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ARS Home » Research » Publications at this Location » Publication #83702
Title: PREDICTING POTENTIAL AND ACTUAL CROP GROWTH AND YIELD BASED ON A SIMULATION MODEL WITH REMOTELY SENSED SPECTRAL MEASUREMENTS

Author
item INOUE, Y - NIAES JAPAN
item Moran, Mary
item HORIE, T - KYOTO UNIVERSITY JAPAN

Submitted to: International Symposium on Physical Measurements and Signatures in Remote
Publication Type: Proceedings
Publication Acceptance Date: 4/11/1997
Publication Date: N/A
Citation: N/A

Interpretive Summary: Crop managers use many tools for diagnosing and managing plants and soils to improve profits and improve environmental conditions. One tool that is gaining acceptance is the crop simulation model, which can help the farmer predict growth crop and yield. However, such models require a great deal of detailed information about crops, soils, and management practices, and it is tedious and sometimes impossible to collect all such model parameters. A new approach was tested to use on-site radiometers to measure field crop conditions and incorporate this easy-to-obtain information into the crop simulation model. With this approach, radiometric measurements (such as crop temperature) were used to optimize the model inputs and allow accurate predictions of future crop growth and potential yield. Results showed that this was a viable tool for improved management of rice crops. This approach could be put into operation utilizing radiometric measurements from sensors aboard aircrafts to cover all the farmers' fields in a set of images. Such information from aircraft- and satellite-based sensors is currently available at a reasonable cost. The combination of crop simulation models and remote sensing can provide farmers with an operational means of acquiring information for optimal crop management.

Technical Abstract: Crop simulation approach has been increasing its role in agriculture, especially in prediction, diagnosis and management of plant and environmental dynamics. However, it is not easy to take account of all plant and environmental factors such as water, nutrients, soil, disease and insects, etc., in a model, and it is tedious to collect all input- parameters for each canopy. In the present, therefore, remote sensing methods are combined with a crop simulation model for monitoring and predicting the actual crop growth and yield. On the basis of remote sensing, an automated re-calibration system can give optimum physiological parameters such as initial LAI and light use efficiency for each crop canopy and then predict the growth of LAI and dry matter production. A case study based on a data set for rice canopies shows the potential of this approach in the monitoring/prediction of crop growth and yield.