The compact Reconnaissance Imaging Spectrometer for Mars (CRISM) has significantly advanced our understanding of the mineralogy of Mars. With its enhanced spectral and spatial resolution, CRISM has enabled the identification and characterization of various minerals on the Martian surface, providing valuable insights into Mars’ past climate and geologic history, as well as the evolution of the planet’s atmosphere and climate. We present a comprehensive review of mineral identification on Mars using CRISM data. We discuss the data description, pre-processing techniques, different spectrum libraries, geological characteristics used for mineral identification, challenges, and methodologies used for mineral classification, such as learning models, probabilistic methods, and neural networks. We highlight major findings of minerals on the Martian surface and discuss validation techniques. We conclude with a discussion of further research to address the existing gaps and challenges in this field. Overall, we provide a general understanding of mineral classification using CRISM data and could serve as a helpful resource for researchers and scientists interested in planetary remote sensing and mineral identification on the Martian surface.
Spatial Information about Soil moisture over agricultural crops are required for efficient irrigation which in turn helps in saving water and increases crop yield. Soil moisture also useful in prediction of flooded and drought regions. However field measurement of soil moisture is not a practical approach. The main objective of the study is to track soil moisture variation all along the maize growth period in a Semi-Arid region. There are only few studies carried out on soil moisture variation considering whole maize growing period. During the crop growing period soil moisture field investigation are conducted in synchronization with Satellite pass. Sentinel-1a Synthetic Aperture RADAR (SAR) satellite, Interferometric wide swath dual polarized data with 5.405 GHz frequency and central incidence angle of 23 with repeat period of 12 days was used in this study. All in all during growth period 6 satellite pass scenes are acquired and processed by standard procedure using Sentinel Application Platform (SNAP) software. An attempt was made to redeem surface soil moisture for the whole maize growing crop cycle using water cloud model. The whole period of maize crop was divided into 3 parts like seedling, growing and harvesting period and soil moisture is retrieved for each period. The estimated soil moisture was validated with 30 field measured soil moisture samplings. The correlation coefficient of retrieved and actual soil moisture of seedling, growing and harvesting periods are 0.77, 0.72 and 0.6 respectively. The output of this study will be helpful in formulating strategies for irrigation water management.
The present study is concerned with the development and test of an integrated remote sensing and GIS based
methodology for identification of groundwater potential areas in a humid tropical river basin. Indian Remote
Sensing Satellite (IRS 1C-LISS-III) data along with other collateral data such as existing maps and field
observations was utilized to extract information on the hydro-geomorphic features of the terrain. The study
involves two components: (a) demarcation of groundwater potential zones (b) validation of sites with yield data.
In order to demarcate potential groundwater zones, six pertinent thematic layers were integrated and assigned
appropriate rankings. Layers considered were: geology, geomorphology, drainage density, slope, rainfall with
infiltration factor and land cover map. The layer parameters were also rated according to their importance
relative to other classes in the same theme. All the layers were superimposed and analyzed in ARC GIS environment. A linear additive model based on the DRASTIC model concept was used to find the groundwater
potential index (GPI). The map comprised of six categories of groundwater yield. To carry out more focused
investigations on the potential zones, lineament maps were superimposed over it. The validity of different
potential zones identified using the GIS-based model was compared with available borewell yield data and
found to be in good agreement. The map generated can be used in future as a preliminary screening tool in
selecting well sites and as a basic tool in land use planning for groundwater protection.
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