Seasonal and Interannual Variations of Evaporation and their Relations with Precipitation, Net Radiation, and Net Carbon Accumulation for the Gediz Basin Area

A model combining the rate of carbon assimilation with water and energy balance equations has been run using satellite and ancillary data for a period of 60 months (January 1986 to December 1990). Calculations for the Gediz basin area give mean annual evaporation as 395 mm, which is composed of 45% transpiration, 42% soil evaporation and 13% interception. The coefficient of interannual variation of evaporation is found to be 6%, while that for precipitation and net radiation are, respectively, 16% and 2%, illustrating that net radiation has an important effect in modulating interannual variation of evaporation. The mean annual water use efficiency (i.e., the ratio of net carbon accumulation and total evaporation) is ca. 1 g/sq m/mm, and has a coefficient of interannual variation of 5%. A comparison of the mean water use efficiency with field observations suggests that evaporation over the area is utilized well for biomass production. The reference crop evaporation for irrigated areas has annual mean and coefficient of variation as, respectively, 1176 mm and 3%. The total evaporation during three summer months of peak evaporation (June-August) is estimated to be about 575 mm for irrigated crops like maize and cotton. Seasonal variations of the fluxes are presented

On the relation between SMMR 37-GHz polarization difference and the rainfall over Africa and Australia

A major difficulty in interpreting coarse resolution satellite data in terms of land surface characteristics is unavailability of spatially and temporally representative ground observations. Under certain conditions rainfall has been found to provide a proxy measure for surface characteristics, and thus a relation between satellite observations and rainfall might provide an indirect approach for relating satellite data to these characteristics. Relationship between rainfall over Africa and Australia and 7-year average (1979-1985) polarization difference (PD) at 37 GHz from scanning multichannel microwave radiometer (SMMR) on board the Nimbus-7 satellite is studied in this paper. Quantitative methods have been used to screen (accept or reject) PD data considering antenna pattern, geolocation uncertainty, water contamination, surface roughness, and adverse effect of drought on the relation between rainfall and surface characteristics. The rainfall data used in the present analysis are climatologic averages and also 1979-1985 averages, and no screening has been applied to this data. The PD data has been screened considering only the location of rainfall stations, without any regard to rainfall amounts. The present analysis confirms a non-linear relation between rainfall and PD published previously

Evaluation of Special Sensor Microwave/Imager Satellite Data for Regional Soil Moisture Estimation over the Red River Basin

Regional-scale estimation of soil moisture using in situ field observations is not possible due to problemswith the representativeness of the sampling and costs. Remotely sensed satellite data are helpful in this regard.Here, the simulations of 19- and 37-GHz vertical and horizontal polarization brightness temperatures and estimationof soil moistures using data from the Special Sensor Microwave/Imager (SSM/I) for 798 0.258 3 0.258boxes in the southwestern plains region of the United States for the time period between 1 August 1987 and31 July 1988 are presented. A coupled land-canopy–atmosphere model is used for simulating the brightnesstemperatures. The land-surface hydrology is modeled using a thin-layer hydrologic model. The canopy scatteringis modeled using a radiative transfer model, and the atmospheric attenuation is characterized using an empiricalmodel. The simulated brightness temperatures are compared with those observed by the SSM/I sensor aboardthe Defense Metereological Satellite Program satellite. The observed brightness temperatures are used to derivethe soil moistures using the canopy radiative transfer and atmospheric attenuation model. The discrepanciesbetween the SSM/I-based estimates and the simulated soil moisture are discussed. The mean monthly soilmoistures estimated using the 19-GHz SSM/I brightness temperature data are interpreted along with the meanmonthly leaf area index and accumulated rainfall. The soil moistures estimated using the 19-GHz SSM/I dataare used in conjunction with the hydrologic model to estimate cumulative monthly evaporation. The results ofthe simulations hold promise for the utilization of microwave brightness temperatures in hydrologic modelingfor soil moisture estimation

A Soil-Canopy-Atmosphere Model for Use in Satellite Microwave Remote Sensing

Regional and global scale studies of land-surface-atmosphere interactions require the use of observations for calibration and validation. In situ field observations are not representative of the distributed nature of land surface characteristics, and large-scale field experiments are expensive undertakings. In light of these requirements and shortcomings, satellite observations serve our purposes adequately. The use of satellite data in land surface modeling requires developing a hydrological model with a thin upper layer to be compatible with the nature of the satellite observations and that would evaluate the soil moisture and soil temperature of a thin layer close to the surface. This paper outlines the formulation of a thin layer hydrological model for use in simulating the soil moistures and soil temperatures. This thin layer hydrological model is the first step in our attempt to use microwave brightness temperature data for regional soil moisture estimation. The hydrological model presented here has been calibrated using five years (1980–1984) of daily streamflow data for the Kings Creek catchment. The calibrated parameters are used to validate the daily streamflows for the next 5 year period (1985–1989). The comparison of surface soil moistures and surface temperatures for the period of the Intensive Field Campaigns (IFCs) during the First ISLSCP (International Satellite Land Surface Climatology Project) Field Experiment (FIFE) in 1987 is carried out and yields good results. The thin layer hydrological model is coupled with a canopy radiative transfer model and an atmospheric attenuation model to create a coupled soil-canopy-atmosphere model in order to study the effect of the vegetation and the soil characteristics on the Special Sensor Microwave Imager (SSM/I) brightness temperatures. The sensitivities of the brightness temperatures to the soil and vegetation is examined in detail. The studies show that increasing leaf area index masks the polarization difference signal originating at the soil surface

L Band Brightness Temperature Observations Over a Corn Canopy During the Entire Growth Cycle

During a field campaign covering the 2002 corn growing season, a dual polarized tower mounted L-band (1.4 GHz) radiometer (LRAD) provided brightness temperature (T(sub B)) measurements at preset intervals, incidence and azimuth angles. These radiometer measurements were supported by an extensive characterization of land surface variables including soil moisture, soil temperature, vegetation biomass, and surface roughness. During the period from May 22, 2002 to August 30, 2002 a range of vegetation water content (W) of 0.0 to 4.3 kg/square m, ten days of radiometer and ground measurements were available. Using this data set, the effects of corn vegetation on surface emissions are investigated by means of a semi-empirical radiative transfer model. Additionally, the impact of roughness on the surface emission is quantified using T(sub B) measurements over bare soil conditions. Subsequently, the estimated roughness parameters, ground measurements and horizontally (H)-polarized T(sub B) are employed to invert the H-polarized transmissivity (gamma-h) for the monitored corn growing season

Analyzing the discharge regime of a large tropical river through remote sensing, ground-based climatic data, and modeling

This study demonstrates the potential for applying passive microwave satellite sensor data to infer the discharge dynamics of large river systems using the main stem Amazon as a test case. The methodology combines (1) interpolated ground-based meteorological station data, (2) horizontally and vertically polarized temperature differences (HVPTD) from the 37-GHz scanning multichannel microwave radiometer (SMMR) aboard the Nimbus 7 satellite, and (3) a calibrated water balance/water transport model (WBM/WTM). Monthly HVPTD values at 0.25° (latitude by longitude) resolution were resampled spatially and temporally to produce an enhanced HVPTD time series at 0.5° resolution for the period May 1979 through February 1985. Enhanced HVPTD values were regressed against monthly discharge derived from the WBM/WTM for each of 40 grid cells along the main stem over a calibration period from May 1979 to February 1983 to provide a spatially contiguous estimate of time-varying discharge. HVPTD-estimated flows generated for a validation period from March 1983 to February 1985 were found to be in good agreement with both observed arid modeled discharges over a 1400-km section of the main stem Amazon. This span of river is bounded downstream by a region of tidal influence and upstream by low sensor response associated with dense forest canopy. Both the WBM/WTM and HVPTD-derived flow rates reflect the significant impact of the 1982–1983 El Niño-;Southern Oscillation (ENSO) event on water balances within the drainage basin

L Band Brightness Temperature Observations over a Corn Canopy during the Entire Growth Cycle

During a field campaign covering the 2002 corn growing season, a dual polarized tower mounted L-band (1.4 GHz) radiometer (LRAD) provided brightness temperature (TB) measurements at preset intervals, incidence and azimuth angles. These radiometer measurements were supported by an extensive characterization of land surface variables including soil moisture, soil temperature, vegetation biomass, and surface roughness. In the period May 22 to August 30, ten days of radiometer and ground measurements are available for a corn canopy with a vegetation water content (W) range of 0.0 to 4.3 kg m−2. Using this data set, the effects of corn vegetation on surface emissions are investigated by means of a semi-empirical radiative transfer model. Additionally, the impact of roughness on the surface emission is quantified using TB measurements over bare soil conditions. Subsequently, the estimated roughness parameters, ground measurements and horizontally (H)-polarized TB are employed to invert the H-polarized transmissivity (γh) for the monitored corn growing season

A Blended Global Snow Product using Visible, Passive Microwave and Scatterometer Satellite Data

A joint U.S. Air Force/NASA blended, global snow product that utilizes Earth Observation System (EOS) Moderate Resolution Imaging Spectroradiometer (MODIS), Advanced Microwave Scanning Radiometer for EOS (AMSR-E) and QuikSCAT (Quick Scatterometer) (QSCAT) data has been developed. Existing snow products derived from these sensors have been blended into a single, global, daily, user-friendly product by employing a newly-developed Air Force Weather Agency (AFWA)/National Aeronautics and Space Administration (NASA) Snow Algorithm (ANSA). This initial blended-snow product uses minimal modeling to expeditiously yield improved snow products, which include snow cover extent, fractional snow cover, snow water equivalent (SWE), onset of snowmelt, and identification of actively melting snow cover. The blended snow products are currently 25-km resolution. These products are validated with data from the lower Great Lakes region of the U.S., from Colorado during the Cold Lands Processes Experiment (CLPX), and from Finland. The AMSR-E product is especially useful in detecting snow through clouds; however, passive microwave data miss snow in those regions where the snow cover is thin, along the margins of the continental snowline, and on the lee side of the Rocky Mountains, for instance. In these regions, the MODIS product can map shallow snow cover under cloud-free conditions. The confidence for mapping snow cover extent is greater with the MODIS product than with the microwave product when cloud-free MODIS observations are available. Therefore, the MODIS product is used as the default for detecting snow cover. The passive microwave product is used as the default only in those areas where MODIS data are not applicable due to the presence of clouds and darkness. The AMSR-E snow product is used in association with the difference between ascending and descending satellite passes or Diurnal Amplitude Variations (DAV) to detect the onset of melt, and a QSCAT product will be used to map areas of snow that are actively melting