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In this study, ALOS-2 PALSAR L-band dual-pol (HH and HV) synthetic aperture radar (SAR) images were used for above ground biomass (AGB) estimation of Shisham (Dalbergia sissoo) tree species in the ...

Flash floods which occur due to heavy rainfall in hilly and semi-hilly areas may prove deleterious when they hit urban centers. The prediction of such localized and heterogeneous phenomena is a challenge due to a scarcity of in-situ rainfall. A possible solution is the utilization of satellite-based precipitation products. The current study evaluates the efficacy of Tropical Rainfall Measuring Mission (TRMM) Multi-satellite Precipitation Analysis (TMPA) three-hourly products, i.e., 3B42 near-real-time (3B42RT) and 3B42 research version (3B42V7) at a sub-daily time scale. Various categorical indices have been used to assess the capability of products in the detection of rain/no-rain. Hourly rain rates are assessed by employing the most commonly used statistical measures, such as correlation coefficients (CC), mean bias error (MBE), mean absolute error (MAE), and root-mean-square error (RMSE). Further, a diurnal analysis is performed to authenticate TMPA’s performance in specific hours of the day. In general, the results show the good capability of both TMPA products in the detection of rain/no-rain events in all seasons except winter. Specifically, 3B42V7 performed better than 3B42RT. Moreover, both products detect a high number of rainy days falsely in light rain ranges. Regarding rainfall measurements, TMPA products exhibit an overall underestimation. Seasonally, 3B42V7 underestimates rainfall in monsoon and post-monsoon, and overestimates in winter and pre-monsoon. 3B42RT, on the other hand, underestimates rainfall in all seasons. A greater MBE and RMSE are found with both TMPA rain measurements in monsoon and post-monsoon seasons. Overall, a weak correlation and high MBE between the TMPA (3B42RT, 3B42V7) and reference gauge hourly rain rates are found at a three-hourly time scale (CC = 0.41, 0.38, MBE = −0.92, −0.70). The correlation is significant at decadal (CC = 0.79, 0.77) and monthly (CC = 0.91, 0.90) timescales. Furthermore, diurnal rainfall analysis indicates low credibility of 3B42RT to detect flash flooding. Within the parameters of this study, we conclude that the TMPA products are not the best choice at a three-hourly time scale in hilly/semi-hilly areas of Pakistan. However, both products can be used at daily, yet more reliably above daily, time scales, with 3B42V7 preferable due to its consistency.

This study examines ALOS-2 PALSAR L-band dual-polarization (HH and HV) SAR data and Landsat-8 optical imagery for land cover classification. The SAR data has been preprocessed first, which included radiometric calibration, geocoding, and speckle filtering. The HH/HV band ratio has been used to create the third band, and thus a synthetic RGB SAR image was created. The Landsat-8 data was also preprocessed for the classification process. For land cover classification of both SAR and optical datasets, the supervised maximum likelihood classifier was used. Training samples were selected from the Landsat-8 optical imagery with the support of information available in Google Earth; the same pixel locations of training data were used to extract training data from SAR image as well. The Landsat-8 optical imagery was classified and also used for visual assessment of the SAR land cover classification results. Accuracy assessment has been done for both the results of SAR and Landsat-8 data. The SAR classified output gives us accuracy of 93.15% and the Landsat-8 classified map accuracy was 91.34%, while the Kappa coefficient for SAR and Landsat-8 classified images is 0.92 and 0.89, respectively. Classification limitations exist in some cases, such as roads being merged in vegetation areas and some of the barren land is merged in settlements. The land cover classification can be expected to be further improved using polarimetric decomposition methods and fusion of SAR data with optical data.

As compared with conventional methods for measuring ocean surface currents, spaceborne synthetic aperture radar (SAR) offers cloud-penetrating ocean-current observation capability at high spatial resolution. While some studies have shown the potential of SAR for studying ocean surface currents through feature tracking, they have only analyzed a few images to demonstrate the basic measurement technique, and no concise general technique has been developed. This paper shows the application of the maximum cross correlation (MCC) method to generate ocean surface currents from nearly two years of available sequential spaceborne C-band SAR imagery from the Envisat ASAR and ERS-2 Advanced Microwave Instrument SAR sensors over the coastal California Current System. The data processing strategies are discussed in detail, and results are compared with HF radar measured currents. One-dimensional wavenumber spectra of the SAR-derived surface currents agree with the $k^{-2}$ power law, as predicted by submesoscale resolution models. Comparisons with HF radar currents show encouraging results with MCC SAR vectors oriented slightly counterclockwise relative to HF radar vectors. MCC SAR surface currents are found to have larger magnitudes than HF radar currents ( $\approx$ 11 cm/s), which may be due to the fact that SAR penetrates only a few centimeters into the ocean surface whereas HF radar currents are averaged over the top 1 m of the ocean surface. The larger part of this magnitude difference is contained in the along-shore component, which can be attributed to higher HF radar accuracy in the direct radial cross-shore measurements as compared with along-shore components derived from multiple cross-shore radial measurements.

Sustainability of desert ecosystem is highly dependent upon water availability from different sources. Paleochannels are important sources of groundwater, and exploiting such resources involves their identification/mapping and subsequent investigation for fresh groundwater. A study in which multisensor (optical/infrared Landsat 8 OLI and active microwave Envisat ASAR) images of the Cholistan desert of Pakistan were processed and analyzed to identify and map Hakra River paleochannels is presented. Radiometrically corrected optical and synthetic aperture radar datasets were fused using principal components image fusion method. The paleochannels were extracted from the analysis of this fused output, and normalized difference vegetative index analysis of Landsat 8 OLI atmospheric corrected images was used as supporting information. Identification and alignment of an identified paleochannel was validated with geophysical ground measurements (electrical resistivity and conductivity surveys) and historical records. The presence of high apparent electrical resistivity with corresponding low soil water conductivity values intersects well with the paleochannels identified from the remote sensing data. The results were also confirmed with historical evidence such as old wells beside forts and proposed ground water harvesting sites. The proposed methodology in this study could be adopted in other parts of the world for mapping of paleochannels.