REMOTE SENSING FOR CROP AND WATER MANAGEMENT IN IRRIGATED AGRICULTURE
Location: Water Management and Conservation Research
Title: COMBINING REMOTELY SENSED DATA AND GROUND-BASED RADIOMETERS TO ESTIMATE CROP COVER AND SURFACE TEMPERATURES AT DAILY TIME STEPS
Submitted to: Journal of Irrigation and Drainage Engineering
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: August 20, 2009
Publication Date: September 3, 2009
Citation: French, A.N., Hunsaker, D.J., Clarke, T.R., Fitzgerald, G.J., Pinter, Jr., P.J. 2009. COMBINING REMOTELY SENSED DATA AND GROUND-BASED RADIOMETERS TO ESTIMATE CROP COVER AND SURFACE TEMPERATURES AT DAILY TIME STEPS. Journal of Irrigation and Drainage Engineering. 136:4, 232-239.
Interpretive Summary: The ability to measure and predict crop water use is emerging as an important objective for water management in arid lands of the United States. The only way to effectively monitor croplands and their water use during a growing season is to utilize remotely sensed image data since they can provide synoptic views unavailable in any other way. However, current remote sensing platforms do not provide image data frequently enough to be useful by themselves for irrigation decision making. Reasons for this shortcoming include insufficient number of platforms, cloudy skies, insufficient spatial resolution, and high acquisition costs. To help reduce this problem an alternative scheme is proposed that merges available remote sensing data with ground-based observations of crop cover and temperature. The modeling forecasts crop cover at daily time steps and land surface temperatures at sub-hourly time steps. The resulting synthetic images are created between remote sensing observation times and could be very helpful for generating daily estimations of cropland evapotranspiration. Results of this research are helpful to other scientists and engineers seeking better decision making tools for crop water management.
Estimation of evapotranspiration (ET) is important for monitoring crop water stress and for developing decision support systems for irrigation scheduling. Techniques to estimate ET have been available for many years, while more recently remote sensing data have extended ET into a spatially distributed context. However, remote sensing data cannot be easily used in decision system if they are not available frequently. For many crops ET estimates are needed at intervals of a week or less, but unfortunately due to cost, weather and sensor availability constraints, high resolution ($<$100 m) remote sensing data are usually available no more frequently than two weeks. Since resolution of this problem is unlikely to occur soon, a modeling approach has been developed to extrapolate remotely sensed inputs needed to estimate ET. The approach accomplishes this by combining time series observations from ground-based radiometers and meteorological instruments with episodic visible, near infrared, and thermal infrared remote sensing image data. The key components of the model are a vegetation density predictor, a diurnal land surface temperature disaggregator. To illustrate model implementation, remote sensing and ground-based experimental data were collected for cotton grown in 2003 at Maricopa, Arizona, USA. Spatially distributed cotton canopy densities were predicted at daily time steps for eight days using vegetation indices from remote sensing and fractional cover from ground-level photography. Spatially distributed canopy and soil surface temperatures were predicted at 15-minute time steps for the same period by scaling diurnal canopy temperatures according to time of day and degree of vegetative cover. Considering that the predictions included a rapidly growing phase, comparison of spatially projected canopy cover with observed cover were reasonably good, with R2=0.65 and a root mean squared error (RMSE) of 0.13.Comparison of predicted temperatures also showed fair agreement, with RMSE= 2.1 °C. These results show that combining episodic remotely sensed data with continuous ground-based radiometric data is a feasible way to forecast spatially distributed ET over crops.