The current study presents an algorithm to retrieve surface Soil Moisture (SM) from multi-temporal Synthetic Aperture Radar (SAR) data. The developed algorithm is based on the Cumulative Density Function (CDF) transformation of multi-temporal RADARSAT-2 backscatter coefficient (BC) to obtain relative SM values, and then converts relative SM values into absolute SM values using soil information. The algorithm is tested in a semi-arid tropical region in South India using 30 satellite images of RADARSAT-2, SMOS L2 SM products, and 1262 SM field measurements in 50 plots spanning over 4 years. The validation with the field data showed the ability of the developed algorithm to retrieve SM with RMSE ranging from 0.02 to 0. and a correlation coefficient of approximately 0.9. The developed model is compared and found to be better than the change detection and delta index model. The approach does not require calibration of any parameter to obtain relative SM and hence can easily be extended to any region having time series of SAR data available.Remote Sens. 2015, 7 8129
Microgrid with integrated photo-voltaics (PV) and battery storage system (BSS) is a promising technology for future residential applications. Optimally sizing the PV system and BSS can maximise self-sufficiency, grid relief, and at the same time can be cost-effective by exploiting tariff incentives. To that end, this paper presents a comprehensive optimisation model for the sizing of PV, battery, and grid converter for a microgrid system considering multiple objectives like energy autonomy, power autonomy, payback period, and capital costs. The proposed approach involves developing a holistic technoeconomic microgrid model based on variables like PV system power, azimuth angle, battery size, converter ratings, capital investment and electricity tariffs. The proposed method is applied to determine the optimum capacity of a PV system and BSS for two case residential load profiles in the Netherlands and Texas, US to investigate the effect of meteorological conditions on the relative size of PV and battery. Based on the optimisation results, thumb rules for optimal system sizing are derived to facilitate microgrid design engineers during the initial design phase.
Groundwater has rapidly evolved as a primary source for irrigation in Indian agriculture. Over-exploitation of the groundwater substantially depletes the natural water table and has negative impacts on the water resource availability. The overarching goal of the proposed research is to identify the historical evolution of irrigated cropland for the post-monsoon (rabi) and summer cropping seasons in the Berambadi watershed (Area = 89 km 2 ) of Kabini River basin, southern India. Approximately five-year interval irrigated area maps were generated using 30 m spatial resolution Landsat satellite images for the period from 1990 to 2016. The potential of Support Vector Machine (SVM) was assessed to discriminate irrigated and non-irrigated croplands. Three indices, Normalized Difference Vegetation Index (NDVI), Normalized Difference Moisture Index (NDMI) and Enhanced Vegetation Index (EVI), were derived from multi-temporal Landsat satellite images. Spatially distributed intensive ground observations were collected for training and validation of the SVM models. The irrigated and non-irrigated croplands were estimated with high classification accuracy (kappa coefficient greater than 0.9). At the watershed scale, this approach allowed highlighting the contrasted evolution of multiple-cropping (two successive crops in rabi and summer seasons that often imply dual irrigation) with a steady increase in the upstream and a recent decrease in the downstream of the watershed. Moreover, the multiple-cropping was found to be much more frequent in the valleys. These intensive practices were found to have significant impacts on the water resources, with a drastic decline in the water table level (more than 50 m). It also impacted the ecosystem: Groundwater level decline was more pronounced in the valleys and the rivers are no more fed by the base flow.
Inductive power transfer (IPT) is becoming increasingly popular in stationary electric vehicle (EV) charging systems. In this paper, the influence of the different IPT coupler geometries on the performance factors such as efficiency, power density, misalignment tolerance, and stray field is studied. Four different coupler topologies, namely the circular, rectangular, double-D (DD-DD), and the double-D transmitter with double-D-quadrature receiver (DD-DDQ) are considered in this study. The electromagnetic behavior of the couplers is modeled using three-dimensional finiteelement method, which is validated by experiments on a laboratory prototype. A multi-objective optimization (MOO) framework is developed to analyze the Pareto tradeoffs between conflicting performance metrics for the couplers. Optimization results depict that the circular topology performs best among the selected topologies regarding higher coupling coefficient, and efficiency for similar active mass and coupler area. Circular and rectangular couplers perform better than the polarized couplers like DD-DD and DD-DDQ regarding stray field exposure in both vertical and lateral direction of the coupler position in the EV. However, polarized couplers show more tolerance toward misalignment compared to circular and rectangular couplers. Thus, this study provides information regarding the specific strengths and weaknesses of different coupler topologies, which can be used during the initial design phase.
The main objective of this study is to analyze the potential use of L-band radar data for the estimation of soil moisture over tropical agricultural areas under dense vegetation cover conditions. Ten radar images were acquired using the Phased Array Synthetic Aperture Radar/Advanced Land Observing Satellite (PALSAR/ALOS)-2 sensor over the Berambadi watershed (south India), between June and October of 2018. Simultaneous ground measurements of soil moisture, soil roughness, and leaf area index (LAI) were also recorded. The sensitivity of PALSAR observations to variations in soil moisture has been reported by several authors, and is confirmed in the present study, even for the case of very dense crops. The radar signals are simulated using five different radar backscattering models (physical and semi-empirical), over bare soil, and over areas with various types of crop cover (turmeric, marigold, and sorghum). When the semi-empirical water cloud model (WCM) is parameterized as a function of the LAI, to account for the vegetation’s contribution to the backscattered signal, it can provide relatively accurate estimations of soil moisture in turmeric and marigold fields, but has certain limitations when applied to sorghum fields. Observed limitations highlight the need to expand the analysis beyond the LAI by including additional vegetation parameters in order to take into account volume scattering in the L-band backscattered radar signal for accurate soil moisture estimation.
We study the detention statistics of self-propelling droplet microswimmers attaching to microfluidic pillars. These droplets show negative autochemotaxis: they shed a persistent repulsive trail of spent fuel that biases them to detach from pillars in a specific size range after orbiting them just once. We have designed a microfluidic assay recording swimmers in pillar arrays of varying diameter, derived detention statistics via digital image analysis and interpreted these statistics via the Langevin dynamics of an active Brownian particle model. By comparing data from orbits with and without residual chemical field, we can independently estimate quantities like hydrodynamic and chemorepulsive torques, chemical coupling constants and diffusion coefficients, as well as their dependence on environmental factors like wall curvature. arXiv:1907.09924v2 [cond-mat.soft]
Simple Summary: -A novel algorithm delivering high resolution soil moisture maps is developed by merging active (SAR) and passive microwave.-MAPSM is based on the concept of Water Change Capacity. -A case study using MAPSM is presented by using the RADARSAT-2 and SMOS retrieved soil moisture data products over Berambadi watershed, Karnataka, India. -The algorithm parameters show scalability from the spatial resolution of 20 m to 2000 m.Abstract: Availability of soil moisture observations at a high spatial and temporal resolution is a prerequisite for various hydrological, agricultural and meteorological applications. In the current study, a novel algorithm for merging soil moisture from active microwave (SAR) and passive microwave is presented. The MAPSM algorithm-Merge Active and Passive microwave Soil Moisture-uses a spatio-temporal approach based on the concept of the Water Change Capacity (WCC) which represents the amplitude and direction of change in the soil moisture at the fine spatial resolution. The algorithm is applied and validated during a period of 3 years spanning from 2010 to 2013 over the Berambadi watershed which is located in a semi-arid tropical region in the Karnataka state of south India. Passive microwave products are provided from ESA Level 2 soil moisture products derived from Soil Moisture and Ocean Salinity (SMOS) satellite (3 days temporal resolution and 40 km nominal spatial resolution). Active microwave are based on soil moisture retrievals from 30 images of RADARSAT-2 data (24 days temporal resolution and 20 m spatial resolution). The results show that MAPSM is able to provide a good estimate of soil moisture at a spatial resolution of 500 m with an RMSE of 0.025 m 3 /m 3 and 0.069 m 3 /m 3 when comparing it to soil moisture from RADARSAT-2 and in-situ measurements, respectively. The use of Sentinel-1 and RISAT products in MAPSM algorithm is envisioned over other areas where high number of revisits is available. This will need an update of the algorithm to take into account the angle sampling and resolution of Sentinel-1 and RISAT data.
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