Aedes aegypti and Aedes albopictus abundance, landscape coverage and spectral indices effects in a subtropical city of Argentina

The presence, abundance and distribution of Aedes (Stegomyia) aegypti (Linnaeus 1762) and Aedes (Stegomyia) albopictus (Skuse 1894) could be conditioned by different data obtained from satellite remote sensors. In this paper, we aim to estimate the effect of landscape coverage and spectral indices on the abundance of Ae. aegypti and Ae. albopictus from the use of satellite remote sensors in Eldorado, Misiones, Argentina. Larvae of Aedes aegypti and Ae. albopictus were collected monthly from June 2016 to April 2018, in four outdoor environments: tire repair shops, cemeteries, family dwellings, and an urban natural park. The proportion of each land cover class was determined by Sentinel-2 image classification. Furthermore spectral indices were calculated. Generalized Linear Mixed Models were developed to analyze the possible effects of landscape coverage and vegetation indices on the abundance of mosquitoes. The model's results showed the abundance of Ae. aegypti was better modeled by the minimum values of the NDVI index, the maximum values of the NDBI index and the interaction between both variables. In contrast, the abundance of Ae. albopictus has to be better explained by the model that includes the variables bare soil, low vegetation and the interaction between both variables.

Research on a Partial Aperture Factor Measurement Method for the Agri Onboard Calibration Assembly

A partial aperture onboard calibration method can solve the onboard calibration problems of some large aperture remote sensors, which is of great significance for the development trend of increasingly large apertures in optical remote sensors. In this paper, the solar diffuser reflectance degradation monitor (SDRDM) in the onboard calibration assembly (CA) of the FengYun-4 (FY-4) advanced geostationary radiance imager (AGRI) is used as the reference radiometer for measuring the partial aperture factor (PAF) for the AGRI onboard calibration. First, the linear response count variation relationship between the two is established under the same radiance source input. Then, according to the known bidirectional reflection distribution function (BRDF) of the solar diffuser (SD) in the CA, the relative reflectance ratio coefficient between the AGRI observation direction and the SDRDM observation direction is calculated. On this basis, the response count value of the AGRI and the SDRDM is used to realize the high-precision measurement of the PAF of the AGRI B1 ~ B3 bands by simulating the AGRI onboard calibration measurement under the illumination of a solar simulator in the laboratory. According to the determination process of the relevant parameters of the PAF, the measurement uncertainty of the PAF is analyzed; this uncertainty is better than 2.04% and provides an important reference for the evaluation of the onboard absolute radiometric calibration uncertainty after launch.

Arctic Sea Ice Classification Based on CFOSAT SWIM Data at Multiple Small Incidence Angles

Sea ice type is the key parameter of Arctic sea ice monitoring. Microwave remote sensors with medium incidence and normal incidence modes are the primary detection methods for sea ice types. The Surface Wave Investigation and Monitoring instrument (SWIM) on the China-France Oceanography Satellite (CFOSAT) is a new type of sensor with a small incidence angle detection mode that is different from traditional remote sensors. The method of sea ice detection using SWIM data is also under development. The research reported here concerns ice classification using SWIM data in the Arctic from October 2019 to April 2020. Six waveform features are extracted from the SWIM echo data at small incidence angles, then the distinguishing capabilities of a single feature are analyzed using the Kolmogorov-Smirnov distance. The classifiers of the k-nearest neighbor and support vector machine are established and chosen based on single features. Moreover, sea ice classification based on multi-feature combinations is carried out using the chosen KNN classifier, and optimal combinations are developed. Compared with sea ice charts, the overall accuracy is up to 81% using the optimal classifier and a multi-feature combination at 2°. The results reveal that SWIM data can be used to classify sea water and sea ice types. Moreover, the optimal multi-feature combinations with the KNN method are applied to sea ice classification in the local regions. The classification results are analyzed using Sentinel-1 SAR images. In general, it is concluded that these multifeature combinations with the KNN method are effective in sea ice classification using SWIM data. Our work confirms the potential of sea ice classification based on the new SWIM sensor, and highlight the new sea ice monitoring technology and application of remote sensing at small incidence angles.

Current Status and Future Opportunities for Grain Protein Prediction Using On- and Off-Combine Sensors: A Synthesis-Analysis of the Literature

The spatial information about crop grain protein concentration (GPC) can be an important layer (i.e., a map that can be utilized in a geographic information system) with uses from nutrient management to grain marketing. Recently, on- and off-combine harvester sensors have been developed for creating spatial GPC layers. The quality of these GPC layers, as measured by the coefficient of determination (R2) and the root mean squared error (RMSE) of the relationship between measured and predicted GPC, is affected by different sensing characteristics. The objectives of this synthesis analysis were to (i) contrast GPC prediction R2 and RMSE for different sensor types (on-combine, off-combine proximal and remote); (ii) contrast and discuss the best spatial, temporal, and spectral resolutions and features, and the best statistical approach for off-combine sensors; and (iii) review current technology limitations and provide future directions for spatial GPC research and application. On-combine sensors were more accurate than remote sensors in predicting GPC, yet with similar precision. The most optimal conditions for creating reliable GPC predictions from off-combine sensors were sensing near anthesis using multiple spectral features that include the blue and green bands, and that are analyzed by complex statistical approaches. We discussed sensor choice in regard to previously identified uses of a GPC layer, and further proposed new uses with remote sensors including same season fertilizer management for increased GPC, and in advance segregated harvest planning related to field prioritization and farm infrastructure. Limitations of the GPC literature were identified and future directions for GPC research were proposed as (i) performing GPC predictive studies on a larger variety of crops and water regimes; (ii) reporting proper GPC ground-truth calibrations; (iii) conducting proper model training, validation, and testing; (iv) reporting model fit metrics that express greater concordance with the ideal predictive model; and (v) implementing and benchmarking one or more uses for a GPC layer.

Observation of aerosol induced ‘lower tropospheric cooling’ over Indian core monsoon region

Aerosols play a significant role in regional scale pollution that alters the cloud formation process, radiation budget, and climate. Here, using long-term (2003-2019) observations from multi-satellite and ground-based remote sensors, we show robust aerosol-induced instantaneous daytime lower tropospheric cooling during the pre-monsoon season over the Indian core monsoon region (ICMR). Quantitatively, an average cooling of -0.82±0.11 °C to -1.84±0.25 °C is observed in the lower troposphere. The observed cooling is associated with both aerosol-radiation and aerosol-cloud-radiation interactions processes. The elevated dust and polluted-dust layers cause extinction of the incoming solar radiation, thereby decreasing the lower tropospheric temperature. The aerosol-cloud interactions also contribute to enhancement of cloud fraction which further contributes to the lower tropospheric cooling. The observed cooling results in a stable lower tropospheric structure during polluted conditions, which can also feedback to cloud systems. Our findings suggest that aerosol induced lower tropospheric cooling can strongly affect the cloud distribution and circulation dynamics over the ICMR, a region of immense hydroclimatic importance.

Remote sensors of Vegetable Activity to protect areas resistant to low soil moisture

Corte de Madera has a high vegetable activity measured by satellite despite the extreme drought conditions of the State during the years 2020-2021. Napa Valley suffered a severe decline in vegetable activity just after the rainy season of 2020 in April. It happened before the wildfires that affect the region in August 2020. Dixie region had a declining rainy season of 2020 and 2021 (November to April). The wildfires started in August. Satellite images could be a low-cost strategy to build an Early Warning System for wildfires.

Sensors and Their Application in Precision Agriculture

The paper depicts sensors in precision agriculture. It encompasses the most significant and frequently used sensors in agriculture. Furthermore, the paper explains the main sensor types according to their design, the recorded range of electromagnetic spectrum, as well as the way of detection, recording, measuring, and representation of the detected energy. The development of remote research has provided deeper understanding of remote sensors and their advantages. The sensors installed on soil testing equipment, fertilizing and crop protection machinery, as well as crop picking machinery have been analyzed relative to precision farming. The paper depicts widely known sensors OptRx, ISARIA and VRT technology. The results of the paper assess the data collected by sensors and processed in order to produce maps for agrotechnical operations. The application of maps decreases the employment of human resources, heightens the capacity of data collection, increases the precision of agricultural activities, and finally results in decreasing the cost of final products. The technological progress over the past decade has enabled the development of technology with variable application standards (VRT) that, according to current needs, enables input optimization.

THE CONCEPT OF IOT SYSTEMS FOR AGRICULTURAL SERVICES WITH A HIGH DEGREE OF AUTONOMY

В статье рассматриваются вопросы, связанные с использованием новейшей технологии IOT в сельском хозяйстве, показана основная роль Интернета вещей в сельском хозяйстве, которая заключается в том, чтобы можно было контролировать всю важную информацию благодаря оснащённому последними передовыми технологиями оборудованию. А также проанализированы возможности внедрения технологии IOT в агропромышленности, благодаря которой можно с легкостью отслеживать необходимые данные, такие как влажность, качество почвы, температура воздуха, также показаны возможности, которые появляются благодаря использованию дистанционных датчиков. Цель использования технологии, ее необходимость для повышения урожайности и планирования более результативной системы полива и составления прогнозов. Описана необходимость, технологии Интернета вещей в исследовании биологов, для воздействия геномов и микроклимата на степень урожайности, для возможного увеличения качества и производительности продукции. Идет описание вопросов касательно экологических проблем. The article examines the issues related to the use of the latest IOT technology in agriculture, shows the main role of the Internet of Things in agriculture, which is to control all important information thanks to the equipment equipped with the latest advanced technologies. It also analyzed the possibilities of introducing IOT technology in the agricultural industry, thanks to which it is possible to easily track the necessary data, such as moisture, soil quality, air temperature, and also shows the possibilities that arise due to the use of remote sensors. The purpose of using the technology is its need for increasing yields and planning a more efficient irrigation system and making forecasts. The necessity of the Internet of Things technology in the study of biologists, for the influence of genomes and microclimate on the degree of productivity, for a possible increase in the quality and productivity of products is described. There is a description of questions regarding environmental issues.

Standard Test Methods for Rating of Solar Reflectance of Built-Up Surfaces and Potential Use of Satellite Remote Sensors

More and more attention is being paid to the solar reflectance of built-up surfaces due to its influence on the summer heating of buildings and urban areas and the consequent effects on energy needs for air conditioning, as well as on the peak load of the electric grid. Several standard test methods are available for measuring solar reflectance in the laboratory or in the field, based on different devices and approaches. A convergence of some methods has been achieved by rating programs in the U.S. and, more recently, in Europe and other areas. However, laboratory or field measurements are impractical for characterizing a large number of urban surfaces—whether it is for identifying critical issues, developing policies, or verifying compliance with building requirements. In this regard, satellite remote sensors have recently become available, through which it is possible to estimate the reflectance of roof and pavement surfaces thanks to a spatial resolution that is suitable for identifying and characterizing individual built-up surfaces. In the present paper, the most-used standard test methods for rating of solar reflectance are reviewed. Subsequently, some publicly accessible satellite sensors are examined, through which comparable measurements could be obtained.