Viticulture, the science of cultivating grapevines, demands particular attention and care throughout the year to ensure a high-quality harvest. Thanks to the advancements in precision agriculture, the early detection of potential dangers or diseases can be achieved without harming the crops. Understanding the diverse characteristics exhibited by different grapevine varieties is of primary importance, including chlorophyll content, canopy growth, stress response, and interactions with specific soil substances. To deal with these challenges, multispectral images captured from unmanned aerial vehicles function as a novel and powerful means of analyzing the spectral characteristics of vine canopies. In this context, this study aims to create groups of different varieties located on the same area based on their common spectral characteristics.

The methodology involved an experiment conducted in a four-acre vineyard in the northern part of Attica Region, Greece, encompassing 112 unique grapevine varieties, where a multispectral camera (Micasense RedEdge-M) was deployed, capturing aerial data across five spectral bands, namely Red, Green, Blue, RedEdge and Near-InfraRed. The images were photogrammetrically processed, creating an orthoimage of the vineyard. Exploiting the vast potential of machine learning, supervised algorithms were applied to segregate the grapevine areas within the orthoimage; the Maximum Likelihood algorithm achieved a remarkable classification accuracy of 98.79% for correctly identifying the vine pixels. The produced grapevine masks facilitated the creation of distinct polygons, each representing a particular variety. Subsequently, seven different vegetation indices (Chlorophyll Index-Green (CIG), Chlorophyll Index–RedEdge (CIRE), Chlorophyll Vegetation Index (CVI), Normalized Difference Vegetation Index (NDVI), Green Normalized Difference Vegetation Index (GNDVI), Enhanced Vegetation Index2 (EVI2) and Ratio Vegetation Index (RVI)) were calculated for each polygon, providing valuable insights into the characteristics of each variety.

To investigate the relationship between varieties, two pairs of the above-mentioned indices (pair1: CVI-RVI, pair2: CLGR-CLRE) were carefully selected based on their minimal correlation. Continuing, two clustering algorithms, the k-Means and Gaussian Mixture Model (GMM), were applied aiming at categorizing the varieties in three distinct groups. The k-means and GMM algorithms categorized 73 and 58 out of 112 varieties respectively on three groups. These varieties were classified on the same groups by both pair of indices. The rest varieties remained unclassified. Combining the algorithms' results, 25 out of 112 varieties belong to the same groups (Group 1-2-3: 13-4-8 varieties), where each group represents specific spectral properties. The implementation of two clustering methods and two pairs of indices, resembles a holistic approach that enables robustness and efficacy.

This process of identifying varieties with similar characteristics provides farmers invaluable insights on the growth and health of their grapevines. Through obtaining this knowledge, farmers can optimize the yield of each variety, ripening time, disease resistance, and other growth attributes. Furthermore, this approach empowers farmers to employ targeted agricultural practices for varieties with comparable properties, including irrigation, fertilization, and harvesting, thereby enhancing efficiency and sustainability in viticulture. Ultimately, the fusion of precision agriculture, machine learning, and multispectral analysis proclaim a new era of scientific viticulture, pushing the grape-growing industry into a booming and knowledge-driven future.

Chilika Lake is the Asia largest lake and largest tropical lagoon in the world which contains wide range of sub ecosystems such as mudflats, freshwater marshes, sand dunes and a shallow brackish lake. It is the first Indian wetland which got international importance under the Ramasar convention due to its rich biodiversity with over 400 different types of brackish and fresh water species, but the lake is under constant pressure of natural and anthropogenic activities which can lead to ecological transformations. This study demonstrates the spatiotemporal changes of Chilika Lake in the period of 1988-2017 using multi-temporal Landsat 5-TM and Landsat 8-OLI images. The satellite derived Normalized Difference Water Index (NDWI) was used to investigate the dynamics of surface water, extracted from Landsat data. Minimum (Min.), Maximum (Max.) and Mean pixel value extraction method from each NDWI image was applied and generated annual composites for the above years, to identify the lake area. Results obtained from Min., Max., and Mean method, indicates the increasing and decreasing trend in lake surface area in the period of 1988-2017. Results from Min. and Mean methods shows the Increase in lake area from 282 to 440 km² and 665 to 768 km² respectively; while results obtained from Max., shows slight decrease in lake area from 959 to 957 km² in the period 1988-2017.

Recently, the Italian government has announced IRIDE a new Earth Observation Program. Probably, IRIDE will be completed by 2026 under the management of the European Space Agency (ESA) and with the support of the Italian Space Agency (ASI). IRIDE is an end-to-end system made up of a set of sub-constellations (with radar and optical sensors) and services intended for the Italian Public Administration. The aims of this work are twofold: firstly, to disseminate information within the scientific community regarding the IRIDE program by highlighting key constellation characteristics as outlined in the latest ASI technical communications; secondly, to put forth valuable recommendations for the global applicability of this data, adopting a bottom-up perspective.

Eutrophication of lakes promotes the development of cyanobacterial blooms that threaten aquatic environment and human health. They can produce cyanotoxins that prevents the proper use of water, result in human intoxication and fish kill. Cyanobacterial biomass can be estimated using traditional field sampling techniques, laboratory analysis, and cell counting method. Despite being accurate, this method is time-consuming, labor-intensive, and cost-ineffective. Remote sensing is considered an alternative monitoring method that is cost and time efficient, and feasible for repetitive and continuous monitoring. The aim of this research is to test the potential of various algorithms (models) to retrieve phycocyanin concentration in a Mediterranean Lake after comparison with standard classical methods. For that, field spectroradiometric measurements to produce spectral signatures, and field sampling were performed during 2016 and 2017. Field and laboratory analysis showed that phycocyanin concentration varied between 18 and 170 µg/l during the different field campaigns in 2016 and 2017. Phycocyanin was heterogenous throughout the lake and showed considerable variation in 02 November 2016. Two main cyanobacterial genera (Microcystis sp. and Chrysoporum sp.) were identified during the campaigns in which field spectroradiometer measurements were performed in 2016 and 2017. The potential of 10 developed algorithms was tested to retrieve phycocynanin concentration. Results obtained proved that various ratio-models can be used for the estimation of phycocyanin with the model R₇₀₀/R₆₀₀ (reflectance ratio of wavelengths 700 and 600) being the most suitable model presenting the highest coefficient of correlation (R² = 0.716). The importance of these algorithms is that they can be used as indicators to choose between different satellite imagery to map cyanobacterial blooms, based on their visible and NIR bands. The opportunities of using different potential satellite images from Worldview-2, Sentinel-2 images, and to a lesser extent from Landsat-8 images is to directly derive phycocyanin to help map and manage cyanobacterial blooms.

Forest fires are common occurrences in Kazakhstan, particularly from June until September, and damage extensively to the country's forest resources. The mapping of burned areas is crucial for fire management to implement the proper mitigation strategies and restoration actions following the fire season. The mapping of burned areas enables a thorough evaluation of the damage caused by fires to forests. The unique characteristics of forest plants and soil are dramatically altered by the fire's destruction, leading to a dramatic shift in reflectance. The destruction caused by fires can be mitigated, and vegetation can be replanted, with the use of maps depicting the affected areas. Accurate and timely mapping of burned areas is critical for fire prevention methods such as planning, mitigation, and vegetation regeneration. The country Kazakhstan launched two satellites KazEOSat 1 and KazEOSat 2 as part of the Earth Remote Sensing Satellite System (ERSSS) for the management of natural resources and monitoring. The KazEOSat 1 is a high-resolution observation satellite, launched in Sun-synchronous orbit at an altitude of about 630 km, consisting 4 spectral bands (4m) and very high panchromatic (1m) band. In this study, KazEOsat 1 satellite datasets were used to map the burned area in various parts of Kazakhstan. Three different spectral indices viz. Global Environmental Monitoring Index (GEMI), Ashburn Vegetation Index (AVI) and Burn Area Index (BAI) are used and the findings are compared to the best burnt area discrimination index using KazEOsat 1 satellite datasets. The results show that the BAI shows the higher accuracy than other indices to map the burnt area using the KazEOsat 1 satellite datasets.

Aerosols are one of the major reasons to decline air quality world-wide. Vehicular/industrial emissions and crop waste burnings are the major contributors involved in deteriorating the air quality including metrological factors such as lack of rainfall and high relative humidity. Lahore Division of Punjab Pakistan has been affected by smog pollution during October and November since 2017. So, goal of the present study is to identify the key air pollution generating sources and their contribution in smog formation. In this study, aerosol optical depth (AOD) and thermal anomalies have been analyzed and examined with three metrological elements i.e. temperature, humidity and rainfall of October & November during 2018-2022 using satellite data. Transport, industrial and agricultural data derived from secondary sources has also been considered in this study. Results of the study exhibited that transport sector is playing leading role by having 43% share of pollutants emission into the ambient air. Whereas, results of the satellite imagery showed that AOD level increased in Lahore Division due to significant variation in metrological factors and thermal anomalies. Hence, results of the study suggest multi-sectoral short, medium and long term plans to tackle air pollution for environmental, social and economic sustainability.

Proper information on forest cover change and its spatio-temporal characteristics are essential for natural resource management and sustainable development. It is one of the major factor that affect the ecosystem and the services it provides. In this study, we used remote sensing techniques and a geographical information system to extract the forest cover categories based on the Object- based Image Analysis (OBIA) techniques from Landsat TM/ETM/OLI satellite in the Chure and Terai region from 1994-2018. In addition, ecosystem service value coefficient per unit area contributed by forest cover was taken from the previous studies and adjusted by taking accounts of yearly inflation or buying power to bring about the proper estimation of the ESV in terms of money impacting forest cover area. In 1994(40.02%), forest covered the highest area and found decreased quite faster till 2004(28.87%), and afterwards a gradual decrease arriving to 2018(26.52%). The highest ESV in 1994 was found to be 78.7 billion and lowest in 2018 was found to be 43.2 billion in current fiscal year. The spatial distribution of the Ecosystem Service Value is shown by the map where immediate action can be taken. The findings of this study could facilitate the strong forest conservation policy formulation further and development management intervention.

Hapke radiative transfer model has been widely used in the field of soil remote sensing, such as characterizing soil reflectance characteristics, and inverting soil particle size, water content, and surface roughness. However, the latest development of the Hapke model used piecewise fitting of soil spectral reflectance, which brought great difficulties to soil parameter inversion. This paper presents a method to calculate the imaginary part of the soil complex refraction index using the spectral reflectance of dry soil, which can effectively solve the problem of piecewise fitting the soil spectral reflectance by the Hapke model, and then improve Hapke radiation transfer model. Finally, the improved Hapke model is coupled with the MARMIT-2 model to invert soil water content. The results show that the improved Hapke can effectively characterize the spectral characteristics of soil and show higher fitting accuracy (RMSE < 0.012), especially with high soil water content (>30%). Therefore, the improved Hapke radiative transfer model can better understand soil physical properties, improve the inversion accuracy of soil-vegetation physical parameters, which can be use to enhance agricultural water use efficiency, promote optimal allocation of water resources and ensure food production security.

Implementation of combined lidar and radiometer sounding of atmospheric aerosol with application of CALIOP lidar data, measured in the area of radiometric station (LRS-C technique) has increased the number of potential LRS-S measuring sites up to 500 all over the planet, except for polar regions with a latitude greater than 80°. However, scattered solar radiation background significantly reduces the signal-to-noise ratio of the recorded lidar signals, especially the depolarized component of the backscattered signal at 532 nm. It results in problems with application of LRS-C technique in regions with a low aerosol load. An aerosol load is considered low if aerosol optical depth (AOD) at the wavelength of 500 nm is below 0.1.

We propose a statistical approach to the formation of input data set and retrieving aerosol parameters from LRS-C data. A lot of lidar signals measured in the vicinity of a radiometric station during a certain time period constitute the “lidar” part of the statistical ensemble of input data. The radiometric information is represented by the columnar optical characteristics of the aerosol layer, retrieved from the radiometric observations coordinated with the satellite overpass.

The basic system of equations determines the relationship between the statistical characteristics of input data set and parameters of the aerosol model.

At the stage of solving the inverse problem, lidar information is represented by the average values and variance of lidar signals calculated from a large number input data that provide resulting high signal-to-noise ratio of averaged lidar signals.

In the frame of this work the LIRIC-2 code was developed for processing LRS-C data. LIRIC-2 contains program modules for creating the ensemble of input data and options for calculating the statistical characteristics of vertical distributions of aerosol concentration and optical parameters.

The statistical version of the LRS-C technique and the LIRIC-2 code were used to study the aerosol annual and seasonal changes in the Europe regions, in Antarctica and in mountainous areas.

Soil moisture (SM) is an important variable related to the health of terrestrial ecosystems, agriculture, continental water cycle, etc. It also provides an opportunity for drought monitoring, flood forecasting, weather forecasting, and calibration of hydrological models. This study aims to estimate surface soil moisture at high spatial resolution (10m) by combining radar and optical remote sensing data and improving spatial resolution and accuracy. Synthetic Aperture Radar (SAR) operates with the competence to acquire data in any weather condition. SAR images were acquired by C-band SAR sensors in the VV polarization boarded on Sentinel-1 satellites and optical images were obtained from a Sentinel-2 multi-spectral instrument. The main algorithm involves the retrieval of soil moisture using radar data through a change detection (CD) method that is somehow combined with the WCM model (parameters include vegetation descriptors and model coefficients) to estimate SM and reduce the effect of vegetation cover. The method is applied in 13 months of time-series satellite data from November 7, 2019, to October 20, 2020, over Salamanca (western Spain) and is validated using field data acquired at a study site with the use TDR sensor. The results showed good accuracy between retrieves and ground measurement, soil moisture data (Root Mean Square Error (RMSE) of 0.53 m^3/m^3 and the obtained accuracy is promising compared to recent similar works.