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This review article provides an overview of various aspects of lunar exploration, including missions to the Moon, collection and analysis of lunar sample data in laboratories, and the processing and analysis of remote sensing data, particularly using radar techniques. Both remote sensing and in-situ methods are critical for advancing our understanding of the lunar surface and its properties. This review article focuses on the identification and quantification of water–ice deposits located in areas such as Permanently shadowed areas (PSRs) and the lunar poles (section " Lunar Poles and PSRs: A Special Environment "). These volatile resources have the potential to serve as valuable sources of fuel for future missions, making it crucial for the lunar community to determine their abundance and distribution. After thoroughly examining lunar samples using high-precision laboratory techniques, many preconceptions were dispelled which is highlighted in the section " Laboratory Investigation of Lunar samples ". But as in-situ observations are difficult to acquire, especially terrestrial bodies samples, remote sensing techniques allow the global understanding of the surface. The article specifically highlights the importance of understanding the electrical characteristics of the lunar surface and how radar inversion can provide valuable information in this regard. The " Conclusion " section of this review article serves as a key takeaway for readers, underscoring the critical role that both in-situ and remote sensing techniques play in advancing our understanding of the Moon. [ABSTRACT FROM AUTHOR]

The peculiar illumination and insolation conditions at the poles of the Moon lead to the formation of Permanently Shadowed Regions (PSR) that act as cold traps for volatiles and are important for In Situ Resource Utilization (ISRU). However, since these regions lie in shadows of surrounding topographic highs, the ability of optical remote sensing in imaging these regions for photo reconnaissance is limited and using other modes of remote sensing becomes imperative. In this work, reflectance from optical data, backscatter from active microwave data and temperature from thermal data from the surfaces of three craters – de Gerlache, Haworth and Shoemaker – on the South Pole of the Moon, in close proximity to the Artemis III candidate landing sites, has been studied. Images from Clementine UVVIS, LRO WAC and LRO NAC have been compared with fully polarimetric data from the L-bands sensor of Chandrayaan-2 DFSAR. Further, the temperatures at the surface of the craters have been studied using data from LRO DIVINER. Since large parts of the craters under study lie in permanent shadows, Clementine UVVIS and LRO WAC could image only some parts of the rims. LRO NAC, with its longer exposure times, could image some parts within the crater however the walls of the crater and the floor near the walls remained in shadow. DFSAR was found to be useful in viewing the permanently shadowed regions of the craters. Overall, the backscatter in HH and VV (co-pol) channels was higher than the backscatter in HV and VH (cross-pol) channels. The elevated rims of the craters were clearly visible in co-pol channels and the rough texture of the ejecta of the craters had a distinct high backscatter in cross-pol channels. This fully polarimetric data can be used to map the interiors of the craters where optical data has low albedo. Analysis of the thermal data revealed that within the craters, in the regions that were in perpetual shadows, the maximum summer temperatures were less than 150 K making these regions cold traps for volatiles and possible locations of surface water–ice. The parts of craters lying in the shadows of surrounding higher features had lower maximum temperatures in summer and the parts facing them had higher maximum temperatures due to reflected insolation. Seleno Referenced Image (SRI) product from Chandrayaan-2 DFSAR was found to be a viable alternative to optical data for viewing the PSRs and for their geologic mapping. [ABSTRACT FROM AUTHOR]

Imaging spectrometry is a powerful tool among remote sensing techniques to analyze the chemical compositions and physical properties of the lunar regolith. Recalibration of the classical method byLucey and co-workers with Moon Mineralogy Mapper (M3) data provides an opportunity to understand the FeO, TiO 2, and optical maturity distributions over the lunar nearside on a global scale. We estimated the chemical compositionsand optical maturity from the M3 spectral reflectance around the 1 µm region. The standard chemical compositions of the lunar landing sites were estimated based on origin optimization and data fitting. We obtained an optimized origin of (0.08, 1.18) for FeO and (0.07, 0.71) for TiO 2. The Spectrophotometric evaluations of Catharina and Cyrillus craters, located in the lunar nearside are categorized under the matured category. The estimation results reveal that the FeO wt % of the Catharina crater varies from 0.0002 to 19.62, and that of the Cyrillus crater varies from 0.0001 to 12.32. TiO 2 wt% of Catharina crater varies from 0.01 to 86.12, while Cyrillus Crater varies from 0.01 to 4.34. The optical maturity (OMAT) of the Catharina crater varies from 0.0001 to 0.19 to that of the Cyrillus Crater Varies from 0.01 to 0.17. The overall summary of the results points out that lunar regolith is a fabulous repository of FeO and TiO 2 and quantitatively this metal abundance may be more than the anticipated values. [ABSTRACT FROM AUTHOR]

Naval transportations have grown exponentially in the past few years' and the quantity of illegitimate oil discharges in the oceans has developed with the volume of traffic imposing a severe threat to marine lives and ecosystem. Oil spills dampen the capillary wave's effect hence create a dark signature on satellite image which is often called a dark object/matter that can be easily detected by SAR sensors. This study reviews the potential of quad-pol and hybrid-pol datasets in detecting oil spills. The data used for this study is the UAVSAR quad-pol and RISAT-1 hybrid/compact-pol dataset. These datasets were acquired during an oil spill experiment named the Norwegian Radar oil Spill Experiment (NORSE2015) which was carried out in the North Sea in June 2015. Basic pre-processing and decomposition modeling techniques namely Freeman and Durden, Van-Zyl, Yamaguchi, and Multiple-Component Scattering Model (MCSM) were applied on UAVSAR dataset, while Raney and Compact-Pol decompositions were applied to RISAT-1 dataset to know different surface backscattering behavior shown by the sea and oil patches. It was observed that Van-Zyl decomposition for UAVSAR and Compact-Pol decomposition for the RISAT-1 dataset gave the most suitable outcome based on the separability analysis to distinguish between oil and water. Two classification techniques Wishart Supervised Classifier (WSC) and Support Vector Machine (SVM) classifications were implemented on decomposed scattering elements of Van-Zyl and Compact-Pol. Radial Basis Function (RBF) kernel parameter values in SVM were used to precisely classify the oil patches from seawater. All the applied decomposition experiments provide good separability between water and oil. Later on, WSC and SVM had been used to finally classify both the dataset outputs. WSC had given better results with an accuracy of 83% for UAVSAR and 86% for RISAT-1, with a larger window size by minimizing the misclassification of sea class into oil spill class. Later the paper concludes the scope of further research possibilities for making a robust system for detecting the oil spills, both precisely and effectually. [ABSTRACT FROM AUTHOR]

The regulation of the carbon cycle is a critical ecosystem service provided by forests globally. It is, therefore, necessary to have robust techniques for speedy assessment of forest biophysical parameters at the landscape level. It is arduous and time taking to monitor the status of vast forest landscapes using traditional field methods. Remote sensing and GIS techniques are efficient tools that can monitor the health of forests regularly. Biomass estimation is a key parameter in the assessment of forest health. Polarimetric SAR (PolSAR) remote sensing has already shown its potential for forest biophysical parameter retrieval. The current research work focuses on the retrieval of forest biophysical parameters of tropical deciduous forest, using fully polarimetric spaceborne C-band data with Polarimetric SAR Interferometry (PolInSAR) techniques. PolSAR based Interferometric Water Cloud Model (IWCM) has been used to estimate aboveground biomass (AGB). Input parameters to the IWCM have been extracted from the decomposition modeling of SAR data as well as PolInSAR coherence estimation. The technique of forest tree height retrieval utilized PolInSAR coherence based modeling approach. Two techniques – Coherence Amplitude Inversion (CAI) and Three Stage Inversion (TSI) – for forest height estimation are discussed, compared and validated. These techniques allow estimation of forest stand height and true ground topography. The accuracy of the forest height estimated is assessed using ground-based measurements. PolInSAR based forest height models showed enervation in the identification of forest vegetation and as a result height values were obtained in river channels and plain areas. Overestimation in forest height was also noticed at several patches of the forest. To overcome this problem, coherence and backscatter based threshold technique is introduced for forest area identification and accurate height estimation in non-forested regions. IWCM based modeling for forest AGB retrieval showed R 2 value of 0.5, RMSE of 62.73 (t ha −1 ) and a percent accuracy of 51%. TSI based PolInSAR inversion modeling showed the most accurate result for forest height estimation. The correlation between the field measured forest height and the estimated tree height using TSI technique is 62% with an average accuracy of 91.56% and RMSE of 2.28 m. The study suggested that PolInSAR coherence based modeling approach has significant potential for retrieval of forest biophysical parameters. [ABSTRACT FROM AUTHOR]

Highlights: [•] Recalibration of Lucey et al. (2000b) with hyperspectral data. [•] Origin is optimized for M3 sensor As (0.08,1.18). [•] Optical maturity and FeO contents of two craters are estimated. [ABSTRACT FROM AUTHOR]

Mechanisms to mitigate climate change in tropical countries such as India require information on forest structural components i.e., biomass and carbon for conservation steps to be implemented successfully. The present study focuses on investigating the potential use of a one time, QuadPOL ALOS PALSAR L-band 25 m data to estimate above-ground biomass (AGB) using a water cloud model (WCM) in a wildlife sanctuary in India. A significant correlation was obtained between the SAR-derived backscatter coefficient ( σ °) and the field measured AGB, with the maximum coefficient of determination for cross-polarized (HV) σ ° for Shorea robusta , and the weakest correlation was observed with co-polarized (HH) σ ° for Tectona grandis forests. The biomass of S. robusta and that of T. grandis were estimated on the basis of field-measured data at 444.7 ± 170.4 Mg/ha and 451 ± 179.4 Mg/ha respectively. The mean biomass values estimated using the WCM varied between 562 and 660 Mg/ha for S. robusta ; between 590 and 710 Mg/ha for T. grandis using various polarized data. Our results highlighted the efficacy of one time, fully polarized PALSAR data for biomass and carbon estimate in a dense forest. [ABSTRACT FROM AUTHOR]