Accuracy of crop classification for high
spatial resolution satellite imagery in the intensively cultivated lands of the
Egyptian Nile delta is still low. The main objective of this research was to
determine the optimal hyperspectral wavebands in the spectral range of (400 - 2500
nm) to discriminate between two winter crops (wheat and clover) and two summer
crops (maize and rice). This was considered as the first step to improve crop
classification through satellite imagery in the intensively cultivated areas in
Egypt.
Hyperspectral
ground measurements of ASD field Spec3 spectroradiometer was used to monitor
the spectral reflectance profile during the period of the maximum growth stage
of the four crops. 1-nm-wide was aggregated to 10-nm-wide bandwidths. After
accounting for atmospheric windows and/or areas of significant noise, a total
of 2150 narrow bands in 400 - 2500 nm were used in the analysis.
Spectral reflectance was divided into six spectral zones: blue, green, red,
near-infrared, shortwave infrared-I and shortwave infrared-II. One Way ANOVA
and Tukey’s HSD post hoc analysis was performed to choose the optimal spectral
zone that could be used to differentiate the different crops. Then, linear
regression discrimination (LDA) was used to identify the specific optimal
wavebands in the spectral zones in which each crop could be spectrally
identified.
The results of
Tukey’s HSD showed that blue, NIR, SWIR-1 and SWIR-2 spectral zones were more
sufficient in the discrimination between wheat and clover than green and red
spectral zones. At the same time, all spectral zones were quite sufficient to
discriminate between rice and maize. The results of LDA showed that the
wavelength zone (727:1299 nm) was the optimal to identify clover crop while
three zones (350:712, 1451:1562, 1951:2349 nm) could be used to identify wheat
crop. The spectral zone (730:1299 nm) was the optimal to identify maize crop
while three spectral zones were the best to identify rice crop (350:713,
1451:1532, 1951:2349 nm). An average of thirty measurements for each crop was
considered in the process.
These results would
be used in machine learning process to improve the performance of the existing
remote sensing software’s to isolate the different crops in intensive
cultivated lands.
Article by Sayed
M. Arafat, et al, from National Authority for Remote Sensing and Space Sciences
(NARSS), Cairo, Egypt.
Full access: http://mrw.so/2c9WGm
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