Satellite-data-based analysis of ecotope diversity on different spatial scales in China

Seaweed is a marine plant or algae which has economic value in many parts of the world. The purpose of this study is to evaluate different satellite sensors such as high-resolution WorldView-2 (WV2) satellite data and Landsat 8 30-meter resolution satellite data for mapping seaweed resources along the coastal waters of Karachi. The continuous monitoring and mapping of this precious marine plant and their breeding sites may not be very efficient and cost effective using traditional survey techniques. Remote Sensing (RS) and Geographical Information System (GIS) can provide economical and more efficient solutions for mapping and monitoring coastal resources quantitatively as well as qualitatively at both temporal and spatial scales. Normalized Difference Vegetation Indices (NDVI) along with the image enhancement techniques were used to delineate seaweed patches in the study area. The coverage area of seaweed estimated with WV-2 and Landsat 8 are presented as GIS maps. A more precise area estimation wasachieved with WV-2 data that shows 15.5Ha (0.155 Km2)of seaweed cover along Karachi coast that is more representative of the field observed data. A much larger area wasestimated with Landsat 8 image (71.28Ha or 0.7128 Km2) that was mainly due to the mixing of seaweed pixels with water pixels. The WV-2 data, due to its better spatial resolution than Landsat 8, have proven to be more useful than Landsat 8 in mapping seaweed patches

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Wavelet-based spatial comparison technique for analysing and evaluating two-dimensional geophysical model fields

Geoscientific Model Development ◽

10.5194/gmd-5-223-2012 ◽

2012 ◽

Vol 5 (1) ◽

pp. 223-230 ◽

Cited By ~ 20

Author(s):

S. Saux Picart ◽

M. Butenschön ◽

J. D. Shutler

Keyword(s):

Satellite Data ◽

Fishery Management ◽

Numerical Models ◽

Spatial Scales ◽

Marine Ecosystem ◽

Original Method ◽

Precipitation Forecast ◽

Spatial And Temporal Scales ◽

Geographical Patterns ◽

Marine Ecosystem Model

Abstract. Complex numerical models of the Earth's environment, based around 3-D or 4-D time and space domains are routinely used for applications including climate predictions, weather forecasts, fishery management and environmental impact assessments. Quantitatively assessing the ability of these models to accurately reproduce geographical patterns at a range of spatial and temporal scales has always been a difficult problem to address. However, this is crucial if we are to rely on these models for decision making. Satellite data are potentially the only observational dataset able to cover the large spatial domains analysed by many types of geophysical models. Consequently optical wavelength satellite data is beginning to be used to evaluate model hindcast fields of terrestrial and marine environments. However, these satellite data invariably contain regions of occluded or missing data due to clouds, further complicating or impacting on any comparisons with the model. This work builds on a published methodology, that evaluates precipitation forecast using radar observations based on predefined absolute thresholds. It allows model skill to be evaluated at a range of spatial scales and rain intensities. Here we extend the original method to allow its generic application to a range of continuous and discontinuous geophysical data fields, and therefore allowing its use with optical satellite data. This is achieved through two major improvements to the original method: (i) all thresholds are determined based on the statistical distribution of the input data, so no a priori knowledge about the model fields being analysed is required and (ii) occluded data can be analysed without impacting on the metric results. The method can be used to assess a model's ability to simulate geographical patterns over a range of spatial scales. We illustrate how the method provides a compact and concise way of visualising the degree of agreement between spatial features in two datasets. The application of the new method, its handling of bias and occlusion and the advantages of the novel method are demonstrated through the analysis of model fields from a marine ecosystem model.

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Biophysical remote sensing of arctic environments

Progress in Physical Geography Earth and Environment ◽

10.1191/0309133303pp358ra ◽

2003 ◽

Vol 27 (1) ◽

pp. 44-68 ◽

Cited By ~ 33

Author(s):

Gita J. Laidler ◽

Paul Treitz

Keyword(s):

Remote Sensing ◽

Satellite Data ◽

Spatial Scales ◽

Temporal Distribution ◽

Remote Sensing Data ◽

Vegetation Mapping ◽

General Purpose ◽

The Arctic ◽

Natural Systems ◽

Tundra Ecosystem

Various remote sensing studies have been conducted to investigate methods and applications of vegetation mapping and analysis in arctic environments. The general purpose of these studies is to extract information on the spatial and temporal distribution of vegetation as required for tundra ecosystem and climate change studies. Because of the recent emphasis on understanding natural systems at large spatial scales, there has been an increasing interest in deriving biophysical variables from satellite data. Satellite remote sensing offers potential for extrapolating, or ‘scaling up’ biophysical measures derived from local sites, to landscape and even regional scales. The most common investigations include mapping spatial vegetation patterns or assessing biophysical tundra characteristics, using medium resolution satellite data. For instance, Landsat TM data have been shown to be useful for broad vegetation mapping and analysis, but not accurately representative of smaller vegetation communities or local spatial variation. It is anticipated, that high spatial resolution remote sensing data, now available from commercial remote sensing satellites, will provide the necessary sampling scale to link field data to remotely sensed reflectance data. As a result, it is expected that these data will improve the representation of biophysical variables over sparsely vegetated regions of the Arctic.

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Large variability in continental shelf production of phytoplankton carbon revealed by satellite

Biogeosciences Discussions ◽

10.5194/bgd-7-8953-2010 ◽

2010 ◽

Vol 7 (6) ◽

pp. 8953-8978

Author(s):

B. F. Jönsson ◽

J. E. Salisbury ◽

A. Mahadevan

Keyword(s):

Ocean Circulation ◽

Satellite Data ◽

Gulf Of Maine ◽

Spatial Scales ◽

Light Attenuation ◽

Satellite Image ◽

Circulation Model ◽

Ocean Circulation Model ◽

Large Variability ◽

Chl A

Abstract. We estimate the net production of phytoplankton in the Gulf of Maine (GoM) over a 3-year period using satellite ocean color data in conjunction with surface velocities from a high-resolution operational ocean circulation model. Chlorophyll (chl-a) and light attenuation (K490) products are combined with a carbon to chlorophyll model to estimate the phytoplankton carbon (PC) stock in the euphotic layer. A satellite-based productivity, termed NCPe in analogy with net community production (NCP), is derived by tracking changes in satellite-derived PC from one satellite image to the next, along water parcel trajectories calculated with surface velocities from the ocean circulation model. Such an along-trajectory analysis of satellite data discounts the effect of advection that would otherwise contribute to the temporal change between consecutive images viewed in the fixed reference frame. Our results show a high variability of up to ± 500 mg C m−2 d−1 in NCPe on spatial scales of 10–100 km. A region-wide median NCPe of 40–50 mg C m−2 d−1 is often prevalent in the Gulf, while blooms attain peak values of 400 mg C m−2 d−1 for a few days. The spatio-temporal variability of NCPe in this region, though conditioned by seasonality, is dominated by events lasting a few days, which if integrated, lead to large inter-annual variability in the annual carbon budget. This study is a step toward achieving synoptic and time-dependent estimates of oceanic productivity and NCP from satellite data.

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Improvement of uncertainty estimation in global hydrological models by using high resolution satellite data as an interpolator

10.5194/egusphere-egu2020-4323 ◽

2020 ◽

Author(s):

Rogier Westerhoff ◽

Frederika Mourot ◽

Conny Tschritter

Keyword(s):

High Resolution ◽

Water Balance ◽

Groundwater Recharge ◽

Satellite Data ◽

Spatial Scales ◽

Remotely Sensed ◽

Google Earth ◽

Data Driven ◽

Hydrological Models ◽

Water Balance Components

Global hydrological models often ingest remotely-sensed observations supported by ground-truthed data in attempts to better quantify water balance components, e.g. soil water content, evapotranspiration, runoff/discharge, groundwater recharge. However, the scaling up process from local observations to that global, coarse, scale contains large uncertainty, often undermining the relevance of water balance calculations.With recent more advanced high-resolution satellite data, freely available at 10m spatial resolution and (sub-) weekly temporal resolution, there is a possibility to reduce uncertainty in that upscaling. However, there are two challenges in doing so when working with global models: exponential increase of computational effort, and the need for quantifying the yet unknown uncertainty of assumptions that coarse global model cells and their underlying equations bring.This study hypothesises that a bottom-up approach with high-resolution satellite data and in situ observations will better constrain and quantify uncertainty. By using these more spatially-explicit data, we make the case that the estimation of water balance components should become more data-driven. We propose a more data-driven model that improves uncertainty of estimation and scalability by using more sophisticated, remotely-sensed interpolation layers.Our case study shows New Zealand-wide estimates of evapotranspiration and groundwater recharge at two resolutions: 1km x 1km, using an earlier developed model and MODIS satellite data; and a novel approach at 10m x 10m using Sentinel-1 and Sentinel-2 data to better incorporate impervious areas (e.g., anthropogenic urbanised land covers) and small land patches (e.g., small forestry areas). We then study the implications of using different spatial scales and quantify the differences between 10m x 10m versus 1km x 1km model pixel estimates. Our method is applied in the Google Earth Engine, a free-for-research high performance cloud computing facility, hence providing powerful computational resources and making our approach easily scalable to global, regional and catchment scales.&#160;Finally, we discuss what underlying model assumptions in traditional models could be changed to facilitate a method that works consistently at these different scales.

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The Hamburg Ocean Atmosphere Parameters and Fluxes from Satellite Data – HOAPS-3

Earth System Science Data ◽

10.5194/essd-2-215-2010 ◽

2010 ◽

Vol 2 (2) ◽

pp. 215-234 ◽

Cited By ~ 112

Author(s):

A. Andersson ◽

K. Fennig ◽

C. Klepp ◽

S. Bakan ◽

H. Graßl ◽

...

Keyword(s):

Time Series ◽

Satellite Data ◽

Spatial Scales ◽

Heat Fluxes ◽

Freshwater Flux ◽

Climate Modelling ◽

Turbulent Heat ◽

Data Sampling ◽

Data Set ◽

Ocean Atmosphere

Abstract. The availability of microwave instruments on satellite platforms allows the retrieval of essential water cycle components at high quality for improved understanding and evaluation of water processes in climate modelling. HOAPS-3, the latest version of the satellite climatology "Hamburg Ocean Atmosphere Parameters and Fluxes from Satellite Data" provides fields of turbulent heat fluxes, evaporation, precipitation, freshwater flux and related atmospheric variables over the global ice-free ocean. This paper describes the content, methodology and retrievals of the HOAPS climatology. A sophisticated processing chain, including all available Special Sensor Microwave Imager (SSM/I) instruments aboard the satellites of the Defense Meteorological Satellites Program (DMSP) and careful inter-sensor calibration, ensures a homogeneous time-series with dense data sampling and hence detailed information of the underlying weather situations. The completely reprocessed data set with a continuous time series from 1987 to 2005 contains neural network based algorithms for precipitation and wind speed and Advanced Very High Resolution Radiometer (AVHRR) based SST fields. Additionally, a new 85 GHz synthesis procedure for the defective SSM/I channels on DMSP F08 from 1988 on has been implemented. Freely available monthly and pentad means, twice daily composites and scan-based data make HOAPS-3 a versatile data set for studying ocean-atmosphere interaction on different temporal and spatial scales. HOAPS-3 data products are available via http://www.hoaps.org.

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Dead Fuel Moisture Content (DFMC) Estimation Using MODIS and Meteorological Data: The Case of Greece

Remote Sensing ◽

10.3390/rs13214224 ◽

2021 ◽

Vol 13 (21) ◽

pp. 4224

Author(s):

Eleni Dragozi ◽

Theodore M. Giannaros ◽

Vasiliki Kotroni ◽

Konstantinos Lagouvardos ◽

Ioannis Koletsis

Keyword(s):

Moisture Content ◽

Satellite Data ◽

Spatial Scales ◽

Meteorological Data ◽

Water Vapor Pressure ◽

Field Measurements ◽

Fuel Moisture ◽

Weather Station ◽

Physically Based ◽

Fuel Moisture Content

The frequent occurrence of large and high-intensity wildfires in the Mediterranean region poses a major threat to people and the environment. In this context, the estimation of dead fine fuel moisture content (DFMC) has become an integrated part of wildfire management since it provides valuable information for the flammability status of the vegetation. This study investigates the effectiveness of a physically based fuel moisture model in estimating DFMC during severe fire events in Greece. Our analysis considers two approaches, the satellite-based (MODIS DFMC model) and the weather station-based (AWSs DFMC model) approach, using a fuel moisture model which is based on the relationship between the fuel moisture of the fine fuels and the water vapor pressure deficit (D). During the analysis we used weather station data and MODIS satellite data from fourteen wildfires in Greece. Due to the lack of field measurements, the models’ performance was assessed only in the case of the satellite data by using weather observations obtained from the network of automated weather stations operated by the National Observatory of Athens (NOA). Results show that, in general, the satellite-based model achieved satisfactory accuracy in estimating the spatial distribution of the DFMC during the examined fire events. More specifically, the validation of the satellite-derived DFMC against the weather-station based DFMC indicated that, in all cases examined, the MODIS DFMC model tended to underestimate DFMC, with MBE ranging from −0.3% to −7.3%. Moreover, in all of the cases examined, apart from one (Sartis’ fire case, MAE: 8.2%), the MAE of the MODIS DFMC model was less than 2.2%. The remaining numerical results align with the existing literature, except for the MAE case of 8.2%. The good performance of the satellite based DFMC model indicates that the estimation of DFMC is feasible at various spatial scales in Greece. Presently, the main drawback of this approach is the occurrence of data gaps in the MODIS satellite imagery. The examination and comparison of the two approaches, regarding their operational use, indicates that the weather station-based approach meets the requirements for operational DFMC mapping to a higher degree compared to the satellite-based approach.

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Mapping riverbed sediment size from Sentinel 2 satellite data

10.31223/x5qw65 ◽

2021 ◽

Author(s):

Giulia Marchetti ◽

Simone Bizzi ◽

Barbara Belletti ◽

Barbara Lastoria ◽

Francesco Comiti ◽

...

Keyword(s):

Grain Size ◽

Size Distribution ◽

Grain Size Distribution ◽

Satellite Data ◽

Spatial Scales ◽

Image Texture ◽

Mapping Procedure ◽

Bed Sediments ◽

Sediment Size ◽

Sentinel 2

A comprehensive understanding of river dynamics requires quantitative knowledge of the grain size distribution of bed sediments and its variation across different temporal and spatial scales. Several techniques are already available for grain size assessment based on field and remotely sensed data. However, the existing methods are only applicable on small spatial scales and on short time scales. Thus, the operational measurement of grain size distribution of river bed sediments at the catchment scale remains an open problem. A solution could be the use of satellite images as the main imaging platform. However, this would entail retrieving information at sub-pixel scales. In this study, we propose a new approach to retrieve sub-pixel scale grain size class information from Copernicus Sentinel-2 imagery building upon a new image-based grain size mapping procedure. Three Italian gravel-bed rivers featuring different morphologies were selected for Unmanned Aerial Vehicle (UAV) acquisitions coupled to field surveys and lab analysis meant to serve as ground truth grain size data. Grain size maps on river bars were generated in each study site by exploiting image texture measurements, upscaled and co-registered with Sentinel-2 data resolution. Relationships between the grain sizes measured and the reflectance values in Sentinel-2 imagery were analyzed by using a machine learning framework. Results show statistically significant predictive models (MAE of ±8.34 mm and R2=0.92). The trained model was applied on 300 km of the Po river in Italy and allows to detect grain size longitudinal variation and to identify the gravel-sand transition occurring along this river length.Our proposed approach based on freely available satellite data calibrated by low-cost automated drone technology can provide reasonably accurate estimates of surface grain size classes, in the range of sand to gravel, for bar sediments in medium to large river channels, over lengths of hundreds of kilometers.

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New methods for the retrieval of chlorophyll red fluorescence from hyperspectral satellite instruments: simulations and application to GOME-2 and SCIAMACHY

Atmospheric Measurement Techniques ◽

10.5194/amt-9-3939-2016 ◽

2016 ◽

Vol 9 (8) ◽

pp. 3939-3967 ◽

Cited By ~ 106

Author(s):

Joanna Joiner ◽

Yasuko Yoshida ◽

Luis Guanter ◽

Elizabeth M. Middleton

Keyword(s):

Satellite Data ◽

Spatial Scales ◽

Emission Feature ◽

Terrestrial Vegetation ◽

Ozone Monitoring Instrument ◽

Solar Absorption ◽

Time Space ◽

Wide Range ◽

Gas Measurements ◽

Satellite Instruments

Abstract. Global satellite measurements of solar-induced fluorescence (SIF) from chlorophyll over land and ocean have proven useful for a number of different applications related to physiology, phenology, and productivity of plants and phytoplankton. Terrestrial chlorophyll fluorescence is emitted throughout the red and far-red spectrum, producing two broad peaks near 683 and 736 nm. From ocean surfaces, phytoplankton fluorescence emissions are entirely from the red region (683 nm peak). Studies using satellite-derived SIF over land have focused almost exclusively on measurements in the far red (wavelengths  >  712 nm), since those are the most easily obtained with existing instrumentation. Here, we examine new ways to use existing hyperspectral satellite data sets to retrieve red SIF (wavelengths  <  712 nm) over both land and ocean. Red SIF is thought to provide complementary information to that from the far red for terrestrial vegetation. The satellite instruments that we use were designed to make atmospheric trace-gas measurements and are therefore not optimal for observing SIF; they have coarse spatial resolution and only moderate spectral resolution (0.5 nm). Nevertheless, these instruments, the Global Ozone Monitoring Instrument 2 (GOME-2) and the SCanning Imaging Absorption spectroMeter for Atmospheric CHartographY (SCIAMACHY), offer a unique opportunity to compare red and far-red terrestrial SIF at regional spatial scales. Terrestrial SIF has been estimated with ground-, aircraft-, or satellite-based instruments by measuring the filling-in of atmospheric and/or solar absorption spectral features by SIF. Our approach makes use of the oxygen (O2) γ band that is not affected by SIF. The SIF-free O2 γ band helps to estimate absorption within the spectrally variable O2 B band, which is filled in by red SIF. SIF also fills in the spectrally stable solar Fraunhofer lines (SFLs) at wavelengths both inside and just outside the O2 B band, which further helps to estimate red SIF emission. Our approach is then an extension of previous approaches applied to satellite data that utilized only the filling-in of SFLs by red SIF. We conducted retrievals of red SIF using an extensive database of simulated radiances covering a wide range of conditions. Our new algorithm produces good agreement between the simulated truth and retrievals and shows the potential of the O2 bands for noise reduction in red SIF retrievals as compared with approaches that rely solely on SFL filling. Biases seen with existing satellite data, most likely due to instrumental artifacts that vary in time, space, and with instrument, must be addressed in order to obtain reasonable results. Our 8-year record of red SIF observations over land with the GOME-2 allows for the first time reliable global mapping of monthly anomalies. These anomalies are shown to have similar spatiotemporal structure as those in the far red, particularly for drought-prone regions. There is a somewhat larger percentage response in the red as compared with the far red for these areas that are drought sensitive. We also demonstrate that good-quality ocean fluorescence line height retrievals can be achieved with GOME-2, SCIAMACHY, and similar instruments by utilizing the full complement of radiance measurements that span the red SIF emission feature.

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Large variability in continental shelf production of phytoplankton carbon revealed by satellite

Biogeosciences ◽

10.5194/bg-8-1213-2011 ◽

2011 ◽

Vol 8 (5) ◽

pp. 1213-1223 ◽

Cited By ~ 17

Author(s):

B. F. Jönsson ◽

J. E. Salisbury ◽

A. Mahadevan

Keyword(s):

Ocean Circulation ◽

Satellite Data ◽

Gulf Of Maine ◽

Spatial Scales ◽

Light Attenuation ◽

Satellite Image ◽

Circulation Model ◽

Ocean Circulation Model ◽

Large Variability ◽

Chl A

Abstract. We estimate the net production of phytoplankton in the Gulf of Maine (GoM) over a 3-yr period using satellite ocean color data in conjunction with surface velocities from a high-resolution operational ocean circulation model. Chlorophyll (Chl-a) and light attenuation (K490) products are combined with a carbon to chlorophyll model to estimate the phytoplankton carbon (PC) stock in the euphotic layer. A satellite-based productivity, termed NCPe in analogy with net community production (NCP), is derived by tracking changes in satellite-derived PC from one satellite image to the next, along water parcel trajectories calculated with surface velocities from the ocean circulation model. Such an along-trajectory analysis of satellite data discounts the effect of advection that would otherwise contribute to the temporal change between consecutive images viewed in the fixed reference frame. Our results show a high variability of up to ±500 mg C m−2 d−1 in NCPe on spatial scales of 10–100 km. A region-wide median NCPe of 40–50 mg C m−2 d−1 is often prevalent in the Gulf, while blooms attain peak values of 400 mg C m−2 d−1 for a few days. The spatio-temporal variability of NCPe in this region, though conditioned by seasonality, is dominated by events lasting a few days, which if integrated, lead to large inter-annual variability in the annual carbon budget. This study is a step toward achieving synoptic and time-dependent estimates of oceanic productivity and NCP from satellite data.

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