Assessment of Crop Condition and Estimation of Yields by Remote Sensing technique

The use of remote sensing data for estimating crop acreage estimation has reached a stage near operational level. Studies carried out for estimating acreage under different crops in many countries show a near 90 percent accuracy level. In many countries, production forecasting of certain crops, crop yield modeling and crop stress detection are done using remote sensing data. Yield is influenced by many factors, such as crop genotype, soil characteristics, cultural practices adopted, meteorological conditions and influences of diseases and pests. Many approaches have been followed to determine the integrated effects of various parameters that affect crop growth and crop yield. Several yield models have been developed in which data obtained from various types of satellites to cover some of the parameters have been used (Gupta, 1993; Doraiswamy et al, 1996).

A major constraint however is a cloudy sky during the cropping season when normal optical remote sensing cannot give good data. However with the emergence of microwave remote sensed data, this can be overcome as such instruments can penetrate through clouds. To ensure complete success in predicting yields, some more research experiments may be needed to evolve a foolproof system.

Many countries have developed methods to assess crop growth and development from several sources of information such as, surveys of farm operators, crop condition reports from field surveys and local weather information. Remote sensing technology can provide supplemental spatial data to provide timely information on crop condition and potential yields. The timely evaluation of potential yields is increasingly important because of the growing economic impact of agricultural production on world markets. The use of the NDVI parameter to estimate crop yields is a specific extension of the above general concept. The seasonal accumulated NDVI values correlate well with the reported crop yields in semi-arid regions (Groten, 1993).

Crop growth simulation models have been successfully used for predicting crop yields at the field level. However, numerous input requirements that are specific to the crop type, soil characteristics and management practices limit their applicability for regional studies. Integrating parameters derived from remotely sensed data with a growth model provides spatial integrity and near real time "calibration" of crop growth simulations. Remotely sensed data are incorporated in simulations of agricultural crop yields to calibrate or adjust model parameters during the simulation period to ensure agreement between the modeled and satellite observed parameters (Maulin et al, 1995).

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