High Spatial Resolution Satellite Data Research Articles

Fusion of spectral and topographic features for land use mapping using a machine learning framework for a regional scale application.

This study investigated the dynamics of land use and land cover (LULC) modelling, mapping, and assessment in the Kegalle District ofSri Lanka, where policy decision-making is crucial in agricultural development where LULC temporal datasets are not readily available.Employing remotely sensed datasets and machine learning algorithms, the work presented here aims to compare the accuracy of three classification approaches in mapping LULC categories across the time in the study area primarily using the Google Earth Engine (GEE). Three classifiers namely random forest (RF), support vector machines (SVM), and classification and regression trees (CART) were used in LULC modelling, mapping, and change analysis. Different combinations of input features were investigated to improve classification performance. Developed models were optimised using the grid search cross-validation (CV) hyperparameter optimisation approach. It was revealed that the RF classifier constantly outstrips SVM and CART in terms of accuracy measures, highlighting its reliability in classifying the LULC. Land cover changes were examined for two periods, from 2001 to 2013 and 2013 to 2022, implying major alterations such as the conversion of rubber and coconut areas to built-up areas and barren lands. For suitable classification with higher accuracy, the study suggests utilising high spatial resolution satellite data, advanced feature selection approaches, and a combination of several spatial and spatial-temporal data sources. The study demonstrated practical applications of derived temporal LULC datasets for land management practices in agricultural development activities in developing nations.

IMPROVING THE ASSESSMENT OF THE ECOLOGICAL STATE OF SURFACE WATERS BY SATELLITE DATA

The article presents the results of research into the ecological state of surface waters using newly developed scientific approaches based on the use of high spatial resolution satellite data. The systematization of all available indicators and the development of new methodological approaches significantly improves the existing methodology for determining the ecological state of water bodies, which is the goal of research. In the studies of water bodies within the city of Kyiv, Obolon Bay and Lake Verbne, the optimal set of such indicators as spectral indices was determined, which made it possible to comprehensively assess the state of water bodies: NDWI - for distinguishing vegetation and water bodies, GCI - to assess the extent of blue-green algae, NDTI - to determine turbidity, IO - to determine the presence of soluble iron in water, NDSI - to assess the extent of flooding. According to the NDSI, NDWI indices and a combination of the red and infrared channels of the Sentinel-2 L2A satellite, the flooding of the floodplain of the Irpin River was traced, caused by the destruction of a hydraulic structure near the village of Kozarovichi. Wave abrasion of the shores was studied on the example of the Kremenchuk Reservoir near the villages of Pronozivka and Mozoliivka using Landsat4 (1984) and Landsat8 (2016) satellite images. The study of the reshaping of the coastline near the village of Tsybli in the Kyiv region used the method of determining spatio-temporal changes of the coastline as a result of its erosion. The assessment of spatio-temporal changes of the coastline should be carried out taking into account the water levels on the dates of the measurements. The conducted studies established that all the identified evaluation indicators can be grouped as biological, hydro morphological, and physicochemical, and it can be concluded that methodical approaches to assessing the ecological state of surface waters using satellite data are based on established cause-and-effect relationships of processes affecting water objects.

Photoperiod alone does not explain the variations of leaf senescence onset across Europe

The onset of autumnal leaf senescence (TLSO) plays a crucial role in understanding and modeling how plants prepare for winter dormancy, but it has remained under-explored. Previous research, based on limited in situ observations, have proposed that the year-to-year invariant photoperiod is the primary trigger of TLSO, indicating potential insensitivity to climate change. This study verifies this hypothesis using high spatial resolution satellite data and ground-based observations throughout the European continent. Surprisingly, our findings reveal a widespread delay in TLSO during 2001–2019 (approximately 73 %, significant in 19 % of the continent, p < 0.05), closely aligned with delayed leaf senescence end (TLSD) as reported in previous studies. Moreover, as the delaying trend was larger for TLSO than for TLSD, it is apparent that climate change does not only postpone the onset of the leaf senescence process but also shortens its duration across the continent. This finding indicates that a reduced duration for plants to get ready for dormancy could potentially undermine their resilience to cold and other winter stresses. Contrary to the hypothesized photoperiodic control, TLSO exhibited opposing inter-annual temporal trends along temperature gradients, despite similar daylengths. Ground based observations failed to support photoperiodic influence either, highlighting the substantial spatial variation in TLSO responses to climate change. Intriguingly, complex climate-TLSO interactions were observed, with radiation dominating TLSO changes in 37.0 % of the study area. This analysis underscores the necessity of considering multiple variables to comprehend the patterns of TLSO at continental and vegetation scales.

Validation of satellite-derived water-leaving reflectance in contrasted French coastal waters based on HYPERNETS field measurements

Since 2021, two autonomous HYPERNETS (A new hyperspectral radiometer integrated in automated networks of water and land bidirectional reflectance measurements for satellite validation) stations are operated in contrasted French coastal waters: one in the center of an optically complex coastal lagoon and one at the mouth of a highly turbid estuary. These stations perform predefined sequences of above-water hyperspectral radiometric measurements following a strict viewing geometry. The data recorded by the ®HYPSTAR radiometer is automatically transmitted to servers for quality-controls then computation of the water-leaving reflectance signal. Numerous matchups were identified with high (Sentinel2-MSI and Landsat8/9-OLI) and medium (Sentinel3-OLCI and Aqua-MODIS) spatial resolution satellite data and are analyzed to assess the performance of different atmospheric correction algorithms (Sen2Cor, ACOLITE, POLYMER, iCOR, C2RCC, GRS, BPAC, NIR-SWIR). Considering the specifications of each site (i.e., spatial and temporal variations of water optical properties), optimized matchup protocols are first established to guaranty high quality comparisons between satellite products and field measurements. The matchup results highlight the failure and limits of several atmospheric correction algorithms in complex/turbid coastal waters. The importance of accurate sun glint corrections in low to moderately-turbid waters (with the good performances of POLYMER, C2RCC and GRS processors, e.g., errors (MAPE) lower than 25% in the green spectral region) is also shown while the use of dark targets and spectral fitting to estimate the aerosol contributions is proved to be the most accurate method in the case of turbid waters (with Sen2Cor and ACOLITE errors (MAPE) lower than 20% in the visible and near-infrared spectral regions).

Cascading Machine Learning to Monitor Volcanic Thermal Activity Using Orbital Infrared Data: From Detection to Quantitative Evaluation

Several satellite missions are currently available to provide thermal infrared data at different spatial resolutions and revisit time. Furthermore, new missions are planned thus enabling to keep a nearly continuous ‘eye’ on thermal volcanic activity around the world. This massive volume of data requires the development of artificial intelligence (AI) techniques for the automatic processing of satellite data in order to extract significant information about volcano conditions in a short time. Here, we propose a robust machine learning approach to accurately detect, recognize and quantify high-temperature volcanic features using Sentinel-2 MultiSpectral Instrument (S2-MSI) imagery. We use the entire archive of high spatial resolution satellite data containing more than 6000 S2-MSI scenes at ten different volcanoes around the world. Combining a ‘top-down’ cascading architecture, two different machine learning models, a scene classifier (SqueezeNet) and a pixel-based segmentation model (random forest), we achieved a very high accuracy, namely 95%. These results show that the cascading approach can be applied in near-real time to any available satellite image, providing a full description of the scene, with an important contribution to the monitoring, mapping and characterization of volcanic thermal features.

Estimating of heavy metal concentration in agricultural soils from hyperspectral satellite sensor imagery: Considering the sources and migration pathways of pollutants

The evolution of hyperspectral remote sensing and artificial intelligence technologies has led to a surge in their application for predicting Soil Heavy Metal Concentrations (SHMC). Nevertheless, the preponderance of existing research within this sphere centers around data procured from ground-based and airborne hyperspectral sources. Studies that employ satellite-based methodologies typically rely on medium spatial resolution hyperspectral or multispectral satellite data. The application of high spatial and spectral resolution satellite data, such as that obtained from GaoFen-5 (GF-5), remains conspicuously underexplored. Furthermore, the impact of geographical environmental factors (GEFs) on the accuracy of predictions has been infrequently considered. In the context of this backdrop, the present study introduces stacking models designed to estimate SHMC. This approach integrates reflectance spectral features (SFs) derived from GF-5 hyperspectral imagery and GEFs, including topography and pollution sources. The results demonstrate a notable improvement in the predictive accuracy of SHMC using our Stacking model, as compared to single models. The incorporation of GEFs into the method results in a varying degree of reduction in the Root Mean Square Error (RMSE), along with an enhancement in the R2 on the training set. The predictive performance improvement is most prominent for Cd and As, with the RMSE decreasing by 52% and 48%, respectively. Notably, apart from Pb, there is an improvement in performance for all elements within the test set. This study confirms the effectiveness of integrating GEFs into SHMC prediction models to enhance accuracy. Applying this technique to predict soil pollution at a regional scale and to demarcate heavily polluted areas can yield satisfactory results. In the future, we plan to apply this technique to other research areas or datasets to expand its universality. Furthermore, we aim to delve more deeply into the potential of GEFs to enhance the predictive capacity of SHMC.

A comparison of moderate and high spatial resolution satellite data for modeling gross primary production and transpiration of native prairie, alfalfa, and winter wheat

Although agroecosystems have a significant potential to offset carbon dioxide (CO2), the amount of CO2 captured can vary significantly depending on management practices. Accurate estimation of gross primary production (GPP) and transpiration (T) of agroecosystems at the field scale are essential for the study of food security and water resource management. To date, the carbon and water fluxes data products for commercial agroecosystems are limited, mostly at the moderate spatial resolution (MSR, hundreds of meters), which cannot be used to assess the temporal dynamics of GPP and T at the field scale. This study used the vegetation photosynthesis model (VPM) and vegetation transpiration model (VTM) to estimate field-level daily GPP (GPPVPM) and T (TVTM), respectively, in native prairie, alfalfa (Medicago sativa L.), and winter wheat (Triticum aestivum L.) in central Oklahoma, USA. We evaluated the reliability and advantages of vegetation indices (enhanced vegetation index, EVI and land surface water index, LSWI) in monitoring the land surface phenology using moderate spatial resolution data from Moderate Resolution Imaging Spectroradiometer (MODIS) and high spatial resolution (HSR, tens of meters) data from Landsat and Sentinel-2. The accuracy of GPPVPM and TVTM estimates at different spatial scales was evaluated using GPP (GPPEC) and evapotranspiration (ETEC) from the eddy flux tower sites, respectively. Results demonstrate the capacity of VPM and VTM to estimate the field-level carbon and water flux dynamics and their responses to weather conditions. The use of HSR vegetation indices helped to address certain challenges faced by MSR indices, especially in capturing the crop phenology in smaller areas with conservation measures or disturbances. The findings highlight the importance of using HSR GPP estimates to reduce uncertainty in quantifying CO2 fluxes for croplands and grasslands. The findings also demonstrate the ability of the models to track field-level vegetation phenology, carbon uptake, and water use in agroecosystems under different management practices.

Mapping sugarcane residue burnt areas in smallholder farming systems using machine learning approaches

Satellite remote sensing methods have been proven to quantify the burnt areas resulting from different fire activities reliably. However, they were reported to perform poorly to identify crop residue burnt areas, particularly in smallholder systems due to lack of frequent and high spatial resolution satellite data. In this study, we used Harmonized Landsat Sentinel-2 (HLS) observations and evaluated two machine learning classifiers (i.e., Support Vector Machines (SVM) and Artificial Neural Networks (ANN)) to map sugarcane burnt areas for the 2019–20 season in a smallholder farming region in Thailand.Results showed that both classifiers performed well in identifying the spatial patterns of sugarcane burnt areas in the region. The ANN outperformed SVM at both pixel and regional scales. At pixel level, ANN accuracy was 93.4 % while SVM's best-performing Polynomial kernel accuracy was 82.7 %. The ANN estimated average percent burnt area (51.1 %) in the region was closer to reported value (48.7 %) by Thailand's Office of Cane and Sugar Board (OCSB), compared to the SVM estimate (62.9 %). The total estimated burnt areas by ANN and SVM (315 and 418 thousand ha, respectively) deviated more from OCSB's area (240 thousand ha) compared to percent burnt area. However, area estimates from classifiers had significantly better accuracy than the estimates of MODIS burnt products.Overall, this study demonstrated that HLS observations provided required spectral information to build promising models to map burnt areas in smallholder systems with higher accuracy than global products. Our mapping algorithm using the ANN classifier showed the potential to monitor sugarcane burnt areas reliably, and contribute to the successful implementation of regulatory policies in Thailand.

Kuala Lumpur Green Space Fragmentation Index Analysis and Green Corridors Suggestion using Geographic Information System

More than half of the world’s population lives in urban areas, and the United Nations has projected that nearly all future global population growth will occur in urban areas. Urbanization has caused rapid industrial, transportation and residential development at the expense of environmental deterioration. Activities involving large-scale land clearances are conducted to satisfy human needs for expansion. The clearances have caused green area fragmentation and patches, especially in the rapidly developing urban areas. This study is aimed at detecting the current coverage of green space in the metropolitan city of Kuala Lumpur (KL), the capital city of Malaysia and monitor its fragmentation using a very high spatial resolution satellite data. Green cover which includes trees, grass, and shrubs of KL was first extracted from the SPOT 6 data using Object Oriented method which can classified the green covers. The fragmented green spaces in KL were analyzed using a range of fragmentation indices namely Number of Patches, Edge Density, Class Area, Total Landscape Area, Mean Patch Size and Mean Shape Index using the Patch Analyst tool embedded in the ArcGIS software version 10.0 to study the patterns of green patches. The connectivity of the green patches was studied and proposed to overcome the fragmentation problems. Kuala Lumpur (KL) was chosen as the case study as it is a rapidly developing city with a significant loss of green space. The urban green spaces were more fragmented as there were more built-up areas. Results of the green cover extraction reveal that the KL city centre had the lowest percentage of green cover (2.09 %) area to the urban area compared to the other five zones (Sentul Manjalara 5.03 %, Wangsa Maju Maluri 6.09 %, Damansara Penchala 11.22 %, Bukit Jalil Seputeh 7.07 % and Bandar Tun Razak Sg Besi 7.42 %). Since KL’s urban growth predominantly consists of compact building blocks, the highest land use was believed to be for commercial purposes. Around 66% of industrial and commercial activities are concentrated in the KL city centre, resulting in a diverse green space structure and a growing gap between patches in the city centre zone. The green patches were then connected to create green corridors in the city centre. Several parameters were involved in the analysis, including area, distance, types of land use (green areas and place of interest for the origin and destination), slopes under 7% value (topology with flat surfaces) and road networks. The origin and destination points were analyzed using the network analysis method in Quantum GIS to produce routes with the shortest path algorithm. Results showed that six routes were suitable for designing green corridors in the KL city centre area connecting green spaces and places of interest. All six routes were analyzed every 400 meters using Google Earth Street View to see the surrounding facilities. The Green Corridor Route 6 was the most appropriate for people, as it included parks and places of interest for city dwellers. The green corridor could then be promoted as a walkable area to minimize carbon emissions in the urban area, reducing the urban heat island phenomenon and increasing green connectivity for a better urban life.

Reconstruct the AMSR-E/2 thin ice thickness algorithm to create a long-term time series of sea-ice production in Antarctic coastal polynyas

This study presented an extended time series of ice production in Antarctic coastal polynyas for 20 years of 2002–21 using high spatial resolution satellite data from passive microwave sensors: the Advanced Microwave Scanning Radiometer for EOS (AMSR-E) and the Advanced Microwave Scanning Radiometer 2 (AMSR2). For this, we reconstructed the previously presented thin ice thickness algorithms and re-estimated ice production by replacing atmospheric input data for the heat flux calculations with the newly released ECMWF Reanalysis v5 (ERA5). The consistency of ice production between AMSR-E and AMSR2, whose operation periods do not overlap, was confirmed from comparisons with ice production by the Special Sensor Microwave/Imager (SSM/I) and the Special Sensor Microwave Imager Sounder (SSMIS) with relatively coarse spatial resolution. The open ocean area, which cannot be detected from the thin ice thickness algorithm, was defined using sea-ice concentration (SIC) from AMSR-E and AMSR2. The satellite derived SIC has been suggested to be underestimated in the new thin ice (polynya) area. The effect of the underestimation on the ice production estimation was evaluated. The underestimation of SIC from three major algorithms was also investigated from comparisons with the thin ice thickness presented in this study. The coastal polynya dataset covering 20 years with the higher spatial resolution presented in this study is vital for climate-change-related studies.

Features of the joint use of visible-range satellite and satellite radar data for monitoring arctic icebergs based on data from Franz Josef Land area

Given the availability of data, in order to monitor icebergs in the vast Arctic region, it is necessary to ensure an optimal ratio of the satellite radar data cost (with a wide swath and high spatial resolution) and the reliability of the results obtained. The aim of the study was to evaluate the effectiveness of using only non-commercial satellite information for monitoring icebergs. To achieve this goal, an iceberg detection technique was applied based on statistical criteria of searching for gradient zones in analyzing two-dimensional fields of satellite images. As a source of information, we used high spatial resolution satellite data obtained from non – commercial satellites Sentinel-1 (radar data) and Landsat-8 (visible spectral range data) with a pixel size of 40 × 20 and 15 m, respectively.Through comparison with the data of the visible spectral range of ultra-high resolution, the error was estimated of measuring the size of icebergs using the visible range equipment of the Landsat-8 satellite and the radar of the Sentinel-1 satellite. Based on the analysis of satellite data of the visible spectral range and radar data, the spatial dimensions of icebergs formed by the outlet glaciers of Franz Josef Land n 2020–2022 were estimated. The materials were collected for the period August-September, corresponding to the conditions of minimum ice extent in the Franz Josef Land area and sufficient natural illumination for using the data of the visible spectral range in the Arctic. In total, about 100 satellite images were analyzed. The sources of error in the satellite measurements are discussed, as well as the reasons for the impossibility in some cases to identify Franz Josef Land icebergs on radar images. It is shown that the largest contribution to the iceberg runoff of Franz Josef Land is made by outflow glaciers located on Georg Land, Wilczek Land, Gall, Salisbury and Champa islands. According to the results of the study, it is concluded that radar data with a pixel size of 40 m allow one to obtain essentially reliable information on the spatial distribution of icebergs in the water area. However, it is not possible to obtain a reliable function of the distribution of icebergs by size since most of the icebergs in the Franz Josef Land water area have small and medium sizes and are rarely identified in images of such spatial resolution.

Forest status assessment in China with SDG indicators based on high-resolution satellite data

ABSTRACT To assess the status and change trend of forest in China, an indicator framework was developed using SDG sub-indicators. In this paper, we propose an improved methodology and a set of workflows for calculating SDG indicators. The main modification include the use of moderate and high spatial resolution satellite data, as well as state-of-the-art machine learning techniques for forest cover classification and estimation of forest above-ground biomass (AGB). This research employs GF-1 and GF-2 data with enhanced texture information to map forest cover, while time series Landsat data is used to estimate forest AGB across the whole territory of China. The study calculate two SDG sub-indicators: SDG15.1.1 for forest area and SDG15.2.1 for sustainable forest management. The evaluation results showed that the total forest area in China was approximately 219 million hectares at the end of 2021, accounting for about 23.51% of the land area. The average annual forest AGB from 2015 to 2021 was estimated to be 105.01Mg/ha, and the overall trend of forest AGB change in China was positive, albeit with some spatial differences.

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.

Evaluation of the nitrogen oxide emission inventory with TROPOMI observations

To compensate for the emissions missed or underestimated in the national bottom-up emission inventories, we apply the high spatial resolution satellite data from TROPOspheric Monitoring Instrument (TROPOMI) to estimate the top-down nitrogen oxide (NOx) emissions in regional scales. The NOx chemical lifetime is derived based on ground-based measurements of ozone photolysis rate, ultra-violet (UV) index, and temperature. For five designated regions of western Taiwan, the derived lifetime is about 1–2 h in summer and 2–4 h in winter. The retrieved 2021 annual emissions for regions near two major pollution sources, Taichung thermal power plant and Mailiao Industrial Zone, are comparable with the emission from the Continuous Emission Monitoring System (CEMS), with a difference of 6% and −12%, respectively. After validating the data and methods, the NOx emissions for five regions of western Taiwan are derived and applied to evaluate the bottom-up inventories. For northern and southern Taiwan, the top-down emissions agree well with emission inventories. The top-down emissions are 12%, 23%, and 16% higher than emission inventories for north-central, central, and south-central Taiwan, respectively. This indicates that the bottom-up inventories are underestimated from north-central to south-central Taiwan, which may be associated with the uncertainties from traffic sources. Given the various complex pollution sources, deriving NOx emissions from space allows us to acquire a better understanding of emissions on urban scales and improve the bottom-up emission inventories.

Using Sentinel-1, Sentinel-2, and Planet satellite data to map field-level tillage practices in smallholder systems.

Remote sensing can be used to map tillage practices at large spatial and temporal scales. However, detecting such management practices in smallholder systems is challenging given that the size of fields is smaller than historical readily-available satellite imagery. In this study we used newer, higher-resolution satellite data from Sentinel-1, Sentinel-2, and Planet to map tillage practices in the Eastern Indo-Gangetic Plains in India. We specifically tested the classification performance of single sensor and multiple sensor random forest models, and the impact of spatial, temporal, or spectral resolution on classification accuracy. We found that when considering a single sensor, the model that used Planet imagery (3 m) had the highest classification accuracy (86.55%) while the model that used Sentinel-1 data (10 m) had the lowest classification accuracy (62.28%). When considering sensor combinations, the model that used data from all three sensors achieved the highest classification accuracy (87.71%), though this model was not statistically different from the Planet only model when considering 95% confidence intervals from bootstrap analyses. We also found that high levels of accuracy could be achieved by only using imagery from the sowing period. Considering the impact of spatial, temporal, and spectral resolution on classification accuracy, we found that improved spatial resolution from Planet contributed the most to improved classification accuracy. Overall, it is possible to use readily-available, high spatial resolution satellite data to map tillage practices of smallholder farms, even in heterogeneous systems with small field sizes.

Mapping of aeolian deposits of an industrial site in the arid region using the TIR bands of ASTER and study of physicochemical characters and stabilization of sand erosion

This study describes the spectral emissive character of silicate and carbonate minerals of aeolian deposits and maps the deposits and sand encroachments that occurred in and around the site 5/6 of Qatar Fertilizer Company (QAFCO), Qatar using thermal infrared (TIR) bands of ASTER. The results of studies show that the quartz and unaltered silicates have spectral features between 8.12 and 9.27 µm, and the calcite and dolomite have emissivity minima near 11.4 and 11.2 µm, respectively. The mapping of deposits, dunes, and carbonate formations using the bands, and quartz index (QI), and carbonate index (CI) displayed their occurrence, distribution, and direction of sand movement from NW to SE. The sand encroachment was mapped using high spatial resolution satellite data of WorldView-2. The study of physicochemical characteristics of field samples showed the occurrence of sand grains up to 99.81% and the XRD and geochemical analyses represented the presence of quartz, calcite, dolomite, albite, and halite minerals in the deposits. In addition, the bacterial strains isolated from the samples indicated high urease activity leading to precipitation of carbonate minerals via microbially induced calcite precipitation (MICP) processes, and demonstrated high potential for utilization for sand stabilization of the QAFCO site.

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.

Evaluation Of Machine Learning Classification Methods For Rice Detection Using Earth Observation Data: Case Of Senegal

Agriculture is considered one of the most vulnerable sectors to climate change. In addition to rainfed agriculture, irrigated crops such as rice have been developed in recent decades along the Senegal River. This new crop requires reliable information and monitoring systems. Remote sensing data have proven to be very useful for mapping and monitoring rice fields. In this study, a rice classification system based on machine learning to recognize and categorize rice is proposed. Physical interpretations of rice with other land cover classes in relation to the spectral signature should identify the optimal periods for mapping rice plots using three machine learning methods including Support Vector Machine (SVM), Random Forest (RF), and Classification and Regression Trees (CART). The database is composed of field data collected by GPS and high spatial resolution (10 to 30 m) satellite data acquired between January and May 2018. The analysis of the spectral signature of different land cover show that the ability to differentiate rice from other classes depends on the level of rice development. The results show the efficiency of the three classification algorithms with overall accuracies and Kappa coefficients for SVM (96.2%, 94.5%), for CART (97.6%, 96.5%) and for RF (98% 97.1%) respectively. Unmixing analysis was made to verify the classification and compare the accuracy of these three algorithms according to their performance.

Maize Yield Estimation in Intercropped Smallholder Fields Using Satellite Data in Southern Malawi

Satellite data provide high potential for estimating crop yield, which is crucial to understanding determinants of yield gaps and therefore improving food production, particularly in sub-Saharan Africa (SSA) regions. However, accurate assessment of crop yield and its spatial variation is challenging in SSA because of small field sizes, widespread intercropping practices, and inadequate field observations. This study aimed to firstly evaluate the potential of satellite data in estimating maize yield in intercropped smallholder fields and secondly assess how factors such as satellite data spatial and temporal resolution, within-field variability, field size, harvest index and intercropping practices affect model performance. Having collected in situ data (field size, yield, intercrops occurrence, harvest index, and leaf area index), statistical models were developed to predict yield from multisource satellite data (i.e., Sentinel-2 and PlanetScope). Model accuracy and residuals were assessed against the above factors. Among 150 investigated fields, our study found that nearly half were intercropped with legumes, with an average plot size of 0.17 ha. Despite mixed pixels resulting from intercrops, the model based on the Sentinel-2 red-edge vegetation index (VI) could estimate maize yield with moderate accuracy (R2 = 0.51, nRMSE = 19.95%), while higher spatial resolution satellite data (e.g., PlanetScope 3 m) only showed a marginal improvement in performance (R2 = 0.52, nRMSE = 19.95%). Seasonal peak VI values provided better accuracy than seasonal mean/median VI, suggesting peak VI values may capture the signal of the dominant upper maize foliage layer and may be less impacted by understory intercrop effects. Still, intercropping practice reduces model accuracy, as the model residuals are lower in fields with pure maize (1 t/ha) compared to intercropped fields (1.3 t/ha). This study provides a reference for operational maize yield estimation in intercropped smallholder fields, using free satellite data in Southern Malawi. It also highlights the difficulties of estimating yield in intercropped fields using satellite imagery, and stresses the importance of sufficient satellite observations for monitoring intercropping practices in SSA.