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Chilika Lake, a RAMSAR site, is an environmentally and ecologically pivotal coastal lagoon in India facing significant emerging environmental challenges due to anthropogenic activities and natural processes. Traditional in situ water quality monitoring methods are often labor intensive and time consuming. This study presents a novel approach for ex situ water quality monitoring in Chilika Lake, located on the east coast of India, utilizing Google Earth Engine (GEE) and spectral indices, such as the Normalized Difference Turbidity Index (NDTI), Normalized Difference Chlorophyll Index (NDCI), and total suspended solids (TSS). The methodology involves the integration of multi-temporal satellite imagery and advanced spectral indices to assess key water quality parameters, such as turbidity, chlorophyll-a concentration, and suspended sediments. The NDTI value in Chilika Lake increased from 2019 to 2021, and the Automatic Water Extraction Index (AWEI) method estimated the TSS concentration. The results demonstrate the effectiveness of this approach in providing accurate and comprehensive water quality assessments, which are crucial for the sustainable management of Chilika Lake. Maps and visualization are presented using GIS software. This study can effectively detect floating algal blooms, identify pollution sources, and determine environmental changes over time. Developing intuitive dashboards and visualization tools can help stakeholders engage with data-driven insights, increase community participation in conservation, and identify pollution sources. [ABSTRACT FROM AUTHOR]

Effective monitoring of water quality in reservoirs is imperative to comprehend pollution sources and secure a sustainable water supply for various needs, notably irrigation. Thus, a case study was undertaken at Punjab Agricultural University, Ludhiana to provide a comprehensive understanding of the spatio-temporal water quality in Chohal and Damsal dam reservoirs located in the Kandi region of Punjab, India for facilitating informed decision-making for water resource management and environmental protection. The study employed multimodal technique based on satellite-based analysis (Normalize Difference Turbidity Index, NDTI) and on-site investigations (sample analysis and Water Quality Index (WQI) determination) to provide composite picture of water quality dynamics. NDTI values for 2016 and 2022 for Chohal dam ranged from −0.0034 to 0.4758 and −0.0107 to 0.2931, respectively. For Damsal dam for these years, values were −0.0313 to 0.3381 and −0.0539 to 0.1902, respectively. Consistently low NDTI values across both reservoirs indicated suitable irrigation water quality. Variations in NDTI values over time implied changes in water turbidity. Likewise, WQI values indicated water quality suitable for irrigation, but showed changes between pre- and post-monsoon periods, reflecting fluctuations. Average WQI values for Chohal and Damsal dams decreased by more than 20% during post-monsoon, implying worsened quality, likely due to monsoon related factors. Comparing pre- and post-monsoon water quality data helped to discern significant changes stemming from agricultural runoff, industrial discharges, and sediment transport. Identifying post-monsoon deterioration would help pinpoint pollution sources, crucial for water management. These findings provide valuable information for policymakers to protect the water quality of Chohal and Damsal dams, thus ensuring a consistent and trustworthy source of irrigation water. [ABSTRACT FROM AUTHOR]

In the remote sensing monitoring of conservation tillage, the acquisition of remote sensing data with high spatial and temporal resolution is critical. The current optical remote sensing images cannot realize both temporal and spatial resolution, especially under cloud and rain interference. Thus, this study employs the enhanced spatial and temporal adaptive reflectance fusion model (ESTARFM) to obtain the normalized difference tillage index (NDTI) with both temporal and spatial resolution estimated by Sentinel−2 and MODIS using the Index−then−Blend (IB) and Blend−then−Index (BI) fusion schemes. After comparison, the IB scheme was better than the BI scheme in predicting results and prediction efficiency. The NDTI predicted by ESTARFM and Sentinel−2 on June 12, 2020 was compared. A coefficient of determination R2 of 0.73 and RMSE of 0.000117 was obtained, indicating a high prediction accuracy, which meets the prediction requirements. Based on the predicted ESTARFM NDTI of the study area on May 17, 2021, the maize residue cover (MRC) of the study area was estimated using the previously constructed MRC unary linear regression model. The MRC of the sampling points of the remote sensing images was estimated by verifying the predicted ESTARFM NDTI with the MRC of the sampling points taken in the field extracted by the maximum likelihood classifier, which has a coefficient of determination R2 of 0.78 and RMSE of 0.00676, signifying better prediction results. The proposed method provides considerable data sources for the remote sensing monitoring studies of conservation tillage. [ABSTRACT FROM AUTHOR]

This study focused on optimizing the placement of shortwave infrared (SWIR) bands for pixel-level estimation of fractional crop residue cover (fR) for the upcoming Landsat Next mission. We applied an iterative wavelength shift approach to a database of crop residue field spectra collected in Beltsville, Maryland, USA (n = 916) and computed generalized two- and three-band spectral indices for all wavelength combinations between 2000 and 2350 nm, then used these indices to model field-measured fR. A subset of the full dataset with a Normalized Difference Vegetation Index (NDVI) < 0.3 threshold (n = 643) was generated to evaluate green vegetation impacts on fR estimation. For the two-band wavelength shift analyses applied to the NDVI < 0.3 dataset, a generalized normalized difference using 2226 nm and 2263 nm bands produced the top fR estimation performance (R2 = 0.8222; RMSE = 0.1296). These findings were similar to the established two-band Shortwave Infrared Normalized Difference Residue Index (SINDRI) (R2 = 0.8145; RMSE = 0.1324). Performance of the two-band generalized normalized difference and SINDRI decreased for the full-NDVI dataset (R2 = 0.5865 and 0.4144, respectively). For the three-band wavelength shift analyses applied to the NDVI < 0.3 dataset, a generalized ratio-based index with a 2031–2085–2216 nm band combination, closely matching established Cellulose Absorption Index (CAI) bands, was top performing (R2 = 0.8397; RMSE = 0.1231). Three-band indices with CAI-type wavelengths maintained top fR estimation performance for the full-NDVI dataset with a 2036–2111–2217 nm band combination (R2 = 0.7581; RMSE = 0.1548). The 2036–2111–2217 nm band combination was also top performing in fR estimation (R2 = 0.8690; RMSE = 0.0970) for an additional analysis assessing combined green vegetation cover and surface moisture effects. Our results indicate that a three-band configuration with band centers and wavelength tolerances of 2036 nm (±5 nm), 2097 nm (±14 nm), and 2214 (±11 nm) would optimize Landsat Next SWIR bands for fR estimation. [ABSTRACT FROM AUTHOR]

Assessing the extent of hailstorm-affected crop is one of the thrust areas for quantifying mid-season adversities under crop insurance values chain. This study evaluated the pre- and post-hailstorm responses of potato crop, over different spectral bands and vegetation indices derived from Sentinel-2 data, towards assessment of damage severity classes. The potato crop was mapped using pre-event satellite data with overall accuracy of 88% (k = 0.82). Pair-wise Games–Howell t-test showed significant differences among the post-hailstorm potato severity classes in red, near infrared and short-wave infrared (SWIR) bands and normalized vegetation indices. Percentage change (from pre- to post-event) in band reflectance and vegetation indices showed a better sensitivity in differentiating damage severities. Differential behaviour of SWIR-1 (Band-11) and SWIR-2 (Band-12) was observed within severely affected potato crop under dry and wet soil conditions. Decision matrix based on percentage change in normalized difference vegetation index (ΔNDVI) and normalized difference tillage index (ΔNDVI) could able to capture the damage severity classes with an overall accuracy of 86.7%. Higher proportion of affected area was found to be associated with larger percentage of potato yield reduction based on measured yield data at insurance unit level. The proposed methodological approach could be adopted for operational assessment of the impact of hailstorm events on other crops with required modifications. [ABSTRACT FROM AUTHOR]

During the outbreak of the COVID-19, China implemented an urban lockdown in the first period. These measures not only effectively curbed the spread of the virus but also brought a positive impact on the ecological environment. The water quality of urban inland river has a significant impact on urban ecology and public health. This study uses Sentinel-2 visible and near-infrared band reflectance and the Normalized Difference Turbidity Index (NDTI) to analyze the water quality of the Haihe River Basin during the control period of COVID-19. It is found that during the lockdown period, the river water quality was significantly improved compared to the same period in 2019. The average NDTI of the Haihe River Basin in March decreased by 0.27, a decrease of 219.06%; in April, it increased by 0.07, that is 38.38%. Further exploration using VIIRS lights found that the brightness of the lights in the main urban area was significantly lower in February, the beginning of the lockdown. However, as the city was unblocked, the lights rose sharply in March and then recovered to normal. There is obvious asynchrony in changes between river turbidity and light. The results can help understand the impact of human activities on the natural environment. [ABSTRACT FROM AUTHOR]

Small unoccupied aerial systems (sUASs) are increasingly applied to study hydrologic processes and water quality. Here, we evaluate a novel application of sUAS to stream turbidity monitoring, with the goal of extending analyses implemented with satellite remote sensing to enable high resolution, rapid collection of turbidity imagery along smaller waterbodies. To accomplish this, we collected multispectral imagery using two sUAS platforms under a range of environmental conditions along a local creek in Syracuse, NY. In addition, we collected in situ turbidity observations immediately after each flight along several transects along the creek, as well as within a clear plume created by a natural spring entering the main channel of the creek. The in situ turbidity values were compared with the mean and standard deviation of several single‐band and multiband indices extracted along similar transects from the sUAS flights. On the basis of data collected across several flights, we found optical metrics obtained from multispectral imagery correlated well with in situ turbidity measurements. Though many optical metrics yielded strong relationships considering only values within the main channel, values associated with the red band were strongly related to turbidity estimates from the main channel as well as lower turbidity values observed in the spring plume. Although there are still limitations of this approach associated with variable field conditions, results from this proof of concept analysis show that sUASs offer a promising avenue for cost‐effective turbidity monitoring. [ABSTRACT FROM AUTHOR]

Maintaining permanent coverage of the soil using crop residues is an important and commonly recommended practice in conservation agriculture. Measuring this practice is an essential step in improving knowledge about the adoption and impact of conservation agriculture. Different data collection methods can be implemented to capture the field level crop residue coverage for a given plot, each with its own implication on survey budget, implementation speed and respondent and interviewer burden. In this paper, six alternative methods of crop residue coverage measurement are tested among the same sample of rural households in Ethiopia. The relative accuracy of these methods are compared against a benchmark, the line-transect method. The alternative methods compared against the benchmark include: (i) interviewee (respondent) estimation; (ii) enumerator estimation visiting the field; (iii) interviewee with visual-aid without visiting the field; (iv) enumerator with visual-aid visiting the field; (v) field picture collected with a drone and analyzed with image-processing methods and (vi) satellite picture of the field analyzed with remote sensing methods. Results of the methodological experiment show that survey-based methods tend to underestimate field residue cover. When quantitative data on cover are needed, the best estimates are provided by visual-aid protocols. For categorical analysis (i.e., >30% cover or not), visual-aid protocols and remote sensing methods perform equally well. Among survey-based methods, the strongest correlates of measurement errors are total farm size, field size, distance, and slope. Results deliver a ranking of measurement options that can inform survey practitioners and researchers. [ABSTRACT FROM AUTHOR]

Recent reports of antipsychotic medication use in pediatric populations describe large increases in rates of use. Much interest in the increasing use has focused on potentially inappropriate prescribing for non-Food and Drug Administration-approved uses and use amongst youth with no mental health diagnosis. Different studies of antipsychotic use have used different time periods, geographic and insurance populations of youth, and aggregations of diagnoses. We review recent estimates of use and comment on the similarities and dissimilarities in rates of use. We also report new data obtained on 11 health maintenance organizations that are members of the Mental Health Research Network in order to update and extend the knowledge base on use by diagnostic indication. Results indicate that most use in pediatric populations is for disruptive behaviors and not psychotic disorders. Differences in estimates are likely a function of differences in methodology; however, there is remarkable consistency in estimates of use by diagnosis. [ABSTRACT FROM AUTHOR]

Climate change stressors like rising and warmer seas, increased storms and droughts, and acidifying oceans are rapidly threatening coastal zones, which are the world's most densely inhabited places. This research assesses the effects of Palm Jumeirah Island (PJI) construction on its surrounding water quality and temperature, using Landsat-7 and 8 spectral and thermal bands for the years 2001, 2014, 2016, 2019, and 2020. To aid in this goal, the changes in water spectral reflectance was observed and interpreted, based on previous research and measurements, to discover the correlation between water quality and its spectral reflectance. Then, the sea surface temperature (SST) was calculated for the years under review and changes in water temperature were evaluated. Finally, the Green Normalized Difference Vegetation Index (GNDVI) and the Normalized Difference Turbidity Index (NDTI) were calculated to estimate water chlorophyll levels and water turbidity, respectively, and changes were observed and interpreted for the time period under review. The present study showed that the PJI construction not only increased the water reflectance in the 0.5–0.8 µm of wavelength, which can be considered to be the increase of suspended sediments and chlorophyll but the water temperature also increased by 7.5 °C during the 19 years. In addition, a gradual increase in the values of GNDVI (by 0.097–0.129) and NDTI (by 0.118~0.172) were observed. A drop in chlorophyll and suspended sediment spectral reflectance and GNDVI and NDTI values were also observed in 2020 compared to 2019 which can be attributed to the 63 to 82% decrease in tourists in Dubai in 2020 as a result of the COVID-19 pandemic. This study aims to draw attention to environmental issues by clarifying the effect of creating artificial islands in the sea and our analysis and results are a suitable reference for specialized hydrological and environmental studies based on spectral information and distance measurements, as presented in this paper. [ABSTRACT FROM AUTHOR]

This research reports the findings of a Landsat Next expert review panel that evaluated the use of narrow shortwave infrared (SWIR) reflectance bands to measure ligno-cellulose absorption features centered near 2100 and 2300 nm, with the objective of measuring and mapping non-photosynthetic vegetation (NPV), crop residue cover, and the adoption of conservation tillage practices within agricultural landscapes. Results could also apply to detection of NPV in pasture, grazing lands, and non-agricultural settings. Currently, there are no satellite data sources that provide narrowband or hyperspectral SWIR imagery at sufficient volume to map NPV at a regional scale. The Landsat Next mission, currently under design and expected to launch in the late 2020's, provides the opportunity for achieving increased SWIR sampling and spectral resolution with the adoption of new sensor technology. This study employed hyperspectral data collected from 916 agricultural field locations with varying fractional NPV, fractional green vegetation, and surface moisture contents. These spectra were processed to generate narrow bands with centers at 2040, 2100, 2210, 2260, and 2230 nm, at various bandwidths, that were subsequently used to derive 13 NPV spectral indices from each spectrum. For crop residues with minimal green vegetation cover, two-band indices derived from 2210 and 2260 nm bands were top performers for measuring NPV (R2 = 0.81, RMSE = 0.13) using bandwidths of 30 to 50 nm, and the addition of a third band at 2100 nm increased resistance to atmospheric correction residuals and improved mission continuity with Landsat 8 Operational Land Imager Band 7. For prediction of NPV over a full range of green vegetation cover, the Cellulose Absorption Index, derived from 2040, 2100, and 2210 nm bands, was top performer (R2 = 0.77, RMSE = 0.17), but required a narrow (≤20 nm) bandwidth at 2040 nm to avoid interference from atmospheric carbon dioxide absorption. In comparison, broadband NPV indices utilizing Landsat 8 bands centered at 1610 and 2200 nm performed poorly in measuring fractional NPV (R2 = 0.44), with significantly increased interference from green vegetation. [ABSTRACT FROM AUTHOR]