Input parameters selection for soil moisture retrieval using an artificial neural network

Factors other than soil moisture which influence the intensity of microwave emission from the soil include surface temperature, surface roughness, vegetation cover and soil texture which make this a non-linear and ill-posed problem. Artificial Neural Networks (ANNs) have been demonstrated to be good solutions to this type of problem. Since an ANN is a data driven model, proper input selection is a crucial step in its implementation as the presence of redundant or unnecessary inputs can severely impair the ability of the network to learn the target patterns. In this paper, the input parameters are chosen in combination with the brightness temperatures and are based on the use of incremental contributions of the variables towards soil moisture retrieval. Field experiment data obtained during the National Airborne Field Experiment 2005 (NAFE'05) are used. The retrieval accuracy with the input parameters selected is compared with the use of only brightness temperature as input and the use of brightness temperature in conjunction with a range of available parameters. Note that this research does not aim at selecting the best features for all ANN soil moisture retrieval problems using passive microwave. The paper shows that, depending on the problem and the nature of the data, some of the data available are redundant as the input of ANN for soil moisture retrieval. Importantly the results show that with the appropriate choice of inputs, the soil moisture retrieval accuracy of ANN can be significantly improved

Use of Soil Moisture Variability in Artificial Neural Network Retrieval of Soil Moisture

Passive microwave remote sensing is one of the most promising techniques for soil moisture retrieval. However, the inversion of soil moisture from brightness temperature observations is not straightforward, as it is influenced by numerous factors such as surface roughness, vegetation cover, and soil texture. Moreover, the relationship between brightness temperature, soil moisture and the factors mentioned above is highly non-linear and ill-posed. Consequently, Artificial Neural Networks (ANNs) have been used to retrieve soil moisture from microwave data, but with limited success when dealing with data different to that from the training period. In this study, an ANN is tested for its ability to predict soil moisture at 1 km resolution on different dates following training at the same site for a specific date. A novel approach that utilizes information on the variability of soil moisture, in terms of its mean and standard deviation for a (sub) region of spatial dimension up to 40 km, is used to improve the current retrieval accuracy of the ANN method.A comparison between the ANN with and without the use of the variability information showed that this enhancement enables the ANN to achieve an average Root Mean Square Error (RMSE) of around 5.1% v/v when using the variability information, as compared to around 7.5% v/v without it. The accuracy of the soil moisture retrieval was further improved by the division of the target site into smaller regions down to 4 km in size, with the spatial variability of soil moisture calculated from within the smaller region used in the ANN. With the combination of an ANN architecture of a single hidden layer of 20 neurons and the dual-polarized brightness temperatures as input, the proposed use of variability and sub-region methodology achieves an average retrieval accuracy of 3.7% v/v. Although this accuracy is not the lowest as comparing to the research in this field, the main contribution is the ability of ANN in solving the problem of predicting “out-of-range” soil moisture values. However, the applicability of this method is highly dependent on the accuracy of the mean and standard deviation values within the sub-region, potentially limiting its routine application

An inverse approach to Einstein's equations for non-conducting fluids

We show that a flow (timelike congruence) in any type $B_{1}$ warped product spacetime is uniquely and algorithmically determined by the condition of zero flux. (Though restricted, these spaces include many cases of interest.) The flow is written out explicitly for canonical representations of the spacetimes. With the flow determined, we explore an inverse approach to Einstein's equations where a phenomenological fluid interpretation of a spacetime follows directly from the metric irrespective of the choice of coordinates. This approach is pursued for fluids with anisotropic pressure and shear viscosity. In certain degenerate cases this interpretation is shown to be generically not unique. The framework developed allows the study of exact solutions in any frame without transformations. We provide a number of examples, in various coordinates, including spacetimes with and without unique interpretations. The results and algorithmic procedure developed are implemented as a computer algebra program called GRSource.Comment: 9 pages revtex4. Final form to appear in Phys Rev

Transitional Justice in a Transnational World: The Ambiguous Role of Law

In situations of military, political or economic transition, the reassessment of the role of law in the transition process becomes a crucial site of a people\u27s or a nation\u27s negotiating the past, present and future. Allusions to a tabula rasa or an annee zero after traumatic collapses of societal order, however, turn into ill-fated attempts to address the challenges of confronting the past when building the future. The law\u27s concern with nations that struggle with transition expresses itself through hybrid concepts such as transitional or post-conflict justice, restorative justice, or reconciliation. This paper revisits these instantiations and places them in the context of an increasingly transnational discourse on transitional justice. In light of the wealth of law and non-law responses to past injustice around the world today, transitional justice emerges as a form of transnational legal pluralism, highlighting the parallels of regulatory challenges confronting transition and established regimes alike

MHCII-mediated dialog between group 2 innate lymphoid cells and CD4+ T cells potentiates type 2 immunity and promotes parasitic helminth expulsion

Group 2 innate lymphoid cells (ILC2s) release interleukin-13 (IL-13) during protective immunity to helminth infection and detrimentally during allergy and asthma. Using two mouse models to deplete ILC2s in vivo, we demonstrate that T helper 2 (Th2) cell responses are impaired in the absence of ILC2s. We show that MHCII-expressing ILC2s interact with antigen-specific T cells to instigate a dialog in which IL-2 production from T cells promotes ILC2 proliferation and IL-13 production. Deletion of MHCII renders IL-13-expressing ILC2s incapable of efficiently inducing Nippostrongylus brasiliensis expulsion. Thus, during transition to adaptive T cell-mediated immunity, the ILC2 and T cell crosstalk contributes to their mutual maintenance, expansion and cytokine production. This interaction appears to augment dendritic-cell-induced T cell activation and identifies a previously unappreciated pathway in the regulation of type-2 immunity

Role of right posterior parietal cortex in maintaining attention to spatial locations over time

Recent models of human posterior parietal cortex (PPC) have variously emphasized its role in spatial perception, visuomotor control or directing attention. However, neuroimaging and lesion studies also suggest that the right PPC might play a special role in maintaining an alert state. Previously, assessments of right-hemisphere patients with hemispatial neglect have revealed significant overall deficits on vigilance tasks, but to date there has been no demonstration of a deterioration of performance over time--a vigilance decrement--considered by some to be a key index of a deficit in maintaining attention. Moreover, sustained attention deficits in neglect have not specifically been related to PPC lesions, and it remains unclear whether they interact with spatial impairments in this syndrome. Here we examined the ability of right-hemisphere patients with neglect to maintain attention, comparing them to stroke controls and healthy individuals. We found evidence of an overall deficit in sustaining attention associated with PPC lesions, even for a simple detection task with stimuli presented centrally. In a second experiment, we demonstrated a vigilance decrement in neglect patients specifically only when they were required to maintain attention to spatial locations, but not verbal material. Lesioned voxels in the right PPC spanning a region between the intraparietal sulcus and inferior parietal lobe were significantly associated with this deficit. Finally, we compared performance on a task that required attention to be maintained either to visual patterns or spatial locations, matched for task difficulty. Again, we found a vigilance decrement but only when attention had to be maintained on spatial information. We conclude that sustaining attention to spatial locations is a critical function of the human right PPC which needs to be incorporated into models of normal parietal function as well as those of the clinical syndrome of hemispatial neglect