Declaration 2 Publications and manuscripts
2.7. Overall challenges and opportunities
30 The data processed through these steps were reduced to a single image, from which the mean digital number (DN) per cultivated sugarcane area was calculated, and the image DNs were subsequently transformed to cane yield (t ha-1) through normalisation. The results showed that this method proved to be efficient in yield prediction and was more accurate than traditional methods used in the Brazilian sugarcane agro-industry. It was recommended that further research in tropical terrains such as Brazil should consider issues about how variables such as topography, sugarcane variety, soil type, and spectral and spatial resolution might affect the quality of the correlation and the forecast of sugarcane yield.
The limitation of the above-mentioned studies is that no operational model for predicting sugarcane yield has been produced, and most of the studies have been undertaken at research level. Moreover, with exception of Bappel et al. (2005), no study implements the combination of sugarcane growth simulation models with sugarcane biophysical parameters derived from remotely-sensed data for providing relatively accurate estimates of sugarcane yield. In addition, testing of fine-resolution sensors for estimates of sugarcane yield is needed, especially for large fields.
31 For the potential use of remote sensing for making day-to-day decisions on sugarcane management, there is overall importance to the sugarcane growers of timely, up-to-date, and accurate data. Despite the recent development of new sensors and processing techniques, very few sugarcane farmers presently have access to regular images of their farms, and even when they do, slow turnaround of the processed product continues to be a problem.
Despite these shortcomings, there is no doubt that remote sensing technology will permeate many aspects of sugarcane farming in the future. Grower acceptance will increase as products with higher spatial, spectral, and temporal resolution become more efficient, affordable, and cost-effective. By selecting appropriate spatial and spectral resolution, and suitable processing techniques for extracting sugarcane spectral information, the remotely-sensed data should find use in sugarcane agriculture in all areas of applications with satisfactory results.
From a research perspective, however, there are several overarching and interrelated challenges that must be dealt with to advance remote sensing beyond today’s largely qualitative applications for sugarcane crop management (Pinter et al., 2003). The first challenge deals with sugarcane nutritional status. Some research in the past has been undertaken to estimate foliar nitrogen and potassium concentrations in experimental fields using handheld spectrometers, with promising results. The use of spectral observations from polar orbiting hyperspectral satellites (e.g., Hyperion) for at-field foliar nitrogen estimation is a challenge for estimating sugarcane foliar nitrogen distribution over large areas, which could be integrated into further planning and management of sugarcane farms.
Second, the attacks on sugarcane crops by many pests and diseases that significantly reduce its yield warrant serious investigation. Detection and delineation of disease- infected (e.g., orange rust) sugarcane areas using hyperspectral remotely-sensed data have been attempted but more work is needed to investigate new insect invaders (e.g., thrips in South Africa). Such work should take advantage of handheld field
32 spectroradiometers and polar orbiting hyperspectral sensors (e.g., Hyperion) as well as the power of sophisticated spatial data processing and analysis software to detect and model sugarcane pest and disease outbreaks and damage. A third research challenge is that in some countries (e.g., South Africa) there are Water Acts and Legislation which levies certain land uses (e.g., commercial forestry, sugarcane) according to their potential for reducing stream flows. No research has yet been carried out on estimating water requirements for sugarcane using process-based models that use remotely-sensed data as input parameters.
The fourth challenge deals with understanding and being able to investigate the techniques that estimate some sugarcane biophysical parameters [e.g., LAI, APAR (Absorbed Photosynthetically Active Radiation)] through spectral data; and the possibility of using these parameters for sugarcane growth simulation model (e.g., CANESIM) to enhance their accuracy which should be studied further. The use of fine spatial resolution sensors (e.g., IKONOS, 2005; NASA, 2006; SIC, 2007) will benefit prediction of sugarcane yield in small fields. Moreover, researchers could use microwave remotely-sensed data (RADARSAT-2, 2006) in wet tropical regions to reduce the problems of cloud cover and to provide accurate information on the physical structure of sugarcane fields. In addition, the use of radar may complement multispectral remotely- sensed data through data fusion.
Fifth, although some work has been undertaken on the prediction of sugarcane yield using remotely-sensed data, there is still a challenge to develop accurate operational sugarcane yield-estimating models that take advantage of improvements in sensor characteristics and processing as well as analysis techniques.
Acknowledgements
The candidate is very grateful to the anonymous reviewers for their useful comments and suggestions.
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CHAPTER THREE
Estimation of sugarcane leaf nitrogen concentration using a handheld spectrometer under laboratory conditions
This chapter is based on
Abdel-Rahman, E. M., Ahmed, F. B. and van den Berg, M., 2008. Imaging spectroscopy for estimating sugarcane leaf nitrogen concentration. Proceedings of SPIE Remote Sensing for Agriculture, Ecosystems, and Hydrology X Conference, V-1 – V-12.
34 Abstract
Spectroscopy can provide real-time high throughput information on growing crops. The spectroscopic data can be obtained from handheld, spaceborne, and airborne sensors.
Such data have been used for assessing the nutritional status of some field crops (e.g., maize, rice, barely, wheat, and potato). In this study a handheld FieldSpec® 3 spectroradiometer in the 350–2500 nm range of the electromagnetic spectrum was evaluated for its use to estimate sugarcane leaf nitrogen (N) concentrations. Sugarcane leaf samples from one variety viz., N19 of two age groups (4–5 and 6–7 month) were subjected to spectral and chemical measurements. Leaf reflectance data were collected under controlled conditions, and leaf N concentrations were obtained using an automated combustion technique (Leco TruSpec® N). The spectral data were transformed to their first-order derivative and continuum-removed spectra. The potential of spectroscopic data for estimating sugarcane leaf N status was evaluated using univariate correlation analysis method with the transformed reflectance data. The variables that presented high correlation with N concentration were used to develop SR-based (Simple Ratio) and NDVI-based (Normalised Difference Vegetation Index) indices. Simple linear regression was then used to select a model that yielded the highest R2. These were the SR (744, 2142)indexfor the 4–5 month old sugarcane crop and the NDVI (2200, 2025) indexfor the 6–7 month old cane crop, with R2 of 0.74 (Root Mean Square Error of Prediction:
RMSEP = 0.120; 7.90% of the mean) and 0.87 (RMSEP = 0.100; 5.87% of the mean), respectively.
Keywords: spectroscopy; hyperspectral; sugarcane; nitrogen estimation
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