David Ndegwa Kuria

Field-Supported Verification and Improvement of a Passive Microwave Surface Emission Model for Rough, Bare, and Wet Soil Surfaces by Incorporating Shadowing Effects

To investigate the potential of passive microwave techniques for observing the atmosphere over land, it is important to understand the nature of emissions from the land surface. The heterogeneity of large-scale land surface emissions has been cited as a major impediment in conducting observations of the atmosphere over land. Many models, both theoretical and empirical, have been developed to explain the surface emission with varying degrees of success. In the past, most field-supported research in soil observations using microwave techniques has concentrated on lower frequencies (L-band). This paper reports on a study, supported by field data, that seeks to improve our understanding of surface emission at various frequencies using passive microwave radiometers. This provides a crucial link between remote sensing of the land surface and the atmosphere. We show that it is important to consider shadowing associated with rough wet surfaces. By incorporating shadowing effects, the advanced integral equation model (AIEM) shows remarkable agreement with observations at all frequencies and polarizations. Although the roughness parameters obtained during our experiment correspond to very rough conditions, by including shadowing effects the AIEM model is able to transition from the not so rough natural condition as observed from space to the very rough as obtained during field experiments

Improving land surface soil moisture and energy flux simulations over the Tibetan plateau by the assimilation of the microwave remote sensing data and the GCM output into a land surface model

The land surface soil moisture is a crucial variable in weather and climate models. This study presents a land data assimilation system (LDAS) that aims to improve the simulation of the land surface soil moisture and energy fluxes by merging the microwave remote sensing data and the general circulation model (GCM) output into a land surface model (LSM). This system was applied over the Tibetan Plateau, using the National Centers for Environmental Prediction (NCEP) reanalysis data as forcing data and the Advanced Microwave Scanning Radiometers for EOS (AMSR-E) brightness temperatures as an observation. The performance of our four data sources, which were NCEP, AMSR-E, LDAS and simulations of Simple Biosphere Model 2 (SiB2), was assessed against 5 months of in situ measurements that were performed at two stations: Gaize and Naqu. For the surface soil moisture, the LDAS simulations were superior to both NCEP and SiB2, and there was more than a one-third reduction in the root mean squared errors (RMSE) for both of the stations. Compared with the AMSR-E soil moisture retrievals, the LDAS simulations were comparable at the Gaize station, and they were superior at the Naqu station. For the whole domain intercomparison, the results showed that the LDAS simulation of the soil moisture field was more realistic than the NCEP and SiB2 simulations and that the LDAS could estimate land surface states properly even in the regions where AMSR-E failed to cover and/or during the periods that the satellite did not overpass. For the surface energy fluxes, the LDAS estimated the latent heat flux with an acceptable accuracy (RMSE less than 35 W/m 2 ), with a one-third reduction in the RMSE from the SiB2. For the 5-month whole plateau simulation, the LDAS produced a much more reasonable Bowen Ratio than the NCEP, and it also generated a clear contrast of the land surface status over the plateau, which was wet in the southeast and dry in the northwest, during the monsoon and post-monsoon seasons. Because the LDAS only uses globally available data sets, this study reveals the potential of the LDAS to improving the land surface energy and water flux simulations in ungauged and/or poorly gauged regions.

Development of the Coupled Atmosphere and Land Data Assimilation System (CALDAS) and Its Application Over the Tibetan Plateau

Land surface heterogeneities are important for accurate estimation of land-atmosphere interactions and their feedbacks on water and energy budgets. To physically introduce existing land surface heterogeneities into a mesoscale model, a land data assimilation system was coupled with a mesoscale model (LDAS-A) to assimilate low-frequency satellite microwave observations for soil moisture and the combined system was applied in the Tibetan Plateau. Though the assimilated soil moisture distribution showed high correlation with Advanced Microwave Scanning Radiometer on the Earth Observing System soil moisture retrievals, the assimilated land surface conditions suffered substantial errors and drifts owing to predicted model forcings (i.e., solar radiation and rainfall). To overcome this operational pitfall, the Coupled Land and Atmosphere Data Assimilation System (CALDAS) was developed by coupling the LDAS-A with a cloud microphysics data assimilation. CALDAS assimilated lower frequency microwave data to improve representation of land surface conditions, and merged them with higher frequency microwave data to improve the representation of atmospheric conditions over land surfaces. The simulation results showed that CALDAS effectively assimilated atmospheric information contained in higher frequency microwave data and significantly improved correlation of cloud distribution compared with satellite observation. CALDAS also improved biases in cloud conditions and associated rainfall events, which contaminated land surface conditions in LDAS-A. Improvements in predicted clouds resulted in better land surface model forcings (i.e., solar radiation and rainfall), which maintained assimilated surface conditions in accordance with observed conditions during the model forecast. Improvements in both atmospheric forcings and land surface conditions enhanced land-atmosphere interactions in the CALDAS model, as confirmed by radiosonde observations.

Mapping groundwater potential in Kitui District, Kenya using geospatial technologies

Kitui district is a semi-arid region characterized by erratic and unreliable rainfall. Despite this, the main economic activity is rain-fed agriculture, with irrigation agriculture taking place on small parcels adjoining riparian reserves. During the dry season, local people travel long distances in search of water, necessitating groundwater potential mapping to support exploitation and complementing other water sources in the district. In this study geospatial technologies are used to identify and map groundwater potential zones using climate, geophysical and geological data. These datasets were appropriately weighted in a modified DRASTIC based overlay scheme. Land-cover data was derived from landsat imagery classification, with lineament density obtained from the same satellite products. A groundwater potential zones map was generated which showed that the central and eastern regions of Kitui district are the most suitable for groundwater exploitation. Existing water points (which were not considered in the study) are situated in this region, hence validating the study.

Managing distribution of national examinations using geospatial technologies: A case study of Pumwani and Central divisions

Most of the spatially referenced data held by the Kenya national examination council (KNEC) are in analogue hard copy format. This necessitates large storage facilities for storing the paper maps, which have low retrieval speeds. Additionally, wear and tear are occasioned during retrieval and handling, and sometimes some of the data is lost. In this form data sharing is difficult and reproduction usually involves high costs per unit. The purpose of this paper is to implement a geographic information system (GIS) which will lower cost per unit, by allowing higher retrieval speeds, smaller storage facilities requirements, while facilitating data sharing. This GIS will perform all the tasks of the current manual system and in addition, provide functionality to aid in the efficient management of the Kenya national examination council data. To accomplish this, existing hardcopy data was digitized and cleaned. New data was collected, processed, analyzed and stored in the form of a geodatabase. This geodatabase stores both the spatially related data and the attribute data. This geodatabase can be used to answer many questions, but for this work, we emphasize the aspect of efficiency in exam distribution. To determine the most efficient routes to follow in the distribution of examinations during the examination period, a geometric network was prepared which was then used to determine the best routes. In this research, a prototype GIS has been developed. Visualization and comparison can be easily performed using the digital maps produced from the implemented system. The GIS database created can be used for purposes of querying and can be revised whenever new information is available. Shortest distances analysis and efficient distribution route determination were performed using spatial analysis and network analysis tools. From the distribution analysis, the service area analysis is demonstrated as giving a more realistic spatial extent of coverage compared to the buffering approach. From these analyses, the services area analysis and buffering approach showed areas of 980.96 and 223.15 Ha being beyond zone 5. Key words:

A coupled Land Atmosphere Radiative-Transfer Model (LA-RTM) for multi-frequency passive microwave remote sensing: development and application over Wakasa Bay and the Tibetan Plateau

Multi-frequency passive microwave sensors herald a new dawn for combined land and atmosphere observations. Past efforts to utilize microwave remote sensing of atmosphere and land surface have proceeded by treating these two areas in a parallel fashion. In this research, a unified approach is presented that can be used to improve both quantitative and qualitative understanding of land and atmosphere constituents. A coupled Land Atmosphere Radiative-Transfer Model (LA-RTM) that can be used as a forward model in retrieval algorithms, or as an observation operator in data-assimilation schemes is developed. This model is validated using data collected during the 2003 Advanced Microwave Scanning Radiometer on board the Earth Observing Satellite (AMSR/AMSR-E) validation experiment over Wakasa Bay in Japan and the Coordinated Enhanced Observing Period (CEOP) dataset for the Tibetan Plateau collected in April and August 2004. These datasets comprise satellite (AMSR-R) observations, ground-based microwave radiometers (GBMRs) and radiosonde atmosphere soundings. In both sites, good agreement between simulated and observed brightness temperatures is demonstrated. To facilitate fast retrievals, a retrieval scheme is proposed that uses LA-RTM as a forward model to generate a look-up table (LUT) for varying land-surface conditions. This LUT is used to retrieve soil-moisture and surface-roughness conditions for the target site. Using this scheme, retrieved soil moisture at in situ stations was shown to have fairly good agreement with observations.

Climate Change Assessment Due to Long Term Soil Moisture Change and Its Applicability Using Satellite Observations

© 2013 Lu et al., licensee InTech. This is an open access chapter distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/3.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Climate Change Assessment Due to Long Term Soil Moisture Change and Its Applicability Using Satellite Observations

Convective cloud discrimination using multi-frequency microwave signatures of the AMSR-E sensor: evaluation over the Tibetan Plateau

Multi-frequency passive microwave remote sensing affords a unique opportunity to understand various phenomena; low-frequency microwaves penetrate clouds and are able to observe Earth surface conditions (∼6–18 GHz), while the higher frequencies are strongly impacted by prevailing atmospheric conditions. By using these relationships, an atmospheric opacity index (AOI) using Advanced Microwave Scanning Radiometer on Earth Observing Satellite (AMSR-E) multi-frequency data is proposed. This index utilizes four AMSR-E frequencies spanning both high- and low-microwave frequency. This AOI can be used to discriminate cloudy atmosphere from clear-sky conditions. This index shows good agreement with current cloud indices. In this research, it is compared against the Moderate Imaging Spectroradiometer (MODIS) and the Geostationary Operational Environmental Satellite 9th series (GOES-9) atmosphere products. It offers the possibility of detecting convective clouds at all times (day and night) due to the advantage of the independence of the microwave sensors on the Sun for illumination.

A Radiative transfer Model for Soil Media with Considering the volume Effects of Soil Particles: field observation and Numerical Simulation

This paper presents the development of an improved soil radiative transfer model (RTM) which considering the volume scattering effect of soil particles, an unexplored part of traditional RTMs, through field experiments and numerical simulations. The field observations were conducted by using the ground based passive microwave radiometer (GBMR) to measure the brightness temperature of dry sand layer over background materials, metal plates or absorbers. The existence of volume scattering effects in the dry sand was demonstrated through field experiments. Then, the observed data were simulated by the dense media radiative transfer (DMRT) model. The simulation results show that the DMRT model which includes the volume scattering effects performers better than the generally used surface emission model which does not include volume scattering effects.

A Coupled Data Assimilation Framework utilizing multifrequency passive microwave remote sensing in retrieval of land surface variables and integrated atmospheric variables: development and application over the Tibetan Plateau

Retrieval of land surface variables and atmospheric variables over land from passive microwave remote-sensing data sets has been a challenge for many years. A lot of progress has been made in these quests such as using cloud-resolving models and data assimilation. Data assimilation allows the integration of observations (including observation errors) into imperfect models, thereby yielding more improved model forecasts. In this work, a coupled data assimilation framework (CDAF) is proposed and applied to predict the evolution of land surface and atmospheric conditions. CDAF comprises a coupling of two data assimilation schemes, namely a land data assimilation scheme (LDAS) and an ice microphysics data assimilation scheme (IMDAS). This system has been developed and evaluated using data for the Tibetan Plateau. In this framework, both low-frequency and high-frequency passive microwave brightness temperatures (T Bs) are assimilated. Low-frequency T Bs are assimilated in the LDAS subsystem and used to obtain land surface conditions, which are subsequently used as improved initial conditions together with high-frequency T Bs and assimilated in the IMDAS subsystem to obtain atmospheric conditions. The retrieved land surface variables and integrated atmospheric variables are demonstrated to show good agreement with observed land and atmosphere conditions such as those derived from point measurements of temperature and soil moisture (using the Soil Moisture and Temperature Measurement System (SMTMS)), sonde, Advanced Infrared Sounder (AIRS) and Global Precipitation Climatology Project (GPCP) products. The distribution of integrated cloud liquid water and cloud ice is shown to follow the observed cloud distribution over the study area. It is shown that by using IMDAS with modifications to account for precipitation and a good description of land surface emission, it is possible to obtain precipitation information of high fidelity over the land surface. Retrieved integrated water vapour using IMDAS shows correspondence with ‘corrected’ AIRS total precipitable water product. It is also shown that the relative humidity profile obtained from IMDAS agrees with the corresponding sonde profile. From the simulations, it is clear that by using the CDAF, there is marked improvement in the forecast conditions compared with the non-assimilation scenario for all of the variables considered. Comparisons with observed land surface conditions and inferences of atmosphere state from the Geostationally Operational Environmental Satellite Series 9 (GOES-9) InfraRed Channel 1 (IR1) brightness temperatures and the GCPCs cumulative daily precipitation indicate that the CDAF is able to generate reliable forecasts that agree with observation-derived products.