Xiaozhou Xin

An Improved Parametric Model for Simulating Cloudy Sky Daily Direct Solar Radiation on Tilted Surfaces

Incoming solar radiation absorbed by the Earths surface is important for simulation models in addressing issues of ecosystem dynamics and climate change. The objective of this study was to simulate the daily direct solar radiation on tilted surfaces under cloudy sky conditions using an improved parametric model that integrates the atmospheric attenuation with the correction of three dimensional effects of cloud shadow and topographic factors. The model is validated by implementing four comparative case studies (Lhasa, Beijing, Kunming and Erjinaqi) based on the daily atmospheric products of MODIS TERRA/AQUA and SRTM DEM. The results show that the proposed parametric model is convincingly efficient, as the computed coefficients of determination (R 2) are relatively high for all stations except Lhasa (0.62 for Lhasa, 0.70 for Kunming, 0.70 for Beijing and 0.78 for Erjinaqi), and the RMSE (root mean square error) are 4.89 MJ/m2 for Lhasa, 4.09 MJ/m2 for Kunming, 4.02 MJ/m2 for Beijing and 3.79 MJ/m2 for Erjinaqi. A possible explanation is that the complex terrain accounts for the greater attenuation of solar radiation at Lhasa, while in our study, the data are retrieved at a spatial resolution of 1 km and the detailed terrain can not be clearly represented. The proposed model also indicates that clouds are the primary contributors to the amount and spatiotemporal distribution of solar radiation. The accuracy of the developed model is largely dependent on the temporal resolution of the data sources, especially the cloud optical thickness data. Meanwhile, the model reveals that topography and the spatial resolution of the DEM are important factors that affect the model results on tilted surfaces.

Modeling Soil Component Temperature Distribution by Extending CUPID Model

Modeling the soil component temperature distribution is useful to study multi-angular thermal remote sensing. SVAT (soil-plant-atmosphere transfer) model could be a good choice because it can predict canopy temperature distribution. However, most of them, including CUPID model 111. were unable to separate shade soil and sunlit soil. They only gave a single temperature for the soil surface. In this paper, based on the difference of net radiance and evaporation rate between the shade and sunlit soil, an extended model from CUPID was proposed to simultaneously retrieve the shaded temperature and sunlit temperature of soil surface. The comparison showed good agreement between simulated soil temperatures and measured ones.

Estimating Subpixel Surface Heat Fluxes through Applying Temperature-Sharpening Methods to MODIS Data

Using high-resolution satellite data to perform routine (i.e., daily to weekly) monitoring of surface evapotranspiration, evapotranspiration (ET) (or LE, i.e., latent heat flux) has not been feasible because of the low frequency of satellite coverage over regions of interest (i.e., approximately every two weeks). Cloud cover further reduces the number of useable observations, and the utility of these data for routine ET or LE monitoring is limited. Moderate-resolution satellite imagery is available multiple times per day; however, the spatial resolution of these data is too coarse to enable the estimation of ET from individual agricultural fields or variations in ET or LE. The objective of this study is to combine high-resolution satellite data collected in the visible and near-infrared (VNIR) bands with data from the MODIS thermal-infrared (TIR) bands to estimate subpixel surface LE. Two temperature-sharpening methods, the disaggregation procedure for radiometric surface temperature (DisTrad) and the geographically-weighted regression (GWR)-based downscaling algorithm, were used to obtain accurate subpixel land surface temperature (LST) within the Zhangye oasis in China, where the surface is heterogeneous. The downscaled LSTs were validated using observations collected during the HiWATER-MUSOEXE (Multi-Scale Observation Experiment on Evapotranspiration) project. In addition, a remote sensing-based energy balance model was used to compare subpixel MODIS LST-based turbulent heat fluxes estimates with those obtained using the two LST downscaling approaches. The footprint validation results showed that the direct use of the MODIS LST approach does not consider LST heterogeneity at all, leading to significant errors (i.e., the root mean square error is 73.15 W·m−2) in LE, whereas the errors in the LE estimates obtained using DisTrad and GWR were 45.84 W·m−2 and 47.38 W·m−2, respectively. Furthermore, additional analysis showed that the ability of DisTrad and GWR to capture subpixel LST variations depends on the value of Shannon’s diversity index (SHDI) and the surface type within the flux contribution source area.

Retrieve component temperature for wheat field with ASTER image

In order to retrieve land surface component temperature from multi-spectral remote sensing images, such as ASTER, we choose a component equivalent emissivity model, and iterative linear regression inversion algorithm. The method is tested with ASTER image of VNIR and TIR channels, as well as supplementary and validation data acquired from ground experiment. The atmospheric effect is corrected with the dark-object method; surface structural information is derived from ASTER VNIR observations; component emissivity is measured in situ with the BOMEN MR-154 spectrometer; validation data are also measured in ground experiments. Finally, the accuracy of the results and sources of error are analyzed.

Experimental study on directionality in thermal infrared observations of corn canopy

In order to investigate the directional distribution of thermal infrared radiance from corn canopy, a field campaign was elaborately schemed in North-China-Plain from July to August, 2000. Corn was designed to be row planted and uniformly planted, to simulate the typical structures of agricultural canopy. One TIR radiometers was set at an automatic multi-angular observation bracket while another was fixed nadir-view over the crop canopy. Directional TIR radiance distributions were measured every two hours at several typical weather conditions during different corn growth stages. Canopy structural parameters and meteorological parameters were also measured simultaneously. After temporally normalized, the directional TIR radiance distributions were extracted, diurnal and seasonal variation of the directionality is analyzed.

A Lookup-Table-Based Approach to Estimating Surface Solar Irradiance from Geostationary and Polar-Orbiting Satellite Data

Incoming surface solar irradiance (SSI) is essential for calculating Earth’s surface radiation budget and is a key parameter for terrestrial ecological modeling and climate change research. Remote sensing images from geostationary and polar-orbiting satellites provide an opportunity for SSI estimation through directly retrieving atmospheric and land-surface parameters. This paper presents a new scheme for estimating SSI from the visible and infrared channels of geostationary meteorological and polar-orbiting satellite data. Aerosol optical thickness and cloud microphysical parameters were retrieved from Geostationary Operational Environmental Satellite (GOES) system images by interpolating lookup tables of clear and cloudy skies, respectively. SSI was estimated using pre-calculated offline lookup tables with different atmospheric input data of clear and cloudy skies. The lookup tables were created via the comprehensive radiative transfer model, Santa Barbara Discrete Ordinate Radiative Transfer (SBDART), to balance computational efficiency and accuracy. The atmospheric attenuation effects considered in our approach were water vapor absorption and aerosol extinction for clear skies, while cloud parameters were the only atmospheric input for cloudy-sky SSI estimation. The approach was validated using one-year pyranometer measurements from seven stations in the SURFRAD (SURFace RADiation budget network). The results of the comparison for 2012 showed that the estimated SSI agreed with ground measurements with correlation coefficients of 0.94, 0.69, and 0.89 with a bias of 26.4 W/m2, −5.9 W/m2, and 14.9 W/m2 for clear-sky, cloudy-sky, and all-sky conditions, respectively. The overall root mean square error (RMSE) of instantaneous SSI was 80.0 W/m2 (16.8%), 127.6 W/m2 (55.1%), and 99.5 W/m2 (25.5%) for clear-sky, cloudy-sky (overcast sky and partly cloudy sky), and all-sky (clear-sky and cloudy-sky) conditions, respectively. A comparison with other state-of-the-art studies suggests that our proposed method can successfully estimate SSI with a maximum improvement of an RMSE of 24 W/m2. The clear-sky SSI retrieval was sensitive to aerosol optical thickness, which was largely dependent on the diurnal surface reflectance accuracy. Uncertainty in the pre-defined horizontal visibility for ‘clearest sky’ will eventually lead to considerable SSI retrieval error. Compared to cloud effective radius, the retrieval error of cloud optical thickness was a primary factor that determined the SSI estimation accuracy for cloudy skies. Our proposed method can be used to estimate SSI for clear and one-layer cloud sky, but is not suitable for multi-layer clouds overlap conditions as a lower-level cloud cannot be detected by the optical sensor when a higher-level cloud has a higher optical thickness.

Modeling daily net shortwave radiation over rugged surfaces using MODIS atmospheric products

As a main component of Net Surface Radiation (NSR), the Net Surface Shortwave Radiation (NSSR) significantly affects the climatic forming and change. However, it is impossible to observe NSSR directly over large areas especially for rugged surfaces such as Qinghai-Tibet Plateau. The primary objective of this study was to estimate daily NSSR over rugged surfaces at 1-km resolution for both clear and cloudy days. All the atmospheric input data were derived from MODerate resolution Imaging Spectroradiometer (MODIS) data, and therefore the errors from multi-sensor architectures were avoided. The performance of the model was evaluated by comparing its results with field measurements at four sites, and the correlation coefficients of NSSR varied between 0.71∼0.93. Possible error may arise from the lower temporal resolution of the cloud products and the spatial resolution of DEM.

Analysis of the Spatial Variability of Land Surface Variables for ET Estimation: Case Study in HiWATER Campaign

Heterogeneity, including the inhomogeneity of landscapes and surface variables, significantly affects the accuracy of evapotranspiration (ET) (or latent heat flux, LE) estimated from remote sensing satellite data. However, most of the current research uses statistical methods in the mixed pixel to correct the ET or LE estimation error, and there is a lack of research from the perspective of the remote sensing model. The method of using frequency distributions or generalized probability density functions (PDFs), which is called the “statistical-dynamical” approach to describe the heterogeneity of land surface characteristics, is a good way to solve the problem. However, in attempting to produce an efficient PDF-based parameterization of remotely sensed ET or LE, first and foremost, it is necessary to systematically understand the variables that are most consistent with the heterogeneity (i.e., variability for a fixed target area or landscape, where the variation in the surface parameter value is primarily concerned with the PDF-based model) of surface turbulence flux. However, the use of PDF alone does not facilitate direct comparisons of the spatial variability of surface variables. To address this issue, the objective of this study is to find an indicator based on PDF to express variability of surface variables. We select the dimensionless or dimensional consistent coefficient of variation (CV), Gini coefficient and entropy to express variability. Based on the analysis of simulated data and field experimental data, we find that entropy is more stable and accurate than the CV and Gini coefficient for expressing the variability of surface variables. In addition, the results of the three methods show that the variability of the leaf area index (LAI) is greater than that of the land surface temperature (LST). Our results provide a suitable method for comparing the variability of different variables.

A New Algorithm of the FPAR Product in the Heihe River Basin Considering the Contributions of Direct and Diffuse Solar Radiation Separately

It remains a challenging issue to accurately estimate the fraction of absorbed photosynthetically-active radiation (FPAR) using remote sensing data, as the direct and diffuse radiation reaching the vegetation canopy have different effects for FPAR. In this research, a FPAR inversion model was developed that may distinguish direct and diffuse radiation (the DnD model) based on the energy budget balance principle. Taking different solar zenith angles and diffuse PAR proportions as inputs, the instantaneous FPAR could be calculated. As the leaf area index (LAI) and surface albedo do not vary in a short periods, the FPAR not only on a clear day, but also on a cloudy day may be calculated. This new method was used to produce the FPAR products in the Heihe River Basin with the Moderate-Resolution Imaging Spectroradiometer (MODIS) LAI and surface albedo products as the input data source. The instantaneous FPAR was validated by using field-measured data (RMSE is 0.03, R2 is 0.85). The daily average FPAR was compared with the MODIS FPAR product. The inversion results and the MODIS FPAR product are highly correlated, but the MODIS FPAR product is slightly high in forest areas, which is in agreement with other studies for MODIS FPAR products.

Retrieval of aerosol optical thickness from HJ-1A/B images using structure function method

Aerosol optical thickness (AOT) is retrieved from HJ-1A/B images using Structure Function Method (SFM) over Beijing and its surrounding area. SFM is discussed by establishing structure function formula, choosing window size and distance value. Retrieved result is validated by the ground-based observation.