Remote Sensing of Nutrient Concentrations of Soils and Crops in Biosolid Amended Soils

An operational overview of the EXport Processes in the Ocean from RemoTe Sensing (EXPORTS) Northeast Pacific field deployment

Elem Sci Anth ◽

10.1525/elementa.2020.00107 ◽

2021 ◽

Vol 9 (1) ◽

Author(s):

David A. Siegel ◽

Ivona Cetinić ◽

Jason R. Graff ◽

Craig M. Lee ◽

Norman Nelson ◽

...

Keyword(s):

Remote Sensing ◽

Optical Properties ◽

Food Web ◽

Mixed Layer ◽

Global Ocean ◽

Nutrient Concentrations ◽

Data Set ◽

Northeast Pacific ◽

Field Deployment ◽

Ocean Station Papa

The goal of the EXport Processes in the Ocean from RemoTe Sensing (EXPORTS) field campaign is to develop a predictive understanding of the export, fate, and carbon cycle impacts of global ocean net primary production. To accomplish this goal, observations of export flux pathways, plankton community composition, food web processes, and optical, physical, and biogeochemical (BGC) properties are needed over a range of ecosystem states. Here we introduce the first EXPORTS field deployment to Ocean Station Papa in the Northeast Pacific Ocean during summer of 2018, providing context for other papers in this special collection. The experiment was conducted with two ships: a Process Ship, focused on ecological rates, BGC fluxes, temporal changes in food web, and BGC and optical properties, that followed an instrumented Lagrangian float; and a Survey Ship that sampled BGC and optical properties in spatial patterns around the Process Ship. An array of autonomous underwater assets provided measurements over a range of spatial and temporal scales, and partnering programs and remote sensing observations provided additional observational context. The oceanographic setting was typical of late-summer conditions at Ocean Station Papa: a shallow mixed layer, strong vertical and weak horizontal gradients in hydrographic properties, sluggish sub-inertial currents, elevated macronutrient concentrations and low phytoplankton abundances. Although nutrient concentrations were consistent with previous observations, mixed layer chlorophyll was lower than typically observed, resulting in a deeper euphotic zone. Analyses of surface layer temperature and salinity found three distinct surface water types, allowing for diagnosis of whether observed changes were spatial or temporal. The 2018 EXPORTS field deployment is among the most comprehensive biological pump studies ever conducted. A second deployment to the North Atlantic Ocean occurred in spring 2021, which will be followed by focused work on data synthesis and modeling using the entire EXPORTS data set.

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Using variations in High Arctic vegetation spectral properties to predict various types of plant, soil, and environmental variables.

Inquiry@Queen's Undergraduate Research Conference Proceedings ◽

10.24908/iqurcp.14722 ◽

2021 ◽

Author(s):

Sandra Yaacoub

Keyword(s):

Remote Sensing ◽

Learning Algorithm ◽

Vegetation Indices ◽

Net Ecosystem Exchange ◽

Environmental Parameters ◽

High Arctic ◽

Hyperspectral Data ◽

Nutrient Concentrations ◽

Polar Regions ◽

Foliar Nitrogen

In comparison to other regions, the High Arctic is experiencing accelerated rates of warming (Meredith et al., 2019). Hyperspectral remote sensing may provide a way to monitor changes in productivity without having to make detailed ground-based measurements. During the 2017 field season researchers on Melville Island, Nunavut, collected in-situ hyperspectral data, plant nutrient concentrations, carbon dioxide gas exchange measurements, and various environmental parameters in a wet-sedge tundra environment. These data were processed with the overall objective of determining if spectral information may be used to quantify changes in productivity across the High Arctic. Using a random forest machine learning algorithm, wavelengths from the hyperspectral data were identified for use in nine vegetation indices (VIs) based on relationships to foliar nitrogen concentrations. Using linear regressions, these VIs were compared to the environmental parameters. Although none correlated significantly to foliar nitrogen, three VIs showed p-values < 0.05 (alpha = 0.05) consistently for the following variables: soil nitrate and ammonia concentrations, net ecosystem exchange (NEE), and gross primary productivity (GPP) values. This shows promise for the use of remote sensing techniques to aid in monitoring the High Arctic. Additional research within this field would help pave way towards increased certainty on the kinds of responses that are in store for these landscapes if warming is to continue at an accelerated rate. This may bring increased monitoring frequency and scale of environmental assessment across the High Arctic, granting communities influenced by warming additional tools to aid in safer regional navigation and improved emergency response preparedness. References Meredith, M., Sommerkorn, M., Cassotta, S., Derksen, C., Ekaykin, A., Hollowed, A., Kofinas, G., Mackintosh, A., Melbourne-Thomas, J., Muelbert, M. M. C. M. M. C., Ottersen, G., Pritchard, H., & Schuur, E. A. G. E. A. G. (2019). Polar Regions. In H.-O. Pörtner, D. C. Roberts, V. Masson-Delmotte, P. Zhai, M. Tignor, E. Poloczanska, K. Mintenbeck, A. Alegría, M. Nicolai, A. Okem, J. Petzold, B. Rama, & N. M. Weyer (Eds.), IPCC Special Report on the Ocean and Cryosphere in a Changing Climate (pp. 203–320). https://www.ipcc.ch/srocc/chapter/chapter-3-2/

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Changes in Nutrient Concentrations in Shenzhen Bay Detected Using Landsat Imagery between 1988 and 2020

Remote Sensing ◽

10.3390/rs13173469 ◽

2021 ◽

Vol 13 (17) ◽

pp. 3469

Author(s):

Jingjing Huang ◽

Difeng Wang ◽

Fang Gong ◽

Yan Bai ◽

Xianqiang He

Keyword(s):

Remote Sensing ◽

Water Quality ◽

Hong Kong ◽

Time Series Data ◽

Inorganic Nitrogen ◽

Landsat Imagery ◽

Machine Learning Algorithms ◽

Series Data ◽

Nutrient Concentrations ◽

Spatial Coverage

Shenzhen Bay (SZB), situated between Shenzhen and Hong Kong, is a typical bay system. The water quality of the bay is notably affected by domestic and industrial discharge. Rivers and various types of drainage outlets carry terrestrial pollutants into SZB, resulting in elevated concentrations of nitrogen and phosphorous as well as relatively poor water quality. For over 200 years, Hong Kong has practiced oyster farming within brackish estuarine waters. Oyster farming is a type of mariculture which includes oyster breeding in oyster rafts. Remote sensing is a monitoring technique characterized by large spatial coverage, high traceability, and low cost, making it advantageous over conventional point-based and ship-borne monitoring methods. In this study, remote-sensing models were established using machine-learning algorithms to retrieve key water-quality factors (dissolved inorganic nitrogen (DIN) and orthophosphate-phosphorous (PO4_P) concentrations, CDIN and CPO4_P, respectively) from long-term time-series data acquired by the Landsat satellites. (1) Spatially, the water quality in Inner SZB was worse than that in Outer SZB. (2) The water quality temporarily deteriorated between the end of the 20th century and the beginning of the 21st century; then it gradually improved in the late 2000s. (3) Monitoring the water quality in an oyster-farming area revealed that oyster farming did not adversely affect the water quality. (4) The result of monitoring the water quality in river estuaries in SZB shows that water quality was mainly affected by river input.

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A Strategy for Estimating Nutrient Concentrations Using Remote Sensing Datasets and Hydrological Modeling

International Journal of Agricultural and Environmental Information Systems ◽

10.4018/jaeis.2012010101 ◽

2012 ◽

Vol 3 (1) ◽

pp. 1-13 ◽

Cited By ~ 5

Author(s):

Vladimir J. Alarcon ◽

William H. McAnally

Keyword(s):

Remote Sensing ◽

Land Use ◽

Total Nitrogen ◽

Total Phosphorus ◽

Hydrological Modeling ◽

Northern Region ◽

Nutrient Concentrations ◽

Nutrient Export

This paper presents a methodology for estimating nutrient concentrations of total phosphorus (TP) and total nitrogen (TN) through the use of hydrological modeling, remote sensing datasets, and nutrient export coefficients. The strategy is applied to the Upper Tombigbee watershed, located in the northern region of the states of Mississippi and Alabama, USA. USGS GIRAS (1986), NASA MODIS MOD12Q1 (2001-2004) land use datasets, and USGS-DEM topographical datasets were used to characterize the physiography of the watershed. TN and TP concentration values estimated using the methodology were compared to values reported in the literature.

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ESTIMATION THE FACTORS AFFECTING ON GROWTH OF ALGAE IN UM EL-NAAJ LAKE BY USING REMOTE SENSING TECHNIQUES

Periódico Tchê Química ◽

10.52571/ptq.v17.n35.2020.21_alnaqeeb_pgs_227_238.pdf ◽

2020 ◽

Vol 17 (35) ◽

pp. 227-238

Author(s):

Neran A. AL NAQEEB ◽

Fouad K. MASHEE ◽

Jinan S. AL HASSANY

Keyword(s):

Remote Sensing ◽

Aquatic Plants ◽

Food Chain ◽

Nutrient Concentrations ◽

Natural Food ◽

Visual Interpretation ◽

Epiphytic Algae ◽

Factors Affecting ◽

Plastic Bags ◽

Remote Sensing Techniques

Epiphytic algae adherent to aquatic plants are an essential link in the composition of the food chain of any ecosystem. Epiphytic algae act as primary producers of the food chain in the aquatic ecosystem and as natural food for herbivorous zooplankton and fish. This study aimed to detect the presence of algae colonies through remote sensing and to analyze factors that affect the growth of algae through field survey and visual interpretation of satellite images in Lake Um El-Naaj. Samples were collected from six locations on Lake Um El-Naaj from November 2018 to June 2019. The algae samples were collected from submerged parts of emerging aquatic plants (aquatic macrophytes Phragmites australis) and stored in plastic bags with little ambient water and solutions for preservation in the field and the laboratory. Water samples were collected to study the nutritional factors that influence the growth of algae, including the concentration of Phosphate, Nitrate, and Silica (silicon dioxide). The results showed that the values of total phosphorus increased by 1.0, 0.9, and 0.8 mg/L, in January, in places 2, 5, and 6, respectively. The highest nitrate value was 11.2 mg/L in December at site 5, while the lowest concentration values were in November (2.2 mg/L at site 6 and 3.7 mg/L at site 4). Besides, the lowest silicate concentration was 0.4 mg/L in November at site 2, while the highest was 2.4 mg/L in June 2019 at site 6. Based on the findings, it is possible to conclude that, during the winter, the water level increased due to the rain. For this reason, nutrient concentrations were low during the last period. Also, with the use of maps and remote sensing techniques, it is possible to determine the expected values around the station's location as future predictive readings that compensate for the difficulty of reaching these areas.

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