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United States Department of Agriculture

Agricultural Research Service

Title: Demonstration of a Remote Sensing/modelling Approach for Irrigation Scheduling and Crop Growth Forecasting 1437

Authors
item Dabrowska-Zielinska, K. - IF&C, REMOTE SESNING CNT
item Moran, Mary
item Maas, S. - TEXAS A & M
item Pinter Jr, Paul
item Kimball, Bruce
item Mitchell, T. - AGILENT TECHNOLOGIES
item Qi, J. - MICHIGAN STATE UNIVERSITY

Submitted to: Journal of Water and Land Development
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: December 20, 2001
Publication Date: December 20, 2001
Citation: Dabrowska-Zielinska, K., Moran, M.S., Maas, S.J., Pinter Jr, P.J., Kimball, B.A., Mitchell, T., Qi, J. 2001. Demonstration of a remote sensing/modelling approach for irrigation scheduling and crop growth forecasting. Journal of Water and Land Development. 5:69-87.

Interpretive Summary: Production of agricultural crops in the arid and semiarid areas of the world is almost totally dependent on irrigation. Nevertheless, farmers are still searching for ways so determine the most beneficial time to apply just the right amount of water. The prototype biomass and evaporation (PROBE) simulation model has been used to monitor plant evaporative water loss, and thus, offer information about plant water status that could be used for irrigation scheduling. In this study, we tested the use of PROBE for cotton irrigation scheduling in Arizona. Results showed that modeled and measured values of evaporative water loss compared well, and PROBE provided information critical for cotton irrigation scheduling. Accurate irrigation scheduling is in the interests of everyone since overwatering can result not only in decreased profits for the farmer but also in pollution of local ground water sources.

Technical Abstract: The PROtotype Biomass and Evaporation (PROBE) model was developed for simulation of daily plant growth and evaporation (E) rates in natural, vegetated ecosystems. The inputs to the model are basic meteorological information and periodic (weekly or bi-weekly) measurements of green leaf area index (GLAI) and E. The model uses an interactive approach with two submodels - a vegetation growth (VG) submodel and soil water balance (SWB) submodel - where the estimate of GLAI from the VG submodel is used in the SWB submodel to calculate E. In turn, the estimate of E is used in a rerun of the VG submodel to refine the estimate of GLAI. This model was tested based on meteorological data and measurements of GLAI and E acquired in a cotton (Gossypium hirsutum L.) field in central Arizona. Overall, the modeled and measured values of GLAI and E corresponded well. Results showed that the time and precision of input data were very important to obtaining accurate estimates of GLAI and E. The model showed promise for use in scheduling crop irrigations.

Last Modified: 12/24/2014
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