Showing papers by "Qiaozhen Mu published in 2013"
TL;DR: In this article, the authors developed a method to generate a near-real-time remotely sensed drought severity index (DSI) to monitor and detect drought globally at 1-km spatial resolution and regular 8-day, monthly, and annual frequencies.
Abstract: Regional drought and flooding from extreme climatic events are increasing in frequency and severity, with significant adverse ecosocial impacts. Detecting and monitoring drought at regional to global scales remains challenging, despite the availability of various drought indices and widespread availability of potentially synergistic global satellite observational records. The authors have developed a method to generate a near-real-time remotely sensed drought severity index (DSI) to monitor and detect drought globally at 1-km spatial resolution and regular 8-day, monthly, and annual frequencies. The new DSI integrates and exploits information from current operational satellite-based terrestrial evapo-transpiration (ET) and vegetation greenness index [normalized difference vegetation index (NDVI)] products, which are sensitive to vegetation water stress. Specifically, this approach determines the annual DSI departure from its normal (2000–11) using the remotely sensed ratio of ET to potential ET (PET) and ...
353 citations
TL;DR: In this article, the accuracy of the MOD16 algorithm at two sites in the Rio Grande basin, Brazil, one characterized by a sugar-cane plantation (USE), the other covered by natural savannah vegetation (PDG) for the year 2001 was evaluated.
Abstract: Remote sensing is considered the most effective tool for estimating evapotranspiration (ET) over large spatial scales. Global terrestrial ET estimates over vegetated land surfaces are now operationally produced at 1-km spatial resolution using data from the Moderate Resolution Imaging Spectroradiometer (MODIS) and the MOD16 algorithm. To evaluate the accuracy of this product, ground-based measurements of energy fluxes obtained from eddy covariance sites installed in tropical biomes and from a hydrological model (MGB-IPH) were used to validate MOD16 products at local and regional scales. We examined the accuracy of the MOD16 algorithm at two sites in the Rio Grande basin, Brazil, one characterized by a sugar-cane plantation (USE), the other covered by natural savannah vegetation (PDG) for the year 2001. Inter-comparison between 8-day average MOD16 ET estimates and flux tower measurements yielded correlations of 0.78 to 0.81, with root mean square errors (RMSE) of 0.78 and 0.46 mm d-1, at PDG and US...
123 citations
01 Jan 2013
TL;DR: In this paper, the authors describe a level 4 MODIS land data product, M 0D 16, the global 8-day (M OD16A2) and annual (MOD16A3) terrestrial ecosystem Evapotranspiration (ET) dataset at 1-km spatial resolution over the 109.03 M illion km^ global vegetated land areas.
Abstract: This Algorithm Theoretical Basis Document (ATBD) describes a level 4 MODIS land data product, M 0D 16, the global 8-day (M OD16A2) and annual (MOD16A3) terrestrial ecosystem Evapotranspiration (ET) dataset at 1-km spatial resolution over the 109.03 M illion km^ global vegetated land areas. The M 0D 16 algorithm is based on the logic o f the Penman-M onteith equation which uses daily meteorological reanalysis data and 8-day remotely sensed vegetation property dynamics from MODIS as inputs. The MOD 16 ET algorithm runs at daily basis and temporally, daily ET is the sum of ET from daytime and night. Vertically, ET is the sum of water vapor fluxes from soil evaporation, wet canopy evaporation and plant transpiration at dry canopy surface. MODIS 8-day FPAR is used as vegetation cover faction to quantify how much surface net radiation is allocated between soil and vegetation; MODIS 8-day albedo and daily surface downward solar radiation and air temperature from daily meteorological reanalysis data are used to calculate surface net radiation and soil heat flux; daily air temperature, vapor pressure deficit (VPD) and relative humidity data, and 8-day MODIS LAI are used to estimate surface stomatal conductance, aerodynamic resistance, wet canopy, soil heat flux and ofher key environmental variables. MODIS land cover is used to specify the biome type for each pixel, and the biome-dependent constant parameters for the algorithm are saved in a Biome-Property-Lookup-Table (BPLUT). Except for minimum daily air temperature and VPD, which are directly adopted from the existing algorithm o f the MODIS global terrestrial gross and net primary production (MODIS GPP/NPP), the BPLUT is tuned largely based on a set o f targeted annual ET for each biome derived from MODIS GPP and water use efficiency calculated from eddy flux fowers. The MOD 16 ET has been validafed wifh ET measured af eddy flux towers and ET estimated from 232 watersheds. Averaged over 2000-2010, the total global annual ET over the vegetated land surface is 63.4X 10 ̂km^, with an average o f 569 ± 358 mm y r '\ comparable to the recent global estimates. Similar to other MODIS level 3 or level 4 MODIS land data products, 8-day and monthly MOD16A2 and annual MOD 16A3 datasets are saved in 10-degree Sinusoidal HDFEOS tiles. Thanks to the powerful internal compression o f HDFEOS, for each year, the size o f the MOD16A2 and MOD 16A3 together takes about 39GB. Since 2006, there have been 193 users from 30 countries requesting MODIS ET data from us and now MOD 16 from 2000 to 2010 are ready and have been released to the public for free download at our ftp site, ftp://ftp.ntsg.umt.edu/pub/M ODIS/M irror/M OD16/.
71 citations