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Elizabeth Walter-Shea

Publications and source records attributed to Elizabeth Walter-Shea.

3 recordsLinked to original sources

Comparison and analysis of empirical equations for soil heat flux for different cropping systems and irrigation methods

We evaluated the performance of four models for estimating soil heat flux density (G) in maize (Zea mays L.) and soybean (Glycine max L.) fields under different irrigation methods (center-pivot irrigated fields at Mead, Nebraska, and subsurface drip irrigated field at Clay Center, Nebraska) and rainfed conditions at Mead. The model estimates were compared against measurements made during growing seasons of 2003, 2004, and 2005 at Mead and during 2005, 2006, and 2007 at Clay Center. We observed a strong relationship between the G and net radiation (Rn) ratio (G/Rn) and the normalized difference vegetation index (NDVI). When a significant portion of the ground was bare soil, G/Rn ranged from 0.15 to 0.30 and decreased with increasing NDVI. In contrast to the NDVI progression, the G/Rn ratio decreased with crop growth and development. The G/Rn ratio for subsurface drip irrigated crops was smaller than for the center-pivot irrigated crops. The seasonal average G was 13.1%, 15.2%, 10.9%, and 12.8% of Rn for irrigated maize, rainfed maize, irrigated soybean, and rainfed soybean, respectively. Statistical analyses of the performance of the four models showed a wide range of variation in G estimation. The root mean square error (RMSE) of predictions ranged from 15 to 81.3 W m-2. Based on the wide range of RMSE, it is recommended that local calibration of the models should be carried out for remote estimation of soil heat flux.

Transactions of the ASABE↗

SPECTRAL data-based estimation of soil heat flux

Numerous existing spectral-based soil heat flux (G) models have shown wide variation in performance for maize and soybean cropping systems in Nebraska, indicating the need for localized calibration and model development. The objectives of this article are to develop a semi-empirical model to estimate G from a normalized difference vegetation index (NDVI) and net radiation (Rn) for maize (Zea mays L.) and soybean (Glycine max L.) fields in the Great Plains, and present the suitability of the developed model to estimate G under similar and different soil and management conditions. Soil heat fluxes measured in both irrigated and rainfed fields in eastern and south-central Nebraska were used for model development and validation. An exponential model that uses NDVI and Rn was found to be the best to estimate G based on r2 values. The effect of geographic location, crop, and water management practices were used to develop semi-empirical models under four case studies. Each case study has the same exponential model structure but a different set of coefficients and exponents to represent the crop, soil, and management practices. Results showed that the semi-empirical models can be used effectively for G estimation for nearby fields with similar soil properties for independent years, regardless of differences in crop type, crop rotation, and irrigation practices, provided that the crop residue from the previous year is more than 4000 kg ha-1. The coefficients calibrated from particular fields can be used at nearby fields in order to capture temporal variation in G. However, there is a need for further investigation of the models to account for the interaction effects of crop rotation and irrigation. Validation at an independent site having different soil and crop management practices showed the limitation of the semi-empirical model in estimating G under different soil and environment conditions.

Transactions of the ASABE↗

Southeastern U.S. vegetation response to ENSO Events (1989–1999)

El Niño/Southern Oscillation (ENSO) is considered one of the most powerful forces driving anomalous global weather patterns. Large-scale seasonal precipitation and temperature changes influenced by ENSO have been examined in many areas of the world. The southeastern United States is one of the regions affected by ENSO events. In this study, remote sensing detection of vegetation response to ENSO phases is demonstrated with one-kilometer biweekly Normalized Difference Vegetation Index (NDVI) data (1989–1999) derived from the Advanced Very High Resolution Radiometer(AVHRR). The impacts of three ENSO phases, cold, warm and neutral, on vegetation were analyzed with a focus on two vegetation cover types, two seasons and two geographic regions within the southeastern U.S. Significant ENSO effects on vegetation were found in cropland and forest vegetation cover types based on image and statistical analysis of the NDVI data. The results indicate that vegetation condition was optimal during the ENSO neutral phase for both agricultural and natural vegetation.

Climatic Change↗