Spaceborne L-Band Synthetic Aperture Radar Data for Geoscientific Analyses in Coastal Land Applications: A Review

The coastal zone offers among the world’s most productive and valuable ecosystems and is experiencing increasing pressure from anthropogenic impacts: human settlements, agriculture, aquaculture, trade, industrial activities, oil and gas exploitation and tourism. Earth observation has great capability to deliver valuable data at the local, regional and global scales and can support the assessment and monitoring of land‐ and water‐related applications in coastal zones. Compared to optical satellites, cloud‐cover does not limit the timeliness of data acquisition with spaceborne Synthetic Aperture Radar (SAR) sensors, which have all‐weather, day and night capabilities. Hence, active radar systems demonstrate great potential for continuous mapping and monitoring of coastal regions, particularly in cloud‐prone tropical and sub‐tropical climates. The canopy penetration capability with long radar wavelength enables L‐band SAR data to be used for coastal terrestrial environments and has been widely applied and investigated for the following geoscientific topics: mapping and monitoring of flooded vegetation and inundated areas; the retrieval of aboveground biomass; and the estimation of soil moisture. Human activities, global population growth, urban sprawl and climate change‐induced impacts are leading to increased pressure on coastal ecosystems causing land degradation, deforestation and land use change. This review presents a comprehensive overview of existing research articles that apply spaceborne L‐band SAR data for geoscientific analyses that are relevant for coastal land applications

Assessment of coastal aquaculture ponds in Asia with high resolution SAR data

Aquaculture is one of the fastest-growing animal food production sectors worldwide and is the main source of valuable animal protein in many countries. The farming of fish, crustaceans and mollusks generated income and employment for many people in rural coastal areas and has attracted considerable attention for its high export value and potentials in terms of protein supply and global food security. Rising demand for fish, crustaceans and mollusks, and international trade has driven the rapid expansion of the total global aquaculture production with an increase from 17.8 Mio tones in 1993 to 70.2 Mio tones in 2013. Farmed aquatic products account for 43 percent of the total volume of aquatic food produced in 2013 and it is foreseen that aquaculture will be the main source of aquatic animal food in human consumption since capture fisheries production stagnated over the past years. Asia alone generates nearly 90 percent of the total aquaculture production worldwide – and China is by far the largest producing country with a share of more than 65 percent of the global aquaculture production. Aquaculture has mainly been developed in valuable fertile environments along the coasts in Southeast Asian and East Asian countries and caused large-scale land use changes, destruction and loss of coastal wetlands and pollution of waters and soils. Quantitative assessment of the spatial extent and distribution of aquaculture is of utmost importance for a sustainable management of land and water resources. Such information can further be used to identify and investigate environmental impacts which might deserve special attention in terms of future human and ecosystem health. However, inventory data barely exists on national or regional level but is crucial to analyze current status and trends of aquaculture development around the globe. Here, earth observation can effectively support the planning and management of aquaculture practices and the implementation of adequate regulations and protection measures. In this presentation we address the potentials of radar data processing to map large-scale coastal aquaculture areas in Asia, highlighting the opportunities to delineate coastal pond structures using advanced SAR data. Radar instruments provide all-weather capabilities obtaining cloud-free imagery which increases capabilities to monitor and map aquaculture structures – such as ponds – and is therefore a promising data source for aquaculture research. As part of the established European Copernicus program, the Sentinel-1A C-band SAR satellite has been launched in 2014 imaging the earth’s surface at high spatial resolution for more than a year now. The free and open access data policy of the Sentinel fleet opens up new opportunities to process large-scale and timely data to analyze aquaculture on local, regional and even global scale. In our study, Sentinel-1A interferometric wide swath mode imagery is being used to test the capabilities of radar imagery to delineate aquaculture ponds in coastal test areas in China and Vietnam

Mapping pond aquaculture for the entire coastal zone of Asia using high resolution Sentinel-1 and Sentinel-2 data

Asia is the world’s most important region for aquaculture and generates almost 90 percent of the total production. The farming of fish and shrimp in land-based aquaculture systems expanded mainly along the shorelines of South Asia, Southeast Asia, and East Asia, and is a primary protein source for millions of people. The production of fish and shrimp in freshwater and brackish water ponds in coastal regions of Asia has increased rapidly since the 1990s due to the rising demand for protein-rich foods from a growing (world) population. The aquaculture sector generates income, employment and contributes to food security, has become a billion-dollar industry with high socio-economic value, but has also led to severe environmental degradation. In this regard, geospatial information on aquaculture can support the management of this growing food sector for the sustainable development of coastal ecosystems, resources and human health. With free and open access to the rapidly growing volume of data from the European Sentinel satellites as well as using machine learning algorithms and cloud computing services, we extracted coastal aquaculture at a continental-scale. We present a multi-sensor approach which utilizes Earth Observation time series data for the mapping of pond aquaculture within the entire Asian coastal zone, defined as a buffer of 200km from the coastline. In this research, we developed an object-based framework to detect and extract aquaculture at single pond level based on temporal features derived from high spatial resolution SAR and optical satellite acquired from the Sentinel-1 and Sentinel-2 satellites. In a second step, we performed spatial and statistical data analyses of the Earth observation derived aquaculture dataset to investigate spatial distribution and to identify production hotspots in various administrative units at regional, national, and sub-national scale

Mapping Aquaculture Ponds for the Coastal Zone of Asia with Sentinel-1 and Sentinel-2 Time Series

Asia dominates the world’s aquaculture sector, generating almost 90 percent of its total annual global production. Fish, shrimp, and mollusks are mainly farmed in land-based pond aquaculture systems and serve as a primary protein source for millions of people. The total production and area occupied for pond aquaculture has expanded rapidly in coastal regions in Asia since the early 1990s. The growth of aquaculture was mainly boosted by an increasing demand for fish and seafood from a growing world population. The aquaculture sector generates income and employment, contributes to food security, and has become a billion-dollar industry with high socio-economic value, but has also led to severe environmental degradation. In this regard, geospatial information on aquaculture can support the management of this growing food sector for the sustainable development of coastal ecosystems, resources, and human health. With free and open access to the rapidly growing volume of data from the Copernicus Sentinel missions as well as machine learning algorithms and cloud computing services, we extracted coastal aquaculture at a continental scale. We present a multi-sensor approach that utilizes Earth observation time series data for the mapping of pond aquaculture within the entire Asian coastal zone, defined as the onshore area up to 200 km from the coastline. In this research, we developed an object-based framework to detect and extract aquaculture at a single-pond level based on temporal features derived from high-spatial-resolution SAR and optical satellite data acquired from the Sentinel-1 and Sentinel-2 satellites. In a second step, we performed spatial and statistical data analyses of the Earth-observation-derived aquaculture dataset to investigate spatial distribution and identify production hotspots at various administrative units at regional, national, and sub-national scale

Estimating rice production in the Mekong Delta, Vietnam, utilizing time series of Sentinel-1 SAR data

Rice is the most important food crop in Asia and rice exports can significantly contribute to a country's GDP. Vietnam is the third largest exporter and fifth largest producer of rice, the majority of which is grown in the Mekong Delta. The cultivation of rice plants is important, not only in the context of food security, but also contributes to greenhouse gas emissions, provides man-made wetlands as an ecosystem, sustains smallholders in Asia and influences water resource planning and run-off water management. Rice growth can be monitored with Synthetic Aperture Radar (SAR) time series due to the agronomic flooding followed by rapid biomass increase affecting the backscatter signal. With the advent of Sentinel-1 a wealth of free and open SAR data is available to monitor rice on regional or larger scales and limited data availability should not be an issue from 2015 onwards. We used Sentinel-1 SAR time series to estimate rice production in the Mekong Delta, Vietnam, for three rice seasons centered on the year 2015. Rice production for each growing season was estimated by first classifying paddy rice area using superpixel segmentation and a phenology based decision tree, followed by yield estimation using random forest regression models trained on in situ yield data collected by surveying 357 rice farms. The estimated rice production for the three rice growing seasons 2015 correlates well with data at the district level collected from the province statistics offices with R2s of 0.93 for the Winter–Spring, 0.86 for the Summer–Autumn and 0.87 for the Autumn–Winter season

Large-Scale Assessment of Coastal Aquaculture Ponds with Sentinel-1 Time Series Data

We present an earth observation based approach to detect aquaculture ponds in coastal areas with dense time series of high spatial resolution Sentinel-1 SAR data. Aquaculture is one of the fastest-growing animal food production sectors worldwide, contributes more than half of the total volume of aquatic foods in human consumption, and offers a great potential for global food security. The key advantages of SAR instruments for aquaculture mapping are their all-weather, day and night imaging capabilities which apply particularly to cloud-prone coastal regions. The different backscatter responses of the pond components (dikes and enclosed water surface) and aquaculture’s distinct rectangular structure allow for separation of aquaculture areas from other natural water bodies. We analyzed the large volume of free and open Sentinel-1 data to derive and map aquaculture pond objects for four study sites covering major river deltas in China and Vietnam. SAR image data were processed to obtain temporally smoothed time series. Terrain information derived from DEM data and accurate coastline data were utilized to identify and mask potential aquaculture areas. An open source segmentation algorithm supported the extraction of aquaculture ponds based on backscatter intensity, size and shape features. We were able to efficiently map aquaculture ponds in coastal areas with an overall accuracy of 0.83 for the four study sites. The approach presented is easily transferable in time and space, and thus holds the potential for continental and global mapping

Opportunities and Challenges for the Estimation of Aquaculture Production Based on Earth Observation Data

Aquaculture makes a crucial contribution to global food security and protein intake and is a basis for many livelihoods. Every second fish consumed today is produced in aquaculture systems, mainly in land-based water ponds situated along the coastal areas. Satellite remote sensing enables high-resolution mapping of pond aquaculture, facilitating inventory analyses to support sustainable development of the planet’s valuable coastal ecosystems. Free, full and open data from the Copernicus earth observation missions opens up new potential for the detection and monitoring of aquaculture from space. High-resolution time series data acquired by active microwave instruments aboard the Sentinel-1 satellites and fully automated, object-based image analysis allow the identification of aquaculture ponds. In view of the diversity and complexity in the production of aquaculture products, yield and production varies greatly among species. Although national statistics on aquaculture production exist, there is a large gap of pond-specific aquaculture production quantities. In this regard, earth observation-based mapping and monitoring of pond aquaculture can be used to estimate production and has great potential for global production projections. For the deltas of the Mekong River, Red River, Pearl River, and Yellow River, as one of the world’s most significant aquaculture production regions, we detected aquaculture ponds from high spatial resolution Sentinel-1 Synthetic Aperture Radar (SAR) data. We collected aquaculture production and yield statistics at national, regional and local levels to link earth observation-based findings to the size, number and distribution of aquaculture ponds with production estimation. With the SAR derived mapping product, it is possible for the first time to assess aquaculture on single pond level at a regional scale and use that information for spatial analyses and production estimation

Modeling River Discharge Using Automated River Width Measurements Derived from Sentinel-1 Time Series

Against the background of a worldwide decrease in the number of gauging stations, the estimation of river discharge using spaceborne data is crucial for hydrological research, river monitoring, and water resource management. Based on the at-many-stations hydraulic geometry (AMHG) concept, a novel approach is introduced for estimating river discharge using Sentinel-1 time series within an automated workflow. By using a novel decile thresholding method, no a priori knowledge of the AMHG function or proxy is used, as proposed in previous literature. With a relative root mean square error (RRMSE) of 19.5% for the whole period and a RRMSE of 15.8% considering only dry seasons, our method is a significant improvement relative to the optimized AMHG method, achieving 38.5% and 34.5%, respectively. As the novel approach is embedded into an automated workflow, it enables a global application for river discharge estimation using solely remote sensing data. Starting with the mapping of river reaches, which have large differences in river width over the year, continuous river width time series are created using high-resolution and weather-independent SAR imaging. It is applied on a 28 km long section of the Mekong River near Vientiane, Laos, for the period from 2015 to 2018