Max Bleiweiss

Using ASTER satellite data to calculate riparian evapotranspiration in the Middle Rio Grande, New Mexico

Riparian evapotranspiration (ET) in the Rio Grande Basin in New Mexico, USA is a major component of the hydrological system. Over a period of several years, ET has been measured in selected locations of dense saltcedar and cottonwood vegetation. Riparian vegetation varies in density, species and soil moisture availability, and to obtain accurate measurements, multiple sampling points are needed, making the process costly and impractical. An alternative solution involves using remotely sensed data to estimate ET over large areas. In this study, daily ET values were measured using eddy covariance flux towers installed in areas of saltcedar and cottonwood vegetation. At these sites, remotely sensed satellite data from the National Aeronautics and Space Administration (NASA) Terra Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) were used to calculate the albedo, normalized difference vegetation index (NDVI) and surface temperature. A surface energy balance model was used to calculate ET values from the ASTER data, which were available for 7 days in the year. Comparison between the daily ET values of saltcedar and cottonwood measured from the flux towers and calculated from remote sensing resulted in a mean square error (MSE) of 0.16 and 0.37 mm day−1, respectively. The regional map of ET generated from the remote sensing data demonstrated considerable variation in ET, ranging from 0 to 9.8 mm day−1, with a mean of 5.5 mm day−1 and standard deviation of 1.85 mm day−1 (n = 427481 pixels) excluding open water. This was due to variations in plant variety and density, soil type and moisture availability, and the depth to water table.

Estimation of the fractional canopy cover of pecan orchards using Landsat 5 satellite data, aerial imagery, and orchard floor photographs

Pecan orchards are the largest agricultural water consumer in the lower part of the Mesilla Valley, NM, USA. Knowledge of fractional canopy (FC) cover allows better crop water use assessment and orchard management. FC can be estimated from vegetation indices (VIs), such as the normalized difference vegetation index (NDVI), the soil-adjusted vegetation index (SAVI), the simple ratio (SR), and the triangular vegetation index (TVI), using satellite imagery. The main objective of this research is to develop an approach to determine the FC from a simultaneous imagery campaign consisting of aerial imagery, orchard floor photographs, and satellite images. All the required data were collected based on satellite overpass times at three different times during the initial part of the growing season to enhance the quality of data and reduce errors. The data were processed using the software package Environment for Visualizing Images (ENVI® 4.6.1; ITT Research Systems Inc.). The orchard floor digital photographs were used as a ground truth data set that gave a good correlation to the aerial photography. The aerial images were then used to determine the relationship between the FC and the VIs using these ‘corrected FCs’. The results showed significant correlation between NDVI and FC (R 2 = 0.80; p < 0.0001). Likewise, the calculated SR not only showed good correlation to the FCs but also verified the calculated NDVI. The results indicated that the methodology of this research can be applied to other tree crops as an aid in estimating the FC.

Sensitivity of the snowmelt runoff model to underestimates of remotely sensed snow covered area

Three methods for estimating snow covered area (SCA) from Terra MODIS data were used to derive conventional depletion curves for input to the Snowmelt Runoff Model (SRM). We compared the MOD10 binary and fractional snow cover products and a method for estimating sub-pixel snow cover using spectral mixture analysis (SMA). All three methods underestimated SCA and this contributed to underestimates in runoff modeled by SRM. The closest relationship between measured and computed runoff was achieved when SRM was run with conventional depletion curves derived from the MODIS binary snow cover product (R2 = 0.91). Although the MODIS fractional snow cover product and SMA did not perform as well as the binary snow cover product (R2 = 0.70 and R2 = 0.72 respectively) we anticipate that either of these methods may be reworked to better account for forest cover in our study area and so improve SCA estimates.

Monitoring Water Use In The Rio Grande Valley Using Remotely Sensed Data

Crop evapotranspiration (ET) is a major component of the hydrologic system. Knowledge of ET is used in irrigation water management, water rights allocation, hydrologic and atmospheric modeling and water resource planning and management. Traditionally, ET has been estimated using crop coefficients and climatic parameters. Point measurements of ET can be made through soil moisture monitoring, vapor flux measurement using either the energy balance Bowen ratio method or the eddy‐covariance method. However, traditional methods will only provide point measurements of ET and they do not account for spatial variability of ET at larger scales. Recent advances in remote sensing have made it possible to monitor water use at large scales with good precision. Here we describe a methodology where data from the Advanced Spaceborne Thermal Emission and Reflection (ASTER) radiometer on NASA’s Terra satellite were used to evaluate water use in an agricultural setting in the Rio Grande Valley of southern New Mexico.