Development of a simplified technique for gap filling of Normalize Difference Vegetation Index (NDVI) time series data

The presence of gaps or missing values in time series prevents the practical use of such data. The current research aims at developing a simplified, straightforward technique for gap-filling the time series data of the Normalize Difference Vegetation Index (NDVI) generated using Moderate Resolution Imaging Spectroradiometer (MODIS). This research assumes that a relationship exists between the pixel location, date of acquisition and its NDVI value within a defined timeline. Therefore, two relatively simple methods were tested: the Multiple Linear Regression (MLR) analysis and the Artificial Neural Networks (ANN)to fill the NDVI missing values. While MLR is a well-known simple statistical method, the ANN has been successfully applied for the analysis of various scientific data, including the gap-filling of time series data. Nevertheless, ANN proved its supremacy in such approach. The accuracy of estimation utilizing the developed ANN model reached an average of r2 of 0.8, while the average accuracy of MLR was about 0.3. Nevertheless, the developed model could only be applied within the same timeframe of the images used for developing the model. Otherwise, the accuracy of determination was reduced significantly. The results showed that according to its performance, ANN are promising for filling missing data of NDVI time series and could be applied to any other vegetation indices as well

Performance evaluation of Satellite-based actual evapotranspiration technique

Estimating crop evapotranspiration is vital for the calculation of irrigation water requirements. Remote sensing data have proven to be a valuable and efficient tool for estimating evapotranspiration. It has been used intensively over the past decade due to free, high temporal and spectral resolution data availability. The main aim of this study was to estimate the evapotranspiration (ET) over a selected area in El-Beheira governorate, Egypt based on the Simplified Surface Energy Balance Index (S-SEBI) using nine Landsat-8 images acquired from January to December 2020. The performance of the studied method was compared with the CROPWAT-8 model. The results revealed the acceptable accuracy of the ET estimated from S-SEBI algorithms with Landsat 8 images according to the coefficient of determination (r2 = 0.82) and root mean square error (RMSE = 0.53 mm/day). Therefore, it is recommended to use the S-SEBI to calculate the spatial evapotranspiration distribution using Landsat-8 images to provide the required information for determining irrigation water requirements and suggesting an efficient water management strategy

Utilizing neural networks for image downscaling and water quality monitoring

Remotely sensed images are becoming highly required for various applications, especially those related to natural resource management. The Moderate Resolution Imaging Spectroradiometer (MODIS) data has the advantages of its high spectral and temporal resolutions but remains inadequate in providing the required high spatial resolution. On the other hand, Sentinel-2 is more advantageous in spatial and temporal resolution but lacks a solid historical database. In this study, four MODIS bands in the visible and near-infrared spectral regions of the electromagnetic spectrum and their matching Sentinel-2 bands were used to monitor the turbidity in Lake Nasser, Egypt. The MODIS data were downscaled to Sentinel-2, which enhanced its spatial resolution from 250 and 500m to 10m.Furthermore, it provided a historical database that was used to monitor the changes in lake turbidity. Spatial approach based on neural networks was presented to downscale MODIS bands to the spatial resolution of the Sentinel-2 bands. The correlation coefficient between the predicted and actual images exceeded 0.70 for the four bands. Applying this approach, the downscaled MODIS images were developed and the neural networks were further employed to these images to develop a model for predicting the turbidity in the lake. The correlation coefficient between the predicted and actual measurements reached 0.83. The study suggests neural networks as a comparatively simplified and accurate method for image downscaling compared to other methods. It also demonstrated the possibility of utilizing neural networks to accurately predict lake water quality parameters such as turbidity from remote sensing data compared to statistical methods

Endomorphisms of quantized Weyl algebras

Belov-Kanel and Kontsevich conjectured that the group of automorphisms of the n'th Weyl algebra and the group of polynomial symplectomorphisms of C^2 are canonically isomorphic. We discuss how this conjecture can be approached by means of (second) quantized Weyl algebras at roots of unity

Young children's explorations of average through informal inferential reasoning

This study situates children's early notions of average within an inquiry classroom to investigate the rich inferential reasoning that young children drew on to make sense of the questions: Is there a typical height for a student in year 3? If so, what is it? Based on their deliberations over several lessons, students' ideas about average and typicality evolved as meaning reasonable, contrary to atypical, most common (value or interval), middle, normative, and representative of the population. The case study reported here documents a new direction for the development of children's conceptions of average in a classroom designed to elicit their informal inferential reasoning about data