Description: Complexity of the temporal sediment variability within the Gulf of Kachchh is resolved into major modes of variations using empirical orthogonal function (EOF) analysis on sequential OCM derived suspended sediment concentration (SSC) images during spring intermonsoon period of 2011. Variance accounted collectively by the first four modes is around 80% and the principal component (PC) of each mode is correlated with different hydro-meteorological forces influencing the hydrodynamics of the gulf. Hydro-meteorological forces are classified in the order of precedence of influencing the sediment dynamics of the region. PC1 contributing 50% of the total variance is correlated with average of the simulated current velocities between consecutive images, indicating the variation of tides from neap-spring ranges ( ${rm{r}^{2}} = 0.86$ ). The ocean state parameters like tidal height, wind speed, and current speed at the time of OCM pass contribute to around 20%, 6%, and 4%, respectively, to the total variability of the temporal dynamics of SSC. The interchange of tides from neap and spring is the major contributor toward the temporal variability of sediment concentration within the gulf, where the sediments are subjected to deposition and resuspension. The gulf being highly tide dominated, the suspended sediment variability also reverberates with the variability of tidal characteristics and subtly with the wind conditions.

Description: This study assesses the successive Version-6 and Version-7 TRMM Multisatellite Precipitation Analysis (TMPA) products including near-real-time products (3B42RTV6 and 3B42RTV7) and post-real-time research products (3B42V6 and 3B42V7) from March 2002 to February 2008 over Ganjiang Basin in the humid southeastern China, located in the lower reach of Yangtze River. Direct comparison of TMPA rainfall estimates with ground observation shows that the spatial and temporal rainfall characteristics over this region are well captured by 3B42V6, 3B42RTV7, and 3B42V7. In terms of daily grid-based comparison, 3B42RTV7 has been improved over 3B42RTV6 by reducing relative bias (RB) from –30.25% to 4.93%; 3B42V6, 3B42RTV7, and 3B42V7 show close performance with each other with RB less than 5% and moderate correlative coefficient (CC, 0.59). Daily hydrologic simulation with Xin’anjiang hydrologic model using these TMPA products as input shows that: 1) 3B42V6 and 3B42V7 demonstrate very comparable hydrologic skills, which are close to those of the reference rainfall with high Nash-Sutcliffe index (NSCE, 0.71 and 0.72, respectively) and strong correlation (CC = 0.88); and 2) 3B42RTV7 displays a better hydrologic performance than 3B42RTV6 by increasing the NSCE from 0.56 to 0.59, improving CC from 0.81 to 0.87, and reducing RB from –33.15% to 20.93%. This improvement of real-time TMPA product shows its potential hydrologic utility in water resource management and flood forecast. Finally, this study provides useful reference for TMPA developers and insights for the end users in their applications.

Description: We address the problem of detecting a stealth aircraft flying far away from an observer with limited visibility conditions using their multispectral signature. In such environment, the aircraft is a very low-contrast target, i.e., the target spectral signature may have a similar magnitude to the background clutter. Therefore, methods accounting only for the spectral features of the target, while leaving aside its spatial pattern, may either lead to poor detection statistics or high false alarm rate. We propose a new detection method which accounts for both spectral and spatial dispersions, by inferring level sets of the Mahalanobis transform of the multispectral image. This combines the approach of the well-known Reed Xiaoli (RX) detector with some elements of the level set methods for shapes analysis. This algorithm is in turn used to specify the wavelength bands which maximize an aircraft detection probability, for a given false alarm rate. This methodology is illustrated in a typical scenario, consisting of a daylight air-to-ground full-frontal attack by a generic combat aircraft flying at low altitude, over a database of 30 000 simulated multispectral infrared signature (IRS). The results emphasize that, in the context of aircraft detection, there is great interest in using multispectral IRS rather than integrated IRS, as long as the IR bands are well chosen.

Description: A fast and efficient estimation of crop biophysical parameters is significantly important in many agricultural, ecological, and meteorological applications. This study investigated the potential of airborne LiDAR and satellite GF-1 data for estimating three biophysical parameters of maize: 1) leaf area index (LAI); 2) average canopy height $left({{{bf H}_{{bf canopy}}}} right)$ ; and 3) aboveground biomass (AGB) during the peak growing season. First, classification data of maize was produced using normalized surface height, GF-1 NDVI, and terrain slope through decision-making. Second, four representative remotely sensed (RS) metrics which have been widely used in forest studies were tested to develop multiplicative models with similar shapes for estimating each biophysical parameter of maize, respectively. Third, the estimation results were obtained and validated through leave-one-out cross-validation method yielding a root-mean-square error (rmse) of 0.37 for LAI, 0.17 m for ${{bf H}_{{bf canopy}}}$ , and ${0}.{49},{bf kg}/{{bf m}^{2}}$ for AGB. Finally, contributions to the estimation models from each RS metric were analyzed, and spatial patterns of the biophysical parameters across the entire study area were mapped. Based on these results, the following conclusions were drawn. 1) The four selected metrics from airborne LiDAR and satellite GF-1 data are also applicable and promising in estimating biophysical parameters of maize during the peak growing season. 2) Multiplicative model was proved to be a fast, simple but effective alternative by combining LiDAR-derived structure information and spectral content from GF-1 NDVI. These conclusions provide valuable information for estimation of biophysical parame- ers of maize during the peak growing season.

Description: The squinted imaging mode has been successfully applied to synthetic aperture radar (SAR) mounted on moving platforms of high speed. The developed focusing algorithms for squint SAR data usually perform the linear range walk correction (LRWC) in the azimuth time domain as the first step, to mitigate the bulk range-azimuth coupling. Unfortunately, when applied on high-speed platforms with large squint angle and large azimuth scene length, these algorithms for airborne SAR considering only the first-order azimuth dependence of the Doppler frequency-modulation (FM) rate will introduce a large residual phase error, and therefore degrade the final radar image quality. Meanwhile, those algorithms, originally designed for full-aperture data, cannot be applied directly to the small-aperture data. Due to these problems, a high-order phase correction approach (HPCA) combined with SPECAN operation for focusing high-squint SAR (HS-SAR) small-aperture data is developed in this paper to deal with the above issues. In this approach, both the first- and the second-order azimuth dependence of the Doppler FM rate are compensated for, and the same to the cubic phase term, by introducing a correction phase term in the azimuth frequency domain. The presented approach has been successfully used to focus the real airborne radar data recently.

Description: Land surface temperature (LST) is a key parameter in the processes of energy and water exchange between land and atmosphere. However, the MODIS LST products are often obscured by clouds and other atmospheric disturbances, resulting in severe data loss. Traditional interpolation methods cannot be effectively applied when there is large area of missing data. Thus, in this study, an effective LST interpolation method is developed to address this issue and is used to interpolate MODIS/Terra LST data on the Qinghai–Tibet Plateau in 2005. This method assumes that some pixels with spatially valid LSTs may follow a change trend over time similar to the null pixels, and the focus thus becomes to locate those similar pixels to interpolate each null pixel. First, LST images with a small amount of missing data, chosen as the reference images, were interpolated using a traditional interpolation method. Then, for each null pixel, other pixels with similar temporal changes of LST were identified by a similarity function. Finally, a transfer function for each null pixel was established based on those pixels most similar to it in the interpolated image and the corresponding reference image. The results were found to be much superior to those interpolated by traditional methods, such as regression Kriging, ordinary Kriging, and IDW. A specially designed experiment on an area that had ample valid LSTs confirmed that the proposed method can produce more favorable results than the other methods, and performed especially well when there was a significant lack of data.

Description: This paper addresses the subject of the regression models used for the wet troposphere path delay correction for range measurements by satellite radar altimeters. The objective of this study is twofold: 1) to find which regression method is better suited for the retrieval between a neural network algorithm and a log-linear regression and 2) to determine whether the use of the altimeter backscattering coefficient at Ka- or Ku-band can substitute for the use of the radiometer brightness temperature at 18 GHz as an input for the retrieval. Several configurations of algorithms, including those used in the operational processing of altimetry missions such as Jason-1 or Envisat, are built and compared on the same learning and test database to determine which retrieval strategy is more appropriate. The importance of each input is analyzed and the performances of the different algorithms are assessed in terms of bias and standard of the errors and also in terms of their geographical distribution and correlations with other environmental variables. Their performances are then assessed on Jason-2 radiometer measurements using the criterion of variance in sea-surface height differences at crossovers. The study shows that the neural network formalism is better suited for the retrieval of the wet tropospheric path delay than the log-linear regression. In terms of variable selection, better results were obtained when the brightness temperature at 18 GHz was used instead of the backscattering coefficient. Overall, the best results are obtained with the combination of a three-channel radiometer and a neural network algorithm.

Description: Microwave (MW) radiometers at low-frequency bands play unreplaceable roles in Earth remote sensing, especially in the areas of numerical weather prediction (NWP) and climate research. All MW radiometers have been flown, so far, at low Earth orbit (LEO), which provide relatively narrow swath with long revisit time. There are increasing demands for applying MW radiometer at geostationary Earth orbit (GEO) for the purpose of having temporally continuous and spatially large coverage. Unfortunately, the required ground spatial resolution leads to a large-sized antenna that prevents MW radiometer applications, especially at low-frequency bands. The new concept of clock scanning microwave interferometric radiometer (CS-MIR) provides an opportunity to resolve this problem. By virtue of the simple and deployable array structure, it has the potential to work at GEO and produce hourly hemispheric imagery. This paper investigates the essential theory of CS-MIR and presents some approaches about its design principles in aspects of array configuration, sampling patterns, polarization corrections, and image reconstruction. The potential GEO applications of CS-MIR in frequencies lower than 50 GHz are investigated. Finally, a conceptual design of CS-MIR for SST measurement from GEO and the associated numerical simulations of image reconstruction are presented.