Temporal Resolution Satellite Data Research Articles

Cross-Attention-Based High Spatial-Temporal Resolution Fusion of Sentinel-2 and Sentinel-3 Data for Ocean Water Quality Assessment

Current marine research that leverages remote sensing data urgently requires gridded data of high spatial and temporal resolution. However, such high-quality data is often lacking due to the inherent physical and technical constraints of sensors. A necessary trade-off therefore exists between spatial, temporal, and spectral resolution in satellite remote sensing technology: increasing spatial resolution often reduces the coverage area, thereby diminishing temporal resolution. This manuscript introduces an innovative remote sensing image fusion algorithm that combines Sentinel-2 (high spatial resolution) and Sentinel-3 (relatively high spectral and temporal resolution) satellite data. The algorithm, based on a cross-attention mechanism and referred to as the Cross-Attention Spatio-Temporal Spectral Fusion (CASTSF) model, accounts for variations in spectral channels, spatial resolution, and temporal phase among different sensor images. The proposed method enables the fusion of atmospherically corrected ocean remote sensing reflectance products (Level 2 OSR), yielding high-resolution spatial data at 10 m resolution with a temporal frequency of 1–2 days. Subsequently, the algorithm generates chlorophyll-a concentration remote sensing products characterized by enhanced spatial and temporal fidelity. A comparative analysis against existing chlorophyll-a concentration products demonstrates the robustness and effectiveness of the proposed approach, highlighting its potential for advancing remote sensing applications.

Ecosystems threatened by intensified drought with divergent vulnerability

Global climate change is projected to increase the severity and duration of droughts across many regions of the world, which could push ecosystems across their tipping points. To protect the most vulnerable ecosystems, it is critical to understand if ecosystems are likely to collapse rapidly or have a certain buffer capacity with aggravating drought (as characterized by their onset, duration and severity). However, such ecosystem vulnerability has been rarely characterized. Here, we present a novel, multi-faceted approach to quantify the ecosystem vulnerability and associated impacts of drought characteristics across different ecosystems at the continental-scale in Europe using high spatial and temporal resolution satellite data. We observed substantially different vulnerabilities across ecosystems where the vegetation damage increases with earlier, longer and more intense droughts. In particular, irrigated croplands are under prominently high risk when facing intensified droughts with the shortest delay in response to drought (0.67 times that of other ecosystems) and a fast increase in damage to drought (1.27 times that of other ecosystems). Mixed forests have a prominently low vulnerability in terms of a notably slow increase in damage to drought (0.67 times that of other ecosystems) and a relatively long response time (longer than over half ecosystems). Moreover, the vulnerability of most ecosystems largely increases with increasing drought severity. Consequently, vegetation tends to succumb more quickly to intensified droughts. Our multi-faceted approach to ecosystem vulnerability characterization indicates that under future intensified droughts in the 21st century, the functioning of a vast range of ecosystems will be threatened.

Linkage of Strong Intraseasonal Events of the East Asian Winter Monsoon to the Tropical Convections over the Western Pacific

The East Asian winter monsoon (EAWM) is the most important climate system for transporting Arctic cold air to the tropics in boreal winter. Rapid intensification of the EAWM, such as a cold surge, can lead to increased tropical convection over the western Pacific, but the possible effects from the intraseasonal variation of EAWM is unclear. Using high temporal and spatial resolution satellite data, including Outgoing Longwave Radiation (OLR) and Tropical Rainfall Measuring Mission (TRMM) precipitation, we show that strong intraseasonal EAWM events are associated with increased tropical convection over the western Pacific for about 6–8 days. Our statistical analysis shows that the lifetime of a strong intraseasonal EAWM event is about 2 weeks, with the beginning, peak, and ending phases occurring at days −6, 0, and 6, respectively. During days 0 to 8, increased convection is observed over the western tropical Pacific, due to the anomalous convergence associated with the strengthened northerly winds over the South China Sea. Over land, increased precipitation is observed over Vietnam, northwestern Kalimantan, and the southern Philippines. In addition, the East Asian local Hadley circulation is strengthened during these days, in association with the enhanced tropical convection.

Preliminary Application of Space-Based Remote Sensing and Geospatial Technology for Investigation on the Geo-Environmental Consequences of Cyclone Aila 2009 in the NW 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.

Monitoring coastal water flow dynamics using sub-daily high-resolution SkySat satellite and UAV-based imagery

Sub-daily tracking of dynamic features and events using high spatial resolution satellite imagery has only recently become possible, with advanced observational capabilities now available through tasking of satellite constellations. Here, we provide a first of its kind demonstration of using sub-daily 0.50 m resolution SkySat imagery to track coastal water flows, combining these data with object-based detection and a machine-learning approach to map the extent and concentration of two dye plumes. Coincident high-frequency unmanned aerial vehicle (UAV) imagery was also employed for quantitative modeling of dye concentration and evaluation of the sub-daily satellite-based dye tracking. Our results show that sub-daily SkySat imagery can track dye plume extent with low omission (8.73–16.05%) and commission errors (0.32–2.77%) and model dye concentration (coefficient of determination = 0.73; root mean square error = 28.68 ppb) with the assistance of high-frequency UAV data. The results also demonstrate the capabilities of using UAV imagery for scaling between field data and satellite imagery for tracking coastal water flow dynamics. This research has implications for monitoring of water flows and nutrient or pollution exchange, and it also demonstrates the capabilities of higher temporal resolution satellite data for delivering further insights into dynamic processes of coastal systems.

A case study of a severe hailstorm in Mendoza, Argentina, during the RELAMPAGO-CACTI field campaign

Storms in central Argentina are known for producing large and severe hail. Environmental conditions and strong interaction with topography in this region provide unique conditions for the initiation and intensification of severe storms. The RELAMPAGO (Remote sensing of Electrification, Lightning, And Mesoscale/microscale Processes with Adaptive Ground Observations) and CACTI (Clouds, Aerosols, and Complex Terrain Interactions) field campaigns deployed between October 2018 and April 2019 over west-central Argentina have collected unprecedented periods of intensive observation in the region. During the Intense Observation Period (IOP) number 10 (November 26, 2018), 4 cm diameter hail was reported over wide areas of the province of Mendoza. This was the first time a severe hailstorm was measured with multi-platform observations in Mendoza as the IOP deployment included multiple atmospheric soundings, observations from the Center for Severe Weather Research (CSWR) Doppler on Wheel radars (DOWs) and high temporal resolution satellite data from the Advanced Baseline Imager (ABI) and the Geostationary Lightning Mapper (GLM) onboard GOES-16. High-temporal frequency soundings and surface observations collected prior to the convection initiation are analyzed, allowing a detailed description of the pre-convective environment. The establishment of the upslope flow and the role of boundary layer processes that resulted in the development of the severe hailstorm are also analyzed. In addition, several GOES-16 ABI and GLM derived products are investigated, in order to test their ability to forecast convective initiation and provide hail proxies in the region.

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.

Assessment of Multisource Remote Sensing Image Fusion by several dissimilarity Methods

Recently, advancements in remote sensing technology have made it easier to obtain various temporal and spatial resolution satellite data. Remote sensing techniques can be a useful tool to detect vegetation and soil conditions, monitor crop diseases and natural disaster prevention, etc. Although the same scene taken by different sensors belong to the same ground object, the information that they offered are redundant, complementary and collaborative due to the spatial, spectral and temporal resolution are different. The method of image fusion can integrate an image with rich details and valuable information from multi-source remote sensing images, which aim to obtain more comprehensive and precise observations of the ground object. By using aspects from multi-source image fusion, this review presents the current status and future trends in remote sensing image fusion. First, different image properties and their applications are presented for remote sensing datasets at home and abroad. Second, a general summary and inductive analysis of the challenging difficulty of different types of multisource image fusion methods is conducted. Third, experiments are tested on eight different methodological approaches, and experimental results demonstrate that GSA method is the best alternative in terms of obtaining high spatial resolution and retaining the spectral information.

Displacement Analysis of Geothermal Field Based on PSInSAR And SOM Clustering Algorithms A Case Study of Brady Field, Nevada—USA

The availability of free and high temporal resolution satellite data and advanced SAR techniques allows us to analyze ground displacement cost-effectively. Our aim was to properly define subsidence and uplift areas to delineate a geothermal field and perform time-series analysis to identify temporal trends. A Persistent Scatterer Interferometry (PSI) algorithm was used to estimate vertical displacement in the Brady geothermal field located in Nevada by analyzing 70 Sentinel-1A Synthetic-Aperture Radar (SAR) images, between January 2017 and December 2019. To classify zones affected by displacement, an unsupervised Self-Organizing Map (SOM) algorithm was applied to classify points based on their behavior in time, and those clusters were used to determine subsidence, uplift, and stable regions automatically. Finally, time-series analysis was applied to the clustered data to understand the inflection dates. The maximum subsidence is –19 mm/yr with an average value of –6 mm/yr within the geothermal field. The maximum uplift is 14 mm/yr with an average value of 4 mm/yr within the geothermal field. The uplift occurred on the NE of the field, where the injection wells are located. On the other hand, subsidence is concentrated on the SW of the field where the production wells are located. The coupling of the PSInSAR and the SOM algorithms was shown to be effective in analyzing the direction and pattern of the displacements observed in the field.

20 years of landscape dynamics within the world's largest multinational network of protected areas

The world's largest network of protected areas (PAs), Natura 2000, is facing different types of disturbances and pressures, however, it still remains unclear the impact they have on the conservation status of sites. Remote sensing big data analysis and satellite data were used to quantify dynamics of the dominant land cover category, landscape structure, and vegetation greenness, as indicators of conservation status, as well as drivers of change, between 2000 and 2018, within each Natura 2000 protected area, across the entire European Union. Our results show that the majority of sites are ‘favourable’ on natural land cover range and areas, but heading to ‘unfavourable’ status regarding the landscape structure, while an alarmingly high number of sites experience both net loss of the dominant land cover type and degradation of landscape structure, labeled consequently as having an ‘unfavourable’ conservation status. The results also showed high differences between biogeographic regions and countries, with an extremely low number of sites suffering dramatic changes to other dominant land cover types, mainly among grasslands. Mediterranean region showed a high net forest increase (mainly extension of existing forests) as well as insignificant changes of landscape fragmentation and diversity (predominantly in Greece, Spain and, Italy), related to the intensification of forest planting, and to a high loss of grassland area and cropland (land abandonment). High net forest gain, but increasing landscape fragmentation, was observed in the Continental region (mainly in Bulgaria, Poland, Germany and, Italy), suggesting that forest developed in numerous new smaller patches, due to the development of invasive species through natural processes (agricultural land abandonment) and natural system modifications. The Alpine region also showed a low positive net forest change, but with significant dynamics of gains due to reducing of agricultural activities and human disturbances, and losses due to natural catastrophes such as natural fires, storms, avalanches or landslides. Contrarily, the Boreal and Atlantic regions recorded considerable net forest loss during the analyzed period, caused mainly by the occurrence of natural catastrophes, natural biotic and abiotic processes (erosion, parasitism, diseases), and the increase of forestry clearance. These results show the high potential of moderate resolution remote sensing big data in assessing PAs, even more as higher spatial and temporal resolution satellite data are continuously emerging.

A Google Earth Engine Tool to Investigate, Map and Monitor Volcanic Thermal Anomalies at Global Scale by Means of Mid-High Spatial Resolution Satellite Data

Several satellite-based systems have been developed over the years to study and monitor thermal volcanic activity. Most of them use high temporal resolution satellite data, provided by sensors like the Moderate Resolution Imaging Spectroradiometer (MODIS) that if on the one hand guarantee a continuous monitoring of active volcanic areas on the other hand are less suited to map thermal anomalies, and to provide accurate information about their features. The Multispectral Instrument (MSI) and the Operational Land Imager (OLI), respectively, onboard the Sentinel-2 and Landsat-8 satellites, providing Short-Wave Infrared (SWIR) data at 20 m (MSI) and 30 m (OLI) spatial resolution, may make an important contribution in this area. In this work, we present the first Google Earth Engine (GEE) App to investigate, map and monitor volcanic thermal anomalies at global scale, integrating Landsat-8 OLI and Sentinel-2 MSI observations. This open tool, which implements the Normalized Hot spot Indices (NHI) algorithm, enables the analysis of more than 1400 active volcanoes, with very low processing times, thanks to the high GEE computational resources. Performance and limitations of the tool, such as its next upgrades, aiming at increasing the user-friendly experience and extending the temporal range of data analyses, are analyzed and discussed.

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.

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.

How the variety of satellite remote sensing data over volcanoes can assist hazard monitoring efforts: The 2011 eruption of Nabro volcano

Satellite remote sensing is becoming an increasingly essential component of volcano monitoring, especially at little-known and remote volcanoes where in-situ measurements are unavailable and/or impractical. Moreover the synoptic view captured by satellite imagery over volcanoes can benefit hazard monitoring efforts. By monitoring, we mean both following the changing styles and intensities of the eruption once it has started, as well as nowcasting and eventually forecasting the areas potentially threatened by hazardous phenomena in an eruptive scenario. Here we demonstrate how the diversity of remote sensing data over volcanoes and the mutual interconnection between satellite observations and numerical simulations can improve lava flow hazard monitoring in response to effusive eruption. Time-averaged discharge rates (TADRs) obtained from low spatial/high temporal resolution satellite data (e.g. MODIS, SEVIRI) are complemented, compared and fine-tuned with detailed maps of volcanic deposits with the aim of constraining the conversion from satellite-derived radiant heat flux to TADR. Maps of volcanic deposits include the time-varying evolution of lava flow emplacement derived from multispectral satellite data (e.g. EO-ALI, Landsat, Sentinel-2, ASTER), as well as the flow thickness variations, retrieved from the topographic monitoring by using stereo or tri-stereo optical data (e.g. Pléiades, PlanetScope, ASTER). Finally, satellite-derived parameters are used as input and validation tags for the numerical modelling of lava flow scenarios. Our strategy is applied to the first historic eruption of Nabro volcano (Eritrea), occurred in June 2011. This eruptive event was characterized by the extraordinary quantity of SO2 emitted into the atmosphere and the extent of the long lava flows, which had a significant impact on the inhabitants of the Eritrea-Ethiopia border region despite the low population density. Because of its remote position, little was known about this eruption regarding the quantity of volcanic deposits and the timing and mechanisms of their emplacement. We found that the total volume of deposits, calculated from differences of digital elevation models (DEMs), is about 580 × 106 m3, of which about 336 × 106 m3 is the volume of the main lava flow that advanced eastward beyond the caldera. Multi-spectral satellite observations indicate that the main lava flow had reached its maximum extent (~16 km) within about 4 days of the eruption onset on midnight 12 June. Lava flow simulations driven by satellite-derived parameters allow building an understanding of the advance rate and maximum extent of the main lava flow showing that it is likely to have reached 10.5 km in one day with a maximum speed of ~0.44 km/h.

Trends in Remote Sensing Accuracy Assessment Approaches in the Context of Natural Resources

The utility of land cover maps for natural resources management relies on knowing the uncertainty associated with each map. The continuous advances typical of remote sensing, including the increasing availability of higher spatial and temporal resolution satellite data and data analysis capabilities, have created both opportunities and challenges for improving the application of accuracy assessment. There are well established accuracy assessment methods, but their underlying assumptions have not changed much in the last couple decades. Consequently, revisiting how map error and accuracy have been performed and reported over the last two decades is timely, to highlight areas where there is scope for better utilization of emerging opportunities. We conducted a quantitative literature review on accuracy assessment practices for mapping via remote sensing classification methods, in both terrestrial and marine environments. We performed a structured search for land and benthic cover mapping, limiting our search to journals within the remote sensing field, and papers published between 1998–2017. After an initial screening process, we assembled a database of 282 papers, and extracted and standardized information on various components of their reported accuracy assessments. We discovered that only 56% of the papers explicitly included an error matrix, and a very limited number (14%) reported overall accuracy with confidence intervals. The use of kappa continues to be standard practice, being reported in 50.4% of the literature published on or after 2012. Reference datasets used for validation were collected using a probability sampling design in 54% of the papers. For approximately 11% of the studies, the sampling design used could not be determined. No association was found between classification complexity (i.e. number of classes) and measured accuracy, independent from the size of the study area. Overall, only 32% of papers included an accuracy assessment that could be considered reproducible; that is, they included a probability-based sampling scheme to collect the reference dataset, a complete error matrix, and provided sufficient characterization of the reference datasets and sampling unit. Our findings indicate that considerable work remains to identify and adopt more statistically rigorous accuracy assessment practices to achieve transparent and comparable land and benthic cover maps.

Developing and applying a multi-purpose land cover validation dataset for Africa

The production of global land cover products has accelerated significantly over the past decade thanks to the availability of higher spatial and temporal resolution satellite data and increased computation capabilities. The quality of these products should be assessed according to internationally promoted requirements e.g., by the Committee on Earth Observation Systems-Working Group on Calibration and Validation (CEOS-WGCV) and updated accuracy should be provided with new releases (Stage-4 validation). Providing updated accuracies for the yearly maps would require considerable effort for collecting validation datasets. To save time and effort on data collection, validation datasets should be designed to suit multiple map assessments and should be easily adjustable for a timely validation of new releases of land cover products. This study introduces a validation dataset aimed to facilitate multi-purpose assessments and its applicability is demonstrated in three different assessments focusing on validating discrete and fractional land cover maps, map comparison and user-oriented map assessments. The validation dataset is generated primarily to validate the newly released 100 m spatial resolution land cover product from the Copernicus Global Land Service (CGLS-LC100). The validation dataset includes 3617 sample sites in Africa based on stratified sampling. Each site corresponds to an area of 100 m × 100 m. Within site, reference land cover information was collected at 100 subpixels of 10 m × 10 m allowing the land cover information to be suitable for different resolution and legends. Firstly, using this dataset, we validated both the discrete and fractional land cover layers of the CGLS-LC100 product. The CGLS-LC100 discrete map was found to have an overall accuracy of 74.6 ± 2.1% (at 95% confidence level) for the African continent. Fraction cover products were found to have mean absolute errors of 9.3, 8.8, 16.2, and 6.5% for trees, shrubs, herbaceous vegetation and bare ground, respectively. Secondly, for user-oriented map assessment, we assessed the accuracy of the CGLS-LC100 map from four user groups' perspectives (forest monitoring, crop monitoring, biodiversity and climate modelling). Overall accuracies for these perspectives vary between 73.7% ± 2.1% and 93.5% ± 0.9%, depending on the land cover classes of interest. Thirdly, for map comparison, we assessed the accuracy of the Globeland30-2010 map at 30 m spatial resolution. Using the subpixel level validation data, we derived 15,252 sample pixels at 30 m spatial resolution. Based on these sample pixels, the overall accuracy of the Globeland30-2010 map was found to be 66.6 ± 2.4% for Africa. The three assessments exemplify the applicability of multi-purpose validation datasets which are recommended to increase map validation efficiency and consistency. Assessments of subsequent yearly maps can be conducted by augmenting or updating the dataset with sample sites in identified change areas.

Increasing extent and intensity of thunderstorms observed over the Congo Basin from 1982 to 2016

Recent studies found a long-term drought and resulting declines in vegetation greenness and canopy water content over the Congo Basin, the second largest rainforest in the world after the Amazon. Since most precipitation in tropical latitudes stems from convection, this paper analyzed 35 years of high-resolution (8 km spatial resolution and 3 h temporal resolution) satellite data to document the long-term trends in the number, size and intensity of thunderstorms activity over the Congo Basin during April, May, and June (AMJ) for the period 1982–2016. Changes in the magnitude and area of cold cloud top brightness temperatures (Tb) at different thresholds were used as a proxy to quantify the number and size of individual thunderstorms at different intensities. We found that the areal extent and intensity of thunderstorms increased over the past 35 years, particularly over Northern Congo Basin, and these changes are consistent with other satellite datasets. Combined with a reanalysis dataset, our work suggests that thunderstorms over the Congo Basin are becoming taller and wider, and likely resulting in a moister (drier) upper (lower) troposphere.

Spring green-up date derived from GIMMS3g and SPOT-VGT NDVI of winter wheat cropland in the North China Plain

Satellite temporal resolution affects the fitting accuracy of vegetation growth curves. However, there are few studies that evaluate the impact of different satellite data (including temporal resolution and time series change) on spring green-up date (GUD) extraction. In this study, four GUD algorithms and two different temporal resolution satellite data (GIMMS3g during 1982–2013 and SPOT-VGT during 1999–2013) were used to investigate winter wheat GUD in the North China Plain. Four GUD algorithms included logistic-NDVI (normalized difference vegetation index), logistic-cumNDVI (cumulative NDVI), polynomial-NDVI and polynomial-cumNDVI algorithms. All algorithms and data were first regrouped into eight controlled cases. At site scale, we evaluated the performance of each case using correlation coefficient (r), bias and root mean square error (RMSE). We further compared spatial patterns and inter-annual trends of GUD inferred from different algorithms, and then analyzed the difference between GIMMS3g-based GUD and SPOT-VGT-based GUD. Our results showed that all satellite-based GUD were correlated with observations with r ranging from 0.32 to 0.57 (p<0.01). SPOT-VGT-based GUD generally had better correlations with observed GUD than those of GIMMS3g. Spatially, SPOT-VGT-based GUD performed more reasonable spatial distributions. Inter-annual regional averaged satellite-based GUD presented overall advanced trends during 1982–2013 (0.3–2.0days/decade) while delayed trends were observed during 1999–2013 (1.7–7.4days/decade for GIMMS3g and 3.8–7.4days/decade for SPOT-VGT). However, their significance levels were highly dependent on the data and algorithms used. Our findings suggest cautions on previous results of inter-annual variability of phenology from a single data/method.

Application of geo-spatial technology in schistosomiasis modelling in Africa: a review.

Schistosomiasis continues to impact socio-economic development negatively in sub-Saharan Africa. The advent of spatial technologies, including geographic information systems (GIS), Earth observation (EO) and global positioning systems (GPS) assist modelling efforts. However, there is increasing concern regarding the accuracy and precision of the current spatial models. This paper reviews the literature regarding the progress and challenges in the development and utilization of spatial technology with special reference to predictive models for schistosomiasis in Africa. Peer-reviewed papers identified through a PubMed search using the following keywords: geo-spatial analysis OR remote sensing OR modelling OR earth observation OR geographic information systems OR prediction OR mapping AND schistosomiasis AND Africa were used. Statistical uncertainty, low spatial and temporal resolution satellite data and poor validation were identified as some of the factors that compromise the precision and accuracy of the existing predictive models. The need for high spatial resolution of remote sensing data in conjunction with ancillary data viz. ground-measured climatic and environmental information, local presence/absence intermediate host snail surveys as well as prevalence and intensity of human infection for model calibration and validation are discussed. The importance of a multidisciplinary approach in developing robust, spatial data capturing, modelling techniques and products applicable in epidemiology is highlighted.