Microwave Remote Sensing

This study examines the wave correlation effects that arise in active microwave remote sensing of the environment. These correlation effects, or coherent interference effects, are not accounted for by the regular phenomenological transport and radar equations, in which intensities, as a rule, are added incoherently. In particular, two types of correlation effects are examined: those associated with the medium and

Organic soils play a key role in global warming because they store large amount of soil carbon which might be degraded with changing soil temperatures or soil water contents. There is thus a strong need to monitor these soils and, in particular, their hydrological characteristics using, for instance, space-borne L-band brightness temperature observations. However, there are still open issues with respect to soil moisture retrieval techniques over organic soils. In view of this, organic soil blocks with their vegetation cover were collected from a heathland in the Skjern River catchment in western Denmark and then transported to a remote sensing field laboratory in Germany where their structure was reconstituted. The controlled conditions at this field laboratory made it possible to perform tower-based L-band radiometer measurements of the soils over a period of two months. Brightness temperature data were inverted using a radiative transfer (RT) model for estimating the time variations in the soil dielectric permittivity and the vegetation optical depth. In addition, the effective vegetation scattering albedo parameter of the RT model was retrieved based on a two-step inversion approach. The remote estimations of the dielectric permittivity were compared to in situ measurements. The results indicated that the radiometer-derived dielectric permittivities were significantly correlated with the in situ measurements, but their values were systematically lower compared to the in situ ones. This could be explained by the difference between the operating frequency of the L-band radiometer (1.4 GHz) and that of the in situ sensors (70 MHz). The effective vegetation scattering albedo parameter was found to be polarization dependent. While the scattering effect within the vegetation could be neglected at horizontal polarization, it was found to be important at vertical polarization. The vegetation optical depth estimated values over time oscillated between 0.10 and 0.19 with a mean value of 0.13. This study provides further insights into the characterization of the L-band brightness temperature signatures of organic soil surface layers and, in particular, into the parametrization of the RT model for these specific soils. Therefore, the results of this study are expected to improve the performance of space-borne remote sensing soil moisture products over areas dominated by organic soils.

To design the pile foundation considering the live load, spacing of pile by referring the code book IS456-2000 (plain and reinforced concrete) and IS 6403-1981 (determination of safe bearing capacity of soil for deep foundation) and using this software as STADD Pro and STADD Foundation
For this design M 25 Grade concrete and Fe 415 HYSD Bars (High yield strength deformed bars) are used.

In this paper it has been discussed about microwave remote sensing Characteristics of soil for agriculture. The quality of soil is controlled by physical, chemical and biological components of soil and their interactions. The soil has physical, chemical as well as electrical properties, colour, texture, grain size, bulk density etc. comprise the physical properties, nutrients, organic matter, pH etc, comprise chemical properties while, electrical properties include dielectric constant, electrical conductivity and permeability. The concept of soil health and soil quality has consistently evolved with an increase in the understanding of soils and soil quality attributes. It has been found that scattering cross-section increases with frequency and decrease will visibility. It has also been seen that the intensity of radiation from soil depends directly on the frequency of exciting field. Thus, the increase in frequency is bound to increase the scatters, radiation, which ultimately changes the overall loss of energy.

Soil moisture is an important state variable in the terrestrial system because it controls the exchange of water and energy between the land surface and the atmosphere. In this study, we review recent advances in noninvasive techniques that allow continuous noninvasive and contactless measurements of soil moisture dynamics at the field to basin scale. In particular, we report on (1) cosmic-ray neutron probes, (2)GlobalNavigation Satellite System reflectometry, (3) ground-based microwave radiometry, (4) gamma-ray monitoring, (5) terrestrial gravimetry, and (6) low-frequency electromagnetic surface waves. Each method is described in terms of its basic principle, measurement scales, calibration issues, measurement accuracy, and applications. We hope that this review will further stimulate the community to invest in the continued development of novel soil moisture sensing methods that address the need for large-scale soil water content measurements with sufficiently high temporal resolution.

Journal of Oceanography, Vol. 58, pp. 137 to 151, 2002 Review Microwave Remote Sensing of Tropical Cyclones from Space Kristina В. Katsaros1*, Paris W. Vachon2, W. Timothy Liu3 and Peter G. Black1 1 NOAA/Atlantic Océanographie and Meteorological Laboratory, 4301 ...

The Oceanagold mine which is located 270 kilometers north of Manila is a copper and gold producer in the Philippines. It began its operation in 2013 and has an expected mine life until 2030 or longer. The Duterte administration advocates a responsible mining and just recently, all operating mines were audited in terms of environmental and social compliances.Interferometric SAR data were used and analyzed in this paper to be able to detect changes in the landscape.

The 2011 Tōhoku earthquake, also known as the “Great East Japan Earthquake”, (Japanese: Higashi Nihon Daishinsai) was a magnitude 9.0 (Mw) undersea megathrust earthquake off the coast of Japan that occurred at 14:46 JST (05:46 UTC) on Friday, 11 March 2011. Simulations of the inundated and devastated areas  of the tsunami were conducted using data from Shuttle Radar Topography Mission (SRTM; 90m-3 arc second resolution). Digital elevation models (DEM) draped with Landsat TM (pre-disaster) images were produced, onto
which tsunami/sea-level heights were simulated at scenarios of 1-meter, 3-meters, 5-meters, 7-meters, 10-meters,
12-meters, 15-meters, 17-meters and 20-meters, along coastal towns of northeastern Japan. Validation and accuracy
assessment of resulting coastal (simulated) inundations were done and were visually compared with the postdisaster
images of the actual tsunami-devastated areas. The simulated inundation scenarios show considerable
accuracies. The technique may have promising applications on tsunami/coastal hazard zoning, as well as, forecasting sea-level rise scenarios.

A dual-wavelength radio frequency observation of Venus was performed on April 5, 1996 with the Very Large Array to investigate potential variations in the vertical and horizontal distribution of temperature and the sulfur compounds sulfur dioxide (SO2) and sulfuric acid vapor (H2SO4(g)) in the Venus atmosphere. Brightness temperature maps were produced which feature significantly darkened polar regions compared to the brighter low-latitude regions at both observed frequencies. This is the first time such polar features have been seen unambiguously in radio wavelength observations of Venus. Vertical profiles of temperature and abundance of H2SO4(g) were retrieved from the maps given an assumed sub-cloud abundance of SO2. The results indicate a substantially higher abundance of H2SO4(g) at high latitudes (above 45o) than in the low-latitude regions. The temperature profiles are significantly warmer than the profile obtained by the Pioneer Venus sounder probe at altitudes <20-40 km (depending on location). The H2SO4(g) profiles obtained at high latitudes are consistent with those derived from the Magellan radio occultations, peaking at around 8 ppm at an altitude of 46 km and decaying rapidly away from that altitude. At low latitudes, no significant H2SO4(g) is observed. This contrasts with Mariner 10 measurements between 44 and 50 km which followed saturation abundance to ~ 9 ppm at ~ 47 km. Our results are also not consistent with the value of 130 ppm for SO2 reported by Bézard et al. (1993) from near-IR measurements. Such a high abundance of SO2, along with their cooler assumed temperature structure, is not supported by our data - either a lower abundance of SO2 or higher atmospheric temperature is required. The retrieved temperature profile and higher abundance of H2SO4(g) in polar regions is consistent with a strong equatorial to polar cloud level Hadley cell type circulation in the atmosphere of Venus.

An agricultural land use classification of Oltrepo&#39; Pavese region (in Northern Italy) is performed through integration of remote sensing data from optical and radar sensors. The source of optical data was a Landsat-TM image acquired on April 1994, while the source of microwave data was a contemporaneous image of the new radar sensor SIR-C/X-SAR which was flown on-board the Space

Planting and harvesting times drive cropland phenology. There are few datasets that derive explicit phenological metrics, and these datasets use the visible to near infrared (VNIR) spectrum. Many different methods have been used to derive phenometrics such as Start of Season (SOS) and End of Season (EOS), leading to differing results. This discrepancy is partly due to spatial and temporal compositing of the VNIR satellite data to minimize data gaps resulting from cloud cover, atmospheric aerosols, and solar illumination constraints. Phenometrics derived from the downward Convex Quadratic model (CxQ) include Peak Height (PH) and Thermal Time to Peak (TTP), which are more consistent than SOS and EOS because they are minimally affected by snow and frost and other non-vegetation related issues. Here, we have determined PH using the vegetation optical depth (VOD) in three microwave frequencies (6.925, 10.65 and 18.7 GHz) and accumulated growing degree-days derived from AMSR-E (Advanced Microwave Scanning Radiometer on EOS) data at a spatial resolution of 25 km. We focus on 50 AMSR-E cropland pixels in the major grain production areas of Northern Eurasia (Ukraine, southwestern Russia, and northern Kazakhstan) for 2003–2010. We compared the land surface phenologies of AMSR-E VOD and MODIS NDVI data. VOD time series tracked cropland seasonal dynamics similar to that recorded by the NDVI. The coefficients of determination for the CxQ model fit of the NDVI data were high for all sites (0.78 < R 2 < 0.99). The 10.65 GHz VOD (VOD 1065GHz) achieved the best linear regression fit (R 2 = 0.84) with lowest standard error (SEE = 0.128); it is therefore recommended for microwave VOD studies of cropland land surface phenology. Based on an Analysis of Covariance (ANCOVA) analysis, the slopes from the linear regression fit were not significantly different by microwave frequency, whereas the intercepts were significantly different, given the different magnitudes of the VODs. PHs for NDVI and VOD were highly correlated. Despite their strong correspondence, there was generally a lag of AMSR-E PH VOD 10.65GHz by about two weeks compared to MODIS peak greenness. To evaluate the utility of the PH determination based on maximum value, we correlated the CxQ derived and maximum value determined PHs of NDVI and found that they were highly correlated with R 2 of 0.87, but with a one-week bias. Considering the one-week bias between the two methods, we find that PH of VOD 10.65GHz lags PH of NDVI by three weeks. We conclude, therefore, that maximum-value based PH of VOD can be a complementary phenometric for the CxQ model derived PH NDVI, especially in cloud and aerosol obscured regions of the world.

Soil moisture is an important variable in a number of environmental processes – specifically the hydrological cycle, in the water-limited environments. Therefore, soil moisture data is important as an input variable in hydrologic, climatic modelling and agricultural applications. Many of these applications require high-resolution soil moisture data. However, most of the available soil moisture measurements are rarely available at high resolution, therefore unable to capture the spatial heterogeneity of soil moisture with required accuracy levels. Thus, upscaling or downscaling of soil moisture observations to higher spatial resolution is an essential requirement for these multidisciplinary applications. A long-term high-resolution soil moisture dataset is useful for planning and decision making in agriculture, climatology and hydrology. Developing a historic soil moisture dataset at high spatial resolution over a long period requires the use of different satellite soil moisture products. However, the use of different satellite products results in incompatibilities among each other due to discrepancies in overpass times, the wavelengths used, retrieval algorithms, orbital parameters and sensor errors. Therefore, validation and comparison of soil moisture retrievals from different satellite sensors and their downscaled products is important in evaluating the consistency of a long-term time series dataset of high-resolution soil moisture. This study focusses on a downscaling algorithm based on the thermal inertia theory at two sub-catchments of the Goulburn River in southeastern Australia, Krui and Merriwa River catchments. The goal is to downscale the radiometric soil moisture retrievals of Soil Moisture Active Passive (SMAP) and Soil Moisture and Ocean Salinity (SMOS) missions along with validation using established in-situ observation networks. A linear regression model was developed between the daily surface temperature difference and daily mean soil moisture values from the in-situ observations of the Scaling and Assimilation of Soil Moisture and Streamflow (SASMAS) project. This relationship is modulated by the vegetation cover and soil attributes. The MODerate-resolution Imaging Spectroradiometer (MODIS) derived land surface temperature difference values were fitted into the lookup algorithms to estimate surface soil moisture at fine spatial resolution at 1 km. The coarse-resolution SMAP (36 km) and SMOS (25 km) radiometric soil moisture products were downscaled to 1 km. The coarse-resolution SMAP and SMOS soil moisture datasets were compared with each other, and then against the SASMAS in-situ measurements. SMAP 36 km datasets show a reasonable agreement with the in-situ data with RMSEs of 0.07 and 0.05 cm 3 /cm 3 over two SMAP pixels. However, SMOS 25 km soil moisture products show a general underestimation as compared to SMAP and SASMAS datasets. Therefore, the SMOS data were calibrated with SMAP data. Subsequently, the SMAP, SMOS and adjusted SMOS datasets over the Krui and Merriwa River catchments for the year 2015 were downscaled and compared. The results show that the accuracy of the downscaled soil moisture datasets are highly influenced by the accuracy of the coarse-resolution satellite soil moisture products. The downscaled data were compared with in-situ data of five SASMAS monitoring stations. The downscaled SMAP, SMOS and adjusted SMOS datasets respectively showed average RMSEs of 0.10 (standard deviation, σ= 0.05), 0.19 (σ= 0.07) and 0.13 (σ= 0.02) cm 3 /cm 3 with the SASMAS in-situ measurements. The three downscaled datasets of SMAP, SMOS and adjusted SMOS show consistent soil moisture pattern over the study catchments. The downscaled adjusted SMOS data displayed a better agreement with downscaled SMAP soil moisture data compared to the non-adjusted SMOS data.

Hydrological applications, i.e. physically-based, distributed hydrological modelling, require areal input for parametrization and validation of water cycle components. Active microwave remote sensing research shows the eligibility of multifrequent, multipolarimetric and interferometric SAR data to retrieve land use, topography, soil moisture and vegetation information. In this study data of the multifrequent, multipolarimetric and interferometric airborne &quot;Experimental Synthetic Aperture Radar (E-SAR)&quot; were

Malaria is endemic problem in the low and middle income countries, especially, sub-Saharan Africa, is caused by Plasmodium falciparum contributed on the major parts, and Plasmodium vivax parasites in the minor parts claim for millions of morbidity and mortality on the global level. Mainly due to the climate change, monsoon failure, declining agriculture crop production, population movements on poverty, mushroom growth of unplanned urbanization, landscape and land cover changes. Multispectral (MSS) satellite data and Synthetic Aperture Radar (SAR) imagery has been used for the replacement of conventional survey methods for the assessment of the problems. Remote sensing of environmental information has been used to study the variations of climate conditions; land use/ land cover changes and its impact on natural environmental transitions, assess breeding potentiality, and forecast malaria for the past 4 decades. It provides the reliable, picturesque, repetitive, precise, speed, and low cost comparatively. Remote sensing technology has been applied as alternative tool, a scientific method to develop spatial models for forecast malaria for lager areas; regional, national, and global scale. Malaria is prolonged public health challenging problem in Africa continent, tropical countries, and sub-tropical regions for several decades, it claims 2 million death tolls every year, especially, in the sub-Saharan Africa regions excessively tremendous problem, despite, all kinds control measures. The perceptions of spatial model for malaria prediction/ forecast malaria epidemics have been attracted by many researchers for past 4 decades. Therefore, present study is aimed to review relevant studies of the use of multispectral satellite data, and synthetic aperture radar imagery to analyze recurrent weather environment (temperature, precipitation, relative humidity, and saturation deficiency), land surface temperature (LST), sea surface temperature (SST), vector breeding potentiality, deforestation; land use/ land cover changes for forecast malaria.

Microwave radiation with the inherent advantage of its ability to partially penetrate clouds is ideally suited for remote measurements of precipitation, especially over the oceanic regions. The retrieval problem is of great practical interest, as precipitation over the oceans has to be necessarily remotely sensed. More importantly, precipitation is also a crucial input in many weather and climate models. A downward looking space borne radiometer such as the TRMM's Microwave Imager (TMI), however, does not sense the surface rain fall directly. Rather, it measures the upwelling radiation coming from the top-of-atmosphere which depends on the total quantities of the parameters that can affect the radiation in the chosen frequency range. In addition to precipitation, the attenuation and augmentation of total cloud content and integrated precipitable water content by absorption and emission, respectively, affect the microwave brightness temperatures in various frequencies. In the present work, a systematic study has been conducted to investigate the effect of total cloud and precipitable water contents on surface rainrate retrievals from the TMI measured brightness temperatures (BT). While a Bayesian framework is used to assimilate radar reflectivities into hydrometeor structures, a neural network is used to correlate rain and cloud parameters with brightness temperatures. The correlation between the TMI brightness temperatures and the TRMM's Precipitation Radar (TRMM-PR) is used as the benchmark for comparison. A community developed software meso-scale Weather Research and Forecast (WRF) is used to simulate the cloud and precipitable water content, along with the surface rainfall rate for several rain events in the past. Four cases are considered: (a) TRMM PR's near surface rain rate is correlated with TMI brightness temperatures directly; (b) the total cloud and precipitable water contents along with surface rainfall simulated using WRF are correlated with TMI BTs;- (c) similar to case (b) with near surface rainfall taken from TRMM PR measurements; (d) total cloud and precipitable water contents and the surface rain rate are corrected with PR vertical reflectivity profile in a Bayesian framework. The freely available QuickBeam software has been used for simulation of reflectivities at the TRMM PR frequency. The corrected data are then correlated with TMI BTs. Results show that surface rain fall retrievals can be radically improved by using TRMM PR vertical rain corrected total cloud and precipitable water content.