Testing of global pressure/temperature (GPT) model and global mapping function (GMF) in GPS analyses

Several sources of a priori meteorological data have been compared for their effects on geodetic results from GPS precise point positioning (PPP). The new global pressure and temperature model (GPT), available at the IERS Conventions web site, provides pressure values that have been used to compute a priori hydrostatic (dry) zenith path delay z _h estimates. Both the GPT-derived and a simple height-dependent a priori constant z _h performed well for low- and mid-latitude stations. However, due to the actual variations not accounted for by the seasonal GPT model pressure values or the a priori constant z _h, GPS height solution errors can sometimes exceed 10 mm, particularly in Polar Regions or with elevation cutoff angles less than 10 degrees. Such height errors are nearly perfectly correlated with local pressure variations so that for most stations they partly (and for solutions with 5-degree elevation angle cutoff almost fully) compensate for the atmospheric loading displacements. Consequently, unlike PPP solutions utilizing a numerical weather model (NWM) or locally measured pressure data for a priori z _h, the GPT-based PPP height repeatabilities are better for most stations before rather than after correcting for atmospheric loading. At 5 of the 11 studied stations, for which measured local meteorological data were available, the PPP height errors caused by a priori z _h interpolated from gridded Vienna Mapping Function-1 (VMF1) data (from a NWM) were less than 0.5 mm. Height errors due to the global mapping function (GMF) are even larger than those caused by the GPT a priori pressure errors. The GMF height errors are mainly due to the hydrostatic mapping and for the solutions with 10-degree elevation cutoff they are about 50% larger than the GPT a priori errors.     

Geoid undulation computations at laser tracking stations

This paper studies the use of two new methods for gravimetric geoid undulation computations: The Molodenskii's and Sjöberg's methods that both modify the original Stokes'function so that certainrms errors are minimized. These new methods were checked against the traditional methods of Stokes' and Meissl's modification with the criterion of the globalrms undulation error that each method implies. Sjöberg's method gave consistently the smallest globalrms undulation error of all the other methods for capsizes 0° to 10°. However with the exception of Stokes' method, for capsizes between 0° to 5°, all the methods gave approximately (within±5cm) the same globalrms undulation error. Actual gravity data within a cap of 2° and potential coefficient information were then combined to compute the undulation of 39 laser stations distributed around the world. Therms discrepancy between the gravimetric undulations using all the four methods and the undulations computed as the ellipsoidal minus the orthometric height of 28 at the above stations was±1.70,±1.65,±1.66,±1.65m for the Stokes', Meissl's, Molodenskii's and Sjöberg's method respectively. For five oceanic laser stations where no terrestrial gravity data was available, theGEOS-3/SEASAT altimeter sea surface heights were used to compute the undulations of these stations in a collocation method. Therms discrepancy between the altimeter derived undulation and the ellipsoidal mirus orthometric value of the undulation was ±1.30m for the above five laser stations.     

Assessing reference evapotranspiration at regional scale based on remote sensing,weather forecast and GIS tools

Reference evapotranspiration (ET_o) is a key component in efficient water management, especially in arid and semi-arid environments. However, accurate ET_o assessment at the regional scale is complicated by the limited number of weather stations and the strict requirements in terms of their location and surrounding physical conditions for the collection of valid weather data. In an attempt to overcome this limitation, new approaches based on the use of remote sensing techniques and weather forecast tools have been proposed.Use of the Land Surface Analysis Satellite Application Facility (LSA SAF) tool and Geographic Information Systems (GIS) have allowed the design and development of innovative approaches for ET_o assessment, which are especially useful for areas lacking available weather data from weather stations. Thus, by identifying the best-performing interpolation approaches (such as the Thin Plate Splines, TPS) and by developing new approaches (such as the use of data from the most similar weather station, TS, or spatially distributed correction factors, CITS), errors as low as 1.1% were achieved for ET_o assessment. Spatial and temporal analyses reveal that the generated errors were smaller during spring and summer as well as in homogenous topographic areas.The proposed approaches not only enabled accurate calculations of seasonal and daily ET_o values, but also contributed to the development of a useful methodology for evaluating the optimum number of weather stations to be integrated into a weather station network and the appropriateness of their locations. In addition to ET_o, other variables included in weather forecast datasets (such as temperature or rainfall) could be evaluated using the same innovative methodology proposed in this study.     

Application of Remotely-Sensed Data to Estimate a Water Budget for Data-Scarce Endorheic Basins: A Case Study of Lake Urmia basin,Iran

Estimating the water budgets of large basins is a challenge because of the lack of data and information. It becomes more complicated in endorheic basins that consist of separate land and water phases. The application of remotely-sensed data is one solution in this regard. The present study addresses this issue and develops a modeling framework to evaluate a water budget based on remotely-sensed data for endorheic basins. To explore the methodology, Lake Urmia basin was selected as a case study. The lake water level has declined steeply since 1995 and stakeholders have agreed to allocate 3100 MCM of water per year to the lake. This makes it necessary to monitor river inflow into the lake to fulfill the agreement. Gauging stations have been employed around the lake, but they could not account for shortages such as water uptake below the stations. To do this, separate water budgets for the water body and the land were required. More specifically, it was necessary to estimate actual evapotranspiration (ET _a ) from freshwater (E _f ) and saltwater (E _s ) estimated using the SEBAL model. Different methods were applied to estimate soil moisture, groundwater exploitation, and surface-groundwater inflow into the lake. A comparison of the observed and estimated amounts showed good agreement. For instance, the coefficient of determination for the observed/reported and estimated ET _a and E _f were 0.83 and 0.84, respectively. The average annual inflow was estimated to be 2.2 BCM/year for 2002–2008 using the RS model, which is about 84 % of the total inflow from the last recording stations before the lake and shows influence of water exploitation after these stations. Future study should focus on increasing temporal and spatial resolution of the method     

Estimation of satellite position,clock and phase bias corrections

Precise point positioning with integer ambiguity resolution requires precise knowledge of satellite position, clock and phase bias corrections. In this paper, a method for the estimation of these parameters with a global network of reference stations is presented. The method processes uncombined and undifferenced measurements of an arbitrary number of frequencies such that the obtained satellite position, clock and bias corrections can be used for any type of differenced and/or combined measurements. We perform a clustering of reference stations. The clustering enables a common satellite visibility within each cluster and an efficient fixing of the double difference ambiguities within each cluster. Additionally, the double difference ambiguities between the reference stations of different clusters are fixed. We use an integer decorrelation for ambiguity fixing in dense global networks. The performance of the proposed method is analysed with both simulated Galileo measurements on E1 and E5a and real GPS measurements of the IGS network. We defined 16 clusters and obtained satellite position, clock and phase bias corrections with a precision of better than 2 cm.     

First results with the transportable absolute gravity meter JILAG-3

A transportable absolute gravity meter has been built at the Joint Institute for Laboratory Astrophysics (JILA) of the University of Colorado and delivered in January 1986 to the Institut für Erdmessung (IFE), Universität Hannover. Instrumental investigations, software improvements and absolute gravity determinations on 15 stations performed byIFE in 1986 are reported here. The main conclusion from one year experience with the instrument is that absolute gravity can be observed on a station within one day with a precision of a few μgal and an accuracy of about 10 μgal.     

Combining consecutive short arcs into long arcs for precise and efficient GPS Orbit Determination

The final products of theCODE Analysis Center (Center for Orbit Determination in Europe) of theInternational GPS Service for Geodynamics (IGS) stem fromoverlapping 3-day-arcs. Until 31 December, 1994 these long arcs were computedfrom scratch, i.e. by processing three days of observations of about 40 stations (by mid 1995 about 60 stations were used) of the IGS Global Network in our parameter estimation program GPSEST. Becauseone-day-arcs have to be produced first (for the purpose of error detection etc.) the actual procedure was rather time-consuming. In the present article we develop the mathematical tools necessary to form long arcs based on the normal equation systems of consecutive short arcs (one-day-solutions in the case of CODE). The procedure in its simplest version is as follows:

• Each short arc is described bysix initial conditions and a number of dynamical orbit parameters (e.g. radiation pressure parameters). The resulting long arc in turn shall be based onn consecutive short arcs and described bysix initial conditions and again the same number of dynamical parameters as in the short arcs..
• By asking position and velocity to be continuous at the boundaries of the short arcs we obtain a long arc which is actually defined by one set of initial conditions andn sets of dynamical parameters (ifn short arcs are combined)..
• By asking the dynamical parameters to be identical in consecutive short arcs, the resulting long arc is characterized by exactly the same number of orbit parameters as each of the short arcs.
• This procedure isnot yet optimized becauseformally all n sets of orbit parameters have to be set up and solved for in the long arc solution (although they are not independent). In order to allow for an optimized solution we derive all necessary relations to eliminate the unnecessary parameters in the combination. Each long arc is characterized by the actual number of independent orbit parameters. The resulting procedure isvery efficient.

From the point of view of the result the new procedure iscompletely equivalent to an actual re-evaluation of all observations pertaining to the long arc. It is much more efficient and flexible, however because it allows us to construct 2-day-arcs, 3-day-arcs, etc. based on the previously stored daily normal equation systems without requiring much additional CPU time. The theory is developed in the first four sections. Technical aspects are dealt with in appendices A and B. The actual implementation into the Bernese GPS Software system and test results are given in section 5.     

Agriculture pest and disease risk maps considering MSG satellite data and land surface temperature

Pest risk maps for agricultural use are usually constructed from data obtained from in-situ meteorological weather stations, which are relatively sparsely distributed and are often quite expensive to install and difficult to maintain. This leads to the creation of maps with relatively low spatial resolution, which are very much dependent on interpolation methodologies. Considering that agricultural applications typically require a more detailed scale analysis than has traditionally been available, remote sensing technology can offer better monitoring at increasing spatial and temporal resolutions, thereby, improving pest management results and reducing costs. This article uses ground temperature, or land surface temperature (LST), data distributed by EUMETSAT/LSASAF (with a spatial resolution of 3 × 3 km (nadir resolution) and a revisiting time of 15 min) to generate one of the most commonly used parameters in pest modeling and monitoring: “thermal integral over air temperature (accumulated degree-days)”. The results show a clear association between the accumulated LST values over a threshold and the accumulated values computed from meteorological stations over the same threshold (specific to a particular tomato pest). The results are very promising and enable the production of risk maps for agricultural pests with a degree of spatial and temporal detail that is difficult to achieve using in-situ meteorological stations.     

A globally applicable, season-specific model for estimating the weighted mean temperature of the atmosphere

In GPS meteorology, the weighted mean temperature is usually obtained by using a linear function of the surface temperature T _s. However, not every GPS station can measure the surface temperature. The current study explores the characteristics of surface temperature and weighted mean temperature based on the global pressure and temperature model (GPT) and the Bevis T _m–T _s relationship (T _m =?a?+?bT _s). A new global weighted mean temperature (GWMT) model has been built which directly uses three-dimensional coordinates and day of the year to calculate the weighted mean temperature. The data of year 2005–2009 from 135 radiosonde stations provided by the Integrated Global Radiosonde Archive were used to calculate the model coefficients, which have been validated through examples. The result shows that the GWMT model is generally better than the existing liner models in most areas according to the statistic indexes (namely, mean absolute error and root mean square). Then we calculated precipitable water vapor, and the result shows that GWMT model can also yield high precision PWV.     

Validation of MODIS integrated water vapor product against reference GPS data at the Iberian Peninsula

In this work, the water vapor product from MODIS (MODerate-resolution Imaging Spectroradiometer) instrument, on-board Aqua and Terra satellites, is compared against GPS water vapor data from 21 stations in the Iberian Peninsula as reference. GPS water vapor data is obtained from ground-based receiver stations which measure the delay caused by water vapor in the GPS microwave signals. The study period extends from 2007 until 2012. Regression analysis in every GPS station show that MODIS overestimates low integrated water vapor (IWV) data and tends to underestimate high IWV data. R² shows a fair agreement, between 0.38 and 0.71. Inter-quartile range (IQR) in every station is around 30–45%. The dependence on several parameters was also analyzed. IWV dependence showed that low IWV are highly overestimated by MODIS, with high IQR (low precision), sharply decreasing as IWV increases. Regarding dependence on solar zenith angle (SZA), performance of MODIS IWV data decreases between 50° and 90°, while night-time MODIS data (infrared) are quite stable. The seasonal cycles of IWV and SZA cause a seasonal dependence on MODIS performance. In summer and winter, MODIS IWV tends to overestimate the reference IWV value, while in spring and autumn the tendency is to underestimate. Low IWV from coastal stations is highly overestimated (∼60%) and quite imprecise (IQR around 60%). On the contrary, high IWV data show very little dependence along seasons. Cloud-fraction (CF) dependence was also studied, showing that clouds display a negligible impact on IWV over/underestimation. However, IQR increases with CF, except in night-time satellite values, which are quite stable.     

Hyperspectral radiometric observation of the northeast Arabians Sea during April 2006

Parameters were retrieved from the hyperspectral radiometer like upwelling and downwelling radiance (Lu and Ed) upwelling and downwelling attenuation (K-Lu and K-Ed) for 9 stations in the northeast Arabian Sea between 16–26 April 2006. Data was analyzed for 5 offshore and 4 coastal stations of the cruise SS-244, on board FORV “Sagar Sampada” between latitude 9-22oN and longitude 68–74°E. The peak for all parameters was observed to be different respectively for depths 1, 5, 10, 20, 30, 40, 50 meters in coastal and offshore stations. Each peak in the respective wavelength is due to a particular composition; phytoplankton pigments have spectral peaks at 443, 490, 555, 670 nm, suspended matter, sediments have peaks at 630 and 670 nm. Detailed analysis with High Performance Liquid Chromatography (HPLC) data and comparison with the water composition of our hyperspectral radiometer results show that the marine cyanophyte, Trichodesmium bloom produces high pigment concentrations of chlorophyll-a, zeaxanthin, β-carotene and pheophytin and their absorptions are interpreted at wavelengths 443, 490, 515 and 536 nanometers, respectively. A dip around 515 nm was seen in the Ed and Lu profiles in our study.     

Geocenter variations derived from a combined processing of LEO- and ground-based GPS observations

GNSS observations provided by the global tracking network of the International GNSS Service (IGS, Dow et al. in J Geod 83(3):191–198, 2009) play an important role in the realization of a unique terrestrial reference frame that is accurate enough to allow a detailed monitoring of the Earth’s system. Combining these ground-based data with GPS observations tracked by high-quality dual-frequency receivers on-board low earth orbiters (LEOs) is a promising way to further improve the realization of the terrestrial reference frame and the estimation of geocenter coordinates, GPS satellite orbits and Earth rotation parameters. To assess the scope of the improvement on the geocenter coordinates, we processed a network of 53 globally distributed and stable IGS stations together with four LEOs (GRACE-A, GRACE-B, OSTM/Jason-2 and GOCE) over a time interval of 3 years (2010–2012). To ensure fully consistent solutions, the zero-difference phase observations of the ground stations and LEOs were processed in a common least-squares adjustment, estimating all the relevant parameters such as GPS and LEO orbits, station coordinates, Earth rotation parameters and geocenter motion. We present the significant impact of the individual LEO and a combination of all four LEOs on the geocenter coordinates. The formal errors are reduced by around 20% due to the inclusion of one LEO into the ground-only solution, while in a solution with four LEOs LEO-specific characteristics are significantly reduced. We compare the derived geocenter coordinates w.r.t. LAGEOS results and external solutions based on GPS and SLR data. We found good agreement in the amplitudes of all components; however, the phases in x- and z-direction do not agree well.     

Global sea-level rise and its relation to the terrestrial reference frame

We examined the sensitivity of estimates of global sea-level rise obtained from GPS-corrected long term tide gauge records to uncertainties in the International Terrestrial Reference Frame (ITRF) realization. A useful transfer function was established, linking potential errors in the reference frame datum (origin and scale) to resulting errors in the estimate of global sea level rise. Contrary to scale errors that are propagated by a factor of 100%, the impact of errors in the origin depends on the network geometry. The geometry of the network analyzed here resulted in an error propagation factor of 50% for the Z component of the origin, mainly due to the asymmetry in the distribution of the stations between hemispheres. This factor decreased from 50% to less than 10% as the geometry of the network improved using realistic potential stations that did not yet meet the selection criteria (e.g., record length, data availability). Conversely, we explored new constraints on the reference frame by considering forward calculations involving tide gauge records. A reference frame could be found in which the scatter of the regional sea-level rates was limited. The resulting reference frame drifted by 1.36 ± 0.22? mm/year from the ITRF2000 origin in the Z component and by ?0.44 ± 0.22?mm/year from the ITRF2005 origin. A bound on the rate of global sea level rise of 1.2 to 1.6?mm/year was derived for the past century, depending on the origin of the adopted reference frame. The upper bound is slightly lower than previous estimates of 1.8?mm/year discussed in the IPCC fourth report.     

Accuracy analysis for the NAD 83 geodetic reference system

The North American Datum of 1983 (NAD 83) provides horizontal coordinates for more than 250,000 geodetic stations. These coordinates were derived by a least squares adjustment of existing terrestrial and space-based geodetic data. For pairs of first order stations with interstation distances between 10km and 100km, therms discrepancy between distances derived fromNAD 83 coordinates and distances derived from independentGPS data may be suitably approximated by the empirical rulee=0.008 K^0.7 where e denotes therms discrepancy in meters and K denotes interstation distance in kilometers. For the same station pairs, therms discrepancy in azimuth may be approximated by the empirical rule e=0.020 K^0.5. Similar formulas characterize therms discrepancies for pairs involving second and third order stations. Distance and orientation accuracies, moreover, are well within adopted standards. While these expressions indicate that the magnitudes of relative positional accuracies depend on station order, absolute positional accuracies are similar in magnitude for first, second, and third order stations. Adjustment residuals reveal a few local problems with theNAD 83 coordinates and with the weights assigned to certain classes of observations.     

A model of present-day tectonic plate motions from 12 years of DORIS measurements

In the frame of the International DORIS Service (IDS), the Laboratoire d’Etudes en Géophysique et Océanographie Spatiales (LEGOS)/Collecte Localisation Satellites (CLS) Analysis Center (LCA) processes DORIS measurements from the SPOT, TOPEX/Poseidon and Envisat satellites and provides weekly station coordinates of the whole network to the IDS. Based on DORIS measurements, the horizontal and vertical velocities of 57 DORIS sites are computed. The 3D positions and velocities of the stations with linear motion are estimated simultaneously from the 12-year (1993–2004) combined normal equation matrix. We include 35 DORIS sites assumed to be located in the stable zones of 9 tectonic plates. For the motion of these plates, we propose a model (LCAVEL-1) of angular velocities in the ITRF2000 reference frame. Based on external comparison with the most recent global plate models (PB2002, REVEL, GSRM-1 and APKIM2000) and on internal analysis, we estimate an average velocity error of the DORIS solution of less than 3 mm/year. The LCAVEL-1 model presents new insights of the Somalia/Nubia pair of plates, as the DORIS technique has the advantage of having a few stations located on those two plates. We also computed (and provide in this article) the horizontal motion of the sites located close to plate boundaries or in the deformation zones defined in contemporary models. These computations could be used in further analysis for these particular regions of the Earth not moving as rigid plates.     

A soft-classification-based chlorophyll-a estimation method using MERIS data in the highly turbid and eutrophic Taihu Lake

Soft-classification-based methods for estimating chlorophyll-a concentration (C_chla) by satellite remote sensing have shown great potential in turbid coastal and inland waters. However, one of the most important water color sensors, the MEdium Resolution Imaging Spectrometer (MERIS), has not been applied to the study of turbid or eutrophic lakes. In this study, we developed a new soft-classification-based C_chla estimation method using MERIS data for the highly turbid and eutrophic Taihu Lake. We first developed a decision tree to classify Taihu Lake into three optical water types (OWTs) using MERIS reflectance data, which were quasi-synchronous (±3 h) with in situ measured C_chla data from 91 sample stations. Secondly, we used MERIS reflectance and in situ measured C_chla data in each OWT to calibrate the optimal C_chla estimation model for each OWT. We then developed a soft-classification-based C_chla estimation method, which blends the C_chla estimation results in each OWT by a weighted average, where the weight for each MERIS spectra in each OWT is the reciprocal value of the spectral angle distance between the MERIS spectra and the centroid spectra of the OWT. Finally, the soft-classification based C_chla estimation algorithm was validated and compared with no-classification and hard-classification-based methods by the leave-one-out cross-validation (LOOCV) method. The soft-classification-based method exhibited the best performance, with a correlation coefficient (R²), average relative error (ARE), and root-mean-square error (RMSE) of 0.81, 33.8%, and 7.0 μg/L, respectively. Furthermore, the soft-classification-based method displayed smooth values at the edges of OWT boundaries, which resolved the main problem with the hard-classification-based method. The seasonal and annual variations of C_chla were computed in Taihu Lake from 2003 to 2011, and agreed with the results of previous studies, further indicating the stability of the algorithm. We therefore propose that the soft-classification-based method can be effectively used in Taihu Lake, and that it has the potential for use in other optically-similar turbid and eutrophic lakes, and using spectrally-similar satellite sensors.     

THE SURVEY FRAMEWORK OF NIGERIA

Abstract

Angular and Linear Errors.—In the results which follow, the number of stations quoted on the chains is exclusive of all the stations on the base-extension nets at Minna, Rijau, Chafe, and Naraguta. The total number of stations can therefore be obtained by adding the numbers on all the chains and the base-extension nets and deducting the number of points common to other chains. The quoted fractional misclosures in length are the actual misclosures obtained and the theoretical fractional misclosures which might be expected to be developed through the chains from the probable errors of the adjusted angles.     

Devising stable geometrical reference frames for use in geodetic studies of vertical crustal motion

We present a method for constructing and assessing the stability of a geometrical reference frame for use in vertical crustal motion studies. Our approach exploits the fact that when we transform GPS velocity solutions from one reference frame (RF) to another one using a Helmert transformation, only the frame translation rate parameters produce significant changes in the vertical station velocities expressed in the final RF. Loosely speaking, one can select and impose a ‘vertical RF’ from an ensemble of candidate frames, without any reference to the ‘horizontal RF’ (which can be selected and imposed afterwards), by seeing how the frame translation rates vary as one moves across the ensemble of frames. We order this ensemble according to the number of stations, N, incorporated into the set VREF whose RMS vertical motion is minimized in order to realize each frame. The value of N controls the level of scatter in, and hence the degree of similarity between the vertical velocities of the stations composing VREF. We characterize a specific vertical RF as stable if all of the frames located in a large neighborhood of the ensemble which includes the specific frame are characterized by very small relative frame translation rates. In this case, the expression of vertical GPS station velocities in any of these frames would lead to very similar results. We present a case study using a very large global time series in which we find a large RF neighborhood in which vertical station velocities are globally stable at the $\sim $ 0.2 mm/year level, and a slightly smaller neighborhood in which vertical stability improves to $\sim $ 0.1 mm/year level in polar regions.     

Comparison of ocean color chlorophyll algorithms for IRS-P4 OCM sensor usingin-situ data

In-situ chlorophyll concentration data and remote sensing reflectance (R_rs) measurements collected in six different ship campaigns in the Arabian Sea were used to evaluate the accuracy, precision, and suitability of different ocean color chlorophyll algorithms for the Arabian Sea. The bio-optical data sets represent the typical range of biooptical conditions expected in this region and are composed of 47 stations encompassing chlorophyll concentration, between 0.072 and 5.90 mg m^-3, with 43 observations in case I water and 4 observations in case II water. Six empirical chlorophyll algorithms [i.e. Aiken-C, POLDER-C, OCTS-C, Morel-3, Ocean Chlorophyll-2 (OC2) and Ocean Chlorophyll-4 (OC4)] were selected for analysis on the Arabian Sea data set. Numerous statistical and graphical criterions were used to evaluate the performance of these algorithms. Among these six chlorophyll algorithms two chlorophyll algorithms (i.e. OC2 and OC4) performed well in the case I waters of the Arabian Sea. The OC2 algorithm, a modified cubic polynomial function which uses ratio of R_rs490 nm and R_rs555 nm (where, R_rs is remote sensing reflectance), performed well with r²=0.85; rms =0.15. The OC4 algorithm, a four-band (443, 490, 510, 555 nm), maximum band ratio formulation was found best on the basis of statistical analysis results with r²=0.85 and rms=0.14. Both OC2 and OC4 algorithms failed to estimate chlorophyll inTrichodesmium dominated waters. The OC2 algorithm was preferred over OC4 algorithm for routine processing of the OCM data to generate chlorophyll-a images, as it uses a band ratio of 490/555 nm and atmospheric correction is more accurate in 490 nm compared to 443 nm band, which is used by OC4 algorithm.     

Remote detection of bare soil moisture using a surface-temperature-based soil evaporation transfer coefficient

An approach for estimating soil moisture is presented and tested by using surface-temperature-based soil evaporation transfer coefficient (h_a), a coefficient recently proposed through the equation h_a = (T_s − T_a)/(T_sd − T_a), where T_s, T_sd, and T_a are land surface temperature (LST), reference soil (dry soil without evaporation) surface temperature, and air temperature respectively. Our analysis and controllable experiment indicated that h_a closely related to soil moisture, and therefore, a relationship between field soil moisture and h_a could be developed for soil moisture estimation. Field experiments were carried out to test the relationship between h_a and soil moisture. Time series Aqua-MODIS images were acquired between 11 Sep. 2006 and 1 Nov. 2007. Then, MODIS derived h_a and simultaneous measured soil moisture for different soil depths were used to establish the relations between the two variables. Results showed that there was a logarithmic relationship between soil moisture and h_a (P < 0.01). These logarithmic models were further validated by introducing another ground-truth data gathered from 46 meteorological stations in Hebei Province. Good agreement was observed between the measured and estimated soil moisture with RMSE of 0.0374 cm³/cm³ and 0.0503 cm³/cm³ for surface energy balance method at two soil depths (10 cm and 20 cm), with RMSE of 0.0467 cm³/cm³ and 0.0581 cm³/cm³ for maximum temperature method at two soil depths. For vegetated surfaces, the ratio of h_a and NDVI suggested to be considered. The proposed approach has a great potential for soil moisture and drought evaluation by remote sensing.