Integration of In Situ and Remote Sensing Measurements for the Management of Harmful Cyanobacteria Blooms. A Lesson from a Strategic Multiple-Uses Reservoir (Lake Occhito, South Italy)

Harmful cyanobacteria blooms (HCBs) are one of the main water quality threats affecting reservoirs. Guidelines suggest integrating laboratory, real-time in situ, and remote sensing data in the monitoring of HCBs. However, this approach is still little adopted in institutional measuring programs. We demonstrated that this integration improves frequency and spatial resolution of the data collection. Data were from an intense HCB (Planktothrix rubescens), which occurred in a south Italy multiple-uses reservoir (Lake Occhito) between 2008 and 2009 and regarded both the lake and the irrigation network. Laboratory and in situ fluorometric data were related to satellite imagery, using simple linear regression models, to produce surface lake-wide maps reporting the distribution of both P. rubescens and microcystins. In the first node of the distribution network, microcystin concentrations (4–10 µg L−1) reached values potentially able to damage the culture and to accumulate during cultivation. Nevertheless, our study shows a decrease in the microcystin content with the distance from the lake (0.05 µg L−1 km−1), with a reduction of about 80% of the microcystin concentrations at the furthest tanks. Recent improvements in the spatial resolution (i.e., tens of meters) of satellite imagery allow us to monitor the main tanks of large and complex irrigation systems.

Download Full-text

Global Land Surface Temperature from historical NOAA AVHRR datasets (1981-2000)

10.5194/egusphere-egu2020-3102 ◽

2020 ◽

Author(s):

Jin Ma ◽

Ji Zhou ◽

Frank-Michael Göttsche ◽

Shaofei Wang

Keyword(s):

Climate Change ◽

Remote Sensing ◽

Surface Temperature ◽

Spatial Resolution ◽

Land Surface Temperature ◽

Land Surface ◽

Microwave Remote Sensing ◽

Community Land Model ◽

Noaa Avhrr

As one of the most important indicators in the energy exchange between land and atmosphere, Land Surface Temperature (LST) plays an important role in the research of climate change and various land surface processes. In contrast to in-situ measurements, satellite remote sensing provides a practical approach to measure global and local land surface parameters. Although passive microwave remote sensing offers all-weather observation capability, retrieving LST from thermal infra-red data is still the most common approach. To date, a variety of global LST products have been published by the scientific community, e.g. MODIS and (A)ASTR /SLSTR LST products, and used in a broad range of research fields. Several global and regional satellite retrieved LSTs are available since 1995. However, the temporal-spatial resolution before 2000 is generally considerably lower than that after 2000. According to the latest IPCC report, 1983 &#8211; 2012 are the warmest 30 years for nearly 1400 years. Therefore, for global climate change research, it is meaningful to extend the time series of global LST products with a relatively higher temporal-spatial resolution to before 2000, e.g. that of NOAA AVHRR. In this study, global daily NOAA AVHRR LST products with 5-km spatial resolution were generated for 1981-2000. The LST was retrieved using an ensemble of RF-SWAs (Random Forest and Split-Window Algorithm). For a maximum uncertainty in emissivity and water vapor content of 0.04 and 1.0 g/cm2, respectively, the training and testing with simulated datasets showed a retrieval accuracy with MBE of less than 0.1 K and STD of 1.1 K. The generated RF-SWA LST product was also evaluated against in-situ measurements: for water sites of the National Data Buoy Center (NDBC) between 1981 and 2000, it showed an accuracy similar to that for the simulated data, with a small MBE of less than 0.1 K and a STD between 0.79 K and 1.02 K. For SURFRAD data collected between 1995 and 2000, the MBE is -0.03 K with a range of -1.20 K &#8211; 0.54 K and a STD with a mean of 2.55 K and a range of 2.08 K &#8211; 3.0 K (site dependent). As a new global historical dataset, the RF-SWA LST product can help to close the gap in long-term LST data available to climate research. Furthermore, the data can be used as input to land surface process models, e.g. the Community Land Model (CLM). In support of the scientific research community, the RF-SWA LST product will be freely available at the National Earth System Science Data Center of China (http://www.geodata.cn/).

Download Full-text

Remote Sensing Phenology of Antarctic Green and Red Snow Algae Using WorldView Satellites

Frontiers in Plant Science ◽

10.3389/fpls.2021.671981 ◽

2021 ◽

Vol 12 ◽

Author(s):

Andrew Gray ◽

Monika Krolikowski ◽

Peter Fretwell ◽

Peter Convey ◽

Lloyd S. Peck ◽

...

Keyword(s):

Remote Sensing ◽

Cell Density ◽

Spatial Resolution ◽

Satellite Imagery ◽

Algal Blooms ◽

Vegetation Index ◽

Net Primary Productivity ◽

Snow Algae ◽

Antarctic Snow ◽

Sentinel 2

Snow algae are an important group of terrestrial photosynthetic organisms in Antarctica, where they mostly grow in low lying coastal snow fields. Reliable observations of Antarctic snow algae are difficult owing to the transient nature of their blooms and the logistics involved to travel and work there. Previous studies have used Sentinel 2 satellite imagery to detect and monitor snow algal blooms remotely, but were limited by the coarse spatial resolution and difficulties detecting red blooms. Here, for the first time, we use high-resolution WorldView multispectral satellite imagery to study Antarctic snow algal blooms in detail, tracking the growth of red and green blooms throughout the summer. Our remote sensing approach was developed alongside two Antarctic field seasons, where field spectroscopy was used to build a detection model capable of estimating cell density. Global Positioning System (GPS) tagging of blooms and in situ life cycle analysis was used to validate and verify our model output. WorldView imagery was then used successfully to identify red and green snow algae on Anchorage Island (Ryder Bay, 67°S), estimating peak coverage to be 9.48 × 104 and 6.26 × 104 m2, respectively. Combined, this was greater than terrestrial vegetation area coverage for the island, measured using a normalized difference vegetation index. Green snow algae had greater cell density and average layer thickness than red blooms (6.0 × 104 vs. 4.3 × 104 cells ml−1) and so for Anchorage Island we estimated that green algae dry biomass was over three times that of red algae (567 vs. 180 kg, respectively). Because the high spatial resolution of the WorldView imagery and its ability to detect red blooms, calculated snow algal area was 17.5 times greater than estimated with Sentinel 2 imagery. This highlights a scaling problem of using coarse resolution imagery and suggests snow algal contribution to net primary productivity on Antarctica may be far greater than previously recognized.

Download Full-text

Analytical review of remote sensing data sources suitable for mapping of land resource in Chernivtsi region

Visnyk of the Lviv University. Series Geography ◽

10.30970/vgg.2013.46.1499 ◽

2013 ◽

pp. 331-338

Author(s):

J. Smirnov

Keyword(s):

Remote Sensing ◽

Key Words ◽

Spatial Resolution ◽

Satellite Imagery ◽

Remote Sensing Data ◽

Data Sources ◽

Land Resource ◽

Land Resources ◽

Sensing Data ◽

Analytical Review

In the article described the sources of remote sensing data and analyzed their suitability for involvement in the process Chernivtsi region land resources mapping. Taken into account space surveying systems of different spatial resolution and aerial photographic surveys. As a result, have been identified the best sources of data that can be used in the Chernivtsi region land resources mapping. Key words: land resources, remote sensing, satellite imagery, mapping of land resources, sources of remote sensing data.

Download Full-text

Remote Sensing of Broom Snakeweed (Gutierrezia Sarothrae) with Noaa-10 Spectral Image Processing

Weed Technology ◽

10.1017/s0890037x00036642 ◽

1992 ◽

Vol 6 (4) ◽

pp. 1015-1020 ◽

Cited By ~ 26

Author(s):

Albert J. Peters ◽

Bradley C. Reed ◽

Marlen D. Eve ◽

Kirk C. McDaniel

Keyword(s):

Remote Sensing ◽

Image Processing ◽

New Mexico ◽

Spatial Resolution ◽

Satellite Imagery ◽

Spectral Image ◽

Late Season ◽

Shortgrass Prairie ◽

Gutierrezia Sarothrae ◽

Meteorological Satellite

Low-spatial resolution satellite imagery from the NOAA-10 polar-orbiting meteorological satellite was analyzed to determine if central New Mexico grasslands infested by broom snakeweed could be discriminated from unaffected areas. Distinctive phenological characteristics of broom snakeweed, including an early season growth flush and late season flowering, enable moderate to heavily infested areas to be separated from grasslands having few or no weeds present. The procedure used shows promise as a tool for locating and monitoring brown snakeweed and other weeds growing on shortgrass prairie.

Download Full-text

Assessing Water Stress of Desert Tamarugo Trees Using in situ Data and Very High Spatial Resolution Remote Sensing

Remote Sensing ◽

10.3390/rs5105064 ◽

2013 ◽

Vol 5 (10) ◽

pp. 5064-5088 ◽

Cited By ~ 17

Author(s):

Roberto Chávez ◽

Jan Clevers ◽

Martin Herold ◽

Edmundo Acevedo ◽

Mauricio Ortiz

Keyword(s):

Remote Sensing ◽

Water Stress ◽

Spatial Resolution ◽

High Spatial Resolution ◽

In Situ Data ◽

Very High Spatial Resolution ◽

Very High

Download Full-text

Reconstruction of Snow Depth Data at Moderate Spatial Resolution (1 km) from Remotely Sensed Snow Data and Multiple Optimized Environmental Factors: A Case Study over the Qinghai-Tibetan Plateau

Remote Sensing ◽

10.3390/rs13040657 ◽

2021 ◽

Vol 13 (4) ◽

pp. 657

Author(s):

Pengtao Wei ◽

Tingbin Zhang ◽

Xiaobing Zhou ◽

Guihua Yi ◽

Jingji Li ◽

...

Keyword(s):

Remote Sensing ◽

Tibetan Plateau ◽

Environmental Factors ◽

Spatial Resolution ◽

Snow Depth ◽

Microwave Remote Sensing ◽

Passive Microwave ◽

Passive Microwave Remote Sensing ◽

Depth Data

Snow depth distribution in the Qinghai-Tibetan plateau is important for atmospheric circulation and surface water resources. In-situ observations at meteorological stations and remote observation by passive microwave remote sensing technique are two main approaches for monitoring snow depth at regional or global levels. However, the meteorological stations are often scarce and unevenly distributed in mountainous regions because of inaccessibility, so are the in-situ snow depth measurements. Passive microwave remote sensing data can alleviate the unevenness issue, but accuracy and spatial (e.g., 25 km) and temporal resolutions are low; spatial heterogeneity in snow depth is thus hard to capture. On the other hand, optical sensors such as moderate resolution imaging spectroradiometer (MODIS) onboard Terra and Aqua satellites can monitor snow at moderate spatial resolution (1 km) and high temporal resolution (daily) but only snow area extent, not snow depth. Fusing passive microwave snow depth data with optical snow area extent data provides an unprecedented opportunity for generating snow depth data at moderate spatial resolution and high temporal resolution. In this article, a linear multivariate snow depth reconstruction (LMSDR) model was developed by fusing multisource snow depth data, optical snow area extent data, and environmental factors (e.g., spatial distribution, terrain features, and snow cover characteristics), to reconstruct daily snow depth data at moderate resolution (1 km) for 16 consecutive hydrological years, taking Qinghai-Tibetan Plateau (QTP) as a case study. We found that snow cover day (SCD) and environmental factors such as longitude, latitude, slope, surface roughness, and surface fluctuation have a significant impact on the variations of snow depth over the QTP. Relatively high accuracy (root mean square error (RMSE) = 2.26 cm) was observed in the reconstructed snow depth when compared with in-situ data. Compared with the passive microwave remote sensing snow depth product, constructing a nonlinear snow depletion curve product with an empirical formula and fusion snow depth product, the LMSDR model (RMSE = 2.28 cm, R2 = 0.63) demonstrated a significant improvement in accuracy of snow depth reconstruction. The overall spatial accuracy of the reconstructed snow depth was 92%. Compared with in-situ observations, the LMSDR product performed well regarding different snow depth intervals, land use, elevation intervals, slope intervals, and SCD and performed best, especially when the snow depth was less than 3 cm. At the same time, a long-time snow depth series reconstructed based on the LMSDR model reflected interannual variations of snow depth well over the QTP.

Download Full-text

Ulyssys Water Quality Viewer: a satellite-based global near real time water quality visualization

10.5194/egusphere-egu2020-18256 ◽

2020 ◽

Author(s):

András Zlinszky ◽

Gergely Padányi-Gulyás

Keyword(s):

Remote Sensing ◽

Water Quality ◽

Real Time ◽

Suspended Sediment ◽

Satellite Imagery ◽

Quality Information ◽

Custom Script ◽

Chlorophyll Concentrations ◽

Sentinel 2

Sampling-based water quality monitoring networks are inherently spatially sparse. In locations or times where no in-situ water quality data are available, satellite imagery is an essential source of information. Satellite remote sensing can provide high spatial or temporal resolution imagery and has provided a breakthrough for oceanography, but so far, applications for coastal and inland water were limited by data resolution. Recently established satellite systems provide significant advances: Sentinel-2 delivers imagery with 20 m resolution, suitable for viewing even small rivers and ponds. Sentinel-3 delivers daily imagery with 300 m pixel size, which for lakes and coastal seas allows tracking water quality processes at the speed they happen. Information on suspended sediment and chlorophyll concentrations in water can be derived from optical images using simple calculations. The accuracy of these operations will vary across locations and can only be assessed through calibration and validation with in situ data. In absence of such data for all lakes globally, UWQV is based on a small set of algorithms that have been verified on several optically complex water systems to have a close to linear correlation with chlorophyll or suspended sediment concentration. Suspended sediment visualization is based on radiances observed in the 620 or 700 nm spectral bands, while chlorophyll visualization uses fluorescence-based indicators: Fluorescence Line Height, Reflectance Line Height and Maximum Chlorophyll Index. Since remote sensing based chlorophyll retrieval in sediment-laden waters with low transparency is hardly possible, for such cases chlorophyll concentrations are not visualized. The viewer runs as a Custom Script in the Sentinel-Hub EO Browser, which is a global, near real-time satellite data viewing and algorithm testing framework. The Javascript code is open source and enables users to easily tune visualization parameters and select different algorithms for cloud and water masking and chlorophyll and suspended sediment visualization. Wherever in-situ water quality measurements are available, UWQV contributes significant added value by complementing water sample or instrument-based data, providing a map view or even a timelapse of maps; by providing an early warning system for water quality deterioration; by supporting optimization of sampling times and locations based on spatially and temporally explicit information, and &#160;enabling cross-validating water quality information from different sources to reduce uncertainty or identify implausible measurements. Additionally, data-driven spatially explicit models can be verified and tuned based on similarity of their output to situations observed on satellite imagery. UWQV is has all the advantages and drawbacks of a global solution: it will never be more accurate than a locally tuned water quality remote sensing algorithm; however, we hope that it will encourage water quality authorities and stakeholders to initiate the development of locally optimized satellite-based monitoring. By providing easy to read visualizations in a framework accessible to the general public, UWQV can democratize water quality information and raise public awareness of water quality processes and problems.The first version of the algorithm is available in the Sentinel-Hub Custom Script Repository under the following link: https://github.com/sentinel-hub/custom-scripts/tree/master/sentinel-2/ulyssys_water_quality_viewerAn interactive test example of the visualization can be accessed here: tinyurl.com/UWQV-example

Download Full-text

LAI, FAPAR and FCOVER ground-truth map creation from FASat-C satellite imagery and in-situ measurements in Chimbarongo, Chile, for satellite products validation

Revista de Teledetección ◽

10.4995/raet.2016.5691 ◽

2016 ◽

pp. 51 ◽

Cited By ~ 2

Author(s):

C. Latorre-Sánchez ◽

F. Camacho ◽

C. Mattar ◽

A. Santamaría-Artigas ◽

N. Leiva-Büchi ◽

...

Keyword(s):

Spatial Resolution ◽

Satellite Imagery ◽

Field Data ◽

Ground Truth ◽

Landsat 8 ◽

Data Sampling ◽

Least Squares Regression ◽

Ground Field ◽

Global Land

In remote sensing, validation exercises are essential to ensure the quality of the products originated from satellite Earth observations. To assess the measurement uncertainty derived from satellite products, several ground field data from different ecosystems must be available for use. In the same order of importance, it is necessary to define data sampling and up-scaling methodologies to allow a suitable comparison between the ground data and the pixel size of the product. This paper shows the applied methodology used in the FP7 ImagineS project (Implementing Multi-scale Agricultural Indicators Exploiting Sentinels) to validate 10-days global LAI, FAPAR and vegetation cover products at 1km spatial resolution using in-situ data. These global products are derived from PROBA-V observations in the Copernicus Global Land Service. In particular, this case study shows the results of the field-campaign carried out in January of 2015 in the agricultural area of Chimbarongo, Chile. The methodology to scale the ground data and to create ground-based maps using FASat-C Chilean satellite imagery with a 5,8 m spatial resolution using multivariate least squares regression is shown. Finally, the same methodology was used with a 30 m spatial resolution Landsat-8 image to analyze the effect of the field-data input on the ground-truth maps used to validate the results. Our results show the reliability on the presented methodology and the consistency of the method with regard to the input data. Better results and lower RMSE errors were obtained using FASat-C data. The comparison with satellite products at 1 km shows a good agreement with Copernicus Global Land products derived from PROBA-V observations, and systematic negative bias for the MODIS products.

Download Full-text

ESTIMATING GROSS PRIMARY PRODUCTION IN CROPLAND WITH HIGH SPATIAL AND TEMPORAL SCALE REMOTE SENSING DATA

ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences ◽

10.5194/isprs-archives-xlii-3-1009-2018 ◽

2018 ◽

Vol XLII-3 ◽

pp. 1009-1014

Author(s):

S. Lin ◽

J. Li ◽

Q. Liu

Keyword(s):

Remote Sensing ◽

Spatial Resolution ◽

High Spatial Resolution ◽

Gross Primary Production ◽

Remote Sensing Data ◽

Temporal Scale ◽

Estimation Accuracy ◽

Sensing Data ◽

Resolution Data

Satellite remote sensing data provide spatially continuous and temporally repetitive observations of land surfaces, and they have become increasingly important for monitoring large region of vegetation photosynthetic dynamic. But remote sensing data have their limitation on spatial and temporal scale, for example, higher spatial resolution data as Landsat data have 30-m spatial resolution but 16&thinsp;days revisit period, while high temporal scale data such as geostationary data have 30-minute imaging period, which has lower spatial resolution (&gt;&thinsp;1&thinsp;km). The objective of this study is to investigate whether combining high spatial and temporal resolution remote sensing data can improve the gross primary production (GPP) estimation accuracy in cropland. For this analysis we used three years (from 2010 to 2012) Landsat based NDVI data, MOD13 vegetation index product and Geostationary Operational Environmental Satellite (GOES) geostationary data as input parameters to estimate GPP in a small region cropland of Nebraska, US. Then we validated the remote sensing based GPP with the in-situ measurement carbon flux data. Results showed that: 1) the overall correlation between GOES visible band and in-situ measurement photosynthesis active radiation (PAR) is about 50&thinsp;% (R2&thinsp;=&thinsp;0.52) and the European Center for Medium-Range Weather Forecasts ERA-Interim reanalysis data can explain 64&thinsp;% of PAR variance (R2&thinsp;=&thinsp;0.64); 2) estimating GPP with Landsat 30-m spatial resolution data and ERA daily meteorology data has the highest accuracy(R2&thinsp;=&thinsp;0.85, RMSE &lt;&thinsp;3&thinsp;gC/m2/day), which has better performance than using MODIS 1-km NDVI/EVI product import; 3) using daily meteorology data as input for GPP estimation in high spatial resolution data would have higher relevance than 8-day and 16-day input. Generally speaking, using the high spatial resolution and high frequency satellite based remote sensing data can improve GPP estimation accuracy in cropland.

Download Full-text

MULTIDIMENSIONAL STATISTICAL MODEL FOR DETECTING OIL POLLUTION SITES BASED ON SATELLITE IMAGERY

Interexpo GEO-Siberia ◽

10.33764/2618-981x-2021-4-1-11-16 ◽

2021 ◽

Vol 4 (1) ◽

pp. 11-16

Author(s):

Alovsat Shura Guliyev ◽

Tatiana A. Khlebnikova

Keyword(s):

Remote Sensing ◽

Statistical Model ◽

Spatial Resolution ◽

Satellite Imagery ◽

Oil Spills ◽

Oil Pollution ◽

Sliding Window ◽

Synthetic Aperture ◽

The Earth ◽

Homogeneous Regions

The article considers an algorithm for determining the statistical model from several inhomogeneous images of the Earth's surface obtained by different sensors (optoelectronic scanning device, synthetic aperture radar (SAR)) over the sea areas. The object of the study are the methods of remote sensing of the Earth used for detection and mapping of oil spills. The aim of the research was to perform testing for a possible variation of the statistical model inside a non-uniform sliding window based on a semi-automatic approach. The proposed algorithm makes it possible to determine the spatial extent of oil production sites and oil pollution in offshore waters using multi-time RSA data and a multi-zone combined image with a spatial resolution of 10 m. First, homogeneous regions are analyzed in the image, and then the model of the analysis zone is expanded to the more general case of inhomogeneous regions that are observed in the analysis windows.

Download Full-text