Qiang Liu

Watershed Allied Telemetry Experimental Research

[1]xa0The Watershed Allied Telemetry Experimental Research (WATER) is a simultaneous airborne, satellite-borne, and ground-based remote sensing experiment aiming to improve the observability, understanding, and predictability of hydrological and related ecological processes at a catchment scale. WATER consists of the cold region, forest, and arid region hydrological experiments as well as a hydrometeorology experiment and took place in the Heihe River Basin, a typical inland river basin in the northwest of China. The field campaigns have been completed, with an intensive observation period lasting from 7 March to 12 April, from 15 May to 22 July, and from 23 August to 5 September 2008: in total, 120 days. Twenty-five airborne missions were flown. Airborne sensors including microwave radiometers at L, K, and Ka bands, imaging spectrometer, thermal imager, CCD, and lidar were used. Various satellite data were collected. Ground measurements were carried out at four scales, that is, key experimental area, foci experimental area, experiment site, and elementary sampling plot, using ground-based remote sensing instruments, densified network of automatic meteorological stations, flux towers, and hydrological stations. On the basis of these measurements, the remote sensing retrieval models and algorithms of water cycle variables are to be developed or improved, and a catchment-scale land/hydrological data assimilation system is being developed. This paper reviews the background, scientific objectives, experiment design, filed campaign implementation, and current status of WATER. The analysis of the data will continue over the next 2 years, and limited revisits to the field are anticipated.

A novel approach for edge detection based on the theory of universal gravity

This paper presents a new, simple and effective low-level processing edge detection algorithm based on the law of universal gravity. The algorithm assumes that each image pixel is a celestial body with a mass represented by its grayscale intensity. Accordingly, each celestial body exerts forces onto its neighboring pixels and in return receives forces from the neighboring pixels. These forces can be calculated by the law of universal gravity. The vector sums of all gravitational forces along, respectively, the horizontal and the vertical directions are used to compute the magnitude and the direction of signal variations. Edges are characterized by high magnitude of gravitational forces along a particular direction and can therefore be detected. The proposed algorithm was tested and compared with conventional methods such as Sobel, LOG, and Canny using several standard images, with and without the contamination of Gaussian white noise and salt & pepper noise. Results show that the proposed edge detector is more robust under noisy conditions. Furthermore, the edge detector can be tuned to work at any desired scale.

Progresses on microwave remote sensing of land surface parameters

Highly accurate observations at various scales on the land surface are urgently needed for the studies of many areas, such as hydrology, meteorology, and agriculture. With the rapid development of remote sensing techniques, remote sensing has had the capacity of monitoring many factors of the Earth’s land surface. Especially, the space-borne microwave remote sensing systems have been widely used in the quantitative monitoring of global snow, soil moisture, and vegetation parameters with their all-weather, all-time observation capabilities and their sensitivities to the characteristics of land surface factors. Based on the electromagnetic theories and microwave radiative transfer equations, researchers have achieved great successes in the microwave remote sensing studies for different sensors in recent years. This article has systematically reviewed the progresses on five research areas including microwave theoretical modeling, microwave inversion on soil moisture, snow, vegetation and land surface temperatures. Through the further enrichment of remote sensing datasets and the development of remote sensing theories and inversion techniques, remote sensing including microwave remote sensing will play a more important role in the studies and applications of the Earth systems.

Parametrized BRDF for atmospheric and topographic correction and albedo estimation in Jiangxi rugged terrain, China

A method is presented for bi‐directional reflectance distribution function (BRDF) parametrization for topographic correction and surface reflectance estimation from Landsat Thematic Mapper (TM) over rugged terrain. Following this reflectance, albedo is calculated accurately. BRDF is parametrized using a land‐cover map and Landsat TM to build a BRDF factor to remove the variation of relative solar incident angle and relative sensor viewing angle per pixel. Based on the BRDF factor and radiative transfer model, solar direct radiance correction, sky diffuse radiance and adjacent terrain reflected radiance correction were introduced into the atmospheric‐topographic correction method. Solar direct radiance, sky diffuse radiance and adjacent terrain reflected radiance, as well as atmospheric transmittance and path radiance, are analysed in detail and calculated per pixel using a look‐up table (LUT) with a digital elevation model (DEM). The method is applied to Landsat TM imagery that covers a rugged area in Jiangxi province, China. Results show that atmospheric and topographic correction based on BRDF gives better surface reflectance compared with sole atmospheric correction and two other useful atmospheric‐topographic correction methods. Finally, surface albedo is calculated based on this topography‐corrected reflectance and shows a reasonable accuracy in albedo estimation.

Scale effect and scale correction of land-surface albedo in rugged terrain

Influence of topography must be corrected when fine-scale remote sensing data are used to estimate surface albedo in rugged terrain. In the case of coarse-scale satellite remote sensing data, the topographic effect on albedo estimation is generally ignored because the influence to albedo estimation of overall slope in coarse-scale pixels is usually considered negligible; however, because of the scale effect in albedo, neglecting within-pixel topology may cause errors in albedo estimation from coarse-scale data. This paper investigates the scale effect on land-surface albedo estimations, particularly in the case of converting fine-scale albedo to coarse-scale albedo in rugged terrain. Starting from the definition of difference-scale albedo in rugged terrain, a method is presented to convert fine-scale surface albedo to coarse-scale albedo, then deriving a factor to correct coarse-scale land-surface albedo. The performance and accuracy analysis of the method are investigated by using a simulated digital elevation model (DEM) with different mean slopes, as well as real DEM and Thematic Mapper images. Results show that the method is effective for scale-effect correction of land-surface albedo in rugged terrain.

Separating vegetation and soil temperature using airborne multiangular remote sensing image data

Abstract Land surface temperature (LST) is a key parameter in land process research. Many research efforts have been devoted to increase the accuracy of LST retrieval from remote sensing. However, because natural land surface is non-isothermal, component temperature is also required in applications such as evapo-transpiration (ET) modeling. This paper proposes a new algorithm to separately retrieve vegetation temperature and soil background temperature from multiangular thermal infrared (TIR) remote sensing data. The algorithm is based on the localized correlation between the visible/near-infrared (VNIR) bands and the TIR band. This method was tested on the airborne image data acquired during the Watershed Allied Telemetry Experimental Research (WATER) campaign. Preliminary validation indicates that the remote sensing-retrieved results can reflect the spatial and temporal trend of component temperatures. The accuracy is within three degrees while the difference between vegetation and soil temperature can be as large as twenty degrees.

Modeling Directional Brightness Temperature of the Winter Wheat Canopy at the Ear Stage

The ear is the top layer of mature wheat and has very different geometric and thermal characteristics from that of leaves. Compared to the directional brightness temperature (DBT) of wheat canopy without ears, the DBT at the ear stage has specific features, and the ear effects could not be explained by previous models. This paper proposes a hybrid geometric optical and radiative transfer model to reveal the combined influences of the geometric structure of ears and leaf; the temperature distribution of ear, leaf, and soil; and the Sun-target-sensor geometry on the canopy DBT. The soil, leaf, and ear layers are taken into account in the model so it is named as the Soil Leaf Ear Combined (SLEC) DBT model. We compare the model prediction with the field measurement data. The results show that the new SLEC DBT model can simulate the DBT of wheat at the ear stage with an accuracy of 0.78 K.

Estimation of snow depth and snow water equivalent distribution using airborne microwave radiometry in the Binggou Watershed, the upper reaches of the Heihe River basin

Abstract We estimated the spatial distribution of snow depth/snow water equivalent (SD/SWE) in a mountainous watershed (Binggou, which is in the upper reaches of the Heihe River basin) by an airborne microwave radiometry observational experiment. Two microwave radiometers measuring at K band (18.7xa0GHz) and Ka band (36xa0GHz) were used to estimate the volume scatter from snowpacks and infer SD and SWE. Simultaneously, the snow physical properties (such as snow depth, density, grain size and temperature) over four sites were measured, and a simple multi-layer sample scheme was adopted to obtain the stratigraphic information. The microwave emission model of layered snowpacks (MEMLS) was used to simulate the brightness temperatures of snow cover for each measurement point. By comparing TB data that were simulated by MEMLS and observed by radiometers on the aircraft over the four sites, we obtained the retrieval algorithms of SD and SWE based on brightness temperature differences (TBD) at the K- and Ka-bands that are suitable to the local snow properties. The validation shows that the mean absolute and relative errors of SD estimates are approximately 3.5xa0cm and 14.8%, respectively. SWE from airborne microwave radiometers show that blowing snow and sun radiation are two main factors that determine the spatial distribution of SWE in Binggou Watershed. The local angle of incidence of the microwave radiometer observation can be influenced by mountainous topography, and a sensitivity analysis suggests that changes in the local angle of incidence (e.g., the nominal angle of incidence) will not significantly influence the estimation of SD/SWE. The snows stratigraphic condition is not an important factor for estimating SD/SWE in this study because the snow was not very deep in the Binggou Watershed. However, the field sampling scheme should be given more attention to obtain the spatial variations of snow properties and to support pixel-by-pixel validation in next field campaign.

Multi-scale validation strategy for satellite albedo products and its uncertainty analysis

Coarse-resolution satellite albedo products are important for climate change and energy balance research because of their capability to characterize the spatiotemporal patterns of land surface parameters at both the regional and global scales. The accuracy of coarse-resolution products is usually assessed via comparison with in situ measurements. The key issue in the comparison of remote sensing observations with in situ measurements is scaling and uncertainty. This paper presents a strategy for validating 1-km-resolution remote sensing albedo products using field measurements and high-resolution remote sensing observations. Field measurements were collected to calibrate the high-resolution (30 m) albedo products derived from HJ-1a/b images. Then, the calibrated high-resolution albedo maps were resampled (i.e., upscaled) to assess the accuracy of the coarse-resolution albedo products. The samples of field measurements and high-resolution pixels are based on an uncertainty analysis. Two types of coarse-resolution albedo datasets, from global land surface satellite (GLASS) and moderate-resolution imaging spectroradiometer (MODIS), are validated over the middle reaches of the Heihe River in China. The results indicate that the upscaled HJ (Huan Jing means environment in Chinese and this refers to a satellite constellation designed for environment and disaster monitoring by China) albedo, which was calibrated using field measurements, can provide accurate reference values for validating coarse-resolution satellite albedo products. However, the uncertainties in the upscaled HJ albedo should be estimated, and pixels with large uncertainties should be excluded from the validation process.

Component temperatures inversion for remote sensing pixel based on directional thermal radiation model

When the remote sensing pixel is composed of multiple components and a non-isothermal surface, its directional signature of thermal-infrared radiation is mainly determined by the 3D structure of the pixel. In this paper, we present our simple directional thermal radiation model to describe the relation between the pixel thermal emission and the pixel’s component parameters, and invert the model to get the component temperatures. For the inversion algorithm, we focus on how to use the information of given observations in a more effective way. The information content in inversion procedure is studied. Our forward model and inversion method are validated using indoor directional measurement data.