Snow sublimation is an important hydrological process and its spatial and temporal variation remains largely unknown; however, few studies have been conducted to quantify its spatial variability. Our study focuses on the evaluation of two algorithms, Penman–Monteith (P–M) equation and the bulk aerodynamic (BA) parameterization of snow sublimation. The two methods were first evaluated against eddy covariance (EC) measurements of latent heat flux at towers located in the upper reaches of the Heihe River Basin (China). Both methods were in good agreement with the ground observations with high coefficient of determination (R2) and low root mean squared error (RMSE). Next, we estimated subpixel snow sublimation using remote sensing data at a 1-km×1-km spatial resolution. The results based on satellite data were evaluated against ground measurements at the two experimental sites. The P–M equation gave R2=0.75, RMSE=8.4 W m−2 for Dashalong site and R2=0.36, RMSE=9.1 W m−2 for the Dadongshu site and performed better than the BA parameterization, which gave R2=0.65, RMSE=17.5 W m−2 for the Dashalong site and R2=0.06, RMSE=21.2 W m−2 for the Dadongshu site. Overall, the results indicate that P–M is promising for estimating snow sublimation at the regional scale using satellite observations.
The regional surface soil heat flux (G0) estimation is very important for the large-scale land surface process modeling. However, most of the regional G0 estimation methods are based on the empirical relationship between G0 and the net radiation flux. A physical model based on harmonic analysis was improved (referred to as “HM model”) and applied over the Heihe River Basin northwest China with multiple remote sensing data, e.g., FY-2C, AMSR-E, and MODIS, and soil map data. The sensitivity analysis of the model was studied as well. The results show that the improved model describes the variation of G0 well. Land surface temperature (LST) and thermal inertia (Γ) are the two key input variables to the HM model. Compared with in situG0, there are some differences, mainly due to the differences between remote-sensed LST and the in situ LST. The sensitivity analysis shows that the errors from −7 to −0.5 K in LST amplitude and from −300 to 300 J m−2 K−1 s−0.5 in Γ will cause about 20% errors, which are acceptable for G0 estimation.
An exceptional drought struck Henan province during the summer of 2014. It caused directly the financial loss reaching
to hundreds of billion Yuan (RMB), and brought the adverse influence for people’s life, agricultural production as well
as the ecosystem. The study in this paper characterized the Henan 2014 summer drought event through analyzing the
spatial distribution of drought severity using precipitation data from Tropical Rainfall Measuring Mission (TRMM)
sensor and Normalized difference vegetation index (NDVI) and land surface temperature (LST) products from Moderate
Resolution Imaging Spectroradiometer (MODIS) sensor. The trend analysis of the annual precipitation from 2003 to
2014 showed that the region over Henan province is becoming dry. Especially in the east of Henan province, the
decrease of precipitation is more obvious with the maximum change rate of ~48 mm/year. The rainfall in summer (from
June to August) of 2014 was the largest negtive anomaly in contrast with the same period of historical years, which was
43% lower than the average of the past ten years. Drought severity derived from Standardized Precipitation Index (SPI)
indicated that all areas of Henan province experienced drought in summer of 2014 with different severity levels. The
extreme drought, accounting for about 22.7 % of Henan total area, mainly occurred in Luohe, Xuchang, and
Pingdingshan regions, and partly in Nanyang, Zhengzhou, and Jiaozuo. This is consistent with the statistics from local
municipalities. The Normalized Drought Index Anomaly (NDAI), calculated from MODIS NDVI and LST products, can
capture the evolution of the Henan 2014 summer drought effectively. Drought severity classified by NDAI also agreed
well with the result from the SPI.
Information of temporal and spatial variation of aerodynamic roughness length is required in most land surface models. The current research presents a practical approach for determining spatially distributed vegetation aerodynamic roughness length with fine temporal and spatial resolution by combining remote sensing and ground measurements. The basic framework of Raupach (1992), with the bulk surface parameters revised by Jasinski et al. (2005) has been applied to optical remote sensing data of HJ-1A/1B missions. In addition, a method for estimating regional scale vegetation height was introduced, so the aerodynamic roughness length, which is more preferred by users than the height normalized form has been developed. Direct validation on different vegetation classes have finally been performed taking advantage of the data-dense field experiments of Heihe Watershed Allied Telemetry Experimental Research (HiWATER). The roughness model had an overall good performance on most of Eddy Covariance sites of HiWATER. However, deviations still existed on different sites, and these have been further analyzed.
Surface soil heat flux(G0) is an important component of surface energy balance, and it causes large uncertainty in
evapotranspiration estimation. In present study, soil heat flux was calculated at different depths based on the harmonic
analysis method (HM) using field data in Heihe River Basin, northwestern China. The soil heat fluxes at a certain depth
and at the surface were validated by heat-plate measurements and G0 derived from thermal diffusion equation,
respectively. Results showed that HM method obtained good result during the daytime, yet the errors were relatively
large at nighttime mostly due to the assumption of symmetry of G0 during daytime and nighttime. Moreover, a regional
G0 map was provided based on remote sensing data. This study highlighted the simplicity of HM method and its
potential application in large spatial scale mapping. Its internal limit was also discussed here.
A practical algorithm is developed to retrieve LST from the Multi-functional Transport Satellite (MTSAT), which was launched in 2005 by Japan Meteorological Agency (JMA). A classified Split-Window algorithm is developed under various atmospheric and surface conditions through simulations by MODTRAN 4. The coefficients in the algorithm are separated in several groups by a series of different parameters. The retrieved MTSAT LST is compared with that from Fengyun Meteorological Satellite (FY), airborne and ground observations collected in the Heihe river basin, China. The analysis indicates that the classified split-window algorithm can be successfully applied to the LST retrievals from MTSAT data.
Soil water saturation condition is an essential factor that indicates the possible temporal and spatial hazard of inundations
in floodplains. To monitor wetness conditions over a long period of time and large areas, passive microwave data is used
to study the inundation pattern of large floodplains in Asia, such as the Poyang Lake floodplain. The polarization
difference brightness temperature at 37GHz is sensitive to the water extension even under dense forest. However, the
mixing of signals from open water, bare soil and vegetation makes it difficult to obtain the soil-water saturation
conditions from 37GHz data. That is because 37GHz microwave emission is attenuated by the vegetation canopy, which
shows seasonal changes in Asia floodplains. We developed a linear mixing model to eliminate the signal from vegetation
and derive the soil- water saturation condition from 37GHz data. Vegetation attenuation factors, in terms of vegetation
fractional area and LAI, have been estimated by correlation with the NDVI. Thus the vegetation attenuation function is
built according to the relationship between 37GHz and NDVI data of agricultural areas, with the help of Harmonic
analysis of time series to obtain continuous NDVI time series. Comparing the soil-water saturated area from 37GHz and
water extension area of Poyang Lake from SAR image data at higher spatial resolution, our result shows a good fit with
SAR data but relatively higher values.
A practical algorithm is developed to retrieve spatial-temporal land surface temperature (LST) using the Stretched
Visible and Infrared Spin Scan Radiometer (SVISSR) time series data from China Feng-Yun 2C (FY-2C) geostationary
satellite. A cross-calibration method and a general split-window algorithm for FY-2C/SVISSR data are developed. An
automatic procedure is developed to implement the proposed methods for LST retrieval from SVISSR. Results from
cross-calibration show that a good linear relationship between the TOA brightness temperatures from FY-2C/SVISSR
and that from MODIS was found with correlation coefficients R2 as 0.95 notwithstanding the differences of spectral
response function between the two sensors. The results show that the SVISSR derived LST can be evaluated with
aggregated AATSR derived LST and In-situ data. Results indicate that, the SVISSR and aggregated AATSR give
comparable results (within 4K) both in Arou and YK, on the condition that AATSR LST product overestimates by about
3K than the ground measurement.
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