Hua Su

Fuzhou University · The Academy of Digital China

Satellite remote sensing can detect and predict subsurface temperature and salinity structure within the ocean over large scales. In the era of big ocean data, making full use of multisource satellite observations to accurately detect and predict global subsurface thermohaline structure and advance our understanding of the ocean interior processes...

Subsurface density (SD) is a crucial dynamic environment parameter reflecting a three-dimensional ocean process and stratification, with significant implications for the physical, chemical, and biological processes of the ocean environment. Thus, accurate SD retrieval is essential for studying dynamic processes in the ocean interior. However, compl...

Estimating high-resolution ocean subsurface temperature has great importance for the refined study of ocean climate variability and change. However, the insufficient resolution and accuracy of subsurface temperature data greatly limits our comprehensive understanding of mesoscale and other fine-scale ocean processes. In this study, we integrated mu...

Three-dimensional Ocean temperature and salinity data are the basis for studying ocean dynamic processes and warming. Satellite remote sensing observations on the ocean surface are abundant and full-coverage, while in-situ observations in the ocean interior are very sparse and unevenly distributed. Currently, the remote sensing inversion models o...

The accurate extraction of agricultural parcels from remote sensing images is crucial for advanced agricultural management and monitoring systems. Existing methods primarily emphasize regional accuracy over boundary quality, often resulting in fragmented outputs due to uniform crop types, diverse agricultural practices, and environmental variations...

The recognition of sea ice is of great significance for reflecting climate change and ensuring the safety of ship navigation. Recently, many deep learning based methods have been proposed and applied to segment and recognize sea ice regions. However, the diverse scales of sea ice areas, the zigzag and fine edge contours, and the difficulty in disti...

As a result of global warming, major ocean basins have witnessed an increase in the number of extreme warm events and a decrease in the number of extreme cold events, increasing the number of marine heatwave (MHW) events. Previous quantification of MHW events has been limited to simple single metrics, which can only recognize some characteristics f...

Estimating the ocean mixed layer depth (MLD) is crucial for studying the atmosphere-ocean interaction and global climate change. Satellite observations can accurately estimate the MLD over large scales, effectively overcoming the limitation of sparse in situ observations and reducing uncertainty caused by estimation based on in situ and reanalysis...

Tidal wetlands provide a variety of ecosystem services to coastal communities but suffer severe losses due to anthropogenic activities in the Yangtze River Estuary (YRE). However, the detailed dynamics of tidal wetlands have not been well studied with sufficient spatiotemporal resolution. Here, we proposed a rapid classification method that integra...

Tidal flats in northern China are essential parts of the East Asian-Australasian Flyway, the densest pathway for migratory waterbirds, and are of great ecological and economic importance. They are threatened by human activities and climate change, raising the urgency surrounding tracking the spatiotemporal dynamics of tidal flats. However, there is...

Estuarine wetland vegetation (EWV) serves as a critical indicator of ecosystem health but is increasingly threatened by human activities and climate change. Though remote sensing (RS) data provide a holistic view of vegetation change with sufficient spatial resolution, temporal changes in EWV are still ambiguous because of the twisted signals betwe...

Remote sensing scene classification (RSSC) is essential in Earth observation, with applications in land use, environmental status, urban development, and disaster risk assessment. However, redundant background interference, varying feature scales, and high inter-class similarity in remote sensing images present significant challenges for RSSC. To a...

The recognition of sea ice is of great significance for reflecting climate change and ensuring the safety of ship navigation. Recently, many deep learning based methods have been proposed and applied to segment and recognize sea ice regions. However, the diverse scales of sea ice areas, the zigzag and fine edge contours, and the difficulty in disti...

Effectively exploring the impacts of urban spatial structures on carbon dioxide emissions is important for achieving low-carbon goals. However, most previous studies have examined the impact of urban spatial structure on total carbon emissions based only on polycentricity. Fine-grained studies on subsectoral carbon emissions and other dimensions of...

【https://www.mdpi.com/journal/remotesensing/special_issues/4BKYD16M06】 Special Issue Information Dear Colleagues, Ocean environmental monitoring plays a crutial role in understanding and utilization of the ocean, involving many fields such as ocean’s role in climate change, ocean energy development and protection, ocean transportation, fisheries,...

【https://www.mdpi.com/journal/remotesensing/special_issues/0VTM872XS4】 Special Issue Information Dear Colleagues, In recent decades, anthropogenic greenhouse gas emissions have significantly increased, causing global warming via heat trapped in the Earth’s climate system; this has led to a positive Earth energy imbalance (EEI) and global ocean wa...

Concerning the role of ocean dynamics in climate change, retrieving information about the interior of the ocean is the basis of scientific studies. Given the surface limitation of remote sensing techniques and expenses of in-situ observations, the sparse information of the ocean’s three-dimensional structure has hindered the studies about the globa...

Observing the ocean’s interior is becoming extremely important since recent evidence suggests widespread warming in the subsurface and deeper ocean as a response to the Earth’s Energy Imbalance (EEI) in recent decades. However, the ocean’s interior observations are sparse and insufficient, severely constraining the studies of ocean interior dynamic...

As the most relevant indicator of global warming, the ocean heat content (OHC) change is tightly linked to the Earth's energy imbalance. Therefore, it is vital to study the OHC and heat absorption and redistribution. Here we analyzed the characteristics of global OHC variations based on a previously reconstructed OHC dataset (named OPEN) with four...

Rising sea level caused by global climate change may increase extreme sea level events, flood low-lying coastal areas, change the ecological and hydrological environment of coastal areas, and bring severe challenges to the survival and development of coastal cities. Hong Kong is a typical economically and socially developed coastal area. However, i...

Intertidal vegetation plays an essential role in habitat provision for waterbirds but suffers great losses due to human activities. However, it is challenging in tracking the human-driven loss and degradation of intertidal vegetation due to rapid urbanization in a high temporal resolution. In this study, a methodological framework based on full Lan...

Quantitative analysis on the properties of marine phytoplankton bloom events is helpful to understand the marine ecology, environment, and dynamic processes. In the South China Sea, remote sensing is vulnerable to clouds. Previous studies were mostly conducted with discontinuous observation or remote sensing data, which failed to comprehensively un...

Chlorophyll-a (CHL) concentration is an important proxy of the marine ecological environment and phytoplankton production. Long-term trends in CHL of the South China Sea (SCS) reflect the changes in the ecosystem's productivity and functionality in the regional carbon cycle. In this study, we applied a previously reconstructed 15-a (2005-2019) CHL...

The rapid development of marine ranching in recent years provides a new way of tackling the global food crisis. However, the uncontrolled expansion of coastal aquaculture has raised a series of environmental problems. The fast and accurate detection of raft will facilitate scientific planning and the precise management of coastal aquaculture. A new...

The 2030 Agenda for Sustainable Development provides an ambitious vision for global sustainable development in three dimensions: economic, social and environmental. It has, however, run into major challenges posed by such problems as the lack of data, unbalanced progress, and trade-offs between the Sustainable Development Goals (SDGs). At the same...

Accurate and detailed ocean interior salinity data are fundamental to the studies of hydrological cycle, ocean current, and global climate change. This study adopted multisource satellite-based sea surface parameters and Argo float data for downscaling the subsurface salinity with higher spatial resolution based on the Light Gradient Boosting Machi...

The reconstruction of the ocean’s 3D thermal structure is essential to the study of ocean interior processes and global climate change. Satellite remote sensing technology can collect large-scale, high-resolution ocean observation data, but only at the surface layer. Based on empirical statistical and artificial intelligence models, deep ocean remo...

The coastline plays an important role in indicating the conditions of social-economic development in the coastal zone. In this study, an integrated assessment framework was proposed to address the provincial and county-level spatiotemporal dynamics of continental coastlines from the perspectives of length, position, composition, and anthropogenic u...

Coastal wetlands are of great ecological and economic value but face significant degradation and losses because of human activities. Nevertheless, the changes in spatiotemporal landscape patterns, which have occurred as a result of coastal wetland losses, have not been well documented under the rapid urbanization in coastal zones. In this study, an...

Intertidal wetlands are the transitional zone between terrestrial and marine ecosystems, and they are of ecological and economic importance. However, intertidal wetlands are severely damaged due to natural causes (e.g., climate change and sea-level rise) and anthropogenic causes (e.g., coastal reclamation and excessive tourism development). Therefo...

Subsurface ocean observations are sparse and insufficient, significantly constraining studies of ocean processes. Retrieving high-resolution subsurface dynamic parameters from remote sensing observations using specific inversion models is possible but challenging. This study proposed two kinds of machine learning algorithms, namely, Convolutional N...

Global ocean heat content (OHC) is generally estimated using gridded, model and reanal-ysis data; its change is crucial to understanding climate anomalies and ocean warming phenomena. However, Argo gridded data have short temporal coverage (from 2005 to the present), inhibiting understanding of long-term OHC variabilities at decadal to multidecadal...

Chlorophyll-a (chl-a) is an important parameter of water quality and its concentration can be directly retrieved from satellite observations. The Ocean and Land Color Instrument (OLCI), a new-generation water-color sensor onboard Sentinel-3A and Sentinel-3B, is an excellent tool for marine environmental monitoring. In this study, we introduce a new...

In this study, a new LightGBM method combined with Random Forests algorithm are used to predict the global subsurface temperature and salinity anomalies upper 1,000 m depth based on remote sensing data and Argo float data. The methods employ multisource sea surface parameters (sea surface height anomaly (SSHA), sea surface temperature anomaly (SSTA...

Total suspended matter (TSM) is an important indicator to evaluate water quality, and is also one of the key parameters for ocean color remote sensing inversion. The Ocean and Land Color Instrument (OLCI) is a new generation of ocean water color sensor with well spectral and spatio-temporal resolution. This paper adopted CatBoost, Random Forest and...

Retrieving information concerning the interior of the ocean using satellite remote sensing data has a major impact on studies of ocean dynamic and climate changes; however, the lack of information within the ocean limits such studies about the global ocean. In this paper, an artificial neural network, combined with satellite data and gridded Argo p...

Sea ice distribution is an important indicator of ice conditions and regional climate change in the Bohai Sea (China). In this study, we monitored the spatiotemporal distribution of the Bohai Sea ice in the winter of 2017-2018 by developing sea ice information indexes using 300 m resolution Sentinel-3 Ocean and Land Color Instrument (OLCI) images....

Improving ocean interior observation resolution via satellite remote sensing is essential because of the limitation and sparsity of ocean interior observation. Retrieving the multi-temporal and large-scale thermal structure information of the subsurface ocean on the basis of satellite remote sensing is of great importance in understanding the compl...

Using Landsat-8 OLI images and 296 survey samples in Fujian province，we extracted pure vegetation pixels biased on pixel unmixing models，and divided the samples into coniferous forest，broad-leaved forest and mixed forest，then employed tree height，plantation age and slope as attribute information from pure vegetation samples，and also extracted NDVI，...

Retrieving multi-temporal and large-scale thermohaline structure information of the interior of the global ocean based on surface satellite observations is important for understanding the complex and multidimensional dynamic processes within the ocean. This study proposes a new ensemble learning algorithm, extreme gradient boosting (XGBoost), for r...

In order to satisfy the demand of air-pollution monitoring at better spatial details and dense temporal series for Taiwan Island, we estimated ground-level PM2.5 concentrations with overall regression model and Seasonal optimal regression models using the 100m-resolution AOD retrieved from Chinese GF-1 WFV images. The correlation (R) and RMSE betwe...

Accurate estimation of ocean’s interior salinity information based on surface remote sensing data is quite significant for understanding complex dynamic processes in the ocean. This study adopts two kinds of ensemble learning algorithm, Random Forest (RF) and Gradient Boosting Decision Tree (GBDT) to estimate the subsurface salinity anomaly (SSA) i...

Remote sensing technology has the unique advantages of real-time, fast and spatio-temporal continuity and wide coverage scale. Under the background of global climate deterioration, drought monitoring methods based on remote sensing can provide more real-time, accurate, and stable drought information and better assist scientific decision making than...

The supplementary for the publication 'Subsurface temperature estimation from remote sensing data using a clustering-neural network method' in Remote Sensing of Environment

Urbanization has become one of the most important human activities modifying the Earth’s land surfaces; and its impacts on tropical and subtropical cities (e.g., in South/Southeast Asia) are not fully understood. Colombo; the capital of Sri Lanka; has been urbanized for about 2000 years; due to its strategic position on the east–west sea trade rout...

A classification problem involving multi-class samples is typically divided into a set of two-class sub-problems. The pairwise probabilities produced by the binary classifiers are subsequently combined to generate a final result. However, only the binary classifiers that have been trained with the unknown real class of an unlabeled sample are relev...

Chlorophyll-a (Chl-a) concentration is one of the most important water quality parameters, and can be directly retrieved via remote sensing. It can be used to assess the water eutrophication in coastal waters. In this study, we proposed a random forest (RF) machine learning approach based on MODIS time-series images combining in-situ float data to...

Accurately retrieving and describing subsurface temperature on a large scale can provide valuable information that can be used for subsurface dynamic and variability studies. This study develops a new satellite-based geographically weighted regression (GWR) model to estimate a subsurface temperature anomaly (STA) in the upper 2,000 m of the Indian...

The DisTrad (Disaggregation Procedure for Radiometric Surface Temperature) model shows limited applicability for sub-pixel mapping of thermal remote-sensing images in densely vegetated areas due to the phenomenon of normalized difference vegetation index (NDVI) saturation. In this article, we compared the effect of NDVI and enhanced vegetation inde...

According to the characteristics of domestic medium-resolution remote sensing data, we proposed a new method for retrieving aerosol optical depth (AOD) from optical images by integrating the advantages of dark-pixel and deep-blue algorithms. The method was proved to be effective and suitable for AOD estimation over both low-reflectivity and high-re...

Retrieving the subsurface and deeper ocean (SDO) dynamic parameters from satellite observations is crucial for effectively understanding ocean interior anomalies and dynamic processes, but it is challenging to accurately estimate the subsurface thermal structure over the global scale from sea surface parameters. This study proposes a new approach b...

An index is proposed to measure the maximum intensification rates (MIR) of category 4-5 (the Saffir-Simpson scale) Tropical Cyclone (TC) in the western North Pacific and the variations of MIR are investigated in this paper. MIR is defined as the maximum increase in the sustained-wind-speed per 24-hour period during the lifetime of a TC. The new ind...

Subsurface thermal structure of the global ocean is a key factor that reflects the impact of global climate variability and change. Accurately determining and describing the global subsurface and deeper ocean thermal structure from satellite measurements are becoming even more important for understanding the ocean interior anomaly and dynamic proce...

Land surface temperature (LST) is a key parameter of land surface physical processes on global and regional scales, linking the heat fluxes and interactions between the ground and atmosphere. Based on MODIS 8-day LST products (MOD11A2) from the split-window algorithms, we constructed and obtained the monthly and annual LST dataset of Fujian Provinc...

Ocean heat content (OHC) evolutions calculated from the datasets (WOA, MyOcean, ORAS4, and SODA) was examined at different depth ranges in this study. According to the OHC changes, the subsurface and deeper ocean (SDO, 300-2000 m) heat content rapidly increased over the world's ocean basins during 1998-2013, indicating significant warming in the SD...

Forest is one of the vital renewable resources for sustainable development of renewable resources; it plays an important role in global climate change, water and soil conservation, and carbon cycle in terrestrial ecosystem. Forest biomass, therefore, is now attracting attention worldwide from both scholars and policy makers. Using Landsat8 OLI imag...

Digitizing the land surface temperature (Ts) and surface soil moisture (mv) is essential for developing the intelligent Digital Earth. Here, we developed a two parameter physical-based passive microwave remote sensing model for jointly retrieving Ts and mv using the dual-polarized Tb of Aqua satellite advanced microwave scanning radiometer (AMSR-E)...

Image classification of frozen areas and adjacent sea ice is important for monitoring the evolution of ocean freezing. This paper proposes a novel approach to the Moderate Resolution Imaging Spectroradiometer (MODIS) image classification and estimation of the extent of sea ice in frozen areas during recent global surface warming hiatus. We derived...

Estimating the thermal information in the subsurface and deeper ocean from satellite measurements over large basin-wide scale is important but also challenging. This paper proposes a support vector machine (SVM) method to estimate subsurface temperature anomaly (STA) in the Indian Ocean from a suite of satellite remote sensing measurements includin...

An effective methodology for Bohai Sea ice detection based on gray level co-occurrence matrix (GLCM) texture analysis is proposed using MODIS 250 m imagery. The method determines texture measures for sea ice extraction by analyzing the discrepancy of textural features between sea ice and sea water. Sea ice extent and outer edge are recognized accur...

The Annualized Agricultural Non-point Source (AnnAGNPS) pollution model has been widely used to assess and predict runoff, soil erosion, sediment and nutrient loading with a geographic information system. This article presents a case study of the effect of land-use changes on nonpoint source (NPS) pollution using the AnnAGNPS model in the Xizhi Riv...

To estimate sea ice thickness over a large spatial scale is a challenge. In this paper, we propose a direct approach to effectively estimate sea ice thickness over a large spatial area of the Bohai Sea using EOS MODIS data. It is based on the model of an exponential relation between albedo and thickness of sea ice. Eighteen images of EOS MODIS L1B...