Runhe Shi

Evaluation of Satellite Rainfall Estimates over the Chinese Mainland

Benefiting from the high spatiotemporal resolution and near-global coverage, satellite-based precipitation products are applied in many research fields. However, the applications of these products may be limited due to lack of information on the uncertainties. To facilitate applications of these products, it is crucial to quantify and document their error characteristics. In this study, four satellite-based precipitation products (TRMM-3B42, TRMM-3B42RT, CMORPH, GSMaP) were evaluated using gauge-based rainfall analysis based on a high-density gauge network throughout the Chinese Mainland during 2003–2006. To quantitatively evaluate satellite-based precipitation products, continuous (e.g., ME, RMSE, CC) and categorical (e.g., POD, FAR) verification statistics were used in this study. The results are as follows: (1) GSMaP and CMORPH underestimated precipitation (about −0.53 and −0.14 mm/day, respectively); TRMM-3B42RT overestimated precipitation (about 0.73 mm/day); TRMM-3B42, which is the only dataset corrected by gauges, had the best estimation of precipitation amongst all four products; (2) GSMaP, CMORPH and TRMM-3B42RT overestimated the frequency of low-intensity rainfall events; TRMM-3B42 underestimated the frequency of low-intensity rainfall events; GSMaP underestimated the frequency of high-intensity rainfall events; TRMM-3B42RT tended to overestimate the frequency of high-intensity rainfall events; TRMM-3B42 and CMORPH produced estimations of high-intensity rainfall frequency that best aligned with observations; (3) All four satellite-based precipitation products performed better in summer than in winter. They also had better performance over wet southern region than dry northern or high altitude regions. Overall, this study documented error characteristics of four satellite-based precipitation products over the Chinese Mainland. The results help to understand features of these datasets for users and improve algorithms for algorithm developers in the future.

Characterizing Spatiotemporal Dynamics of Methane Emissions from Rice Paddies in Northeast China from 1990 to 2010

Background Rice paddies have been identified as major methane (CH4) source induced by human activities. As a major rice production region in Northern China, the rice paddies in the Three-Rivers Plain (TRP) have experienced large changes in spatial distribution over the recent 20 years (from 1990 to 2010). Consequently, accurate estimation and characterization of spatiotemporal patterns of CH4 emissions from rice paddies has become an pressing issue for assessing the environmental impacts of agroecosystems, and further making GHG mitigation strategies at regional or global levels. Methodology/Principal Findings Integrating remote sensing mapping with a process-based biogeochemistry model, Denitrification and Decomposition (DNDC), was utilized to quantify the regional CH4 emissions from the entire rice paddies in study region. Based on site validation and sensitivity tests, geographic information system (GIS) databases with the spatially differentiated input information were constructed to drive DNDC upscaling for its regional simulations. Results showed that (1) The large change in total methane emission that occurred in 2000 and 2010 compared to 1990 is distributed to the explosive growth in amounts of rice planted; (2) the spatial variations in CH4 fluxes in this study are mainly attributed to the most sensitive factor soil properties, i.e., soil clay fraction and soil organic carbon (SOC) content, and (3) the warming climate could enhance CH4 emission in the cool paddies. Conclusions/Significance The study concluded that the introduction of remote sensing analysis into the DNDC upscaling has a great capability in timely quantifying the methane emissions from cool paddies with fast land use and cover changes. And also, it confirmed that the northern wetland agroecosystems made great contributions to global greenhouse gas inventory.

Light detection and ranging and hyperspectral data for estimation of forest biomass: a review

Abstract Forests are one of the most important sinks for carbon. Estimating the amount of carbon stored in forests is a major task for understanding the global carbon cycle. From local to global scales, remote sensing has been extensively used for forest biomass estimation. With the availability of multisensor image data, fusion has become a valuable method in remote sensing applications. Light detection and ranging (LiDAR) can provide information on the vertical structure of forests, whereas hyperspectral images can provide detailed spectral information of forests. Effective fusion of LiDAR and hyperspectral data is expected to help extract important biophysical parameters of forests. However, it is still unclear as to how forest biophysical and biochemical attributes derived from hyperspectral data relate to structural attributes derived from LiDAR data. A summary of previous research on LiDAR-hyperspectral fusion for forest biomass estimation is valuable for further improvement of biomass estimation methods. A review on the status of hyperspectral data, LiDAR data, and the fusion of these two data sources for forest biomass estimation in the last decade is provided. Some future research topics and major challenges are also discussed.

Trend analysis for evaluating the consistency of Terra MODIS and SPOT VGT NDVI time series products in China

The Normalized Difference Vegetation Index (NDVI) is an important vegetation greenness indicator. Compared to the AVHRR GIMMS NDVI data, the availability of two datasets with 1 km spatial resolution, i.e., Terra MODIS (MOD13A3) monthly composite and SPOT Vegetation (VGT) 10-day composite NDVI, extends the application dimensions at spatial and temporal scales. An overlapping period of 12 years between the datasets now makes it possible to investigate the consistency of the two datasets. Linear regression trend analysis was performed to compare the two datasets in this study. The results show greater consistency in regression slopes in the semi-arid regions of northern China. Alternatively, the results show only slight changes in the Terra MODIS NDVI regression slope in most areas of southern China whereas the SPOT VGT NDVI shows positive changes over a large area. The corresponding regression slope values between Terra MODIS and SPOT VGT NDVI datasets from the linear fit had a fair agreement in the spatial dimension. However, larger positive and negative differences were observed at the junction of the three regions (East China, Central China, and North China). These differences can be partially explained by the positive standard deviation differences distributed over a large area at the junction of these three regions. This study demonstrated that Terra MODIS and SPOT VGT NDVI have a relatively robust basis for characterizing vegetation changes in annual NDVI in most of the semi-arid and arid regions in northern China.

Global validation of FY-3A total ozone unit TOU total ozone columns using ground-based Brewer and Dobson measurements

Analysis of the accuracy and variability of total ozone columns (TOC) has been conducted by many studies, while the TOC observations derived from the total ozone unit (TOU) on board the Chinese FengYun-3A (FY-3A) satellite platform are notably less well documented. Therefore, in this present study, we mainly focus on the global-scale validation of TOU-derived total ozone column data by comparing them with spatially and temporally co-located ground-based measurements from the well-established Brewer and Dobson spectrophotometer for the period July 2009 through December 2011. The results show that TOU-derived total ozone column data yields high accuracy, with the root mean square error less than 5% in comparison with ground-based measurements. Meanwhile, TOU underestimates Brewer measurements by 1.1% in the Northern Hemisphere and overestimates Dobson total ozone 0.3% globally. In addition, TOU-derived total ozone shows no significant dependence on latitude in comparison with either Brewer or Dobson total ozone measurements. Nevertheless, a significant dependence of TOU-derived total ozone is observed on the solar zenith angle (SZA) in comparison with both Brewer and Dobson, demonstrating that TOU underestimates at large SZA and overestimates at small SZA. Finally, the dependence of satellite – ground-based relative difference for total ozone values shows fair agreement when total ozone values are in the range 250–450 Dobson units (DU). Overall, the Chinese FY-3A/TOU performs well on total ozone retrieval with high accuracy, and the total ozone data derived from the TOU can be used as a reliable data source for ozone monitoring and other atmospheric applications.

Analysis of air quality variability in Shanghai using AOD and API data in the recent decade

We use the aerosol optical depth (AOD) measured by the moderate resolution imaging spectrometer (MODIS) onboard the Terra satellite, air pollution index (API) daily data measured by the Shanghai Environmental Monitoring Center (SEMC), and the ensemble empirical mode decomposition (EEMD) method to analyze the air quality variability in Shanghai in the recent decade. The results indicate that a trend with amplitude of 1.0 is a dominant component for the AOD variability in the recent decade. During the World Expo 2010, the average AOD level reduced 30% in comparison to the long-term trend. Two dominant annual components decreased 80% and 100%. This implies that the air quality in Shanghai was remarkably improved, and environmental initiatives and comprehensive actions for reducing air pollution are effective. AOD and API variability analysis results indicate that semi-annual and annual signals are dominant components implying that the monsoon weather is a dominant factor in modulating the AOD and API variability. The variability of AOD and API in selected districts located in both downtown and suburban areas shows similar trends; i.e., in 2000 the AOD began a monotonic increase, reached the maxima around 2006, then monotonically decreased to 2011 and from around 2006 the API started to decrease till 2011. This indicates that the air quality in the entire Shanghai area, whether urban or suburban areas, has remarkably been improved. The AOD improved degrees (IDS) in all the selected districts are (8.6±1.9)%, and API IDS are (9.2±7.1)%, ranging from a minimum value of 1.5% for Putuo District to a maximum value of 22% for Xuhui District.

An artificial neural network approach to estimate evapotranspiration from remote sensing and AmeriFlux data

A simple and accurate method to estimate evapotranspiration (ET) is essential for dynamic monitoring of the Earth system at a large scale. In this paper, we developed an artificial neural network (ANN) model forced by remote sensing and AmeriFlux data to estimate ET. First, the ANN was trained with ET measurements made at 13 AmeriFlux sites and land surface products derived from satellite remotely sensed data (normalized difference vegetation index, land surface temperature and surface net radiation) for the period 2002–2006. ET estimated with the ANN was then validated by ET observed at five AmeriFlux sites during the same period. The validation sites covered five different vegetation types and were not involved in the ANN training. The coefficient of determination (R2) value for comparison between estimated and measured ET was 0.77, the root-mean-square error was 0.62 mm/d, and the mean residual was − 0.28. The simple model developed in this paper captured the seasonal and interannual variation features of ET on the whole. However, the accuracy of estimated ET depended on the vegetation types, among which estimated ET showed the best result for deciduous broadleaf forest compared to the other four vegetation types.

A correlation analysis of monthly mean CO2 retrieved from the Atmospheric Infrared Sounder with surface station measurements

As one of the major greenhouse gases, atmospheric carbon dioxide (CO2) concentrations have been monitored by both top-down satellite observations and air sampling systems on surface stations. The Atmospheric Infrared Sounder (AIRS) on board NASA’s Aqua low Earth orbit (LEO) satellite is a high-resolution infrared sounder that has been in operation for more than 10 years. The World Data Centre for Greenhouse Gases (WDCGG) archives and provides data on CO2 and other greenhouse gases measured mainly from surface stations. In this article, we focus on the correlation between the two different sources of CO2 data and the influencing factors. In general, we find that a linear positive correlation occurs at most stations. However, the variation in the correlation coefficient is large, especially for stations in the Northern Hemisphere. The station’s location, including its latitude, longitude, and altitude, is an important influencing factor because it determines how much its CO2 measurements are influenced by human activities. We also use root mean square difference (RMSD) and bias as evaluation indicators and find that they have similar trends like correlation coefficients.

Retrieval of columnar water vapor using multispectral radiometer measurements over northern China

Water vapor is an important component in hydrological processes that basically involve all types of seasons, including dry (e.g., drought) or wet (e.g., hurricane or monsoon). This study retrieved columnar water vapor (CWV) with the 939.3 nm band of a multifilter rotating shadowband radiometer (MFRSR) using the modified Langley technique. Such an investigation was in concert with the use of the atmospheric transmission model MODTRAN for determining the instrument coefficients required for CWV estimation. Results of the retrieval of CWV by MFRSR from September 23, 2004 to June 20, 2005 at the XiangHe site are presented and analyzed in this paper. To improve the credibility, the MFRSR results were compared with those obtained from the AErosol RObotic NETwork CIMEL sun-photometer measurements, co-located at the XiangHe site, and the moderate resolution imaging spectroradiometer (MODIS) near-infrared total precipitable water product (MOD05), respectively. These comparisons show good agreement in terms of correlation coefficients, slopes, and offsets, revealing that the accuracy of CWV estimation using the MFRSR instrument is reliable and suitable for extended studies in northern China.

Analysis of spatio-temporal variability of aerosol optical depth with empirical orthogonal functions in the Changjiang River Delta, China

This work aims to analyze the spatial and temporal variability of aerosol optical depth (AOD) from 2000 to 2012 in the Changjiang River Delta (CRD), China. US Terra satellite moderate resolution imaging spectroradiometer (MODIS) AOD and Ångström exponent (α) data constitute a baseline, with the empirical orthogonal functions (EOFs) method used as a major data analysis method. The results show that the maximum value of AOD observed in June is 1.00±0.12, and the lowest value detected in December is 0.40±0.05. AOD in spring and summer is higher than in autumn and winter. On the other hand, the α-value is lowest in spring (0.86±0.10), which are affected by coarse particles. High α-value appears in summer (1.32±0.05), which indicate that aerosols are dominated by fine particles. The spatial distribution of AOD has a close relationship with terrain and population density. Generally, high AODs are distributed in the low-lying plains, and low AODs in the mountainous areas. The spatial and temporal patterns of seasonal AODs show that the first three EOF modes cumulatively account for 77% of the total variance. The first mode that explains 67% of the total variance shows the primary spatial distribution of aerosols, i.e., high AODs are distributed in the northern areas and low AODs in the southern areas. The second mode (7%) shows that the monsoon climate probably plays an important role in modifying the distribution of aerosols, especially in summer and winter. In the third mode (3%), this distribution of aerosols usually occurs in spring and winter when the prevailing northwestern or western winds could bring aerosol particles from the inland areas into the central regions of the CRD.