Feasibility study on application of microwave radiometry to monitor contamination level on insulator materials

This paper introduces a novel method for monitoring contamination levels on high voltage insulators based on microwave radiometry. Present contamination monitoring solutions for high voltage insulators are only effective in predicting flashover risk when the contamination layer has been wetted by rain, fog or condensation. The challenge comes where the pollution occurs during a dry period prior to a weather change. Under these conditions, flashover can often occur within a short time period after wetting and is not predicted by measurements taken in the dry period. The microwave radiometer system described in this paper measures energy emitted from the contamination layer and could provide a safe, reliable, contactless monitoring method that is effective under dry conditions. The relationship between equivalent salt deposit density and radiometer output is described using a theoretical model and experimentally verified using a specially designed X-band radiometer. Results demonstrate that the output from the radiometer is able to clearly distinguish between different levels of contamination on insulator materials under dry conditions. This novel contamination monitoring method could potentially provide advance warning of the future failure of wet insulators in climates where insulators can experience dry conditions for extended periods

Determining the Extent of Soil Degradation Processes Using Trend Analyses at a Regional Multispectral Scale

In order to ensure the sustainability of production from agricultural lands, the degradation processes surrounding the fertile land environment must be monitored. Human-induced risk and status of soil degradation (SD) were assessed in the Northern-Eastern part of the Nile delta using trend analyses for years 2013 to 2023. SD hotspot areas were identified using time-series analysis of satellite-derived indices as a small fraction of the difference between the observed indices and the geostatistical analyses projected from the soil data. The method operated on the assumption that the negative trend of photosynthetic capacity of plants is an indicator of SD independently of climate variability. Combinations of soil, water, and vegetation’s indices were integrated to achieve the goals of the study. Thirteen soil profiles were dug in the hotspots areas. The soil was affected by salinity and alkalinity risks ranging from slight to strong, while compaction and waterlogging ranged from slight to moderate. According to the GIS-model results, 30% of the soils were subject to slight degradation threats, 50% were subject to strong risks, and 20% were subject to moderate risks. The primary human-caused sources of SD are excessive irrigation, poor conservation practices, improper utilisation of heavy machines, and insufficient drainage. Electrical conductivity (EC), exchangeable soil percentage (ESP), bulk density (BD), and water table depth were the main causes of SD in the area. Generally, chemical degradation risks were low, while physical risks were very high in the area. Trend analyses of remote sensing indices (RSI) proved to be effective and accurate tools to monitor environmental dynamic changes. Principal components analyses were used to compare and prioritise among the used RSI. RSI pixel-wise residual trend indicated SD areas were related to soil data. The spatial and temporal trends of the indices in the region followed the patterns of drought, salinity, soil moisture, and the difficulties in separating the impacts of drought and submerged on SD on vegetation photosynthetic capacity. Therefore, future studies of land degradation and desertification should proceed using indices as a factor predictor of SD analysis

Review of soil salinity assessment for agriculture across multiple scales using proximal and/or remote sensors

Mapping and monitoring soil spatial variability is particularly problematic for temporally and spatially dynamic properties such as soil salinity. The tools necessary to address this classic problem only reached maturity within the past 2 decades to enable field- to regional-scale salinity assessment of the root zone, including GPS, GIS, geophysical techniques involving proximal and remote sensors, and a greater understanding of apparent soil electrical conductivity (ECa) and multi- and hyperspectral imagery. The concurrent development and application of these tools have made it possible to map soil salinity across multiple scales, which back in the 1980s was prohibitively expensive and impractical even at field scale. The combination of ECa-directed soil sampling and remote imagery has played a key role in mapping and monitoring soil salinity at large spatial extents with accuracy sufficient for applications ranging from field-scale site-specific management to statewide water allocation management to control salinity within irrigation districts. The objective of this paper is: (i) to present a review of the geophysical and remote imagery techniques used to assess soil salinity variability within the root zone from field to regional scales; (ii) to elucidate gaps in our knowledge and understanding of mapping soil salinity; and (iii) to synthesize existing knowledge to give new insight into the direction soil salinity mapping is heading to benefit policy makers, land resource managers, producers, agriculture consultants, extension specialists, and resource conservation field staff. The review covers the need and justification for mapping and monitoring salinity, basic concepts of soil salinity and its measurement, past geophysical and remote imagery research critical to salinity assessment, current approaches for mapping salinity at different scales, milestones in multi-scale salinity assessment, and future direction of field- to regional-scale salinity assessment

Assessment of Drought in Grasslands: Spatio – Temporal Analyses of Soil Moisture and Extreme Climate Effects in Southwestern Mongolia

Soil moisture plays an essential key role in the assessment of hydrological and meteorological droughts that may affect a wide area of the natural grassland and the groundwater resource. The surface soil moisture distribution as a function of time and space is highly relevant for hydrological, ecological, and agricultural applications, especially in water-limited or drought-prone regions. However, gauging soil moisture is challenging because of its high variability. While point-scale in-situ measurements are scarce, the remote sensing tools remain the only practical means to obtain regional and global-scale soil moisture estimates. A Soil Moisture and Ocean Salinity (SMOS) is the first satellite mission ever designed to gauge the Earth’s surface soil moisture (SM) at the near-daily time scales. This work aims to evaluate the spatial and temporal patterns of SMOS soil moisture, determine the effect of the climate extremes on the vegetation growth cycle, and demonstrate the feasibility of using our drought model (GDI) the Gobi Drought Index. The GDI is based on the combination of SMOS soil moisture and several products from the MODIS satellite. We used this index for hydro-meteorological drought monitoring in Southwestern Mongolia. Firstly, we validated bias-corrected SMOS soil moisture for Mongolia by the in-situ soil moisture observations 2000 to 2015. Validation shows satisfactory results for assessing drought and water-stress conditions in the grasslands of Mongolia. The correlation analysis between SMOS and Normalized Difference Vegetation Index (NDVI) index in the various ecosystems shows a high correlation between the bias-corrected, monthly-averaged SMOS and NDVI data (R2 > 0.81). Further analysis of the SMOS and in situ SM data revealed a good match between spatial SM distribution and the rainfall events over Southwestern Mongolia. For example, during dry 2015, SM was decreased by approximately 30% across the forest-steppe and steppe areas. We also notice that both NDVI and rainfall can be used as indicators for grassland monitoring in Mongolia. The second part of this research, analyzed several dzud (specific type of climate winter disaster) events (2000, 2001, 2002, and 2010) related to drought, to comprehend the spatial and temporal variability of vegetation conditions in the Gobi region of Mongolia. We determined how these extreme climatic events affect vegetation cover and local grazing conditions using the seasonal aridity index (aAIZ), NDVI, and livestock mortality data. The NDVI is used as an indicator of vegetation activity and growth. Its spatial and temporal pattern is expected to reflect the changes in surface vegetation density and status induced by water-deficit conditions. The Gobi steppe areas showed the highest degree of vulnerability to climate, with a drastic decline of grassland in arid areas. We found that under certain dzud conditions, rapid regeneration of vegetation can occur. A thick snow layer acting as a water reservoir combined with high livestock losses can lead to an increase of the maximum August NDVI. The snowy winters can cause a 10 to 20-day early peak in NDVI and the following increase in vegetation growth. However, during a year with dry winter conditions, the vegetation growth phase begins later due to water deficiency and the entire year has a weaker vegetation growth. Generally, livestock loss and the reduction of grazing pressure was played a crucial role in vegetation recovery after extreme climatic events in Mongolia. At the last stage of our study, we develop an integrated Gobi drought index (GDI), derived from SMOS and LST, PET, and NDVI MODIS products. GDI can incorporate both, the meteorological and soil moisture drought patterns and sufficiently well represent overall drought conditions in the arid lands. Specifically, the monthly GDI and 1-month standardized precipitation index SPI showed significant correlations. Both of them are useful for drought monitoring in semi-arid lands. But, the SPI requires in situ data that are sparse, while the GDI is free from the meteorological network restriction. Consequently, we compared the GDI with other drought indices (VSWI, NDDI, NDWI, and in-situ SM). Comparison of these drought indices with the GDI allowed assessing the droughts’ behavior from different angles and quantified better their intensity. The GDI maps at fine-scale (< 1km) permit extending the applicability of our drought model to regional and local studies. These maps were generated from 2000 to 2018 across Southwestern Mongolia. Fine-scale GDI drought maps are currently limited to the whole territory for Mongolia but the algorithm is dynamic and can be transported to any region. The GDI drought index can be served as a useful tool for prevention services to detect extremely dry soil and vegetation conditions posing a risk of drought and groundwater resource depletion. It was able to detect the drought events that were underestimated by the National Drought Watch System in Mongolia. In summary, with the help of satellite, climatological, and geophysical data, the integrated GDI can be beneficial for vegetation drought stress characterization and can be a useful tool to monitor the effectiveness of pasture land restoration management practices for Mongolian livelihoods. The future application of the GDI can be extended to monitor potential impacts on water resources and agriculture in Mongolia, which have been impacted by long periods of drought

Geospatial Analysis for Irrigated Land Assessment, Modeling and Mapping

Assessment of irrigated lands by conventional means of survey requires a great deal of time, but the application of geospatial analysis using remote sensing data and GIS techniques minimize time consuming and offer the possibility rapid production of maps and models. This paper gave an overview of the techniques and methods in use at different scales. The presence of salt in the soils and its variation may be because of rise in water table and the difference in elevation in irrigated lands. The combined application of conventional methods with remote sensing and geographical information system techniques in detecting these problems in irrigated lands were examined. Different salinity indexes coupled with ground truthing with the proven results in assessing such problems were also examined thereby depicting indexes as good indicator of soil salinity and water logging, which may influence decision on reclamation of degraded land for proper agricultural land management. Irrigation and drainage managers, planners, farmers, and government agencies for smart agriculture can use models and maps generated through geospatial analysis

Soil Salinity Study in Northern Great Plains Sodium Affected Soil

Climate and land-use changes when combined with the marine sediments that underlay portions of the Northern Great Plains have increased the salinization and sodification risks. The objectives of this dissertation were to compare three chemical amendments (calcium chloride, sulfuric acid and gypsum) remediation strategies on water permeability and sodium (Na) transport in undisturbed soil columns and to develop a remote sensing technique to characterize salinization in South Dakota soils. Fortyeight undisturbed soil columns (30 cm x 15 cm) collected from White Lake, Redfield, and Pierpont were used to assess the chemical remediation strategies. In this study the experimental design was a completely randomized design and each treatment was replicated four times. Following the application of chemical remediation strategies, 45.2 cm of water was leached through these columns. The leachate was separated into 120- ml increments and analyzed for Na and electrical conductivity (EC). Sulfuric acid increased Na leaching, whereas gypsum and CaCl2 increased water permeability. Our results further indicate that to maintain effective water permeability, ratio between soil EC and sodium absorption ratio (SAR) should be considered. In the second study, soil samples from 0-15 cm depth in 62 x 62 m grid spacing were taken from the South Dakota Pierpont (65 ha) and Redfield (17 ha) sites. Saturated paste EC was measured on each soil sample. At each sampling points reflectance and derived indices (Landsat 5, 7, 8 images), elevation, slope and aspect (LiDAR) were extracted. Regression models based on multiple linear regression, classification and regression tree, cubist, and random forest techniques were developed and their ability to predict soil EC were compared. Results showed that: 1) Random forest method was found to be the most effective method because of its ability to capture spatially correlated variation, 2) the short wave infrared (1.5 -2.29 μm) and near infrared (0.75-0.90 μm) were very sensitive to soil salinity; 3) EC prediction model using all 3 season (spring, summer and fall) images was better on state wide validation dataset compared to individual season model. Finally, in eastern South Dakota, the model predicted that from 2008 to 2012, EC increased in 569,165 ha or 13.4% of the land seeded to corn (Zea mays L.) or soybeans (Glycine max L)

Application of remote sensing to selected problems within the state of California

There are no author-identified significant results in this report

Revisiting the global patterns of seasonal cycle in sea surface salinity

Author Posting. © American Geophysical Union, 2021. This article is posted here by permission of American Geophysical Union for personal use, not for redistribution. The definitive version was published in Journal of Geophysical Research: Oceans 126(4), (2021): e2020JC016789, https://doi.org/10.1029/2020JC016789.Argo profiling floats and L-band passive microwave remote sensing have significantly improved the global sampling of sea surface salinity (SSS) in the past 15 years, allowing the study of the range of SSS seasonal variability using concurrent satellite and in situ platforms. Here, harmonic analysis was applied to four 0.25° satellite products and two 1° in situ products between 2016 and 2018 to determine seasonal harmonic patterns. The 0.25° World Ocean Atlas (WOA) version 2018 was referenced to help assess the harmonic patterns from a long-term perspective based on the 3-year period. The results show that annual harmonic is the most characteristic signal of the seasonal cycle, and semiannual harmonic is important in regions influenced by monsoon and major rivers. The percentage of the observed variance that can be explained by harmonic modes varies with products, with values ranging between 50% and 72% for annual harmonic and between 15% and 19% for semiannual harmonic. The large spread in the explained variance by the annual harmonic reflects the large disparity in nonseasonal variance (or noise) in the different products. Satellite products are capable of capturing sharp SSS features on meso- and frontal scales and the patterns agree well with the WOA 2018. These products are, however, subject to the impacts of radiometric noises and are algorithm dependent. The coarser-resolution in situ products may underrepresent the full range of high-frequency small scale SSS variability when data record is short, which may have enlarged the explained SSS variance by the annual harmonic.L. Yu was funded by NASA Ocean Salinity Science Team (OSST) activities through Grant 80NSSC18K1335. FMB was funded by the NASA OSST through Grant 80NSSC18K1322. E. P. Dinnat was funded by NASA through Grant 80NSSC18K1443. This research is carried out in part at the Jet Propulsion Laboratory, California Institute of Technology, under a contract with NASA.2021-09-1