Standardized Doppler lidar processing for operational use in a future network

<p>Fibre-optic based Doppler wind lidars (DL) are widely used for both meteorological research and in the wind energy sector. These compact systems are able to retrieve vertical profiles of kinematic quantities, such as mean wind, from the atmospheric boundary layer as well as from optically thin cloud layers in the free troposphere with high spatio-temporal resolution. It is therefore likely that especially short-term forecasting would benefit from assimilating these data. However, their potential is currently not yet employed operationally.</p> <p>As part of DWD's effort to evaluate ground-based remote sensing systems for their operational readiness, called "Pilotstation", we developed a software client (DL-client) that standardizes the processing of mean wind based on the Velocity Azimuth Display method. Results of a long-term assessment of DLs at the Meteorological Observatory Lindenberg, starting in 2012, show that the DL-client assures a high quality Level-2 product, which is compatible with the EUMETNET's E-PROFILE observation program. We verified the retrieved mean wind speed and direction with the help of independent reference data from a 482 MHz radar wind profiler and 6-hourly radiosonde ascents. Hence, the DL-client not only facilitates processing and archiving of the DL data, but also forms a basis for operational network deployment and data assimilation. Furthermore, through speeding up and standardizing the data processing, the individual users can concentrate on more advanced scientific data analyses.</p> <p>Finally, the software is freely accessible and will be continuously improved to account for different scanning strategies. Its modular build-up of processing steps offers the possibility to extend the list of products with additional retrievals, e.g. for turbulent kinetic energy and wind gusts, which are currently under development at Lindenberg.</p>

Assessment of a Broadband Differential Absorption Lidar (DIAL) for use in a ground-based measurement network

<p>Convective-scale forecasts require more detailed and continuous observational data of thermodynamic profiles and wind profiles in the atmospheric boundary layer (ABL) than currently provided. In order to meet these data requirements in the future, DWD evaluates various surface remote sensing systems targeted on ABL-profiling for routine network operation.</p> <p>One of the candidate systems in operation at the Observatory Lindenberg is a new pre-production broadband DIAL from Vaisala. DIAL instruments are well-established in research activities, but this instrument is developed for operationally providing water vapor profile observations in the ABL during all weather conditions. We present evaluation results of the DIAL’s operational performance regarding the quality of the water vapor profiles and report on its ability to monitor sub-grid scale processes, such as convection and associated weather phenomena. This includes comparisons with radiosounding observations (4 per day) over at least one year of continuous observations and additional comparisons with Raman lidar for a three-month period during summer 2021. Furthermore, we provide observation-minus-background statistics between the DIAL and the ICON limited area model (ICON-LAM) to evaluate the model performance, e.g. under convection, and to identify observational error sources.</p> <p>This contribution provides knowledge regarding the operational viability of the new pre-production broadband DIAL, its value for monitoring water vapour profiles 24/7 and ABL processes for future model applications.</p>

A Review of Crop Water Stress Assessment Using Remote Sensing

Currently, the world is facing high competition and market risks in improving yield, crop illness, and crop water stress. This could potentially be addressed by technological advancements in the form of precision systems, improvements in production, and through ensuring the sustainability of development. In this context, remote-sensing systems are fully equipped to address the complex and technical assessment of crop production, security, and crop water stress in an easy and efficient way. They provide simple and timely solutions for a diverse set of ecological zones. This critical review highlights novel methods for evaluating crop water stress and its correlation with certain measurable parameters, investigated using remote-sensing systems. Through an examination of previous literature, technologies, and data, we review the application of remote-sensing systems in the analysis of crop water stress. Initially, the study presents the relationship of relative water content (RWC) with equivalent water thickness (EWT) and soil moisture crop water stress. Evapotranspiration and sun-induced chlorophyll fluorescence are then analyzed in relation to crop water stress using remote sensing. Finally, the study presents various remote-sensing technologies used to detect crop water stress, including optical sensing systems, thermometric sensing systems, land-surface temperature-sensing systems, multispectral (spaceborne and airborne) sensing systems, hyperspectral sensing systems, and the LiDAR sensing system. The study also presents the future prospects of remote-sensing systems in analyzing crop water stress and how they could be further improved.

Reduction of Spatially Correlated Speckle in Textured SAR Images

Synthetic aperture radars (SARs) provide a lot of images that can be used for numerous applications. A problem with acquired images is that they are corrupted by speckle which is a noise-like phenomenon with multiplicative nature. In addition, speckle is non-Gaussian and it is often spatially correlated. A typical task in SAR image processing is despeckling and many methods have been already proposed. However, most of them do not take noise spatial correlation into account during denoising. In this paper, we show how this can be done in despeckling based on discrete cosine transform. The use of frequency-dependent thresholds leads to sufficient improvement of denoising efficiency in terms of visual quality metrics. Moreover, we consider quite complex structure texture images for which noise removal is usually problematic and can lead to information loss. Comparison to the well-known local statistic Lee and Frost filters, extended DCT-based filter is carried out for different remote sensing systems including Sentinel-1 and Sentinel-2.

Remote Sensing of Wetlands in the Prairie Pothole Region of North America

The Prairie Pothole Region (PPR) of North America is an extremely important habitat for a diverse range of wetland ecosystems that provide a wealth of socio-economic value. This paper describes the ecological characteristics and importance of PPR wetlands and the use of remote sensing for mapping and monitoring applications. While there are comprehensive reviews for wetland remote sensing in recent publications, there is no comprehensive review about the use of remote sensing in the PPR. First, the PPR is described, including the wetland classification systems that have been used, the water regimes that control the surface water and water levels, and the soil and vegetation characteristics of the region. The tools and techniques that have been used in the PPR for analyses of geospatial data for wetland applications are described. Field observations for ground truth data are critical for good validation and accuracy assessment of the many products that are produced. Wetland classification approaches are reviewed, including Decision Trees, Machine Learning, and object versus pixel-based approaches. A comprehensive description of the remote sensing systems and data that have been employed by various studies in the PPR is provided. A wide range of data can be used for various applications, including passive optical data like aerial photographs or satellite-based, Earth-observation data. Both airborne and spaceborne lidar studies are described. A detailed description of Synthetic Aperture RADAR (SAR) data and research are provided. The state of the art is the use of multi-source data to achieve higher accuracies and hybrid approaches. Digital Surface Models are also being incorporated in geospatial analyses to separate forest and shrub and emergent systems based on vegetation height. Remote sensing provides a cost-effective mechanism for mapping and monitoring PPR wetlands, especially with the logistical difficulties and cost of field-based methods. The wetland characteristics of the PPR dictate the need for high resolution in both time and space, which is increasingly possible with the numerous and increasing remote sensing systems available and the trend to open-source data and tools. The fusion of multi-source remote sensing data via state-of-the-art machine learning is recommended for wetland applications in the PPR. The use of such data promotes flexibility for sensor addition, subtraction, or substitution as a function of application needs and potential cost restrictions. This is important in the PPR because of the challenges related to the highly dynamic nature of this unique region.

Using Remote Sensing and an Unmanned Aerial System for Weed Management in Agricultural Crops: A Review

Weeds are unwanted plants that can reduce crop yields by competing for water, nutrients, light, space, and carbon dioxide, which need to be controlled to meet future food production requirements. The integration of drones, artificial intelligence, and various sensors, which include hyperspectral, multi-spectral, and RGB (red-green-blue), ensure the possibility of a better outcome in managing weed problems. Most of the major or minor challenges caused by weed infestation can be faced by implementing remote sensing systems in various agricultural tasks. It is a multi-disciplinary science that includes spectroscopy, optics, computer, photography, satellite launching, electronics, communication, and several other fields. Future challenges, including food security, sustainability, supply and demand, climate change, and herbicide resistance, can also be overcome by those technologies based on machine learning approaches. This review provides an overview of the potential and practical use of unmanned aerial vehicle and remote sensing techniques in weed management practices and discusses how they overcome future challenges.