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Jan Jansonius
NDVI
NOAA-AVHRR remote sensing data has been regularly used by many countries. The Normalized Differential Vegetation Index (NDVI) is a measurement of the greenness of the ground cover, either crops, grazing land, forest or other vegetation and is correlated with plant vigour and potential yield. It is a valuable indicator for crop conditions, especially in areas where rainfall is a major limiting factor. For example a drought sneaking up in remote parts of a country, its spreading and eventual retreat can be closely monitored by successive NDVI images with 10-daily interval.
It should be realized that a sharp reduction in NDVI values could reflect crop maturity or water stress during an earlier crop stage. Therefore, precise information on planting and harvesting period of various crops and varieties is an essential requirement. Regular inspection of field conditions for calibration of the satellite data or “ground truth” is always required.
There are several ways to relate NDVI values to crop yield. One indicator is the total of average monthly values. An other, probably better, indicator is a weighted average of NDVI values during different crop stages, where a higher weight is given to flowering and early grain filling stages. Current NDVI values should always be compared to those of several previous years for the same crop in the same area. A time series of NDVI and crop yield data is required for establishing a regression function. The eruption of the volcano Pinatubo in the Philippines in June 1991 has affected NDVI values during at least two years.
NDVI is ideal for monitoring large areas from one 10-day period to the next, including remote areas where road access is difficult. In semi-desert areas where grazing conditions are studied, the onset of rains will be shown nearly immediately and by spectacular colour change on the NDVI imagery (for example the Ogaden region of Ethiopia).
NDVI has also been used, in favourable circumstances, for estimates of planted crop area and for the assessment of the area affected by flooding or other natural catastrophes.
Meteosat CCD
Meteosat imagery has also been in use in many countries. The FAO ARTEMIS programme produces Cold Cloud Duration (CCD) values, which are correlated with rainfall. Such techniques are normally considered as a complement rather than a substitute for traditional rainfall recording by raingauges.
Difficulties that have been associated with remote sensing techniques are the access to the data, its cost, the cost of the equipment to analyze the data and lack of trained staff.. Thick cloud cover during the monsoon season can be the cause of missing NDVI data.