[1] Satellite remote sensing techniques are widely considered as the most promising way to estimate evapotranspiration (ET) over large geographic extents. In this study, a hybrid dual-source scheme and trapezoid framework-based evapotranspiration model (HTEM) is developed to map evapotranspiration from satellite imagery. It adopts a theoretically determined vegetation index/land surface temperature trapezoidal space to decompose bulk radiative surface temperature into component temperatures (soil and canopy) and uses a hybrid dual-source scheme of the layer approach and patch approach to partition net radiation and estimate sensible and latent fluxes separately from the soil and canopy. The proposed model was tested at the Soil Moisture-Atmosphere Coupling Experiment (SMACEX) site in central Iowa, USA, for 3 days during the campaign in 2002 using Landsat Thematic Mapper/Enhanced Thematic Mapper Plus (TM/ETM+) data, and at the Weishan flux site in the North China Plain during the main growing season of 2007 with Moderate Resolution Imaging Spectroradiometer Terra images. Results indicate that HTEM is capable of estimating latent heat flux (LE) with mean absolute percentage errors of 6.4% and 11.2% for the SMACEX and the Weishan sites, respectively. In addition, the model was found to be able to give reasonable evaporation and transpiration partitioning at both sites. Compared with other models, HTEM generally produced better sensible and latent flux estimates at the two sites and had comparable abilities in estimating net radiation and ground heat flux. Sensitivity analysis suggests that HTEM is most sensitive to temperature variables and less sensitive to other meteorological observations and parameters.Citation: Yang, Y., and S. Shang (2013), A hybrid dual-source scheme and trapezoid framework-based evapotranspiration model (HTEM) using satellite images: Algorithm and model test,

No abstract

A large patch of enhanced chlorophyll a concentration (Chla), lower sea surface temperature (SST), and lower sea surface height (SSH) was revealed in the central South China Sea (SCS) in November 2001 after the passage of typhoon Lingling. Maximum SST reduction of 11 degrees C occurred one day after Lingling's passage on 11/11. Subsequently, against a background level of 0.08 mg/m(3), average Chla within the area of 12.60-16.49 degrees N, 112.17-117.05 degrees E increased to 0.14 mg/m(3) on 11/12 and then to 0.37 mg/m(3) on 11/14. Dissolved organic matter and detritus were differentiated from Chla using a recent bio-optical algorithm. They contributed 64% to the increase of total absorption immediately after Lingling, while most of the changes later (74%) were due to phytoplankton. The area under Lingling's impact covered ca. 3 degrees latitude and 4 degrees longitude, which is much greater than the two summer cases previously observed in the northern SCS. This event lasted for ca. 15 days, and resulted in carbon fixation in the order of 0.4 Mt. Such a drastic response was attributed to the coupling of typhoon-induced nutrient pumping with the pre-established cyclonic gyre in the central SCS driven by the prevailing northeast monsoon

No abstract

[1] The Taiwan Strait is a long and wide shelf-channel where the hydrodynamics is extremely complex, being characterized by strong tides, and where storm surges frequently occur during the typhoon season. Obvious oscillations due to tide-surge interaction were observed by tide gauges along the northern Fujian coast, the west bank of the Taiwan Strait, during Typhoon Dan (1999). Numerical experiments indicate that nonlinear bottom friction (described by the quadratic formula) is a major factor to predict these oscillations while the nonlinear advective terms and the shallow water effect have little contribution. It is found that the tide-surge interaction in the northern portion of the Taiwan Strait is intensified by the strait. Simulations based on simplified topographies with and without the island of Taiwan show that, in the presence of the island, the channel effect strengthens tidal currents and tends to align the major axes of tidal ellipses along the channel direction. Storm-induced currents are also strengthened by the channel. The pattern of strong tidal currents and storminduced currents along the channel direction enhances tide-surge interaction via the nonlinear bottom friction, resulting in the obvious oscillations along the northern Fujian coast.

Accurate estimation of precipitation is critical for hydrological, meteorological, and climate models. This study evaluates the performance of satellite-based precipitation products (SPPs) including Global Precipitation Measurement (GPM)-based Integrated Multi-Satellite Retrievals for GPM (IMERG), Tropical Rainfall Measurement Mission (TRMM) Multi-Satellite Precipitation Analysis (TMPA 3B43-v7), Precipitation Estimation from Remotely-Sensed Information using Artificial Neural Network (PERSIANN), and PERSIANN-CDR (Climate Data Record), over Pakistan based on Surface Precipitation Gauges (SPGs) at spatial and temporal scales. A novel ensemble precipitation (EP) algorithm is developed by selecting the two best SPPs using the Paired Sample t-test and Principal Component Analysis (PCA). The SPPs and EP algorithm are evaluated over five climate zones (ranging from glacial Zone-A to hyper-arid Zone-E) based on six statistical metrics. The result indicated that IMERG outperformed all other SPPs, but still has considerable overestimation in the highly elevated zones (+20.93 mm/month in Zone-A) and relatively small underestimation in the arid zone (−2.85 mm/month in Zone-E). Based on the seasonal evaluation, IMERG and TMPA overestimated precipitation during pre-monsoon and monsoon seasons while underestimating precipitation during the post-monsoon and winter seasons. However, the developed EP algorithm significantly reduced the errors both on spatial and temporal scales. The only limitation of the EP algorithm is relatively poor performance at high elevation as compared to low elevations.

[1] We developed a new method to estimate terrestrial evapotranspiration (ET) from satellite data without using meteorological inputs. By analyzing observations from 20 eddy covariance tower sites across continental North America, we found a strong relationship between monthly gross primary production (GPP) and ET (R 2 = 0.72-0.97), implying the potential of using the remotely sensed GPP to invert ET. We therefore adopted the Temperature-Greenness model which calculates 16 day GPP using MODIS EVI and LST products to estimate GPP and then to calculate ET by dividing GPP with ecosystem water use efficiency (the ratio of GPP to ET). The proposed method estimated 16 day ET very well by comparison with tower-based measurements (R 2 = 0.84, p < 0.001, n = 1290) and provided better ET estimates than the MODIS ET product. This suggests that routine estimation of ET from satellite remote sensing without using fine-resolution meteorological fields is possible and can be very useful for studying water and carbon cycles.

[1] Quantifying carbon fluxes at large spatial scales has attracted considerable scientific attentions. In this study, a novel approach was proposed to estimate the terrestrial ecosystem gross primary production (GPP) using imagery from the satellite-borne Moderate Resolution Imaging Spectroradiometer (MODIS) sensor. The new model (named Temperature and Greenness Rectangle, TGR) uses a combination of MODIS Enhanced Vegetation Index and Land Surface Temperature products as well as in situ measurement of photosynthetically active radiation to estimate GPP at a 16 day interval. Three major advantages are included in the model: (1) the model follows strictly the logic of the light use efficiency model and each parameter has physical meaning; (2) the model reduces the dependency on ground-based meteorological measurements; and (3) the overlap of information in correlated explanatory variables is avoided. The model was calibrated with data from 17 sites within the Ameriflux network and validated at another 13 sites, covering a wide range of climates and eight major vegetation types. Results show that the TGR model explains reasonably well the tower-based measurements of GPP for all vegetation types, except for the evergreen broadleaf forest, with the coefficient of determination in a range from 0.67 to 0.91 and the root mean square error from 9.0 to 31.9 g C/m 2 /16 days. Comparisons with other two models (the TG and GR model) show that the TGR model generally gives better GPP estimates in nearly all vegetation types, especially under dry climate conditions. These results indicate that the TGR model can be potentially used to estimate GPP at regional scale.