Comparative studies on energy balance components and their inter relations on soybean crop and bare soil in kharif season at Pune situated in semi-arid tract

Comparative studies of radiation balance components at different growth stages on soybean crop and bare soil were made at Central Agrometeorological Observatory (CAgMO), Pune. Continuous measurements of net, reflected and global solar radiations were made over cropped field as well as over bare soil all throughout the growth phases in kharif season of 1995. Net and reflected radiations and albedo over canopy were higher by 7, 26 and 25 per cent respectively than bare soil. The net short wave (absorbed) radiation and net long wave (out-going) radiation evaluated over the canopy were less than those over bare soil by 5 and 20 per cent respectively. The mean daily net, reflected, net short wave and net long wave (out-going) radiation were 9.86, 3.86, 15.35 and 5.49 MJm-2 respectively and the albedo was 20 per cent over soybean canopy whereas for bare soil they were 9.23, 3.07, 16.15 and 6.91 MJm-2 and 16 per cent respectively. The mean daily global solar radiation during the crop growing season was 19.20 MJm-2. The highest albedo (26 per cent) of the crop recorded in the 10th week after sowing (WAS) was in correspondence to maximum LAI (5.9) observed at pod formation stage.

Changes in Terrestrial Evaporation across Poland over the Past Four Decades Dominated by Increases in Summer Months

Given the importance of terrestrial evaporation (ET) for the water cycle, a fundamental understanding of the water quantity involved in this process is required. As recent observations reveal a widespread ET intensification across the world, it is important to evaluate regional ET variability. The specific objectives of this study are the following: (1) to assess annual and monthly ET trends across Poland, and (2) to reveal seasons and regions with significant ET changes. This study uses the ET estimates acquired from the Global Land Evaporation Amsterdam Model (GLEAM) dataset allowing for multi-year analysis (1980–2020). The Mann–Kendall test and the Sen’s slope were applied to estimate the significance and magnitude of the trends. The results show that a rising temperature, along with small precipitation increase, led to the accelerated ET of 1.36 mm/y. This was revealed by increased transpiration and interception loss not compensated by a decrease in bare soil evaporation and sublimation. The wide-spread higher water consumption especially occurred during the summer months of June, July, and August. Comparing the two subperiods of 1980–2020, it was found that in 2007–2020, the annual ET increased by 7% compared to the reference period of 1980–2006. These results can serve as an important reference for formulating a water resources management strategy in Poland.

Soil Erosion Susceptibility Prediction in Railway Corridors Using RUSLE, Soil Degradation Index and the New Normalized Difference Railway Erosivity Index (NDReLI)

This study presents a remote sensing-based index for the prediction of soil erosion susceptibility within railway corridors. The empirically derived index, Normalized Difference Railway Erosivity Index (NDReLI), is based on the Landsat-8 SWIR spectral reflectances and takes into account the bare soil and vegetation reflectances especially in semi-arid environments. For the case study of the Botswana Railway Corridor (BRC), the NDReLI results are compared with the RUSLE and the Soil Degradation Index (SDI). The RUSLE model showed that within the BRC, the mean annual soil loss index was at 0.139 ton ha−1 year−1, and only about 1% of the corridor area is susceptible to high (1.423–3.053 ton ha−1 year−1) and very high (3.053–5.854 ton ha−1 year−1) soil loss, while SDI estimated 19.4% of the railway corridor as vulnerable to soil degradation. NDReLI results based on SWIR1 (1.57–1.65 μm) predicted the most vulnerable areas, with a very high erosivity index (0.36–0.95), while SWIR2 (2.11–2.29 μm) predicted the same regions at a high erosivity index (0.13–0.36). From empirical validation using previous soil erosion events within the BRC, the proposed NDReLI performed better that the RUSLE and SDI models in the prediction of the spatial locations and extents of susceptibility to soil erosion within the BRC.

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.

Investigation of Multi-Frequency SAR Data to Retrieve the Soil Moisture within a Drip Irrigation Context Using Modified Water Cloud Model

The objective of this paper was to estimate soil moisture in pepper crops with drip irrigation in a semi-arid area in the center of Tunisia using synthetic aperture radar (SAR) data. Within this context, the sensitivity of L-band (ALOS-2) in horizontal-horizontal (HH) and horizontal-vertical (HV) polarizations and C-band (Sentinel-1) data in vertical-vertical (VV) and vertical-horizontal (VH) polarizations is examined as a function of soil moisture and vegetation properties using statistical correlations. SAR signals scattered by pepper-covered fields are simulated with a modified version of the water cloud model using L-HH and C-VV data. In spatially heterogeneous soil moisture cases, the total backscattering is the sum of the bare soil contribution weighted by the proportion of bare soil (one-cover fraction) and the vegetation fraction cover contribution. The vegetation fraction contribution is calculated as the volume scattering contribution of the vegetation and underlying soil components attenuated by the vegetation cover. The underlying soil is divided into irrigated and non-irrigated parts owing to the presence of drip irrigation, thus generating different levels of moisture underneath vegetation. Based on signal sensitivity results, the potential of L-HH data to retrieve soil moisture is demonstrated. L-HV data exhibit a higher potential to retrieve vegetation properties regarding a lower potential for soil moisture estimation. After calibration and validation of the proposed model, various simulations are performed to assess the model behavior patterns under different conditions of soil moisture and pepper biophysical properties. The results highlight the potential of the proposed model to simulate a radar signal over heterogeneous soil moisture fields using L-HH and C-VV data.

THE CONTRIBUTION OF RADAR REMOTE SENSING VIA SENTINEL-1 DATA AND PHOTO-INTERPRETATION BY GOOGLE EARTH IMAGES FOR WETLAND MAPPING

Wetlands are considered as sensitive ecosystems exposed and threatened by climate change and the urbanization of natural environments. In the purpose of managing these sensitive areas and conservatizing their biodiversity, remote sensing is an efficient way to track environmental variables over large areas as wetlands. However, when it comes to the study of hydrologic dynamics, high temporal and spatial resolutions are essential. Since the access to optical satellite imagery is restrictive because of the large cloud cover that masks the ground, radar sensors that are working in the microwave field, are particularly suited to the characterization of hydrological dynamics due to the sensitivity of their measurements in the presence of water, regardless of the vegetation in place. Recently, radar remote sensing has experienced a real revolution with the launch of the Sentinel-1A satellite in 2014, followed by its twin Sentinel-1B two years later by the European Space Agency as part of the Copernicus program. These sensors acquire C-band data (λ = 5.6 cm) with a temporal resolution of 12 days by satellite and their distribution is open and free. This article aims to assess the potential of Sentinel A1 SAR data for wetland mapping in the city of Benslimane (Central Morocco). The first part is explaining the methodology for mapping water surfaces. We identified a confusion of the C-band radar response of water surfaces and that of certain bare soils. We then showed that the VH polarization is the most suitable for the mapping of water surfaces, comparing four methods of detecting areas in water. It. The second part is discussing the use of unsupervised methods without a priori data demonstrating that the methods taking into account the spatial neighborhood give better results. Temporal filtering has been developed and has made it possible to improve detection and to overcome confusion between bare soil and permanent water surfaces. Water surfaces larger than 0.5 ha are at 80% detected. Classification was performed using the SVM (Support Vector Machine) algorithm. This latter information was then implemented into the thematic map derived from SPOT-4 images to obtain the final weltands map.

Environmental Monitoring at and around the Matuail Landfill Site of Dhaka City using Remote Sensing

Rapid urban population growth and flourishing incomes have increased waste production in Dhaka city. A part of daily produced Municipal Solid Waste (MSW) is disposed of at Matuail sanitary landfill located within Jatrabari Thana, Dhaka. This study has analyzed the environmental impacts at and around this landfill using remote sensing techniques. The objective of this research is to develop a means of environmental monitoring at the landfill site and its surroundings through the implementation of various time-series remote sensing indices e.g., Land Surface Temperature (LST), Normalized Difference Vegetation Index (NDVI), Soil Adjusted Vegetation Index (SAVI), and Modified Soil Adjusted Vegetation Index (MSAVI). LST is used to observe the Spatio-temporal pattern of temperature distribution. NDVI, SAVI, and MSAVI are the Bio-indicators and they are helpful to analyze the vegetation health condition at and around the landfill area. From the result of LST, it is observed that the average temperature of the Jatrabarithana has increased from 23.12℃ in 1993 to an optimum temperature of 35.20℃ in 2013, then it went down to 29.09℃ in 2018. The NDVI result for the study period shows that the percentages of ‘Bare Soil’ and ‘Structural Object’ have increased drastically from 10% to 41.20% and 13.30% to 31.52% respectively for these 25 years in Jatrabarithana. On the other hand, the percentages of ‘Shrub and Grassland’ and ‘Moderate Vegetation’ have decreased from 54.20% to 25.15% and 12.55% to 0% respectively. SAVI and MSAVI also show evidence of increasing the amount of bare soil and structural object and decreasing the amount of vegetation. Due to the waste stabilization process, and inappropriate management system at the Matuail landfill, along with urbanization, industrial activity, and deforestation, a harmful effect has been done to the surrounding environment. As an outcome, the temperature has risen rapidly and the amount of vegetation has declined to a significant extent. Journal of Engineering Science 12(3), 2021, 127-138

The importance of direct and indirect trophic interactions in determining the presence of a locally rare day-flying moth

Ecosystem engineers affect other organisms by creating, maintaining or modifying habitats, potentially supporting species of conservation concern. However, it is important to consider these interactions alongside non-engineering trophic pathways. We investigated the relative importance of trophic and non-trophic effects of an ecosystem engineer, red deer, on a locally rare moth, the transparent burnet (Zygaena purpuralis). This species requires specific microhabitat conditions, including the foodplant, thyme, and bare soil for egg-laying. The relative importance of grazing (i.e., trophic effect of modifying microhabitat) and trampling (i.e., non-trophic effect of exposing bare soil) by red deer on transparent burnet abundance is unknown. We tested for these effects using a novel method of placing pheromone-baited funnel traps in the field. Imago abundance throughout the flight season was related to plant composition, diversity and structure at various scales around each trap. Indirect effects of red deer activity were accounted for by testing red deer pellet and trail presence against imago abundance. Imago abundance was positively associated with thyme and plant diversity, whilst negatively associated with velvet grass and heather species cover. The presence of red deer pellets and trails were positively associated with imago abundance. The use of these sites by red deer aids the transparent burnet population via appropriate levels of grazing and the provision of a key habitat condition, bare soil, in the form of deer trails. This study shows that understanding how both trophic and non-trophic interactions affect the abundance of a species provides valuable insights regarding conservation objectives.

Estimation of soil organic carbon in agricultural fields: A remote sensing approach

Aim: In view of the importance of Soil Organic Carbon (SOC) in agricultural management, a study was conducted to develop a digital SOC map using remotely sensed spectral indices. The present study was conducted on the Tawdeehiya Farms, located in the central region of Saudi Arabia between Al-Kharj and Haradh cities. Methodology: Landsat-8 (L8) and Sentinel-2 (S2A) satellite images were used for the characterization of SOC stocks in the topsoil layer (0-10 cm) of the experimental fields. Soil samples were randomly collected from six (50 ha each) agricultural fields and analyzed in the laboratory for SOC (SOCA) following Walkley and Black method. While, vegetation indices (VI), such as the Normalized Difference Vegetation Index (NDVI), NDVIRedEdge, Enhanced Vegetation Index (EVI), Bare Soil Index (BSI), and Reduced Simple Ratio (RSR) were computed and subsequently used for the development of SOC prediction models. Results: Univariate linear regression technique was employed for the recognition of a suitable band/VI for SOC (SOCP) mapping. The SWIR-1 band of both L8 (R2 = 0.86) and S2A (R2 = 0.77) data was promising for predicting SOC with 16% (S2A) and 18% (L8) of BIAS. Interpretation: The NDVI and BSI (for L8 data) and BSI and RSR (for S2A data) were found most suitable VI in the prediction of SOC. The R2 values of linear regression models were 0.68 (BSI) and 0.78 (RSR), indicating that nearly 68% and 78% of the SOC could be predicted through L8 and S2A datasets, respectively.