Crop Discrimination Using Field Hyper Spectral Remotely Sensed Data

Crop discrimination through satellite imagery is still problematic. Accuracy of crop classification for high spatial resolution satellite imagery in the intensively cultivated lands of the Egyptian Nile delta is still low. Therefore, the main objective of this research is 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 is considered as a 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 are 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 will 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. The study was carried out in Damietta governorate of Egypt.

Internet-Based Spectral Database for Different Land Covers in Egypt

The spectral signatures of natural objects in the visible and near-infrared spectral range are influenced by the object’s physical and biochemical properties. These signatures can be compiled in a database and used to retrieve information of land cover types and their physical composition from actual hyperspectral observations. This paper describes development process of hyperstectral database of reflectance from different land cover types in Egypt. It has been compiled from data obtained using a ground-based spectroradiometer system that covers the spectral range from 350 to 2500 nm at 1 nm resolution. The database is accessible through a website http://www.spectraldb.narss.sci.eg/spectral, where the system includes also metadata that describes the site environment and measurement processes. The system provides flexible mechanisms and friendly interfaces to allow accessing the database by the non-specialized people, whereas spectral data can be sorted by sites, species or selected environmental parameters. The system presents sample results from different vegetation and soil covers. Development of such a database is essential for different remote sensing applications, satellite’s calibrations, data dissemination and linkage with other databases for scientific researches purposes.