High Spatial Resolution Satellite Data Research Articles

USE OF HIGH RESOLUTION SATELLITE DATA (IKONOS IMAGERY) FOR LOGISTIC SUPPORT

Many activities must be done before doing the 3D seismic survey. 3D seismic work needs accurate and precise information to minimize the cost and negative impact, and or dispute with the local people. The data needs information on road for moving instrument, bridge, river and Ancient River. Data of landuse and landcover, building, public facility, demographic and administration are required for estimating compensation. The use of remote sensing is a good alternative to achieve this objective. The 3D seismic survey will involve an amount of labors in the fields where the survey is conducted. During the survey a number of equipments will be planted and connected by wire. Therefore, the seismic survey will directly make contracts with local people and theirs proprietaries for temporary use or damage compensation. 3D seismic survey can be classified as a complex activities, apart from a large number of labors that are needed to carry explosive and equipments which will be used to comply the survey, social problem must be solved first. Especially when the study area is conducted in a well developed area with dense population, cultivation and other utilities. Considering the previous facts, a well plan operation in the office that is supported by detail and up to date surface map is actually needed. High accuracy and up to date surface map was considered as a critical basic information for the 3D seismic planning. Remote sensing technology with the high spatial resolution satellite data can acquire the latest earth surface data with one meter resolution in relatively short time. Ikonos satellite imagery is one of the high spatial resolution satellite imagery can be chosen and utilized as the main source for providing the surface map.

Red Tide Detection Method for HY−1D Coastal Zone Imager Based on U−Net Convolutional Neural Network

Existing red tide detection methods have mainly been developed for ocean color satellite data with low spatial resolution and high spectral resolution. Higher spatial resolution satellite images are required for red tides with fine scale and scattered distribution. However, red tide detection methods for ocean color satellite data cannot be directly applied to medium–high spatial resolution satellite data owing to the shortage of red tide responsive bands. Therefore, a new red tide detection method for medium–high spatial resolution satellite data is required. This study proposes the red tide detection U−Net (RDU−Net) model by considering the HY−1D Coastal Zone Imager (HY−1D CZI) as an example. RDU−Net employs the channel attention model to derive the inter−channel relationship of red tide information in order to reduce the influence of the marine environment on red tide detection. Moreover, the boundary and binary cross entropy (BBCE) loss function, which incorporates the boundary loss, is used to obtain clear and accurate red tide boundaries. In addition, a multi−feature dataset including the HY−1D CZI radiance and Normalized Difference Vegetation Index (NDVI) is employed to enhance the spectral difference between red tides and seawater and thus improve the accuracy of red tide detection. Experimental results show that RDU−Net can detect red tides accurately without a precedent threshold. Precision and Recall of 87.47% and 86.62%, respectively, are achieved, while the F1−score and Kappa are 0.87. Compared with the existing method, the F1−score is improved by 0.07–0.21. Furthermore, the proposed method can detect red tides accurately even under interference from clouds and fog, and it shows good performance in the case of red tide edges and scattered distribution areas. Moreover, it shows good applicability and can be successfully applied to other satellite data with high spatial resolution and large bandwidth, such as GF−1 Wide Field of View 2 (WFV2) images.

Method for monitoring environmental flows with high spatial and temporal resolution satellite data.

Monitoring environmental flows is crucial to maintaining the function and stability of river and lake ecosystems. However, current methods for monitoring environmental flows are expensive and ground based, and the accuracy of the results needs to be verified to evaluate the environmental flows. This evaluation is hampered by the problem of data shortages, such as hydrological and ecological data. In this study, a method for monitoring environmental flows is proposed using multisource high spatial and temporal resolution satellite data. A case study in the Yongding River Basin demonstrates that the method is feasible for monitoring the environmental flows of rivers in semiarid and arid areas. The results show that the environmental flows and months with large water discharges and shortages in the three control sections of the Yongding River Basin were different. Moreover, the downstream river width rarely met the environmental water demand, achieving this only for one period from 2017 to 2019 according to the three typical types of years (an average water year, a dry year, and an extremely dry year). This method and the results have applications in planning environmental flows and could improve the comprehensive management of the ecological environment in river basins.

Preliminary Application of Space-based Remote Sensing and Geospatial Technology for Investigation on the Geo-environmental Consequences of Cyclone Aila 2009 in the Bangladesh

Climate change phenomena have become a great concern in the context of global change and increased frequency and magnitude of natural disasters throughout the world in the recent time. Natural disasters like floods, cyclones, storm surges etc. often cause significant losses of life, large-scale economic and social impacts, and considerable environmental damages. This paper dealt with study on the overall methodological development to investigate the consequences of climate change extremities particularly, the after effects of cyclone using Remote Sensing (RS) and Geographic Information System (GIS) technology. In this connection, individual functional components have been investigated, tested and verified. Time series multi-sensor satellite data particularly of Landsat, World view (from Google origin) have been utilized to infer information on the consequences of Cyclone Aila 2009 hitting part of southwestern Bangladesh as a test case under the present study. Information retrieval mechanism utilized is based on specially designed methodological framework using satellite-based RS technology along with GIS. Developed technical approach consists of a three-fold components – (i) Establishment of input foundation layer with high spatial details to support characterization, recognition and identification of important surface features using high resolution satellite data; (ii) Effective operational procedure to process, analyze, interpret and finally to archive the retrieved information; (iii) Functional computations were made using spatial modeler language (SML) programming environment under ERDAS Imagine image processing software. Satellite image processing, analysis operation together with image-based spectral characterization of surface features under different stressing conditions etc. have been exercised to derive useful surface information. Specially designed geospatial database has been established in GIS using ArcInfo professional software. Varieties of geospatial data from diverse sources have been incorporated categorically as column-based attributes in GIS. Dual spatial data layers have been generated in GIS for two different dates representing the pre-cyclone and post-cyclone time sequences utilizing appropriate high spatial resolution satellite images. Finally, a geospatial image-based combinational technique has been utilized employing high temporal and moderate spatial resolution time series satellite data with low temporal and high spatial resolution satellite data. Such an operation results in an improved spatial and temporal resolution of output products enabling capture of dynamics of surface features in the spatiotemporal domain providing more precision and details in the output. This study has been supplemented with necessary Ground Position System (GPS)-based ground truthing, selected field data collection and field-based group discussions outcomes.

A comparative analysis of different pixel and object-based classification algorithms using multi-source high spatial resolution satellite data for LULC mapping

A comparative analysis of different pixel and object-based classification algorithms using multi-source high spatial resolution satellite data for LULC mapping

Application of Drone and WorldView-4 Satellite Data in Mapping and Monitoring Grazing Land Cover and Pasture Quality: Pre- and Post-Flooding

Frequent flooding worldwide, especially in grazing environments, requires mapping and monitoring grazing land cover and pasture quality to support land management. Although drones, satellite, and machine learning technologies can be used to map land cover and pasture quality, there have been limited applications in grazing land environments, especially monitoring land cover change and pasture quality pre- and post-flood events. The use of high spatial resolution drone and satellite data such as WorldView-4 can provide effective mapping and monitoring in grazing land environments. The aim of this study was to utilize high spatial resolution drone and WorldView-4 satellite data to map and monitor grazing land cover change and pasture quality pre-and post-flooding. The grazing land cover was mapped pre-flooding using WorldView-4 satellite data and post-flooding using real-time drone data. The machine learning Random Forest classification algorithm was used to delineate land cover types and the normalized difference vegetation index (NDVI) was used to monitor pasture quality. This study found a seven percent (7%) increase in pasture cover and a one hundred percent (100%) increase in pasture quality post-flooding. The drone and WorldView-4 satellite data were useful to detect grazing land cover change at a finer scale.

Изучение острова тепла Москвы по данным космических снимков и мезоклиматического моделирования

Using the example of the Moscow region, a detailed comparison of the thermal regime of a highly urbanized territory was carried out according to in-situ data and different-scale satellite images. For the summer season, fundamental differences in the characteristics of the spatial variability of LST and air temperature in the daytime are revealed. It is shown that the relationship between the fields of these quantities is practically absent, primarily for urbanized territories, for which the effect of the heat island in the LST field is pronounced, and is practically not expressed in the air temperature field. However, for the winter season, some cases were identified with a more pronounced relationship between these values. The results obtained are independently confirmed by the results of numerical experiments with the COSMO model and the TERRA_URB parameterization with a 1 km grid step. At the same time, the prospects of using high spatial resolution images for model verification are shown: they made it possible to draw conclusions about the choice of the optimal configuration of model settings. For both summer and winter seasons, a comparison was made of high spatial resolution satellite data from Landsat with low spatial resolution data from MODIS. It is shown that, in spite of the generally good agreement between the two types of data, in some cases there are significant discrepancies between them due to differences in the values of the emissivity used and other factors that require more detailed analysis in further studies.

A High Spatial Resolution Satellite Remote Sensing Time Series Analysis of Cape Bounty, Melville Island, Nunavut (2004–2018)

Changes in vegetation have been observed in areas of the Arctic due to changing climate. This study examines a normalized difference vegetation index (NDVI) time series (2004–2018) of high spatial resolution satellite data (i.e., IKONOS, WorldView-2, WorldView-3) to determine if vegetation abundance has changed over the Cape Bounty Arctic Watershed Observatory, Melville Island, Nunavut. Image data were corrected to top-of-atmosphere reflectance and normalized for time series analysis using the pseudo-invariant feature (PIF) method. Percent vegetation cover measurements and indices derived from local climate data (growing degree days base 5 °C; GDD5) were used to contextualize NDVI trends in different vegetation types and within active layer detachments (ALDs). NDVI showed similar patterns within the different vegetation types and across the ALDs. There was no significant change in NDVI nor in GDD5 over time. However, there were statistically significant (p < 0.05) relationships between the GDD5 and NDVI for all vegetation types. Using field measurements with high spatial resolution remote sensing data helps link changes in NDVI with changes to vegetation and earth surface processes. The challenges of integrating high spatial resolution satellite data from different sensors in a time series analysis are also discussed.

High Resolution Optical Remote Sensing Satellites Challenges and Techniques

High spatial resolution satellite data is essential to identify small objects and extract minute details of the terrain. This data is provided by many satellites and being used in numerous applications. The realization of high resolution satellite is a challenging task. Significant complexity lies in the realization of high spatial resolution camera starting from material selection, high stiffness-low mass opto-mechanical system design, detector selection to high speed camera electronics design. The mass and size of camera increase with the improvement in spatial resolution. Alternate methods such as Step and Stare, and time delay integration methods can be used to achieve high signal to noise ratio images. The performance of satellite bus subsystems like structure, the data handling, and storage system, the data transmission system, attitude sensors and actuators should also be improved to achieve good quality data. The data handling system has to be designed to handle high data rate and data volume. The capacity of the data storage system has to be increased to cater the high data volume storage requirement. The data transmission system needs to be sufficiently capable to transmit the high volume imaging data to the ground station. As the spatial resolution improves, the spacecraft pointing accuracy and drift rate requirements become stringent. Improved attitude sensors and high capacity actuators are essential to meet these stringent requirements. Generally, high resolution cameras are combined with high speed electronics to handle high data rates which need more power. In this paper, we discuss various techniques being employed to obtain high resolution data with reasonable SNR. The challenges involved and the improvements required in various spacecraft subsystems to support these high resolution cameras are presented. Techniques employed by different space agencies to obtain high spatial resolution images are also discussed. The characteristics of high resolution satellites are also tabulated and compared.

Understanding Fields by Remote Sensing: Soil Zoning and Property Mapping

Precision agriculture aims to optimize field management to increase agronomic yield, reduce environmental impact, and potentially foster soil carbon sequestration. In 2015, the Copernicus mission, with Sentinel-1 and -2, opened a new era by providing freely available high spatial and temporal resolution satellite data. Since then, many studies have been conducted to understand, monitor and improve agricultural systems. This paper presents results from the SolumScire project, focusing on the prediction of the spatial distribution of soil zones and topsoil properties, such as pH, soil organic matter (SOM) and clay content in agricultural fields through random forest algorithms. For this purpose, samples from 120 fields were investigated. The zoning and soil property prediction has an accuracy greater than 90%. This is supported by a high agreement of the derived zones with farmer’s observations. The trained models revealed a prediction accuracy of 94%, 89% and 96% for pH, SOM and clay content, respectively. The obtained models for soil properties can support precision field management, the improvement of soil sampling and fertilization strategies, and eventually the management of soil properties such as SOM.

Mapping multiple tree species classes using a hierarchical procedure with optimized node variables and thresholds based on high spatial resolution satellite data

ABSTRACT Tree species distribution mapping using remotely sensed data has long been an important research area. However, previous studies have rarely established a comprehensive and efficient classification procedure to obtain an accurate result. This study proposes a hierarchical classification procedure with optimized node variables and thresholds to classify tree species based on high spatial resolution satellite imagery. A classification tree structure consisting of parent and leaf nodes was designed based on user experience and visual interpretation. Spectral, textural, and topographic variables were extracted based on pre-segmented images. The random forest algorithm was used to select variables by ranking the impact of all variables. An iterating approach was used to optimize variables and thresholds in each loop by comprehensively considering the test accuracy and selected variables. The threshold range for each selected variable was determined by a statistical method considering the mean and standard deviation for two subnode types at each parent node. Classification of tree species was implemented using the optimized variables and thresholds. The results show that (1) the proposed procedure can accurately map the tree species distribution, with an overall accuracy of over 86% for both training and test stages; (2) critical variables for each class can be identified using this proposed procedure, and optimal variables of most tree plantation nodes are spectra related; (3) the overall forest classification accuracy using the proposed method is more accurate than that using the random forest (RF) and classification and regression tree (CART). The proposed approach provides results with 3.21% and 7.56% higher overall land cover classification accuracy and 4.68% and 10.28% higher overall forest classification accuracy than RF and CART, respectively.

In search of floating algae and other organisms in global oceans and lakes

Surface floating macroalgae, microalgae, and other marine and freshwater organisms have been reported in many specific regions around the globe. However, it is technically challenging to identify similar occurrences, or other types of floating organisms or materials, within the vast global oceans and lakes. In this study, we address this challenge through combining global-scale, 375-m resolution false-colored Red-Green-Blue (FRGB) imagery from the Visible Infrared Imaging Radiometer Suite (VIIRS) in NOAA's online Ocean Color Viewer (OCView) for visual inspection and data from several other satellite sensors for spectral diagnostics. In the FRGB imagery, the near-infrared (NIR) band (862 nm) is used as the green channel, which is sensitive to floating algae and organisms/materials on the water surface. Visual inspection of the daily FRGB VIIRS imagery from January 2018 to October 2019 reveals the appearance of various slicks with different colors in many ocean regions and lakes. Combined with spectral diagnostics of the quasi-concurrent Sentinel-3A/3B Ocean and Land Colour Instrument (OLCI) and other higher spatial resolution satellite data as well as knowledge of local oceanography/limnology, most of these elongated or diffuse image features can be identified as Ulva, Sargassum, Noctiluca, Trichodesmium, Microcystis, oil slicks, or pumice rafts. Some of these identified features are found in regions where such occurrences have never been reported before. Some features are of unknown type as they have not been reported previously from remote sensing. In such cases, contemporaneous scientific literature and news reports as well as spectral diagnostics allow for educated inferences to be made. One example is from surface features in the northern Gulf of Maine, Bay of Fundy, and Southwest of Nova Scotia between early June and early July of 2019. Spectral shapes of the FRGB image features indicate transparent materials lacking pigments. Knowledge of local fisheries oceanography and frequent news reports all suggest that these unknown image features may be aggregations or blooms of sea jellies and, to a lesser extent, salps. Another example is from the Great Salt Lake, where image features in the south arm of the lake are speculated to be caused by aggregations of brine shrimp eggs. Once confirmed from field sightings, these findings may represent a milestone in satellite remote sensing because previously remote sensing could only be used to infer oceanographic environments conducive to sea jellies or shrimps. In addition to the findings presented here, the approach in this study may serve as a template to discover various known and unknown types of floating algae and organisms/materials as well as to routinely monitor and track their distributions and movements.

Red tide detection using deep learning and high-spatial resolution optical satellite imagery

ABSTRACT Red tide is one of the most devastating phenomena that have impacted coastal environments and fishery on a local scale in the worldwide seas. Satellite imagery can provide a synoptic view of the red tides over the wide region. Previous methods to detect the red tides have not sufficiently performed and revealed limitations in the study region. This study developed a red tide detection scheme based on the deep-learning method by using Landsat-8 OLI data during unprecedented explosive red tide events in the southern coastal region of the Korean Peninsula in 2013. To develop and validate the red tide detection of this study, we conducted cruise campaigns to obtain in-situ water sampling for red tide species and its density, chlorophyll-a concentration, suspended particulate matter (SPM), and spectrum data of red tides as a target object and other reference data using a spectroradiometer in the coastal regions from 2013 to 2015. The seawater shows different spectral shape by its components such as red tide, chlorophyll-a concentration, and SPM. Spectral characteristics of the red tides demonstrated bimodal peaks over visible wavelengths regardless of the species of the red tide. Considering such spectral characteristics, the red tide detection algorithm was constructed by the deep-learning method with Landsat-8 OLI level-2 reflectance values and in-situ red tide observations. The validation results of the algorithm as compared with the map of in-situ red tide measurements showed a high probability of detection values greater than 0.7. These works have made it possible to monitor the red tides with high-spatial resolution satellite data and provide a tool to minimize a lot of socioecological impacts from red tide.

Pattern recognition of forest canopy using the airborne hyperspectral data and multi-bands high spatial resolution satellite sensor worldview-2 data. A results comparison and accuracy estimation

Pattern recognition of forest surface from remote sensing data: using the airborne hyperspectral data and using multi-bands high spatial resolution satellite sensor WorldView‑2 data are investigated. The early proposed method and standard QDA method for calculations were used. A comparison of calculations results were conducted. A recognition calculation accuracy range for airborne and satellite remote sensing data for three forest surface fragments for different created data bases for recognition system has been assessed. Some opportunities of automatic data preparing of created system were displayed. Some special features of pattern recognition of forest surfaces from hyperspectral airborne data and from multi-bands high spatial resolution satellite data were discussed.

Where are the avalanches? Rapid SPOT6 satellite data acquisition to map an extreme avalanche period over the Swiss Alps

Abstract. Accurate and timely information on avalanche occurrence is key for avalanche warning, crisis management and avalanche documentation. Today such information is mainly available at isolated locations provided by observers in the field. The achieved reliability, considering accuracy, completeness and reliability of the reported avalanche events, is limited. In this study we present the spatially continuous mapping of a large avalanche period in January 2018 covering the majority of the Swiss Alps (12 500 km2). We tested different satellite sensors available for rapid mapping during the first avalanche period. Based on these experiences, we tasked SPOT6 and SPOT7 for data acquisition to cover the second, much larger avalanche period. We manually mapped the outlines of 18 737 individual avalanche events, applying image enhancement techniques to analyze regions in the shade as well as in brightly illuminated ones. The resulting dataset of mapped avalanche outlines, having unique completeness and reliability, is evaluated to produce maps of avalanche occurrence and avalanche size. We validated the mapping of the avalanche outlines using photographs acquired from helicopters just after the avalanche period. This study demonstrates the applicability of optical, very high spatial resolution satellite data to map an exceptional avalanche period with very high completeness, accuracy and reliability over a large region. The generated avalanche data are of great value in validating avalanche bulletins, in completing existing avalanche databases and for research applications by enabling meaningful statistics on important avalanche parameters.

Retrieval and Validation of Cloud Top Temperature from the Geostationary Satellite INSAT-3D

Investigation of cloud top temperature (CTT) and its diurnal variation is highly reliant on high spatial and temporal resolution satellite data, which is lacking over the Indian region. An algorithm has been developed for detection of clouds and retrieval of CTT from the geostationary satellite INSAT-3D. These retrievals are validated (inter-compared) with collocated in-situ (satellite) measurements with specific intent to generate climate-quality data. The cloud detection algorithm employs nine different tests, in accordance with solar illumination, satellite angle and surface type conditions to generate pixel-resolution cloud mask. Validation of cloud mask with cloud-aerosol lidar with orthogonal polarization (CALIOP) shows that probability of detection (POD) of cloudy (clear) sky is 81% (85%), with 83% hit rate. The algorithm is also implemented on similar channels of moderate resolution imaging spectroradiometer (MODIS), which provides 88% (83%) POD of cloudy (clear) sky, with 86% hit rate. CTT retrieval is done at the pixel level, for all cloud pixels, by employing appropriate methods for various types of clouds. Comparison of CTT with radiosonde and cloud-aerosol lidar and infrared pathfinder satellite observations (CALIPSO) shows mean absolute error less than 3%. The study also examines sensitivity of retrieved CTT to the cloud classification scheme and retrieval criteria. Validation results and their close agreements with those of similar satellites demonstrate the reliability of the retrieved product for climate studies.

Red Tide Detection Based on High Spatial Resolution Broad Band Satellite Data: A Case Study of GF-1

Liu, R.-J.; Zhang, J.; Cui, B.-G.; Ma, Y.; Song, P.J., and An, J.-B., 2019. Red tide detection based on high spatial resolution broad band satellite data: A case study of GF-1. In: Jung, H.-S.; Lee, S.; Ryu, J.-H., and Cui, T. (eds.), Advances in Remote Sensing and Geoscience Information Systems of Coastal Environments. Journal of Coastal Research, Special Issue No. 90, pp. 120-128. Coconut Creek (Florida), ISSN 0749-0208.Traditional red tide remote sensing detection methods are based on ocean color satellite data with high spectral resolution (spectral band width < 20 nm), but low spatial resolution. Clearly, fine-scale remote-sensing monitoring of red tide requires high spatial resolution satellite data. Yet, satellite data with high spatial resolution often lack high spectral resolution, with spectral band width > 50 nm. Moreover, systematic research has yet to demonstrate effective detection of red tide using high spatial resolution, but low spectral resolution, satellite data. In this paper, high spatial resolution (16 m), but low spectral resolution, data from the satellite, GF-1, are analyzed to determine if effective fine-scale detection of red tide is possible. The spectral response characteristics of red tide in GF-1 data are analyzed using a 2014 red tide event that occurred in Guangdong Province, China. It was found that, despite broad spectral band widths, GF-1 WFV imaging spectrometer data still exhibits a clear tide detection algorithm for GF-1 WFV data based on this red tide spectral signal. The detection accuracy of red tide for this algorithm is better than 92 %, and the F1-Score value is better than 87 %. Spatial analysis of the Guangdong 2014 red-tide event based on this algorithm showed that the maximum red tide area extracted from GF-1 satellite data is 10 km2, and that the red tide area decreases greatly during the extinction phase. A comparative analysis of MODIS satellite data indicates that, despite the availability of multiple ocean color bands, no ocean color anomaly is detected because of coarse spatial resolution. The developed method was also successfully used to detect red tide occurred in Rizhao, Shandong Province.

A Simple Similarity Index for the Comparison of Remotely Sensed Time Series with Scarce Simultaneous Acquisitions

Emergence of new state-of-the-art technologies has enabled an unprecedented amount of high spatial resolution satellite data having great potential for exploitation of extracted time series for a vast range of applications. Despite the high temporal resolution of time series, the number of real observations of optical data that can be utilized is reduced due to meteorological conditions (such as cloud or haze) prevailing at the time of acquisition. This fact has an effect on the density of the retrieved time series and subsequently on a number of coincidental observations when comparing the similarity of time series from two different data sources for which the simultaneous acquisition date is already scarce. Classical tools for assessing the similarity of such time series can prove to be difficult or even impossible because of a lack of simultaneous observations. In this paper, we propose a simple method in order to circumvent this scarcity issue. In the first step, we rely on an interpolation in order to produce artificial time series on the union of the original acquisition dates. Then, we extend the theory of the correlation coefficient (CC) estimator to these interpolated time series. After validation on synthetic data, this simple approach proved to be extremely efficient on a real case study where Sentinel-2 and PlanetScope NDVI time series on parcels in The Netherlands are compared. Indeed, compared to other methods, it reduced the number of undecided cases while also improving the power of the statistical test on the similarity between both types of time series and the precision of the estimated CC.

Effects of broad bandwidth on the remote sensing of inland waters: Implications for high spatial resolution satellite data applications

High spatial resolution satellite sensors provide opportunities to observe spatial variations of biogeochemical properties of small- and medium-sized inland water bodies. However, high spatial resolution sensors are usually equipped with wider spectral bandwidth (>50 nm) that diminishes the features of the spectrum. Therefore, the effects of the border bandwidth issue need to be evaluated prior to application in aquatic environments. Based on the in situ optical data [remote sensing reflectance (Rrs) and absorption coefficients] and the radiative simulations of hyperspectral remote sensing reflectance and using band specifics of common sensors (e.g., OLCI, VIIRS, MSI, OLI, ETM+ and WFV) as examples, the effects of bandwidth on optical properties of inland waters were analyzed. The results showed the followings. (1) The difference between values at center-wavelength and band-averaged values increased with increasing bandwidth for Rrs and the absorption coefficients. The difference was wavelength-dependent. The difference of Rrs at the visible band was within 0.25% but greater than 0.5% for the spectral bands near 710 nm and 665 nm. (2) The accuracy of the total absorption coefficient derived from QAA-750E, spectral match technique (SMT) and deep neural network (DNN) decreased with increasing bandwidth. The QAA-750E was more sensitive to bandwidth than SMT and DNN. Otherwise, the empirical algorithms for estimating chlorophyll-a (Chla) concentrations were significantly affected by bandwidth. The performance of algorithms for estimating cyanobacterial phycocyanin (PC) and suspended particulate matter (SPM) concentrations changed slightly with a wider bandwidth. Finally, the maximum bandwidth requirement for optical remote sensing in inland waters was proposed. For bandwidth options, it should be within 20 nm for 700–710 nm, ∼30 nm maximum for ∼560 nm and ∼665 nm, 60 nm for ∼620 nm, and ∼80 nm for ∼443 nm and ∼490 nm, respectively. The difference between the Rrs of narrow bands (10–20 nm) and the Rrs of the bands with the recommended bandwidth was within 0.25%. The corresponding bandwidth from MSI and OLI sensors meet this criterion for Chla and SPM. However, the lack of spectral coverage near 700–710 nm may present a challenge to retrieve Chla concentration from OLI images. This study provided helpful theoretical and practical references for the retrieval of inland water parameters by high spatial resolution satellite sensors and its prospective development.

A workflow for Sustainable Development Goals indicators assessment based on high-resolution satellite data

ABSTRACTFor evaluating the progresses towards achieving the Sustainable Development Goals (SDGs), a global indicator framework was developed by the UN Inter-Agency and Expert Group on Sustainable Development Goals Indicators. In this paper, we propose an improved methodology and a set of workflows for calculating SDGs indicators. The main improvements consist of using moderate and high spatial resolution satellite data and state-of-the-art deep learning methodology for land cover classification and for assessing land productivity. Within the European Network for Observing our Changing Planet (ERA-PLANET), three SDGs indicators are calculated. In this research, harmonized Landsat and Sentinel-2 data are analyzed and used for land productivity analysis and yield assessment, as well as Landsat 8, Sentinel-2 and Sentinel-1 time series are utilized for crop mapping. We calculate for the whole territory of Ukraine SDG indicators: 15.1.1 – ‘Forest area as proportion of total land area’; 15.3.1 – ‘Proportion of land that is degraded over total land area’; and 2.4.1 – ‘Proportion of agricultural area under productive and sustainable agriculture’. Workflows for calculating these indicators were implemented in a Virtual Laboratory Platform. We conclude that newly available high-resolution remote sensing products can significantly improve our capacity to assess several SDGs indicators through dedicated workflows.