Jianguang Wen

Heihe Watershed Allied Telemetry Experimental Research (HiWATER): Scientific Objectives and Experimental Design

A major research plan entitled “Integrated research on the ecohydrological process of the Heihe River Basin” was launched by the National Natural Science Foundation of China in 2010. One of the key aims of this research plan is to establish a research platform that integrates observation, data management, and model simulation to foster twenty-first-century watershed science in China. Based on the diverse needs of interdisciplinary studies within this research plan, a program called the Heihe Watershed Allied Telemetry Experimental Research (HiWATER) was implemented. The overall objective of HiWATER is to improve the observability of hydrological and ecological processes, to build a world-class watershed observing system, and to enhance the applicability of remote sensing in integrated ecohydrological studies and water resource management at the basin scale. This paper introduces the background, scientific objectives, and experimental design of HiWATER. The instrumental setting and airborne mission plans a...

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.

Retrieval of Soil Moisture and Vegetation Water Content Using SSM/I Data over a Corn and Soybean Region

The potential for soil moisture and vegetation water content retrieval using Special Sensor Microwave Imager (SSM/I) brightness temperature over a corn and soybean field region was analyzed and assessed using datasets from the Soil Moisture Experiment 2002 (SMEX02). Soil moisture retrieval was performed using a dual-polarization 19.4-GHz data algorithm that requires the specification of two vegetation parameters—single scattering albedo and vegetation water content. Single scattering albedo was estimated using published values. A method for estimating the vegetation water content from the microwave polarization index using SSM/I 37.0-GHz data was developed for the region using extensive datasets developed as part of SMEX02. Analyses indicated that the sensitivity of the brightness temperature to soil moisture decreased as vegetation water content increased. However, there was evidence that SSM/I brightness temperatures changed in response to soil moisture increases resulting from rainfall during the later stages of crop growth. This was partly attributed to the lower soil and vegetation thermal temperatures that typically followed a rainfall. Comparisons between experimentally measured volumetric soil moisture and SSM/I-retrieved soil moisture indicated that soil moisture retrieval was feasible using SSM/I data, but the accuracy highly depended upon the levels of vegetation and atmospheric precipitable water; the standard error of estimate over the 3-week study period was 5.49%. The potential for using this approach on a larger scale was demonstrated by mapping the state of Iowa. Results of this investigation provide new insights on how one might operationally correct for vegetation effects using high-frequency microwave observations.

A multiscale dataset for understanding complex eco-hydrological processes in a heterogeneous oasis system

We introduce a multiscale dataset obtained from Heihe Watershed Allied Telemetry Experimental Research (HiWATER) in an oasis-desert area in 2012. Upscaling of eco-hydrological processes on a heterogeneous surface is a grand challenge. Progress in this field is hindered by the poor availability of multiscale observations. HiWATER is an experiment designed to address this challenge through instrumentation on hierarchically nested scales to obtain multiscale and multidisciplinary data. The HiWATER observation system consists of a flux observation matrix of eddy covariance towers, large aperture scintillometers, and automatic meteorological stations; an eco-hydrological sensor network of soil moisture and leaf area index; hyper-resolution airborne remote sensing using LiDAR, imaging spectrometer, multi-angle thermal imager, and L-band microwave radiometer; and synchronical ground measurements of vegetation dynamics, and photosynthesis processes. All observational data were carefully quality controlled throughout sensor calibration, data collection, data processing, and datasets generation. The data are freely available at figshare and the Cold and Arid Regions Science Data Centre. The data should be useful for elucidating multiscale eco-hydrological processes and developing upscaling methods.

Reconstruction of a Cloud-free Vegetation Index Time Series for the Tibetan Plateau

Abstract In this investigation, an advanced Harmonic Analysis of Time Series (HANTS) technique has been introduced in order to remove cloud contamination from the satellite-based Normalized Difference Vegetation Index (NDVI) over the Tibetan Plateau. Due to high elevation, Asian monsoon, and the prevailing local convective weather, the probability of cloud occurrence in the daytime is very high over the Tibetan Plateau. It is therefore very difficult to directly obtain cloud-free vegetation index images using satellite optical remote sensing data over the entire Tibetan Plateau. Applying the HANTS algorithm made it possible to successfully replace the cloud contamination in NDVI images by the HANTS synthesized results, not only in temporal variation but also in regional distribution. The reconstructed NDVI images are cloud-free and more reliable; the HANTS-generated parameters are also potentially useful in understanding vegetation evolution and phenological characteristics over the mountainous regions of...Abstract In this investigation, an advanced Harmonic Analysis of Time Series (HANTS) technique has been introduced in order to remove cloud contamination from the satellite-based Normalized Difference Vegetation Index (NDVI) over the Tibetan Plateau. Due to high elevation, Asian monsoon, and the prevailing local convective weather, the probability of cloud occurrence in the daytime is very high over the Tibetan Plateau. It is therefore very difficult to directly obtain cloud-free vegetation index images using satellite optical remote sensing data over the entire Tibetan Plateau. Applying the HANTS algorithm made it possible to successfully replace the cloud contamination in NDVI images by the HANTS synthesized results, not only in temporal variation but also in regional distribution. The reconstructed NDVI images are cloud-free and more reliable; the HANTS-generated parameters are also potentially useful in understanding vegetation evolution and phenological characteristics over the mountainous regions of the Tibetan Plateau.

Evaluation of the Airborne CASI/TASI Ts-VI Space Method for Estimating Near-Surface Soil Moisture

High spatial resolution airborne data with little sub-pixel heterogeneity were used to evaluate the suitability of the temperature/vegetation (Ts/VI) space method developed from satellite observations, and were explored to improve the performance of the Ts/VI space method for estimating soil moisture (SM). An evaluation of the airborne ΔTs/Fr space (incorporated with air temperature) revealed that normalized difference vegetation index (NDVI) saturation and disturbed pixels were hindering the appropriate construction of the space. The non-disturbed ΔTs/Fr space, which was modified by adjusting the NDVI saturation and eliminating the disturbed pixels, was clearly correlated with the measured SM. The SM estimations of the non-disturbed ΔTs/Fr space using the evaporative fraction (EF) and temperature vegetation dryness index (TVDI) were validated by using the SM measured at a depth of 4 cm, which was determined according to the land surface types. The validation results show that the EF approach provides superior estimates with a lower RMSE (0.023 m3·m−3) value and a higher correlation coefficient (0.68) than the TVDI. The application of the airborne ΔTs/Fr space shows that the two modifications proposed in this study strengthen the link between the ΔTs/Fr space and SM, which is important for improving the precision of the remote sensing Ts/VI space method for monitoring SM.

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.

An Improved Land-Surface Albedo Algorithm With DEM in Rugged Terrain

The influence of topography on land-surface bidirectional reflectance and albedo should be considered in rugged terrain. However, land-surface albedo algorithms neglect topographic effects, leading to errors in estimating the albedo in rugged terrain. This letter investigates the Angular Bin (AB) algorithm of land-surface albedo and shows that it should be improved when albedo is estimated in rugged terrain. The Terrain AB (TAB), an improved algorithm for albedo estimation with the AB algorithm and digital elevation model (DEM) data set, is presented in this letter. The accuracy and performance of the TAB algorithm was investigated by using the simulated DEM and Bidirectional Reflectance Function as well as the MODIS daily reflectance of the Heihe River Basin. The results show that the TAB algorithm has a better albedo estimation performance in rugged terrain and gives an acceptable accuracy.

Development of a High Resolution BRDF/Albedo Product by Fusing Airborne CASI Reflectance with MODIS Daily Reflectance in the Oasis Area of the Heihe River Basin, China

A land-cover-based linear BRDF (bi-directional reflectance distribution function) unmixing (LLBU) algorithm based on the kernel-driven model is proposed to combine the compact airborne spectrographic imager (CASI) reflectance with the moderate resolution imaging spectroradiometer (MODIS) daily reflectance product to derive the BRDF/albedo of the two sensors simultaneously in the foci experimental area (FEA) of the Heihe Watershed Allied Telemetry Experimental Research (HiWATER), which was carried out in the Heihe River basin, China. For each land cover type, an archetypal BRDF, which characterizes the shape of its anisotropic reflectance, is extracted by linearly unmixing from the MODIS reflectance with the assistance of a high-resolution classification map. The isotropic coefficients accounting for the differences within a class are derived from the CASI reflectance. The BRDF is finally determined by the archetypal BRDF and the corresponding isotropic coefficients. Direct comparisons of the cropland archetypal BRDF and CASI albedo with in situ measurements show good agreement. An indirect validation which compares retrieved BRDF/albedo with that of the MCD43A1 standard product issued by NASA and aggregated CASI albedo also suggests reasonable reliability. LLBU has potential to retrieve the high spatial resolution BRDF/albedo product for airborne and spaceborne sensors which have inadequate angular samplings. In addition, it can shorten the timescale for coarse spatial resolution product like MODIS.