An artificial neural network approach for soil moisture retrieval using passive microwave data

Soil moisture is a key variable that defines land surface-atmosphere (boundary layer) interactions, by contributing directly to the surface energy and water balance. Soil moisture values derived from remote sensing platforms only accounts for the near surface soil layers, generally the top 5cm. Passive microwave data at L-band (1.4 GHz, 21cm wavelength) measurements are shown to be a very effective observation for surface soil moisture retrieval. The first space-borne L-band mission dedicated to observing soil moisture, the European Space Agency's (ESA) Soil Moisture and Ocean Salinity (SMOS) mission, was launched on 2nd November 2009.Artificial Neural Network (ANN) methods have been used to empirically ascertain the complex statistical relationship between soil moisture and brightness temperature in the presence of vegetation cover. The current problem faced by this method is its inability to predict soil moisture values that are 'out-of-range' of the training data.In this research, an optimization model is developed for the Backpropagation Neural Network model. This optimization model utilizes the combination of the mean and standard deviation of the soil moisture values, together with the prediction process at different pre-determined, equal size regions to cope with the spatial and temporal variation of soil moisture values. This optimized model coupled with an ANN of optimum architecture, in terms of inputs and the number of neurons in the hidden layers, is developed to predict scale-to-scale and downscaling of soil moisture values. The dependency on the accuracy of the mean and standard deviation values of soil moisture data is also studied in this research by simulating the soil moisture values using a multiple regression model. This model obtains very encouraging results for these research problems.The data used to develop and evaluate the model in this research has been obtained from the National Airborne Field Experiments in 2005

The role of earth observation in an integrated deprived area mapping “system” for low-to-middle income countries

Urbanization in the global South has been accompanied by the proliferation of vast informal and marginalized urban areas that lack access to essential services and infrastructure. UN-Habitat estimates that close to a billion people currently live in these deprived and informal urban settlements, generally grouped under the term of urban slums. Two major knowledge gaps undermine the efforts to monitor progress towards the corresponding sustainable development goal (i.e., SDG 11—Sustainable Cities and Communities). First, the data available for cities worldwide is patchy and insufficient to differentiate between the diversity of urban areas with respect to their access to essential services and their specific infrastructure needs. Second, existing approaches used to map deprived areas (i.e., aggregated household data, Earth observation (EO), and community-driven data collection) are mostly siloed, and, individually, they often lack transferability and scalability and fail to include the opinions of different interest groups. In particular, EO-based-deprived area mapping approaches are mostly top-down, with very little attention given to ground information and interaction with urban communities and stakeholders. Existing top-down methods should be complemented with bottom-up approaches to produce routinely updated, accurate, and timely deprived area maps. In this review, we first assess the strengths and limitations of existing deprived area mapping methods. We then propose an Integrated Deprived Area Mapping System (IDeAMapS) framework that leverages the strengths of EO- and community-based approaches. The proposed framework offers a way forward to map deprived areas globally, routinely, and with maximum accuracy to support SDG 11 monitoring and the needs of different interest groups

Demarcation of Ground Water Potential Zones using Remote Sensing and GIS Applications

Now-a-days, due to the high demand of water for the human needs, groundwater sources are drastically extracted and causing to least the source. The entire Yearly furnish is contributing from the utmost resource called Groundwater. Globally, groundwater is extracting primarily for the purpose of agricultural fields, domestic and for industrial water supply. Majority of the surface water is in the form of saline water which is not useful for the needs of human beings for their daily needs. Very less amount of fresh surface water is existing on the ground surface. To compensate the needs, it is essential to identify, extract and manage the groundwater which is available at different levels at different areas of the globe. Proper planning is required for the extraction of groundwater using updated technologies for using and maintaining of natural resources like water resources. The prime strive of the selected project area is to map out potential groundwater regions in the Pendlimarri Mandal of Kadapa District by using Geospatial Technology. The main impartial target of the work is to select appropriate methods and assessment criteria of the technology to identify the potential underground demarcations in geographic information system environment with help of ArcGIS software. To demarcate zones of groundwater potential, various key parameters called geology, lineament density, LU / LC, geomorphology, groundwater depths, slope and drainage pattern were prepared by utilizing remote sensing data and secondary data which can collect from concern departments. The thematic layers are to be finally integrated by using weighted overlay analysis of spatial analyst tools of data management tools of ArcMap software to delineate underground water prospects regions output layout of the project. Disparate groundwater prospects levels were categorized, from the range excellent to poor including very good, good and moderate in between. At last, decided that that the applications of geoinformatics are essential and effectively applied for the demarcation of potential zones of groundwater

Astronomy below the survey threshold in the SKA era

Astronomy at or below the 'survey threshold' has expanded significantly since the publication of the original 'Science with the Square Kilometer Array' in 1999 and its update in 2004. The techniques in this regime may be broadly (but far from exclusively) defined as 'confusion' or 'P(D)' analyses (analyses of one-point statistics), and 'stacking', accounting for the flux-density distribution of noise-limited images co-added at the positions of objects detected/isolated in a different waveband. Here we discuss the relevant issues, present some examples of recent analyses, and consider some of the consequences for the design and use of surveys with the SKA and its pathfinders

Estimating global warming potential for agricultural landscapes with minimal field data and cost

Greenhouse gas (GHG) emissions from agriculture comprise 10-12% of anthropocentric global emissions; and 76% of the agricultural emissions are generated in the developing world. Landscape GHG accounting is an effective way to efficiently develop baseline emissions and appropriate mitigation approaches. In a 9,736-hectare case study area dominated by rice and wheat in the Karnal district of Haryana state, India, the authors used a low-cost landscape agricultural GHG accounting method with limited fieldwork, remote sensing, and biogeochemical modeling. We used the DeNitrification-DeComposition (DNDC) model software to simulate crop growth and carbon and nitrogen cycling to estimate net GHG emissions, with information based on the mapping of cropping patterns over time using multi- resolution and multi-temporal optical remote sensing imagery. We estimated a mean net emission of 78,620 tCO2e/yr (tons of carbon dioxide equivalents per year) with a 95% confidence interval of 51,212-106,028 tCO2e/yr based on uncertainties in our crop mapping and soil data. A modeling sensitivity analysis showed soil clay fraction, soil organic carbon fraction, soil density, and nitrogen amendments to be among the most sensitive factors, and therefore critical to capture in field surveys. We recommend a multi-phase approach to increase efficiency and reduce cost in GHG accounting. Field campaigns and aspects of remote sensing image characteristics can be optimized for targeted landscapes through solid background research. An appropriate modeling approach can be selected based on crop and soil characteristics. Soil data in developing world landscapes remain a significant source of uncertainty for studies like these and should remain a key research and data development effort

Global analysis of the controls on seawater dimethylsulfide spatial variability

Dimethylsulfide (DMS) emitted from the ocean makes a significant global contribution to natural marine aerosol and cloud condensation nuclei, and therefore our planet&rsquo;s climate. Oceanic DMS concentrations show large spatiotemporal variability, but observations are sparse, so products describing global DMS distribution rely on interpolation or modelling. Understanding the mechanisms driving DMS variability, especially at local scales, is required to reduce uncertainty in large scale DMS estimates. We present a study of mesoscale and sub-mesoscale (&lt;100 km) seawater DMS variability that takes advantage of the recent expansion in high frequency seawater DMS observations and uses all available data to investigate the typical distances over which DMS varies in all major ocean basins. These DMS spatial variability lengthscales (VLS) are uncorrelated with DMS concentrations. DMS concentrations and VLS can therefore be used separately to help identify mechanisms underpinning DMS variability. When data are grouped by sampling campaigns, almost 80 % of the DMS VLS can be explained using the VLS of sea surface height anomalies, density, and chlorophyll-a. Our global analysis suggests that both physical and biogeochemical processes play an equally important role in controlling DMS variability, in contrast with previous results based on data from the low&ndash;mid latitudes. The explanatory power of sea surface height anomalies indicates the importance of mesoscale eddies in driving DMS variability, previously unrecognised at a global scale and in agreement with recent regional studies. DMS VLS differs regionally, including surprisingly high frequency variability in low latitude waters. Our results independently confirm that relationships used in the literature to parameterise DMS at large scales appear to be considering the right variables. However, contrasts in regional DMS VLS highlight that important driving mechanisms remain elusive. The role of sub-mesoscale features should be resolved or accounted for in DMS process models and parameterisations. Future attempts to map DMS distributions should consider the length scale of variability.</p

A look at land cover classification methods in Northern California with the use of high spatial resolution geospatial data

Land use and land cover (LULC) mapping plays a vital role in understanding the state of the world, showing us a visual representation of the natural and anthropogenic features covering our planet. Northern California in the United States is home to many critical habitats that provide for a variety of endemic and some threatened and engendered species, making it an area of particular concern to better understand and monitor. There is a greater need to identify specific methods for vegetation modeling in Northern California due to its unique species; to do this we examined two case studies with the following objectives: 1) Determine whether unmanned aerial system (UAS) image analysis can provide similar estimates of eelgrass biometrics, such as percent coverage, to those obtained in situ using traditional field survey methods; 2) To develop a GIS data fusion workflow for high-resolution habitat classification in the Napa Watershed of central California with a focus on oak savanna habitat. UAS Imagery for two eelgrass sites were collected during June, 2019 using a DJI Matrice 100 equipped with MicaSense RedEdge Multispectral sensor (5-band). Following UAS image collection, ground survey data were collected at three tidal elevation transects per site, with 20 quadrats stationed randomly along each transect. Eelgrass percent coverage was measured for each quadrat and then compared to eelgrass classification models derived from UAS derived imagery. In the Napa watershed, we examined methods necessary to accurately incorporate ancillary geospatial spatial datasets into a remote sensing land cover classification. By doing so, I developed a habitat distribution dataset that may better analyze interactions of wildlife, humans, and the endemic habitat types of the Napa watershed in California. UAVs provided a means to obtain high resolution remote sensing imagery of eelgrass at a resolution of 3.46 – 3.70 cm per pixel or greater at specific tidal periods, providing a useful methodology that allowed for percent coverage estimates with an R2 value of 0.6496 compared to in situ measurements. While developing a land cover classification workflow for the Napa watershed, I found that by incorporating ancillary geospatial data, remotely sensed data, and threshold classification, I could obtain a LULC model that more accurately depicts the endemic land use and land cover features of the Napa watershed. With an overall accuracy of 70.20% and a kappa statistic of 0.6140, this modeling method proved more accurate than traditional image classification methods. With ground sampled reference data and remotely sensed data gathered at the same temporal and spatial scales these classification methods would be robust and replicable for future analyses

Unpacking dasymetric modelling to correct spatial bias in environmental model outputs

Complex environmental model outputs used to inform decisions often have systematic errors and are of inappropriate resolution, requiring downscaling and bias correction for local applications. Here we provide a new interpretation of dasymetric modelling (DM) as a spatial bias correction framework useful in environmental modelling. DM is based on areal interpolation where estimates of some variable at target zones are obtained from overlapping source zones using ancillary information. We explore DM by downscaling runoff output from a distributed hydrological model using two meta-models and describe the properties of the methodology in detail. Consistent with properties of linear scaling bias correction, results show that the methodology 1) reduces errors compared to the source data and meta-models, 2) improve the spatial structure of the estimates, and 3) improve the performance of the downscaled estimates, particularly where meta-models perform poorly. The framework is simple and useful in ensuring spatial coherence of downscaled products