# Presentation: 661891

## SeaWiFS Highlights

- May 2002
**SeaWiFS Views Highly Productive Waters** **in the Atlantic Ocean**
- On May 11, 2002 SeaWiFS viewed the highly productive waters off the eastern United States and Canada. These two images are derived from the same raw data, but were processed in different ways. The first image is a quasi-true color view formed by using the red, green, and blue spectral bands from the sensor as the red, green, and blue components of the image. The second image is a pseudo-color representation of sea-surface chlorophyll concentrations overlaid on the quasi-true color land image.
- The chlorophyll image brings out more of the complexity in this part of the Atlantic Ocean. Here, the waters over Georges Bank and in the Gulf of Maine exhibit the high chlorophyll concentrations which mark thriving phytoplankton populations; these are the base of the food web for most of the rest of these ecosystems. To the south, chlorophyll concentrations decrease rapidly as one crosses the northern boundary of the meandering Gulf Stream. To the east of Georges Bank, a meander has pinched off into a large warm core ring (light blue).
- Some sense of how different species of phytoplankton are distributed in the area can be gotten by looking at the quasi-true color image and noting how the colors of the various blooms differ from each other. Southeast of Long Island is an aquamarine hue, while over the top of Georges Bank, it is more yellow-green. Around the edge of the Bank and in the middle of the Gulf of Maine, there is a reddish tinge. These color differences likely reflect differences in species composition and in depth distributions of the cells whose pigments and light-scattering characteristics are detected by SeaWiFS.
- POC: 970.2/Gene Feldman
- gene@seawifs.gsfc.nasa.gov

## SeaWiFS Views Highly Productive Waters in the Atlantic Ocean

- Sydney

- May 11, 2002

- True-Color Image

- Pseudo-Color Image

## Relationship between the ERS-2 SAR Radar Cross-Section attenuation and SeaWiFS Chl-a concentration. Typically, 1 mg/m3 of Chl-a is correspondent with a 5 dB reduction of Radar Cross-Section.

- A subscene of the SeaWiFS Chlorophyll-a image collocated with SAR subscene acquired two hours later on 19 August 2000 covering the upwelling region northeast of Taiwan.

- Relationship between the ERS-2 SAR Radar Cross-Section attenuation and SeaWiFS Chl-a concentration. Typically, 1 mg/m3 of Chl-a is correspondent with a 5 dB reduction of Radar Cross-Section.

- Correlation Between Radar Backscatter and Ocean Colour Supported by Sea Truth

- Antony Liu, NASA Goddard Space Flight Center
- I-I Lin, National Center for Ocean Research, Taiwan

## Determinants of SGP97 Surface Soil Moisture Patterns from ESTAR and NOAH

- Abstract
- 	Recently, Peters-Lidard and coworkers (Peters-Lidard et al., 2001) published an analysis of modeled and measured soil moisture during the Washita '92 field campaign suggesting that the spatial scaling of soil moisture is controlled in wet periods by processes controlling infiltration (e.g., including rainfall pattern, soil and vegetation properties) and in dry periods by processes controlling drainage and evaporation (e.g., soils, topography and vegetation properties).   Recent work presented here further explores these issues in the context of the month-long Southern Great Plains '97 hydrology experiment (SGP97), during which daily soil moisture images at 800 m resolution were derived from brightness temperature measurements obtained with the airborne L band ESTAR microwave radiometer.  This set of images, which compared favorably with in-situ volumetric soil moisture measurements, forms the basis of this study, along with the NOAH land surface model (developed by NOAA/NCEP, Oregon State University, the Air Force Weather Agency, and the Office of Hydrology).  In this study, the NOAH model is applied with and without enhancements representing topography for comparisons to the spatial pattern of ESTAR-derived soil moisture estimates over the 11,000 square kilometer SGP97 test region.  Results for both wet and dry periods illustrate the processes controlling the spatial and temporal patterns of soil moisture.
- Reference:  Peters-Lidard, C. D., F. Pan, and E. F. Wood, 2001. A re-examination of modeled and measured soil moisture spatial variability and its implications for land surface modeling. Advances in Water Resources (Special Issue on Non-Linear Propagation of Multi-scale Dynamics Through Hydrologic Subsystems), Vol 24/9-10, pp 1069-1083.

***Determinants of SGP97 Surface Soil Moisture Patterns from ESTAR and NOAH***

## Determinants of SGP97 Surface Soil Moisture Patterns from ESTAR and NOAH

**Christa D. Peters-Lidard****1****, Feifei Pan****2****, **
**Ann Hsu****3****, Peggy O'Neill****1**
**1***Hydrological Sciences Branch, Code 974*
*	**NASA’s Goddard Space Flight Center(GSFC), Greenbelt, MD*
**2***School of Civil and Environmental Engineering, Georgia Institute of Technology, Atlanta, GA*
**3***USDA-ARS Hydrology & Remote Sensing Lab, Beltsville, MD*

***Dr. Christa D. Peters-Lidard cpeters@hsb.gsfc.nasa.gov***

*** ******GSFC Hydrological Sciences Branch***

## Soil moisture-topographic correlation

***Dr. Christa D. Peters-Lidard cpeters@hsb.gsfc.nasa.gov***

*** ******GSFC Hydrological Sciences Branch***

**Notes:**
To seek relationships between soil moisture pattern and topography, we compute time series of correlation coefficients between observed soil moisture and topographic quantities.

Correlation between soil moisture and loss-weighted precipitation is generally higher than correlation between soil moisture and topographic properties. (as shown in next figure)
Correlation between soil moisture and precipitation/topography depends on time since precipitation and magnitude of correlation with slope exceeds that with topographic index, due to correlation of precipitation with elevation.

## Soil moisture-precipitation correlation

***Dr. Christa D. Peters-Lidard cpeters@hsb.gsfc.nasa.gov***

*** ******GSFC Hydrological Sciences Branch***

**Notes:**
To seek relationships between soil moisture pattern and precipitation, we compute time series of correlation coefficients between observed soil moisture and precipitation (moving averages, or loss-function weighted moving averages). 
Correlation between soil moisture and loss-weighted precipitation is generally higher than correlation between soil moisture and topographic properties.
The loss function is a representation of the land surface control on the soil moisture pattern, and to a good approximation is a function of saturated hydraulic conductivity, Ks (surrogate for drainage) and leaf area index LAI (surrogate for transpiration and interception).
With the loss function appropriately defined, the stochastic PDE of Entekhabi and Rodriguez-Iturbe (1994) can be solved to derive an expression for soil moisture at time n, with a depth Zr, using appropriate weighting of precipitation P with the loss function ():

This weighted average is what is shown in the figure above, and is shown to be highly correlated with ESTAR measured soil moisture, particularly close to rainfall events, when the correlation reaches 0.8-0.9.  Even during “dry” periods, the correlation is in excess of 0.4.