Actual Evapotranspiration from UAV Images: A Multi-Sensor Data Fusion Approach

Search and Study of Marked Code Structures for a Spatially Distributed System of Small-Sized Airborne Radars

When forming the radar situation of a terrain, in order to increase its information content and to extract useful information, multi-position spatially distributed systems for integrating multi-angle radar data established by small-sized UAV-based airborne radars are used. In this case, each radar station belonging to a multi-position system as a probing signal must have its own unique marked signal. Such a setup will allow the signals reflected from ground objects and zones to be "attached" to specific receiving-transmitting positions of the multi-position system. This requirement results from the fact that each transceiver position emits one probing signal, and then receives all the echo signals reflected from the underlying surface and previously emitted by other radar devices of the multi-position system. Such a setup of multi-position systems requires the researcher to look for and investigate specialized systems of marked code structures used to modulate the probing signals in order to identify them in a joint radar channel. Thus, the problem at hand is how to look for and investigate novel marked code structures used to generate a system of probing signals, the use of which will allow it to be "attached" to a specific receiving-transmitting position of a multi-position onboard system and to identify them in a joint radar channel in the course of the remote sensing of the underlying surface. The purpose of this work is to conduct a study on the subject of the squeak and choice of a system of code structures that have a low level of side lobes of autocorrelation functions and uniformly distributed values of the levels of the cross-correlation function. To achieve this goal, the relevance of the study is substantiated in the introduction. The second section analyzes the level of side lobes for classical and modified Barker codes with an asymmetric alphabet. On the basis of this analysis, it was concluded that it is expedient to apply this approach for codes longer than Barker codes. Therefore, in the third section, the features of the generation of M-sequences are considered. The fourth section presents a technique for searching for new marked code structures, taking into account their mutual correlation properties for modifying M-sequences in order to implement multi-positional systems. The fifth section presents computer experiments on the search for marked code structures based on the modifications of M-sequences and presents the numerical characteristics of the correlation properties of the considered marked codes. And finally, in the sixth section, the final conclusions of the study are presented and recommendations are given for their further practical application. The practical significance of this study lies in the fact that the proposed new marked code structures are necessary for the synthesis of probing signals in the implementation of spatially distributed systems that function for the high-probability detection and high-precision determination of the coordinates of physical objects and are also necessary for the formation of radar images in a multi-position mode.

Understanding interactive processes: a review of CO2 flux, evapotranspiration, and energy partitioning under stressful conditions in dry forest and agricultural environments

Arid and semiarid environments are characterized by low water availability (e.g., in soil and atmosphere), high air temperature, and irregularity in the spatio-temporal distribution of rainfall. In addition to the economic and environmental consequences, drought also causes physiological damage to crops and compromises their survival in ecosystems. The removal of vegetation is responsible for altering the energy exchange of heat and water in natural ecosystems and agricultural areas. The fluxes of CO₂ are also changed, and environments with characteristics of sinks, which can be sources of CO₂ after anthropic disturbances. These changes can be measured through methods such as sap flow, eddy covariance, remote sensing, and energy balance. Despite the relevance of each method mentioned above, there are limitations in their applications that must be respected. Thus, this review aims to quantify the processes and changes of energy fluxes, CO₂, and their interactions with the surfaces of terrestrial ecosystems in dry environments. Studies report that the use of methods that integrate data from climate monitoring towers and remote sensing products helps to improve the accuracy of the determination of energy fluxes on a global scale, also helping to reduce the dissimilarity of results obtained individually. Through the collection of works in the literature, it is reported that several areas of the Brazilian Caatinga biome, which is a Seasonally Dry Tropical Forest have been suffering from changes in land use and land cover. Similar fluxes of sensible heat in areas with cacti and Caatinga can be observed in studies. On the other hand, one of the variables influenced mainly by air temperature is net radiation. In dry forest areas, woody species can store large amounts of carbon in their biomass above and belowground. The use of cacti can modify the local carbon budget when using tree crops together. Therefore, the study highlights the complexity and severity of land degradation and changes in CO₂, water, and energy fluxes in dry environments with areas of forest, grassland, and cacti. Vegetation energy balance is also a critical factor, as these simulations are helpful for use in forecasting weather or climate change. We also highlight the need for more studies that address environmental conservation techniques and cactus in the conservation of degraded areas.

Evapotranspiration Acquired with Remote Sensing Thermal-Based Algorithms: A State-of-the-Art Review

Almost fifty years have passed since the idea to retrieve a value for Evapotranspiration (ET) using remote sensing techniques was first considered. Numerous ET models have been proposed, validated and improved along these five decades, as the satellites and sensors onboard were enhanced. This study reviews most of the efforts in the progress towards providing a trustworthy value of ET by means of thermal remote sensing data. It starts with an in-depth reflection of the surface energy balance concept and of each of its terms, followed by the description of the approaches taken by remote sensing models to estimate ET from it in the last thirty years. This work also includes a chronological review of the modifications suggested by several researchers, as well as representative validations studies of such ET models. Present limitations of ET estimated with remote sensors onboard orbiting satellites, as well as at surface level, are raised. Current trends to face such limitations and a future perspective of the discipline are also exposed, for the reader’s inspiration.

Building a UAV Based System to Acquire High Spatial Resolution Thermal Imagery for Energy Balance Modelling

High spatial resolution and geolocation accuracy canopy evapotranspiration (ET) maps are well suited tools for evaluation of small plot field trials. While creating such a map by use of an energy balance model is routinely performed, the acquisition of the necessary imagery at a suitable quality is still challenging. An UAV based thermal/RGB integrated imaging system was built using the RaspberryPi (RPi) microcomputer as a central unit. The imagery served as input to the two-source energy balance model pyTSEB to derive the ET map. The setup's flexibility and modularity are based on the multiple interfaces provided by the RPi and the software development kit (SDK) provided for the thermal camera. The SDK was installed on the RPi and used to trigger cameras, retrieve and store images and geolocation information from an onboard GNSS rover for PPK processing. The system allows acquisition of 8 cm spatial resolution thermal imagery from a 60 m height of flight and less than 7 cm geolocation accuracy of the mosaicked RGB imagery. Modelled latent heat flux data have been validated against latent heat fluxes measured by eddy covariance stations at two locations with RMSE of 75 W/m² over a two-year study period.

How High to Fly? Mapping Evapotranspiration from Remotely Piloted Aircrafts at Different Elevations

Recent advancements in remotely piloted aircrafts (RPAs) have made frequent, low-flying imagery collection more economical and feasible than ever before. The goal of this work was to create, compare, and quantify uncertainty associated with evapotranspiration (ET) maps generated from different conditions and image capture elevations. We collected optical and thermal data from a commercially irrigated potato (Solanum tuberosum) field in the Wisconsin Central Sands using a quadcopter RPA system and combined multispectral/thermal camera. We conducted eight mission sets (24 total missions) during the 2019 growing season. Each mission set included flights at 90, 60, and 30 m above ground level. Ground reference measurements of surface temperature and soil moisture were collected throughout the domain within 15 min of each RPA mission set. Evapotranspiration values were modeled from the flight data using the High-Resolution Mapping of Evapotranspiration (HRMET) model. We compared HRMET-derived ET estimates to an Eddy Covariance system within the flight domain. Additionally, we assessed uncertainty for each flight using a Monte Carlo approach. Results indicate that the primary source of uncertainty in ET estimates was the optical and thermal data. Despite some additional detectable features at low elevation, we conclude that the tradeoff in resources and computation does not currently justify low elevation flights for annual vegetable crop management in the Midwest USA.

A Bibliometric Review of the Use of Unmanned Aerial Vehicles in Precision Agriculture and Precision Viticulture for Sensing Applications

This review focuses on the use of unmanned aerial vehicles (UAVs) in precision agriculture, and specifically, in precision viticulture (PV), and is intended to present a bibliometric analysis of their developments in the field. To this aim, a bibliometric analysis of research papers published in the last 15 years is presented based on the Scopus database. The analysis shows that the researchers from the United States, China, Italy and Spain lead the precision agriculture through UAV applications. In terms of employing UAVs in PV, researchers from Italy are fast extending their work followed by Spain and finally the United States. Additionally, the paper provides a comprehensive study on popular journals for academicians to submit their work, accessible funding organizations, popular nations, institutions, and authors conducting research on utilizing UAVs for precision agriculture. Finally, this study emphasizes the necessity of using UAVs in PV as well as future possibilities.

Groundwater Level Modeling with Machine Learning: A Systematic Review and Meta-Analysis

Groundwater is a vital source of freshwater, supporting the livelihood of over two billion people worldwide. The quantitative assessment of groundwater resources is critical for sustainable management of this strained resource, particularly as climate warming, population growth, and socioeconomic development further press the water resources. Rapid growth in the availability of a plethora of in-situ and remotely sensed data alongside advancements in data-driven methods and machine learning offer immense opportunities for an improved assessment of groundwater resources at the local to global levels. This systematic review documents the advancements in this field and evaluates the accuracy of various models, following the protocol developed by the Center for Evidence-Based Conservation. A total of 197 original peer-reviewed articles from 2010–2020 and from 28 countries that employ regression machine learning algorithms for groundwater monitoring or prediction are analyzed and their results are aggregated through a meta-analysis. Our analysis points to the capability of machine learning models to monitor/predict different characteristics of groundwater resources effectively and efficiently. Modeling the groundwater level is the most popular application of machine learning models, and the groundwater level in previous time steps is the most employed input data. The feed-forward artificial neural network is the most employed and accurate model, although the model performance does not exhibit a striking dependence on the model choice, but rather the information content of the input variables. Around 10–12 years of data are required to develop an acceptable machine learning model with a monthly temporal resolution. Finally, advances in machine and deep learning algorithms and computational advancements to merge them with physics-based models offer unprecedented opportunities to employ new information, e.g., InSAR data, for increased spatiotemporal resolution and accuracy of groundwater monitoring and prediction.

Data Fusion in Agriculture: Resolving Ambiguities and Closing Data Gaps

Acquiring useful data from agricultural areas has always been somewhat of a challenge, as these are often expansive, remote, and vulnerable to weather events. Despite these challenges, as technologies evolve and prices drop, a surge of new data are being collected. Although a wealth of data are being collected at different scales (i.e., proximal, aerial, satellite, ancillary data), this has been geographically unequal, causing certain areas to be virtually devoid of useful data to help face their specific challenges. However, even in areas with available resources and good infrastructure, data and knowledge gaps are still prevalent, because agricultural environments are mostly uncontrolled and there are vast numbers of factors that need to be taken into account and properly measured for a full characterization of a given area. As a result, data from a single sensor type are frequently unable to provide unambiguous answers, even with very effective algorithms, and even if the problem at hand is well defined and limited in scope. Fusing the information contained in different sensors and in data from different types is one possible solution that has been explored for some decades. The idea behind data fusion involves exploring complementarities and synergies of different kinds of data in order to extract more reliable and useful information about the areas being analyzed. While some success has been achieved, there are still many challenges that prevent a more widespread adoption of this type of approach. This is particularly true for the highly complex environments found in agricultural areas. In this article, we provide a comprehensive overview on the data fusion applied to agricultural problems; we present the main successes, highlight the main challenges that remain, and suggest possible directions for future research.

Formation of Fused Images of the Land Surface from Radar and Optical Images in Spatially Distributed On-Board Operational Monitoring Systems

This paper considers the issues of image fusion in a spatially distributed small-size on-board location system for operational monitoring. The purpose of this research is to develop a new method for the formation of fused images of the land surface based on data obtained from optical and radar devices operated from two-position spatially distributed systems of small aircraft, including unmanned aerial vehicles. The advantages of the method for integrating information from radar and optical information-measuring systems are justified. The combined approach allows removing the limitations of each separate system. The practicality of choosing the integration of information from several widely used variants of heterogeneous sources is shown. An iterative approach is used in the method for combining multi-angle location images. This approach improves the quality of synthesis and increases the accuracy of integration, as well as improves the information content and reliability of the final fused image by using the pixel clustering algorithm, which produces many partitions into clusters. The search for reference points on isolated contours is carried out on a pair of left and right images of the docked image from the selected partition. For these reference points, a functional transformation is determined. Having applied it to the original multi-angle heterogeneous images, the degree of correlation of the fused image is assessed. Both the position of the reference points of the contour and the desired functional transformation itself are refined until the quality assessment of the fusion becomes acceptable. The type of functional transformation is selected based on clustered images and then applied to the original multi-angle heterogeneous images. This process is repeated for clustered images with greater granularity in case if quality assessment of the fusion is considered to be poor. At each iteration, there is a search for pairs of points of the contour of the isolated areas. Areas are isolated with the use of two image segmentation methods. Experiments on the formation of fused images are presented. The result of the research is the proposed method for integrating information obtained from a two-position airborne small-sized radar system and an optical location system. The implemented method can improve the information content, quality, and reliability of the finally established fused image of the land surface.