Declaration 2 Publications and manuscripts
9.5 The practical and operational use of remote sensing techniques in sugarcane
Unlike other field crops, many sugarcane varieties of different age groups are grown in the same area during the growing season. For example, in April a 5-month old ratoon crop can stand adjacent to a 15-month old plant crop. Hence, the spectral and spatio- temporal characteristics of sugarcane are very complex. Furthermore, sugarcane production systems in some regions like South Africa are very dynamic and characterised by diverse climatic conditions (rainfall, temperature, soil, management etc.). These variations and complexity make the development of a single universal model based on
176 remotely-sensed data for detecting sugarcane stress factors (e.g., N deficiency and thrips damage) and predicting its yield quite a difficult task. Different stress factors may cause similar biophysiological changes, in addition to the visible symptoms thereof on sugarcane crops. Yellowish patches on sugarcane leaves, for example, may be due to thrips damage, N deficiency, disease infection, etc. These confounding effects may be detectable using spectral features in the same region of the electromagnetic spectrum.
Spectral features on the red edge position of the electromagnetic spectrum, for instance, could be used to detect insect damage, disease infection, N deficiencies, and so forth. For practical and operational use the exact cause of the spectral changes within a specific region (e.g., red edge) of the spectrum must be identified and, consequently, the specific stress factor must be detected in order to apply the appropriate agricultural input (insecticides, herbicides, N fertilizers, etc.) to control the cause of the stress.
Given the difficulty of developing a universal model to detect each stress factor separately on the one hand, and the high cost per unit area of the hyperspectral data that are captured from airborne (e.g., AVIRIS) and spaceborne (Hyperion) sensors on the other hand, the future practical and operational applications of remote sensing techniques in sugarcane production could involve the use of multispectral data. This could be done in the near future, after the successful launch of the new specific-centric South African Sumbandilasat microsatellite which carries a multispectral sensor, with 6.25 m spatial resolution and 6 spectral bands (Main et al., 2008; Mostert et al., 2008). Two of its bands (i.e., the red edge band and xanthophyll band) have been strategically placed given an a priori relationship between the vegetation spectral features in these two bands and vegetation status (Main et al., 2008). Multispectral data are relatively inexpensive, accessible, and do not require complex preprocessing and processing techniques. In this regard, the use of multispectral data should be operationalised and implemented to provide informed and near-real-time management practises such as:
• Estimation of area under cane: the broader multispectral wavebands should mask the spectral differences between some sugarcane varieties, close age groups, seasonality, and other vegetation and/or crops and sugarcane etc. Hence the
177 multispectral data can be used on a routine basis for mapping areal extent of sugarcane.
• Monitoring harvest: Mutispectral data could identify and map sugarcane fields of standing crops that need to be harvested, trashed fields, green harvested fields, and fields of bare soil that may have been burnt.
• Detection and mapping of stress factors that cause distinct and clear symptoms on sugarcane crops (e.g., rust disease).
• Detection of areas of anomalies: in this case multispectral data could be used to detect areas in the field with anomalies (within field variations). Then, by conducting field visits, the cause of the anomaly in the field could be identified and the appropriate agricultural input could be applied.
• Enhancing of the accuracy of the process-based growth models that are routinely used for sugarcane production forecasts (e.g. Canesim): Spectral information extracted from multispectral data can be very useful for this.
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