Full text: Remote sensing for resources development and environmental management (Volume 1)

Crop production forecast is an important parameter in the 
food security programme of a developing country. 
Therefore, in order to apply remote sensing effectively the 
following preconditions should be met: 
- The availability of the data during the actual growing 
season must be guaranteed. The acquisition of the 
relevant satellite remote sensing data for crop 
forecasting purposes in the vegetational season is one 
of the major constraints facing the users in many parts 
of Africa. This is mainly due to the lack of ground 
receiving stations that cover the region between the 
Sahara and Southern Africa. Moreover, the period 
between the order and the delivery of the image data 
lies between 4-8 weeks. There are at present serious 
proposals regarding the introduction of mobile 
receiving stations which could be used to support the 
development projects in many developing countries. 
- The data should be interpreted as early as possible. In 
order to be able to deliver information well in time, an 
immediate processing and analysis of the remote 
sensing data is necessary. This requirement would be 
genuinely fulfilled only if enough well-trained and 
motivated personnel is available. 
- The interpretation methods should take the regional or 
the local conditions into consideration. Owing to the 
fact that the derivation of information from remote 
sensing data can be conducted in various methods such 
as the visual image interpretation, digital image class 
ification, or a combination of both, the choice of the 
method of interpretation should suit the available 
- The allocation of enough funds for this purpose should 
be ensured. In order to establish an efficiently functio 
ning crop forecasting programme, financial support 
should be ensured for the technical and administrative 
aspects. The drought affected countries suffer from 
inadequate financial resources and the lack of skilled 
man-power aggravates the situation. These make a 
technical assistance either international or bilateral 
character indispensable. However, this technical assis 
tance should not be unnecessarily prolonged as is with 
many development projects. 
The components of crop production forecasting are the 
identification of crop type and the estimation of crop area, 
monitoring of crop condition, and the estimation of crop 
2.1 Estimation of crop area 
In most of the drought affected countries in Africa, dry 
farming or animal rearing is the main occupation of the 
rural population. The agricultural area is therefore very 
vast and mostly dispersed over several provinces. The 
pattern of cultivation is diversified and the fields are 
irregular. Though the number of newly registered farms 
increases yearly, the land in the traditional hands is usually 
not included in the official statistics. However, in order to 
conduct a sound crop forecasting the estimation of the 
area under crop is essential. Ground field survey would 
take a long time and is costly. The conventional aerial 
survey, though accurate is expensive. Therefore the best 
method is the derivation of information from satellite 
images, aerial photographs and a limited ground field 
survey (fig.l), in combination with the available ancilliary 
data on soil, topography, and location. 
2.2 Monitoring of crop condition and growth 
The rainfall fluctuations - timing, frequency, quantity and 
intensity - is very great in most of the arid and semi-arid 
countries of Africa. The monitoring of crop condition and 
crop growth is therefore an essential part for the estimati- 
Figure 1. A simplified diagramme showing the estimation 
of crop production using remote sensing data. 
on of the eventual area under adversely affected 
conditions. Crop condition monitoring is mainly confined to 
cash-crop plantations. 
This zone is also a fertile breeding area for the desert 
locust which is a great hazard to the crops in the region if 
not carefully and continuously monitored. Several experim 
ents have been made with satellite remote sensing data to 
locate the valleys which may serve as the locations for the 
hatching locust eggs (Hielkma 1980). 
2.3 Estimation of the crop yield 
Crop yield, is among others a function of weather, soil and 
the vigor of the plants. A crop production forecast can be 
conducted early enough before the harvest if the yield 
prospects per hectare are known. There are of course 
several methods of estimation, namely: 
- The forecast of the yield based on characteristics of 
plant or crop, and relation-ships based on weather 
experiences in the former years. This method has been 
intensively described in the agrometeorological liter 
- Estimation of the yield through the spectral values of 
the plant using a digital spectrometer or densitometer. 
The spectral reflectances of such a vegetation, water 
or sand have different characteristics as shown in (fig. 
2). This method is based on the concepts of the high 
correlation between the density of biomass and the 
ratio of the sunlight reflectance of infra-red and red 
The Kenya Rangeland Ecological Monitoring Unit made 
experiments in estimating maize yield in 1984 using a 
digital photometer. Their results according to 
(Peden,Mwenda 1984) were comparable with those of the 
Ministry of Agriculture and Livestock Development. 
However, there are certain limitations for mixed crops 
where the seperation of the responses could be difficult; or 
where the maize plants grow sparsely, as in (fig. 3), 
particularly by the rainfed agricultural areas, in which the

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