Several recommendations have been proposed for detecting land use and land cover change (LULCC) on the environment from, observed climatic records and to modeling to improve its understanding and its impacts on climate. Researchers need to detect LULCCs accurately at appropriate scales within a specified time period to better understand their impacts on climate and provide improved estimates of future climate. The US Climate Reference Network (USCRN) can be helpful in monitoring impacts of LULCC on near-surface atmospheric conditions, including temperature. The USCRN measures temperature, precipitation, solar radiation, and ground or skin temperature. It is recommended that the National Climatic Data Center (NCDC) and other climate monitoring agencies develop plans and seek funds to address any monitoring biases that are identified and for which detailed analyses have not been completed

The land surface influences local, regional and global climate across many time scales. Accurate representation of land surfaces is an important factor for climate modelling studies because land surfaces control the partitioning of available energy and water. Here we introduce new, up‐to‐date and accurate land cover data for the Marmara Region, Turkey derived from Landsat Enhanced Thematic Mapper (ETM+) images into the Weather Research and Forecasting (WRF) model. We used several image processing techniques to create accurate land cover data from Landsat sensor images obtained between 2001 and 2005. By comparing the new land cover data with the default WRF land cover data, we found that there are two types of error in WRF land cover data that caused misrepresentation of the study region. WRF uses Global Land Cover Characteristics (GLCC) data created from images acquired during 1992 and 1993 and it does not reflect current land cover. And the GLCC includes misclassifications. As a result of these errors, GLCC data do not represent urban areas in the cities of Istanbul, Izmit and Bursa and there are spectral mixing problems between classes, e.g. croplands, urban areas and forests. We used WRF land cover and our new land cover data to conduct numerical simulations. Using meteorological station data within the study area, we found that simulation with the new land cover dataset produces more accurate temperature simulations for the region, thus demonstrating the importance of accurate land cover data. Copyright © 2009 Royal Meteorological Society

Object detection from satellite images has been a challenging problem for many years. With the development of effective deep learning algorithms and advancement in hardware systems, higher accuracies have been achieved in the detection of various objects from very high-resolution (VHR) satellite images. This article provides a comparative evaluation of the state-of-the-art convolutional neural network (CNN)-based object detection models, which are Faster R-CNN, Single Shot Multi-box Detector (SSD), and You Look Only Once-v3 (YOLO-v3), to cope with the limited number of labeled data and to automatically detect airplanes in VHR satellite images. Data augmentation with rotation, rescaling, and cropping was applied on the test images to artificially increase the number of training data from satellite images. Moreover, a non-maximum suppression algorithm (NMS) was introduced at the end of the SSD and YOLO-v3 flows to get rid of the multiple detection occurrences near each detected object in the overlapping areas. The trained networks were applied to five independent VHR test images that cover airports and their surroundings to evaluate their performance objectively. Accuracy assessment results of the test regions proved that Faster R-CNN architecture provided the highest accuracy according to the F1 scores, average precision (AP) metrics, and visual inspection of the results. The YOLO-v3 ranked as second, with a slightly lower performance but providing a balanced trade-off between accuracy and speed. The SSD provided the lowest detection performance, but it was better in object localization. The results were also evaluated in terms of the object size and detection accuracy manner, which proved that large- and medium-sized airplanes were detected with higher accuracy.

In arid and semi-arid regions of the world, soil salinization is one of the most crucial environmental problems due to its adverse effects on agricultural productivity and sustainable development. Unconscious irrigation and old irrigation techniques extremely damage fertile land and accelerate water logging and salt accumulation in soil. In addition, some natural factors also exacerbate soil salinity. Therefore, it is an important concern to predict and monitor soil salinity in order to take protective measures against further deterioration of the soil. In this study, emphasis is given to the techniques used for predicting and monitoring soil salinity throughout the different regions of the world. Examples of soil salinity mapping will also be referred to alert especially the soil scientists and farmers. Traditionally, soil salinity prediction and monitoring are often carried out with intensive field work and sampling. Most previous studies have focused on differentiating salinized and non-salinized soil qualitatively by analysing the salinity distribution and monitoring its dynamics. Remote Sensing (RS), Geographical Information Systems (GIS) and modelling have recently outperformed the traditional methods. Mapping has progressed from qualitative to quantitative mapping via multiple-temporal and multi-spectral information obtained from RS observations. In zones of thickly vegetated soils, using vegetation indices in the evaluation, mapping of soil salinity have started to yield promising results; whereas indices have become the appropriate method in the case of exposed soils or soils with low scattered vegetation. This study reviews the application of various satellite images to delineate soil salinity maps by conducting either salinity indices generated by different spectral bands or vegetation indices