heat island effects derived from dense Landsat thermal observations in Nanjing, China

An urban heat island (UHI) is an urban area where temperatures are higher than they are in the surrounding suburban areas. UHIs are of great scientific concern in the context of global environmental change. As a result of rapid economic development over the past three decades, the urban area in China has increased significantly. In the present study, to objectively determine the spatiotemporal patterns of UHI effects in Nanjing, surface brightness temperatures of the urban areas of Nanjing, which originated from remote sensing imagery of Landsat TM/ETM+ from 1988 to 2011, were inversed, and then the UHIs were extracted and graded by choosing a stable forest area as the suburban reference. When the results were combined with the surface meteorological data, a good relationship was found between the brightness temperatures and the ground-truth temperatures. The results of contrast analysis indicated that, over time, most of the heat islands of Nanjing remained over the central portions. The area and the intensity of the heat islands have both increased remarkably over the past 20 years.

Soil Moisture Ocean Salinity (SMOS) salinity data validation over Malaysia coastal water

The study of sea surface salinity (SSS) plays an important role in the marine ecosystem, estimation of global ocean circulation and observation of fisheries, aquaculture, coral reef and sea grass habitats. The new challenge of SSS estimation is to exploit the ocean surface brightness temperature (Tb) observed by the Microwave Imaging Radiometer with Aperture Synthesis (MIRAS) onboard the Soil Moisture Ocean Salinity (SMOS) satellite that is specifically designed to provide the best retrieval of ocean salinity and soil moisture using the L band of 1.4 GHz radiometer. Tb observed by radiometer is basically a function of the dielectric constant, sea surface temperature (SST), wind speed (U), incidence angle, polarization and SSS. Though, the SSS estimation is an ill-posed inversion problem as the relationship between the Tb and SSS is non-linear function. Objective of this study is to validate the SMOS SSS estimates with the ground-truth over the Malaysia coastal water. The LM iteratively determines the SSS of SMOS by the reduction of the sum of squared errors between Tb SMOS and Tb simulation (using in-situ) based on the updated geophysical triplet in the direction of the minimum of the cost function. The minimum cost function is compared to the desired threshold at each iteration and this recursive least square process updates the SST, U and SSS until the cost function converged. The designed LM's non-linear inversion algorithm simultaneously estimates SST, U and SSS and thus, map of SSS over Malaysia coastal water is produced from the regression model and accuracy assessment between the SMOS and in-situ retrieved SSS. This study found a good agreement in the validation with R square of 0.9 and the RMSE of 0.4. It is concluded that the non-linear inversion method is effective and practical to extract SMOS SSS, U and SST simultaneously.

Continuous change detection and classification of land cover using all available Landsat data

A new algorithm for Continuous Change Detection and Classification (CCDC) of land cover using all available Landsat data is developed. It is capable of detecting many kinds of land cover change continuously as new images are collected and providing land cover maps for any given time. A two-step cloud, cloud shadow, and snow masking algorithm is used for eliminating “noisy” observations. A time series model that has components of seasonality, trend, and break estimates surface reflectance and brightness temperature. The time series model is updated dynamically with newly acquired observations. Due to the differences in spectral response for various kinds of land cover change, the CCDC algorithm uses a threshold derived from all seven Landsat bands. When the difference between observed and predicted images exceeds a threshold three consecutive times, a pixel is identified as land surface change. Land cover classification is done after change detection. Coefficients from the time series models and the Root Mean Square Error (RMSE) from model estimation are used as input to the Random Forest Classifier (RFC). We applied the CCDC algorithm to one Landsat scene in New England (WRS Path 12 and Row 31). All available (a total of 519) Landsat images acquired between 1982 and 2011 were used. A random stratified sample design was used for assessing the change detection accuracy, with 250pixels selected within areas of persistent land cover and 250pixels selected within areas of change identified by the CCDC algorithm. The accuracy assessment shows that CCDC results were accurate for detecting land surface change, with producer's accuracy of 98% and user's accuracies of 86% in the spatial domain and temporal accuracy of 80%. Land cover reference data were used as the basis for assessing the accuracy of the land cover classification. The land cover map with 16 categories resulting from the CCDC algorithm had an overall accuracy of 90%.

Research on the Relationship of Surface Temperature and Urban Heat Island Using the Thermal Infrared Remote Sensing Image

The temperature of the earth's land surface plays a very import ant role in the land-air interactions. It is critical parameters in the research of global change. Therefore, the use of satellite remote sensing data for surface temperature retrieval has become an important task of the study. Regional surface temperature is an important parameter of thermal energy distribution in the region, to obtain which is the most convenient and effective method. Thermal infrared remote sensing is an important area, and from thermal infrared remote sensing image information extracting temperature is a prerequisite for applying thermal infrared remote sensing technology .Based on remote sensing image data reading, radiometric calibration, regional cropping pretreatment; Then Landsat band 6 inversion of surface brightness temperature, It introduces the effect of the urban heat island using Landsat remote sensing ,having obtained the changes of spatial distribution of surface temperature, and prospects for future development in this field.

The EUMETSAT OSI SAF near 50 GHz sea ice emissivity model

ABSTRACTA sea ice thermal microwave emission model for 50 GHz was developed under EUMETSAT's Ocean and Sea Ice Satellite Application Facility (OSI SAF) programme. The model is based on correlations between the surface brightness temperature at 18, 36 and 50 GHz. The model coefficients are estimated using simulated data from a combined thermodynamic and emission model. The intention with the model is to provide a first guess sea ice surface emissivity estimate for atmospheric temperature sounding applications in the troposphere in numerical weather prediction (NWP) models assimilating Advanced Microwave Sounding Unit (AMSU) and Special Sensor Microwave Imager/Sounder (SSMIS) data. The spectral gradient ratio is defined as the difference over the sum of the SSMIS brightness temperatures at 18 and 36 GHz vertical linear polarisation (GR1836). The GR1836 is related to the emissivity at the atmospheric temperature sounding channels at around 50 GHz. Furthermore, the brightness temperatures and the polarisation ratio (PR) at the neighbouring 18, 36 and 50 GHz channels are highly correlated. Both the gradient ratio at 18 and 36 GHz and the PR at 36 GHz measured by SSMIS are input into the model predicting the 50 GHz emissivity for horizontal and vertical linear polarisations and incidence angles between 0° and 60° The simulated emissivity is compared to the emissivity derived with alternative methods. The fit to real AMSU observations is investigated using the different emissivity estimates for simulating the observations with atmospheric data from a regional weather prediction model.

The rain effect on Aquarius' L-band sea surface brightness temperature and radar backscatter

We analyze the surface emissivity and radar backscatter measured by the Aquarius L-band radiometer and scatterometer under rainy conditions. The residual signals due to rain are derived from measurements after accounting for roughness due to wind and flat surface emissivity. The wind roughness is accounted for by a geophysical model function (GMF) built using rain-free data. Using more than one year of Aquarius data collocated with SSMI/S, WindSAT rain rate and NCEP wind, our analysis reveals rain rate dependence in radar backscatter and surface emissivity, which become increasingly significant as the wind speed approaches zero and not significant above 15ms−1. The effect of rain on radar backscatter is dominated by raindrop splashing as indicated by the incidence angle dependence and polarization characteristics of the residuals.