Evapotranspiration (ET) plays an important role in surface–atmosphere interactions and can be monitored using remote sensing data. The visible infrared imaging radiometer suite (VIIRS) sensor is a generation of optical satellite sensors that provide daily global coverage at 375- to 750-m spatial resolutions with 22 spectral channels (0.412 to 12.05 μm) and capable of monitoring ET from regional to global scales. However, few studies have focused on methods of acquiring ET from VIIRS images. The objective of this study is to introduce an algorithm that uses the VIIRS data and meteorological variables to estimate the energy budgets of land surfaces, including the net radiation, soil heat flux, sensible heat flux, and latent heat fluxes. A single-source model that based on surface energy balance equation is used to obtain surface heat fluxes within the Zhangye oasis in China. The results were validated using observations collected during the HiWATER (Heihe Watershed Allied Telemetry Experimental Research) project. To facilitate comparison, we also use moderate resolution imaging spectrometer (MODIS) data to retrieve the regional surface heat fluxes. The validation results show that it is feasible to estimate the turbulent heat flux based on the VIIRS sensor and that these data have certain advantages (i.e., the mean bias error of sensible heat flux is 15.23 W m−2) compared with MODIS data (i.e., the mean bias error of sensible heat flux is −29.36 W m−2). Error analysis indicates that, in our model, the accuracies of the estimated sensible heat fluxes rely on the errors in the retrieved surface temperatures and the canopy heights.
Remote sensing (RS) has been recognized as the most feasible means to provide spatially distributed regional evapotranspiration (ET). However, classical RS flux algorithms (SEBS, S-SEBI, SEBAL, etc.) can hardly be used with coarser resolution RS data from sensors like MODIS or AVHRR for no consideration of surface heterogeneity in mixed pixels even they are suitable for assessing the surface fluxes with high resolution RS data.A new model named FAFH is developed in this study to enhance the accuracy of flux estimation in mixed pixels based on high resolution landcover classification data. The area fraction and relative sensible heat fraction of each heterogeneous land use type calculated within coarse resolution pixels are calculated firstly, and then used for the weighted average of modified sensible heat. The study is carried out in the core agricultural land of Zhangye, the middle reaches of Heihe river based on the flux and landcover classification product of HJ-1B in our earlier work. The result indicates that FAFH increases the accuracy of sensible heat by 5% absolutely, 10.64% relatively in the whole research area.
The solar radiation incidence on the horizontal plane is not the true solar radiation (also called surface solar radiation) on the earth surface, it not take the influence of the rugged terrain into account. Topographic correction process is established which necessarily take integrated consideration of the geographic factors and the local topographic factors (i.e. slope, aspect, terrain inter-shielding effect). Based on the high resolution Digital Elevation Model (DEM) data and the horizontal solar radiation as the input data of topographic correction process, using the mountain solar radiation correction model to simulate the topographic correction process and to present the spatial distribution of surface solar radiation in China Ganzi region on June 30, 2010. Because of the influence of the rugged terrain, the spatial distribution of surface solar radiation is accompanied by the strong spatial heterogeneity, and the spatial representativeness of the observed data of meteorological station is limited. By use of the variogram model to calculate the spatial representativeness and to associate the strength of spatial representativeness with the distance. The results indicated that: 1) rugged terrain mainly makes the solar radiation the redistribution effect significantly on sunny/shady slope of local region, and the increase of slope has a subduction effect on radiation. The terrain factor is essential on determining the solar radiation over the complex terrain. 2) The spatial representativeness of Ganzi meteorological station is approximately 350 meters, the strength of spatial representativeness has the negatively correlation with the distance. There is a necessary to consider the spatial representativeness when verifying the retrieved data.
Downward shortwave radiation (DSR) receipt at the Earth’s surface is an important parameter in models of ecosystem dynamics and climate change. This paper presents a methodology to estimate DSR using hourly geostationary satellite (MTSAT-1R) and MODIS BRDF albedo parameter product (MCD43C1). The proposed algorithm retrieves atmospheric parameters directly from MTSAT-1R images by searching and interpolating look-up tables (LUT), which are created by the SBDART. The derived cloud optical thickness together with surface albedo and DEM are used to calculate the instantaneous downward shortwave radiation under cloudy sky. Hourly and daily DSR is calculated by the diurnal cycle integration of hourly instantaneous downward flux. The retrieved daily DSR is compared with ground-based measurements at 96 stations from China Meteorological Administration (CMA). The results show that the estimated DSR is in good agreement with ground measurements over China with a correlation coefficient of 0.93 and a mean bias of 5.8%. Root-mean square differences in the daily DSR are 20.7% for all sky conditions. The daily DSR is also compared with observations on Tibetan Plateau and the results shows a correlation coefficient of 0.91 and a mean bias of 1.53%. Root-mean square differences are 17.5%. The differences between the satellite derived estimates and ground observations may be attributed to calibration uncertainty of the satellite sensor and the ground instruments, undetected cloud shadows, steps of the LUT parameters, uncertainty in determining surface reflectance, and errors in ground observations.
Downward longwave radiation (DLR) at the earth’s surface is a major component of surface radiation budget and thus the climate, and remote sensing provides the most effective method to get surface DLR on a large scale. This paper presents a comparison of several DLR algorithms for both clear-sky and cloudy-sky conditions. These algorithms were applied to MODIS Terra data and extensively validated using one year's ground data at 13 stations around globe. For clear sky conditions, two algorithms using atmospheric parameters, two algorithms using satellite thermal radiances, and an algorithm that combined using satellite thermal data and atmospheric parameters were compared. The validation result indicated that the first type of algorithms often underestimated DLRs over high altitude regions, while the second type of algorithms performed well over these regions but had significant positive errors over arid regions. The third type of algorithm had acceptable results over all types of regions. Furthermore, the study found that using NCEP derived atmospheric parameters could effectively improve the performance of the first algorithms over high altitude regions, compared with MODIS atmospheric product. For cloudy conditions, three parametric algorithms that determined cloud radiative effect by cloud base temperature, and an empirical algorithm that employed cloud water path and cloud ice path were compared. The validation results indicated that the empirical algorithm had best results in most of the sites, while the three parametric algorithms were greatly influenced by the uncertainties of cloud parameters.