Prediction of UT1–UTC,LOD and AAM χ<Subscript>3</Subscript> by combination of least-squares and multivariate stochastic methods

This article presents the application of a multivariate prediction technique for predicting universal time (UT1–UTC), length of day (LOD) and the axial component of atmospheric angular momentum (AAM χ ₃). The multivariate predictions of LOD and UT1–UTC are generated by means of the combination of (1) least-squares (LS) extrapolation of models for annual, semiannual, 18.6-year, 9.3-year oscillations and for the linear trend, and (2) multivariate autoregressive (MAR) stochastic prediction of LS residuals (LS + MAR). The MAR technique enables the use of the AAM χ ₃ time-series as the explanatory variable for the computation of LOD or UT1–UTC predictions. In order to evaluate the performance of this approach, two other prediction schemes are also applied: (1) LS extrapolation, (2) combination of LS extrapolation and univariate autoregressive (AR) prediction of LS residuals (LS + AR). The multivariate predictions of AAM χ ₃ data, however, are computed as a combination of the extrapolation of the LS model for annual and semiannual oscillations and the LS + MAR. The AAM χ ₃ predictions are also compared with LS extrapolation and LS + AR prediction. It is shown that the predictions of LOD and UT1–UTC based on LS + MAR taking into account the axial component of AAM are more accurate than the predictions of LOD and UT1–UTC based on LS extrapolation or on LS + AR. In particular, the UT1–UTC predictions based on LS + MAR during El Niño/La Niña events exhibit considerably smaller prediction errors than those calculated by means of LS or LS + AR. The AAM χ ₃ time-series is predicted using LS + MAR with higher accuracy than applying LS extrapolation itself in the case of medium-term predictions (up to 100 days in the future). However, the predictions of AAM χ ₃ reveal the best accuracy for LS + AR.     

An a priori solar radiation pressure model for the QZSS Michibiki satellite

It has been noted that the satellite laser ranging (SLR) residuals of the Quasi-Zenith Satellite System (QZSS) Michibiki satellite orbits show very marked dependence on the elevation angle of the Sun above the orbital plane (i.e., the \(\beta \) angle). It is well recognized that the systematic error is caused by mismodeling of the solar radiation pressure (SRP). Although the error can be reduced by the updated ECOM SRP model, the orbit error is still very large when the satellite switches to orbit-normal (ON) orientation. In this study, an a priori SRP model was established for the QZSS Michibiki satellite to enhance the ECOM model. This model is expressed in ECOM’s D, Y, and B axes (DYB) using seven parameters for the yaw-steering (YS) mode, and additional three parameters are used to compensate the remaining modeling deficiencies, particularly the perturbations in the Y axis, based on a redefined DYB for the ON mode. With the proposed a priori model, QZSS Michibiki’s precise orbits over 21 months were determined. SLR validation indicated that the systematic \(\beta \)-angle-dependent error was reduced when the satellite was in the YS mode, and better than an 8-cm root mean square (RMS) was achieved. More importantly, the orbit quality was also improved significantly when the satellite was in the ON mode. Relative to ECOM and adjustable box-wing model, the proposed SRP model showed the best performance in the ON mode, and the RMS of the SLR residuals was better than 15 cm, which was a two times improvement over the ECOM without a priori model used, but was still two times worse than the YS mode.     

Retrieval of CDOM and DOC Using In Situ Hyperspectral Data: A Case Study for Potable Waters in Northeast China

Chromophoric dissolved organic matter (CDOM), the light absorbing fraction of dissolved organic carbon (DOC), together with phytoplankton and total suspended matter are the main optically active components could be retrieved by remote sensing data. Generally, different composition of DOC and CDOM corresponds to different water surface reflectance. Absorption properties of CDOM and retrieval models for CDOM and DOC were investigated with data from potable reservoirs located in the central of Jilin Province. Water sampling field surveys were conducted on 15, 16 and 19 of September 2012 across the Shitoukoumen, Erlonghu and Xilicheng reservoirs, respectively. Both empirical regression (single band model and band ratio model) and partial least squares coupled with back-propagation artificial neural models (PLSBPNN) were established to estimate CDOM absorption coefficient at 355 nm [a_CDOM(355)] and DOC concentration with in situ measured remote sensing reflectance. It was found that the band ratio models and PLSBPNN model performed well for estimating DOC concentration while the band ratio models yielded the best result in retrieval CDOM. Moreover, all the three models performed better on the DOC concentration estimation than the performance on a_CDOM(355). Band ratio models outperformed (R ² ?=?0.55) other models for estimating CDOM absorption coefficient, while PLSBPNN model outperformed other models with respect to DOC estimation (R ² ?=?0.93). High spectral slope values indicated that CDOM in the potable waters primarily comprised low molecular weight organic substances; while sources of DOC were mainly coming from exogenous input, which was the main reason lead to the difference of model performances on DOC and a_CDOM(355) estimation. The algorithms developed in this study is needed to be tested and refined with more in situ spectral data, also future work is still needed to be undertaken for characterizing the dynamic of the potable water quality with remotely sensed imagery.     

Improved method for estimating the inter-frequency satellite clock bias of triple-frequency GPS

Considering the contribution of the hardware biases to the estimated clock errors, an improved method for estimating the satellite inter-frequency clock bias (IFCB) is presented, i.e., the difference in the satellite clock error as computed from ionospheric-free pseudorange and carrier phase observations using L1/L2 and P1/P2 versus L1/L5 and P1/P5. The IFCB is composed of a constant and a variable part. The constant part is the inter-frequency hardware bias (IFHB). It contains the satellite and receiver hardware delays and can be expressed as a function of the DCBs [DCB (P1 ? P2) and DCB (P1 ? P5)]. When a reference satellite is selected, the satellite IFHB can be computed but is biased by a reference satellite IFHB. This bias will not affect the utilization of IFCB in positioning since it can be absorbed by the receiver clock error. Triple-frequency observations of 30 IGS stations between June 1, 2013, and May 31, 2014, were processed to show the variations of the IFHB. The IFHB values show a long-term variation with time. When a linear and a fourth-order harmonic function are used to model the estimated IFCB, which contains contributions of the hardware delays and clock errors, the results show that 89 % of the IFCB can be corrected given the current five triple-frequency GPS satellites with the averaged fitting RMS of 1.35 cm. Five days of data are processed to test the estimated satellite clock errors using the strategy presented. The residuals of P1/P5 and L1/L5 have a STD of <0.27 m and 0.97 cm, respectively. In addition, most predicted satellite IFCBs reach an accuracy of centimeter level and its mean accuracy of 5 days is better than 7 cm.     

Volume Height Estimation based on Fusion of Discrete Fourier Transform (DFT) and Least Square (LS) in a Tomographic SAR Application

Tomographic-SAR (Synthetic Aperture Radar) is a 3D Radar imaging technique, based on spectral estimation tools. This technique is used to estimate the distribution of the backscattering signal in the elevation axis, for each azimuth-range resolution cell of the SAR image. Spectral estimation algorithms belong to two families, non parametric estimation algorithms which include DFT (Discrete Fourier Transform), SVD (Single Value Decomposition), MUSIC (Multiple Signal Classification), CAPON and parametric estimation algorithms such as LS (Least Square) and ESPRIT (Estimation of signal parameters via rotation invariance techniques). In this paper we present an inversion algorithm based on the fusion of DFT and LS for the estimation of the reflectivity signal along the elevation axis. With an appropriate combination of these two algorithms and a realistic modeling of the signal distribution, we obtain a high resolution estimate of the reflectivity signal with medium computational effort. The inversion algorithm is tested on a forested area (Västerbotten in northern Sweden), with multibaseline data set acquired in L-band (BioSAR-2008 project). Results are promising with the proposed algorithm. We used MUSIC and RVoG (Random Volume over Ground) inversions for comparison and LIDAR (Laser Imaging Detection And Ranging) image as datasets for validation of the results.     

Adaptive Kalman filtering based on optimal autoregressive predictive model

Conventional Kalman filter (KF) relies heavily on a priori knowledge of the potentially unstable process and measurement noise statistics. Insufficiently known a priori filter statistics will reduce the precision of the estimated states or introduce biases to the estimates. We propose an adaptive KF based on the autoregressive (AR) predictive model for vehicle navigation. First, the AR model is incorporated into the KF for state estimation. The closed-form solution of the AR model coefficients is obtained by solving a convex quadratic programming problem, which is according to the criterion of minimizing the mean-square error, and subject to the polynomial constraint of vehicle motion. Then, an innovation-based adaptive approach is improved based on the KF with the AR predictive model. In the proposed adaptive algorithm, the process noise covariance is computed using the real-time information of the innovation sequence. Simulation results demonstrate that the KF with the AR model has a higher estimated precision than the KF with the traditional discrete-time differential model under the condition of the same parameter setting. Field tests show that the positioning accuracy of the proposed adaptive algorithm is superior to the conventional adaptive KF.     

Analyses of Metro Station Service Area in Shanghai Downtown Based on Traffic Networks

Service area research is one of the pivotal topics in Urban Geography. This article first put forward a model of urban population estimation. And on the basis we measured the size and distribution of population in downtown Shanghai, China. The population model was confirmed well by the traditional survey model. Then we extracted a 1-month actual-time data set contains geo-location by collecting in Sina Weibo data, and generated Voronoi diagram by these data which denoted the service patches. We assigned population to each patch. Second part, we proposed a shortest distance algorithm, a minimum time algorithm and an improved p-median algorithm, took advantages of these three methods to divide the service area of metro stations based on patches. Subsequently, we computed the service population in each service area. Last, we took metro line 1 and 2 as examples to research the relationship among 3 location-allocation methods in detail. The results showed that: The spatial distribution of population of the core city in Shanghai emerged a descending trend from center to periphery clearly. All indicators (including area, population, distance and time) in central city within inner ring road have changed little compared with the region between inner and outer ring road. Yet the improved p-median algorithm has a certain effect of optimization. It presented a scientific and rational travel scheme for citizens cost smallest price to select better starting metro station. The study results should contribute to theoretical and technical support for location-allocation of public service facilities.     

A Directed-Line-Segment-Based Photogrammetry Method to Survey the Tunneling Working Surface of Mine Roadway

Absolute orientation is a basic technical work in digital image geologic logging of underground coal mine. Traditional control-point-based absolute orientation method requires setting object space control points of the known three-dimensional coordinates, which may lead to low efficiency. Therefore, this paper proposed a point-free close-range photogrammetry absolute orientation algorithm, which utilized direction line segments including plumb line segments and line segments with known directions and lengths to identify the dimensional orientation of a stereoscopic model. Experiment results show that the precision of the orientation results is favorable. σ _X and σ _Y are as high as 0.5 mm, and σ _Z is 0.3 mm. Finally, this paper introduced the application of the proposed algorithm in rapid geological logging of coal mine roadway, which was fast and reliable, convenient and feasible.     

<Emphasis Type="Italic">C</Emphasis>/<Emphasis Type="Italic">N</Emphasis><Subscript>0</Subscript> estimators for high-sensitivity snapshot GNSS receivers

We address the problem of estimating the carrier-to-noise ratio (C/N₀) in weak signal conditions. There are several environments, such as forested areas, indoor buildings and urban canyons, where high-sensitivity global navigation satellite system (HS-GNSS) receivers are expected to work under these reception conditions. The acquisition of weak signals from the satellites requires the use of post-detection integration (PDI) techniques to accumulate enough energy to detect them. However, due to the attenuation suffered by these signals, estimating their C/N₀ becomes a challenge. Measurements of C/N₀ are important in many applications of HS-GNSS receivers such as the determination of a detection threshold or the mitigation of near-far problems. For this reason, different techniques have been proposed in the literature to estimate the C/N₀, but they only work properly in the high C/N₀ region where the coherent integration is enough to acquire the satellites. We derive four C/N₀ estimators that are specially designed for HS-GNSS snapshot receivers and only use the output of a PDI technique to perform the estimation. We consider four PDI techniques, namely non-coherent PDI, non-quadratic non-coherent PDI, differential PDI and truncated generalized PDI and we obtain the corresponding C/N₀ estimator for each of them. Our performance analysis shows a significant advantage of the proposed estimators with respect to other C/N₀ estimators available in the literature in terms of estimation accuracy and computational resources.     

GOCE gravitational gradiometry

GOCE is the first gravitational gradiometry satellite mission. Gravitational gradiometry is the measurement of the second derivatives of the gravitational potential. The nine derivatives form a 3 × 3 matrix, which in geodesy is referred to as Marussi tensor. From the basic properties of the gravitational field, it follows that the matrix is symmetric and trace free. The latter property corresponds to Laplace equation, which gives the theoretical foundation of its representation in terms of spherical harmonic or Fourier series. At the same time, it provides the most powerful quality check of the actual measured gradients. GOCE gradiometry is based on the principle of differential accelerometry. As the satellite carries out a rotational motion in space, the accelerometer differences contain angular effects that must be removed. The GOCE gradiometer provides the components V _xx , V _yy , V _zz and V _xz with high precision, while the components V _xy and V _yz are of low precision, all expressed in the gradiometer reference frame. The best performance is achieved inside the measurement band from 5 × 10^–3 to 0.1 Hz. At lower frequencies, the noise increases with 1/f and is superimposed by cyclic distortions, which are modulated from the orbit and attitude motion into the gradient measurements. Global maps with the individual components show typical patterns related to topographic and tectonic features. The maps are separated into those for ascending and those for descending tracks as the components are expressed in the instrument frame. All results are derived from the measurements of the period from November to December 2009. While the components V _xx and V _yy reach a noise level of about \({10\;\rm{\frac{mE}{\sqrt{Hz}}}}\), that of V _zz and V _xz is about \({20\; \rm{\frac{mE}{\sqrt{Hz}}}}\). The cause of the latter’s higher noise is not yet understood. This is also the reason why the deviation from the Laplace condition is at the \({20 \;\rm{\frac{mE}{\sqrt{Hz}}}}\) level instead of the originally planned \({11\;\rm{\frac{mE}{\sqrt{Hz}}}}\). Each additional measurement cycle will improve the accuracy and to a smaller extent also the resolution of the spherical harmonic coefficients derived from the measured gradients.     

Selection criteria for linear regression models to estimate individual tree biomasses in the Atlantic Rain Forest,Brazil

Background

Biomass models are useful for several purposes, especially for quantifying carbon stocks and dynamics in forests. Selecting appropriate equations from a fitted model is a process which can involves several criteria, some widely used and others used to a lesser extent. This study analyzes six selection criteria for models fitted to six sets of individual biomass collected from woody indigenous species of the Tropical Atlantic Rain Forest in Brazil. Six models were examined and the respective fitted equations evaluated by the residual sum of squares, adjusted coefficient of determination, absolute and relative estimates of the standard error of estimate, and Akaike and Schwartz (Bayesian) information criteria. The aim of this study was to analyze the numeric behavior of these model selection criteria and discuss the ease of interpretation of them. The importance of residual analysis in model selection is stressed.

Results

The adjusted coefficient of determination (\( R^{2}_{adj.} \)) and the standard error of estimate in percentage (Syx%) are relative model selection criteria and are not affected by sample size and scale of the response variable. The sum of squared residuals (SSR), the absolute standard error of estimate (Syx), the Akaike information criterion and the Schwartz information criterion, in turn, depend on these quantities. The best fit model was always the same within a given data set regardless the model selection criteria considered (except for SSR in two cases), indicating they tend to converge to a common result. However, such criteria are not always closely related across different data sets. General model selection criteria are indicative of the average goodness of fit, but do not capture bias and outlier effects. Graphical residual analysis is a useful tool to this detection and must always be used in model selection.

Conclusions

It is concluded that the criteria for model selection tend to lead to a common result, regardless their mathematical formulation and statistical significance. Relative measures of goodness of fitting are easier to interpret than the absolute ones. Careful graphical residual analysis must always be used to confirm the performance of the models.

     

Modeling SAR Images by Using a Pathway Model

Statistical modeling of SAR images is one of the important problems in SAR image interpretation. Several types of theoretical models have been used for modeling various SAR data, most of them being highly effective for some particular land-cover typologies. The K-distribution has been used as a flexible tool for the modeling of SAR data over the non-homogeneous areas. In this paper, we examine a q-analogue of the gamma model. The new parameter q makes the density function very flexible for modeling purposes. This q-analogue also produces an extension of the K-distribution. The performance of the extended K model is tested on high-resolution X-band SAR image and compared the result with respect to the K-distribution.     

顾及有色噪声的光纤陀螺随机噪声自适应滤波方法

作为光纤陀螺误差的重要组成部分,随机噪声严重影响着光纤陀螺的精度,对光纤陀螺随机噪声进行准确建模和补偿是提升陀螺精度的有效方式。本文针对光纤陀螺随机噪声的复杂性,难以对其进行精确分析,ARIMA （auto-regressive moving average）模型Kalman滤波中有色噪声不能使用状态扩充法建模的问题,扩展了Harvey方程,实现有色噪声白化。同时,考虑先验噪声的不确定性以及模型参数在线更新导致的参数与状态噪声相互耦合,分析了动态Allan方差估计量测噪声的不足,使用VBAKF （variational Bayesian adaptive Kalman filter）实时修正滤波状态噪声与量测噪声。试验表明,Harvey法较传统滤波建模方式,随机噪声序列方差降低40%,Harvey法结合VBAKF使序列方差降低了54%;VBAKF较动态Allan方差,可以更好地估计量测噪声。结果表明,此方法可有效抑制随机噪声Kalman滤波中有色噪声和随机模型不准确的影响,提高随机误差补偿精度。     

A low-complexity algorithm for fast acquisition of weak DSSS signal in high dynamic environment

The initial acquisition of direct-sequence spread-spectrum (DSSS) signal transmitted in bursts by ground terminals at satellite-borne receiver poses an engineering challenge. We propose a low-complexity acquisition algorithm that is capable of capturing extremely weak DSSS signal in the presence of large Doppler dynamics. The algorithm uses fast Fourier transform (FFT)-based frequency-domain techniques to implement circular correlations between the received signal and the local pseudo-random noise (PRN) code, and it coherently accumulates the correlation results across multiple PRN code periods, to achieve a sufficient signal–noise ratio for reliable acquisition. We invoke another FFT procedure to perform the coherent accumulation and the fine compensation for the residual Doppler frequency offset. To highlight the advantage of the proposed algorithm, we make a complexity comparison among the proposed algorithm and two other benchmark strategies, namely the modified double block zero padding (MDBZP) and two-dimensional exhaustive search (2D-ES). It is shown that the proposed algorithm has the lowest complexity, which is particularly desirable for satellite-borne receivers where the computational resource is limited. The acquisition performance of the proposed algorithm is verified by theoretical analysis and Monte Carlo simulations and compared with that of MDBZP and 2D-ES. Moreover, we have carried out extensive tests on a hardware verification system, and we show the claimed tradeoff between performance and cost is indeed attainable with the suggested algorithm. Numerically, it is found the proposed algorithm can achieve a detection rate of 0.9 and a false alarm rate of \(10^{ - 5}\) at C/N ₀ = 29.5 dBHz over a Doppler frequency offset range of \(\left[ { - 7.5\,{\text{kHz}},7.5\,{\text{kHz}}} \right]\) in floating-point simulation, which coincides with the analytical results. The same performance is achieved at C/N ₀ = 31 dBHz in fixed-point simulation and at C/N ₀ = 31.5 dBHz on a hardware system.     

顾及有色噪声的卡尔曼滤波在多路径误差削弱中的应用

多路径误差为一时空环境效应,难以构建准确数学模型消除其影响,且该误差在基线两端不具有空间相关性,运用现有差分技术也无法很好消除,是高精度短基线测量中主要误差之一.为进一步削弱多路径误差,本文以监测站坐标时间序列中多路径误差为研究对象,根据多路径误差在历元间的时变特性,建立多路径误差状态空间模型,采用标准卡尔曼滤波和顾及有色噪声的卡尔曼滤波从监测站第一天双差固定解坐标残差序列中估计多路径误差改正序列,并根据多路径误差的周日重复特性,利用第一天得到的多路径误差改正序列对之后各天坐标序列进行改正.最后通过实验分析,得出顾及有色噪声的卡尔曼滤波估计方法优于标准卡尔曼滤波的结论.研究方法对提高GNSS定位精度具有重要实用价值.     

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     

Global morphology of ionospheric sporadic E layer from the FormoSat-3/COSMIC GPS radio occultation experiment

Ionospheric sporadic-E (Es) activity and global morphology were studied using the 50 Hz signal-to-noise ratio amplitude and excess phase measurements from the FormoSat-3/Constellation Observing System for Meteorology, Ionosphere and Climate (FS3/COSMIC) GPS radio occultation (RO) observations. The results are presented for data collected during the last sunspot cycle from mid-2006 to the end of 2017. The FS3/COSMIC generally performed more than 1000 complete E-region GPS RO observations per day, which were used to retrieve normalized L1-band amplitude standard deviation (SDL1) and relative electron density (N_e) profiles successfully. More or less 31% of those observations were identified as Es events based on SDL1 and peak SDL1 altitude criteria. We found that the peak Es-event i values are approximately proportional to the logarithms of the corresponding peak N_e differences. Five major geographical zones were identified, in which the seasonal and diurnal Es occurrence patterns are markedly different. These five zones include the geomagnetic equatorial zone (??5°?<?magnetic latitude (ML)?<?5°), two extended geomagnetic mid-latitude zones (15°?<?ML?<?55°, and ??55°?<?ML < ??15°), and two auroral zones (70°?<?ML, and ML < ??70°). The Es climatology, namely its variations with each identified zone, altitude, season, and local time has been documented.     

Cycle slip detection and repair for undifferenced GPS observations under high ionospheric activity

We develop a new approach for cycle slip detection and repair under high ionospheric activity using undifferenced dual-frequency GPS carrier phase observations. A forward and backward moving window averaging (FBMWA) algorithm and a second-order, time-difference phase ionospheric residual (STPIR) algorithm are integrated to jointly detect and repair cycle slips. The FBMWA algorithm is proposed to detect cycle slips from the widelane ambiguity of Melbourne–Wübbena linear combination observable. The FBMWA algorithm has the advantage of reducing the noise level of widelane ambiguities, even if the GPS data are observed under rapid ionospheric variations. Thus, the detection of slips of one cycle becomes possible. The STPIR algorithm can better remove the trend component of ionospheric variations compared to the normally used first-order, time-difference phase ionospheric residual method. The combination of STPIR and FBMWA algorithms can uniquely determine the cycle slips at both GPS L ₁ and L ₂ frequencies. The proposed approach has been tested using data collected under different levels of ionospheric activities with simulated cycle slips. The results indicate that this approach is effective even under active ionospheric conditions.     

Multi-Index Soil Moisture Estimation from Satellite Earth Observations: Comparative Evaluation of the Topographic Wetness Index (TWI), the Temperature Vegetation Dryness Index (TVDI) and the Improved TVDI (iTVDI)

Soil moisture estimation from satellite earth observation has emerged effectively advantageous due to the high temporal resolution, spatial resolution, coverage, and processing convenience it affords. In this paper, we present a study carried out to estimate soil moisture level at every location within Enugu State Nigeria from satellite earth observation. Comparative analysis of multiple indices for soil moisture estimation was carried out with a view to evaluating the robustness, correlation, appropriateness and accuracy of the indices in estimating the spatial distribution of soil moisture level in Enugu State. Results were correlated and validated with In-Situ soil moisture observations from multi-sample points. To achieve this, the Topographic Wetness Index (TWI), based on digital elevation data, the Temperature Vegetation Dryness Index (TVDI) and an improved TVDI (iTVDI) incorporating air temperature and a Digital Elevation Model (DEM) were calculated from ASTER global DEM and Landsat images. Possible dependencies of the indices on land cover type, topography, and precipitation were explored. In-Situ soil moisture data were used to validate the derived indices. The results showed that there was a positive significant relationship between iTVDI versus TVDI (R = 0.53, P value < 0.05), while in iTVDI versus TWI (R = 0.00, P value > 0.05) and TVDI versus TWI (R = ?0.01, P value > 0.05) no significant relationship existed. There was a strong relationship between iTVDI and topography, land cover type, and precipitation than other indices (TVDI, TWI). In situ measured soil moisture values showed negative significant relationship with TVDI (R = ?0.52, P value < 0.05) and iTVDI (R = ?0.63, P value < 0.05) but not with TWI (R = ?0.10, P value > 0.05). The iTVDI outperformed the other two index; having a stronger relationship with topography, precipitation, land cover classes and soil moisture. It concludes that although iTVDI outperformed other indices (TVDI, TWI) in soil moisture estimation, the decision of which index to apply is dependent on available data, the intent of usage and spatial scale.     

Estimation of Rice Yield with a Process-Based Model and Remote Sensing Data in the Middle and Lower Reaches of Yangtze River of China

Accurate simulation of rice yield is very important and vital for agriculture and food security. This study has analyzed the applicability of the RS-P-YEC (the remote-sensing-photosynthesis-yield estimation for crops) model for the rice yield simulation of the Middle and Lower Reaches of Yangtze River (MLRYR) in China. The simulated rice yield was compared with the actual statistical dataset, so as to obtain the accuracy of the model results. The results showed that the correlation coefficients (R) between simulated rice yield and statistical data is 0.708 (P < 0.01), the average relative errors were 9, 6.5, 7.2 %, and the root mean square errors were 777.5, 606.4, 693.4 kg/ha in 2007, 2008 and 2009, respectively. It indicated that the RS-P-YEC model can be used to estimate rice yield in the MLRYR region of China.