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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]

In the field of forestry studies, microwave remote sensing has broad applications due to the penetration into the semi-transparent media. This feature is used for the estimation of biophysical parameters and monitoring of deforestation. Therefore, the estimation of biophysical parameters is essential for assessing carbon stock management. Hence, the aboveground biomass (AGB) using synthetic aperture radar (SAR) data is recognized as typical approaches in forest application. However, the integrated use of polarimetric (PolSAR) and interferometric (PolInSAR) data might be more efficient tools for AGB mapping. Accordingly, in this study with the integrated data, the efficiency of machine learning techniques including random forest regression (RFR) and multiple linear regression (MLR) model were assessed and compared for the prediction of AGB. The analyses were performed using an image pair of fully polarimetric Radarsat-2 C-band data set and the related field data of Malhan Forest Range, Dehradun Forest Division, which were collected using the systematic sampling technique. Particularly, the training and testing of the models were done using the field sample plots. The experimental results showed that the RFR algorithm provided a better prediction result of AGB than the MLR model. The correlation coefficient (R2) and root-mean-square error (RMSE) for the RFR algorithm was estimated to be around 0.65 and 24.33 Mg/ha, respectively, while for the MLR model, R2 and RMSE are estimated as 0.54 and 33.05 Mg/ha, respectively. Therefore, it was concluded that the prediction of AGB through the machine learning technique using PolSAR and PolInSAR data has a significant advantage for accurate estimation of the AGB. [ABSTRACT FROM AUTHOR]

Surface soil moisture has a wide application in climate change, agronomy, water resources, and in many other domain of science and engineering. Measurement of soil moisture at high spatial and temporal resolution at regional and global scale is needed for the prediction of flood, drought, planning and management of agricultural productivity to ensure food security. Recent advancement in microwave remote sensing, especially after the launch of Sentinel operational satellites has enabled the scientific community to estimate soil moisture at higher spatial and temporal resolution with greater accuracy. This study evaluates the potential of Sentinel-1A satellite images to estimate soil moisture in a semi-arid region. Exactly at the time when satellite passes over the study area, we have collected soil samples at 37 different locations and measured the soil moisture from 5 cm below the ground surface using ML3 theta probe. We processed the soil samples in laboratory to obtain volumetric soil moisture using the oven dry method. We found soil moisture measured from calibrated theta probe and oven dry method are in good agreement with Root Mean Square Error (RMSE) 0.025 m 3 /m 3 and coefficient of determination (R 2 ) 0.85. We then processed Sentinel-1A images and applied modified Dubois model to calculate relative permittivity of the soil from the backscatter values ( σ ∘ ). The volumetric soil moisture at each pixel is then calculated by applying the universal Topp's model. Finally, we masked the pixels whose Normalised Difference Vegetation Index (NDVI) value is greater than 0.4 to generate soil moisture map as per the Dubois NDVI criterion. Our modelled soil moisture accord with the measured values with RMSE = 0.035 and R 2 = 0.75. We found a small bias in the modelled soil moisture ( 0.02 m 3 / m 3 ). However, this has reduced significantly ( 0.001 m 3 / m 3 ) after applying a bias correction based on Cumulative Distribution Function (CDF) matching. Our approach provides a first-order estimate of soil moisture from Sentinel-1A images in sparsely vegetated agricultural land. [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]