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Among the techniques that have been developed in spectroscopy, derivative analysis is particularly promising for use with remote sensing data. In the first step of this research we apply the derivative spectrum in a real hyperspectral image and introduce a new target detection approach called “DCEM”. For this purpose, 1st to 5th orders of derivative spectrum were applied to the DCEM. The outcome of this research has shown that the application of derivative spectrum in target detection is perfectly advisable in a specific derivative order for each target. This order can be introduced as an optimized order or the Best DCEM. The spectrum differentiation eliminates low frequency components of the spectrum. Despite the little information included in those low frequency components of a signal or spectrum, their complete elimination cause an information loss problem. Hence, in the second step of this research an ensemble classifier approach was employed for the combined use of both spectra and the best derivative order. This simultaneous use of the derivative and zero order spectra is introduced as “ECEM”. Experiments were conducted via a HyMap hyperspectral airborne image in eastern Iran. The detection results show that both proposed methods significantly outperform CEM in ROC and AUC values. The best performance upgrade in DCEM detection was about 24% for Kaolinite target and about 28% for Alunite target in ECEM. [ABSTRACT FROM AUTHOR]

While Soil Moisture (SM) can be used as a direct indictor of dryness, the Land Surface Temperature (LST) and vegetation status are indirectly related to SM, and consequently, affect the degree of dryness. Each of these three variables has been individually used to assess the dryness. Furthermore, a combination of the LST and vegetation information has also been applied for dryness estimation through the scatter-plot of the LST and Normalized Difference Vegetation Index (NDVI). However, a combination of these three variables has not been used to estimate the dryness. In this study, a 3 Dimensional (3D) space of the LST, Perpendicular Vegetation Index (PVI), and SM was developed to define a new dryness index named the Temperature-Vegetation-soil Moisture Dryness Index (TVMDI). The TVMDI was evaluated against in situ SM and soil temperature data, as well as the NDVI. The results demonstrated that the TVMDI values were highly correlated with the field soil temperature data and the NDVI (R = 0.78 and R = − 0.87, respectively). However, even though the dryness values estimated from the TVMDI were not highly correlated with the in situ SM data, the correlation was still acceptable (R = − 0.65). Furthermore, a comparison between the proposed index and several satellite-based dryness indices, including the Perpendicular Drought Index (PDI), Modified Perpendicular Drought Index (MPDI), Second Modified Perpendicular Drought Index (MPDI1), and Temperature-Vegetation Dryness Index (TVDI), concluded that the TVMDI was the most accurate index. Moreover, using the TVMDI and multi-temporal Landsat 8 images two dryness maps were produced for the Yanco area to show the effectiveness of the proposed index in depicting the temporal and spatial variation of dryness values for medium spatial resolution images. To show the effectiveness of the TVMDI in large scale applications, two dryness maps from the all of Australia and Iran were also produced using the MODerate Resolution Imaging Spectrometer (MODIS) data. Overall, it was concluded that the TVMDI had a high potential to map dryness over different regions, and is a promising index for early awareness of drought risk management. [ABSTRACT FROM AUTHOR]