Nana Yan

An improved satellite‐based approach for estimating vapor pressure deficit from MODIS data

Vapor pressure deficit (VPD) is an important variable widely used in ecosystem and climate models. In this paper, an improved satellite-based approach to estimating VPD was presented that uses several remote sensing products coupled with field measured data. The proposed method exploits an optimized algorithm to derive near-surface actual vapor pressure (ea) from Moderate Resolution Imaging Spectroradiometer (MODIS) data and upgrades Smiths (1966) methodology for estimating ea. The proposed new algorithm for calculating ea was evaluated against in situ measurements at 119 validation sites in China for 2 months in 2013. The mean absolute error (MAE) and root-mean-square error (RMSE) were less than 0.25 kPa and 0.33 kPa, respectively. The near-surface air temperature (Ta), which is an important input data for calculating VPD, was estimated from satellite-retrieved land surface temperature, and had an RMSE of less than 2.5u2009K. The estimated VPD values were validated with ground observation data from the Heihe River Basin for 5 months in 2012 and for all of China for August 2013. A coefficient of determination (R2) of 0.912, MAE of 0.27 kPa, and RMSE value of 0.32 kPa were achieved for the 2012 test data, and corresponding values of 0.88, 0.278 kPa, and 0.367 kPa for the 2013 test data. These results are promising, especially considering the comparatively high spatial resolution (1u2009km) of the VPD map estimated from the satellite data. Potential applications include global evapotranspiration estimation, fire warning, and vegetation analysis.

An Improved Approach for Estimating Daily Net Radiation over the Heihe River Basin

Net radiation plays an essential role in determining the thermal conditions of the Earth’s surface and is an important parameter for the study of land-surface processes and global climate change. In this paper, an improved satellite-based approach to estimate the daily net radiation is presented, in which sunshine duration were derived from the geostationary meteorological satellite (FY-2D) cloud classification product, the monthly empirical as and bs Angstrom coefficients for net shortwave radiation were calibrated by spatial fitting of the ground data from 1997 to 2006, and the daily net longwave radiation was calibrated with ground data from 2007 to 2010 over the Heihe River Basin in China. The estimated daily net radiation values were validated against ground data for 12 months in 2008 at four stations with different underlying surface types. The average coefficient of determination (R2) was 0.8489, and the averaged Nash-Sutcliffe equation (NSE) was 0.8356. The close agreement between the estimated daily net radiation and observations indicates that the proposed method is promising, especially given the comparison between the spatial distribution and the interpolation of sunshine duration. Potential applications include climate research, energy balance studies and the estimation of global evapotranspiration.

A Method to Estimate Sunshine Duration Using Cloud Classification Data from a Geostationary Meteorological Satellite (FY-2D) over the Heihe River Basin

Sunshine duration is an important variable that is widely used in atmospheric energy balance studies, analysis of the thermal loadings on buildings, climate research, and the evaluation of agricultural resources. In most cases, it is calculated using an interpolation method based on regional-scale meteorological data from field stations. Accurate values in the field are difficult to obtain without ground measurements. In this paper, a satellite-based method to estimate sunshine duration is introduced and applied over the Heihe River Basin. This method is based on hourly cloud classification product data from the FY-2D geostationary meteorological satellite (FY-2D). A new index—FY-2D cloud type sunshine factor—is proposed, and the Shuffled Complex Evolution Algorithm (SCE-UA) was used to calibrate sunshine factors from different coverage types based on ground measurement data from the Heihe River Basin in 2007. The estimated sunshine duration from the proposed new algorithm was validated with ground observation data for 12 months in 2008, and the spatial distribution was compared with the results of an interpolation method over the Heihe River Basin. The study demonstrates that geostationary satellite data can be used to successfully estimate sunshine duration. Potential applications include climate research, energy balance studies, and global estimations of evapotranspiration.

Spatial-temporal change analysis of evapotranspiration in the Heihe River Basin

Evapotranspiration (ET) is one important component of hydrological process. It is also a limited factor for ecological environment conservation. The unreasonable utilization of water resources resulted in many problems in the Heihe River Basin, such as the over exploited of water resources, especial the ground water, the river flow decrease, land degradation. A correct understanding of the water consumption characteristics is undoubtedly a base of achieving the sustainable development of society, economy, and improving the eco-environment. In this paper, the time series of ET data estimated by ETWatch model which had been validated for different landscapes during the period of 2000-2010, were analyzed in spatial and temporal scales. The deviation of annual ET estimated by ETWatch with the observation ET data from eddy covariance system (EC) in the four observation stations of the basin was in the range of 5-8%. The conversion of land use type had a great impact on total water consumption across the basin due to lower variance of ET for three vegetation types. Base on the correlation analysis of climate factors and ET, there were apparent impact on ET in the upstream and midstream, but not in the downstream because of complicated human activities. The significant relations between temperature and ET (R=0.842~0.941), and between precipitation and ET (R=0.582~0.840), indicated that these two climate parameters were key influence factors on ET for grass land; The soil water storage was likely another influence factor on ET for the forest besides the temperature which had close relations with ET (R2=0.617~0.822). The spatial-temporal change analysis of ET and driven factors can be contributed to better understanding the eco-hydrological effect of the specific forest in Qilian Mountains.

A Refined Crop Drought Monitoring Method Based on the Chinese GF-1 Wide Field View Data

In this study, modified perpendicular drought index (MPDI) models based on the red-near infrared spectral space are established for the first time through the analysis of the spectral characteristics of GF-1 wide field view (WFV) data, with a high spatial resolution of 16 m and the highest frequency as high as once every 4 days. GF-1 data was from the Chinese-made, new-generation high-resolution GF-1 remote sensing satellites. Soil-type spatial data are introduced for simulating soil lines in different soil types for reducing errors of using same soil line. Multiple vegetation indices are employed to analyze the response to the MPDI models. Relative soil moisture content (RSMC) and precipitation data acquired at selected stations are used to optimize the drought models, and the best one is the Two-band enhanced vegetation index (EVI2)-based MPDI model. The crop area that was statistically significantly affected by drought from a local governmental department, and used for validation. High correlations and small differences in drought-affected crop area was detected between the field observation data from the local governmental department and the EVI2-based MPDI results. The percentage of bias is between −21.8% and 14.7% in five sub-areas, with an accuracy above 95% when evaluating the performance via the data for the whole study region. Generally the proposed EVI2-based MPDI for GF-1 WFV data has great potential for reliably monitoring crop drought at a relatively high frequency and spatial scale. Currently there is almost no drought model based on GF-1 data, a full exploitation of the advantages of GF-1 satellite data and further improvement of the capacity to observe ground surface objects can provide high temporal and spatial resolution data source for refined monitoring of crop droughts.