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

Fusion approach for remotely sensed mapping of agriculture (FARMA):A scalable open source method for land cover monitoring using data fusion

The increasing availability of very-high resolution (VHR; &lt;2 m) imagery has the potential to enable agricultural monitoring at increased resolution and cadence, particularly when used in combination with widely available moderate-resolution imagery. However, scaling limitations exist at the regional level due to big data volumes and processing constraints. Here, we demonstrate the Fusion Approach for Remotely Sensed Mapping of Agriculture (FARMA), using a suite of open source software capable of efficiently characterizing time-series field-scale statistics across large geographical areas at VHR resolution. We provide distinct implementation examples in Vietnam and Senegal to demonstrate the approach using WorldView VHR optical, Sentinel-1 Synthetic Aperture Radar, and Sentinel-2 and Sentinel-3 optical imagery. This distributed software is open source and entirely scalable, enabling large area mapping even with modest computing power. FARMA provides the ability to extract and monitor sub-hectare fields with multisensor raster signals, which previously could only be achieved at scale with large computational resources. Implementing FARMA could enhance predictive yield models by delineating boundaries and tracking productivity of smallholder fields, enabling more precise food security observations in low and lower-middle income countries.</p

Land cover mapping of the Mekong Delta with sentinel-1 synthetic aperture radar

Synthetic aperture radar (SAR) has great potential for land cover/land use (LCLU) mapping, especially in tropical regions, where frequent cloud cover obstructs optical remote sensing. The use of SAR data derived mapping results plays crucial role in urban and suburban extents characterizations, urban services, rice crop distribution delineation, and land use changes detection. As the Mekong Delta is a significant location ecologically, economically, and socially, food security, forest conservation, natural resource management, and urbanization are a matter of great concern. Urban expansion and conversion wetland areas to aquaculture have impacts on natural forest and coastal ecosystems in the Mekong Delta. Therefore, the use of latest Sentinel-1 C-band SAR data characterizing LCLU including urban expansion, aquaculture development, and productive land and unproductive lands is essential for natural resource management and land use planning. This thesis demonstrated the use of Sentinel-1 SAR data and Google Earth Engine to map the LCLU of the Mekong Delta. The research in this thesis is divided into three parts: 1) the classification of multi-temporal Sentinel-1A C-band SAR imagery for characterizing the LCLU to support natural resource management; 2) identifying and mapping persistent building structures from coastal plains to high plateaus, as well as on the sea surface; 3) detecting and mapping persistent surface water and seasonal inundated LCLU. Part 1 of the thesis investigated the classification of multi-temporal Sentinel-1A C-band SAR imagery for characterizing LCLU to support natural resource management for land use planning and monitoring. Twenty-one SAR images acquired in 2016 over Bạc Liêu province, a rapidly developing province of the Mekong Delta, Vietnam were classified. To reduce the effects of rainfall variation confounding the classification, the images were divided into two categories: dry season (Jan–April) and wet season (May–December) and three input image sets were produced: 1) a single-date composite image, 2) a multi-temporal composite image and 3) a multi-temporal and textural composite image. Support Vector Machines (SVM) and Random Forest (RF) classifiers were then applied to characterize urban, forest, aquaculture, and rice paddy field for the three input image sets. A combination of input images and classification algorithms was tested, and the mapping results showed that no matter the classification algorithms used, multi-temporal images had a higher overall classification accuracy than single-date images and that differences between classification algorithms were minimal. The results demonstrated the potential use of SAR as an up-to date complementary data source of land cover information for local authorities, to support their land use master plan and to monitor illegal land use changes. Part 2 of the thesis developed novel and robust methods using time-series data acquired from Sentinel-1 C-band SAR to identify and map persistent building structures from coastal plains to high plateaus, as well as on the sea surface. Mapping building structures is crucial for environmental change and impact assessment and is especially important to accurately estimate fossil fuel CO2 emissions from human settlements. From annual composites of SAR data in the two-dimensional VV-VH polarization space, the VV-VH domain was determined for detecting building structures, whose persistence was defined based on the number of times that a pixel was identified as a building in time-series data. Moreover, the algorithm accounted for misclassified buildings due to water-tree interactions in radar signatures and due to topography effects in complex mountainous landforms. The methods were tested in five cities (Bạc Liêu, Cà Mau, Sóc Trăng, Tân An, and Phan Thiết) in Vietnam located in different socio-environmental regions with a range of urban configurations. Using in-situ data and field observations, the methods were validated, and the results were found to be accurate, with an average false negative rate of 10.9% and average false positive rate of 6.4% for building detection. The new approach was developed to be robust against variations in SAR incidence and azimuth angles. The results demonstrated the potential use of satellite dual-polarization SAR to identify persistent building structures annually across rural–urban landscapes and on sea surfaces with different environmental conditions. The final part of the thesis developed a novel method to map persistent surface water and seasonal inundated land cover and land use. The super-intensive shrimp culture in the Mekong Delta region brings substantial profits to the local economy but it poses major challenges to soil and surface water in wetland areas. The use of geospatial data in monitoring the aquaculture areas is necessary but it has been inadequate in aquaculture areas in the Mekong Delta. In this study, a new algorithm was developed to address the problem of detecting LCLU that contains water such as persistent surface water (permanent lake, permanent rivers, persistently denuded unproductive land) and seasonal inundated land cover (rice paddy and aquaculture) in different environmental conditions. The three-dimensional (3-D) space of VV-VH polarization of the SAR data and Season space was introduced. This study found that the use of the three-dimensional polarization of the SAR and season space is successfully in detecting rice paddy, aquaculture, and persistent surface water. Therefore, the novel method can be utilized to monitor aquaculture in other wetland regions. In conclusion, this thesis demonstrated the potential use of Sentinel-1 C-band SAR data to map LCLU across the urban suburban to rural-natural landscape on level terrains. The proposed methods can be used for urbanization monitoring, aquaculture development monitoring, and illegal land use change

CHARACTERIZING RICE RESIDUE BURNING AND ASSOCIATED EMISSIONS IN VIETNAM USING A REMOTE SENSING AND FIELD-BASED APPROACH

Agricultural residue burning, practiced in croplands throughout the world, adversely impacts public health and regional air quality. Monitoring and quantifying agricultural residue burning with remote sensing alone is difficult due to lack of field data, hazy conditions obstructing satellite remote sensing imagery, small field sizes, and active field management. This dissertation highlights the uncertainties, discrepancies, and underestimation of agricultural residue burning emissions in a small-holder agriculturalist region, while also developing methods for improved bottom-up quantification of residue burning and associated emissions impacts, by employing a field and remote sensing-based approach. The underestimation in biomass burning emissions from rice residue, the fibrous plant material left in the field after harvest and subjected to burning, represents the starting point for this research, which is conducted in a small-holder agricultural landscape of Vietnam. This dissertation quantifies improved bottom-up air pollution emissions estimates through refinements to each component of the fine-particulate matter emissions equation, including the use of synthetic aperture radar timeseries to explore rice land area variation between different datasets and for date of burn estimates, development of a new field method to estimate both rice straw and stubble biomass, and also improvements to emissions quantification through the use of burning practice specific emission factors and combustion factors. Moreover, the relative contribution of residue burning emissions to combustion sources was quantified, demonstrating emissions are higher than previously estimated, increasing the importance for mitigation. The dissertation further explored air pollution impacts from rice residue burning in Hanoi, Vietnam through trajectory modelling and synoptic meteorology patterns, as well as timeseries of satellite air pollution and reanalysis datasets. The results highlight the inherent difficulty to capture air pollution impacts in the region, especially attributed to cloud cover obstructing optical satellite observations of episodic biomass burning. Overall, this dissertation found that a prominent satellite-based emissions dataset vastly underestimates emissions from rice residue burning. Recommendations for future work highlight the importance for these datasets to account for crop and burning practice specific emission factors for improved emissions estimates, which are useful to more accurately highlight the importance of reducing emissions from residue burning to alleviate air quality issues

Land cover mapping of the Mekong Delta with sentinel-1 synthetic aperture radar

Synthetic aperture radar (SAR) has great potential for land cover/land use (LCLU) mapping, especially in tropical regions, where frequent cloud cover obstructs optical remote sensing. The use of SAR data derived mapping results plays crucial role in urban and suburban extents characterizations, urban services, rice crop distribution delineation, and land use changes detection. As the Mekong Delta is a significant location ecologically, economically, and socially, food security, forest conservation, natural resource management, and urbanization are a matter of great concern. Urban expansion and conversion wetland areas to aquaculture have impacts on natural forest and coastal ecosystems in the Mekong Delta. Therefore, the use of latest Sentinel-1 C-band SAR data characterizing LCLU including urban expansion, aquaculture development, and productive land and unproductive lands is essential for natural resource management and land use planning. This thesis demonstrated the use of Sentinel-1 SAR data and Google Earth Engine to map the LCLU of the Mekong Delta. The research in this thesis is divided into three parts: 1) the classification of multi-temporal Sentinel-1A C-band SAR imagery for characterizing the LCLU to support natural resource management; 2) identifying and mapping persistent building structures from coastal plains to high plateaus, as well as on the sea surface; 3) detecting and mapping persistent surface water and seasonal inundated LCLU. Part 1 of the thesis investigated the classification of multi-temporal Sentinel-1A C-band SAR imagery for characterizing LCLU to support natural resource management for land use planning and monitoring. Twenty-one SAR images acquired in 2016 over Bạc Liêu province, a rapidly developing province of the Mekong Delta, Vietnam were classified. To reduce the effects of rainfall variation confounding the classification, the images were divided into two categories: dry season (Jan–April) and wet season (May–December) and three input image sets were produced: 1) a single-date composite image, 2) a multi-temporal composite image and 3) a multi-temporal and textural composite image. Support Vector Machines (SVM) and Random Forest (RF) classifiers were then applied to characterize urban, forest, aquaculture, and rice paddy field for the three input image sets. A combination of input images and classification algorithms was tested, and the mapping results showed that no matter the classification algorithms used, multi-temporal images had a higher overall classification accuracy than single-date images and that differences between classification algorithms were minimal. The results demonstrated the potential use of SAR as an up-to date complementary data source of land cover information for local authorities, to support their land use master plan and to monitor illegal land use changes. Part 2 of the thesis developed novel and robust methods using time-series data acquired from Sentinel-1 C-band SAR to identify and map persistent building structures from coastal plains to high plateaus, as well as on the sea surface. Mapping building structures is crucial for environmental change and impact assessment and is especially important to accurately estimate fossil fuel CO2 emissions from human settlements. From annual composites of SAR data in the two-dimensional VV-VH polarization space, the VV-VH domain was determined for detecting building structures, whose persistence was defined based on the number of times that a pixel was identified as a building in time-series data. Moreover, the algorithm accounted for misclassified buildings due to water-tree interactions in radar signatures and due to topography effects in complex mountainous landforms. The methods were tested in five cities (Bạc Liêu, Cà Mau, Sóc Trăng, Tân An, and Phan Thiết) in Vietnam located in different socio-environmental regions with a range of urban configurations. Using in-situ data and field observations, the methods were validated, and the results were found to be accurate, with an average false negative rate of 10.9% and average false positive rate of 6.4% for building detection. The new approach was developed to be robust against variations in SAR incidence and azimuth angles. The results demonstrated the potential use of satellite dual-polarization SAR to identify persistent building structures annually across rural–urban landscapes and on sea surfaces with different environmental conditions. The final part of the thesis developed a novel method to map persistent surface water and seasonal inundated land cover and land use. The super-intensive shrimp culture in the Mekong Delta region brings substantial profits to the local economy but it poses major challenges to soil and surface water in wetland areas. The use of geospatial data in monitoring the aquaculture areas is necessary but it has been inadequate in aquaculture areas in the Mekong Delta. In this study, a new algorithm was developed to address the problem of detecting LCLU that contains water such as persistent surface water (permanent lake, permanent rivers, persistently denuded unproductive land) and seasonal inundated land cover (rice paddy and aquaculture) in different environmental conditions. The three-dimensional (3-D) space of VV-VH polarization of the SAR data and Season space was introduced. This study found that the use of the three-dimensional polarization of the SAR and season space is successfully in detecting rice paddy, aquaculture, and persistent surface water. Therefore, the novel method can be utilized to monitor aquaculture in other wetland regions. In conclusion, this thesis demonstrated the potential use of Sentinel-1 C-band SAR data to map LCLU across the urban suburban to rural-natural landscape on level terrains. The proposed methods can be used for urbanization monitoring, aquaculture development monitoring, and illegal land use change

Leveraging geospatial technologies for flood management and disaster assessment in the Lower Mekong River Basin

The sixty million inhabitants of the Lower Mekong River Basin (LMB) in Southeast Asia rely on the natural resources provided by the Mekong River and its annual flooding. Extreme events, however, can cause widespread agricultural, structural, and humanitarian loss across the basin. Population increases, land use change, hydropower development, and climate change threaten to increase flood risk and potential damage over the next century. Geospatial technologies such as satellite remote sensing and Geographic Information Systems (GIS) provide powerful tools for assessing historic risk and monitoring real-time events. In this thesis, I first assess trends in flood magnitude and timing over the last 90 years using stream gauge data to understand changes in the annual flooding of the river and associated risk. Next, I develop methods for using satellite data to map flood extent and relative degree, as well as assess risk to agriculture from both floods and droughts based on historic conditions. Lastly, I demonstrate how the satellite-derived flood maps can be integrated with socio-economic data in automated GIS tools to provide timely and reliable estimates of socio-economic damage during disasters.Geography and the Environmen

Rice crop detection using LSTM, Bi-LSTM, and machine learning models from sentinel-1 time series

The Synthetic Aperture Radar (SAR) time series allows describing the rice phenological cycle by the backscattering time signature. Therefore, the advent of the Copernicus Sentinel-1 program expands studies of radar data (C-band) for rice monitoring at regional scales, due to the high temporal resolution and free data distribution. Recurrent Neural Network (RNN) model has reached state-of-the-art in the pattern recognition of time-sequenced data, obtaining a significant advantage at crop classification on the remote sensing images. One of the most used approaches in the RNN model is the Long Short-Term Memory (LSTM) model and its improvements, such as Bidirectional LSTM (Bi-LSTM). Bi-LSTM models are more effective as their output depends on the previous and the next segment, in contrast to the unidirectional LSTM models. The present research aims to map rice crops from Sentinel-1 time series (band C) using LSTM and Bi-LSTM models in West Rio Grande do Sul (Brazil). We compared the results with traditional Machine Learning techniques: Support Vector Machines (SVM), Random Forest (RF), k-Nearest Neighbors (k-NN), and Normal Bayes (NB). The developed methodology can be subdivided into the following steps: (a) acquisition of the Sentinel time series over two years; (b) data pre-processing and minimizing noise from 3D spatial-temporal filters and smoothing with Savitzky-Golay filter; (c) time series classification procedures; (d) accuracy analysis and comparison among the methods. The results show high overall accuracy and Kappa (>97% for all methods and metrics). Bi-LSTM was the best model, presenting statistical differences in the McNemar test with a significance of 0.05. However, LSTM and Traditional Machine Learning models also achieved high accuracy values. The study establishes an adequate methodology for mapping the rice crops in West Rio Grande do Sul

A Review of Earth Observation-Based Drought Studies in Southeast Asia

Drought is a recurring natural climatic hazard event over terrestrial land; it poses devastating threats to human health, the economy, and the environment. Given the increasing climate crisis, it is likely that extreme drought phenomena will become more frequent, and their impacts will probably be more devastating. Drought observations from space, therefore, play a key role in dissimilating timely and accurate information to support early warning drought management and mitigation planning, particularly in sparse in-situ data regions. In this paper, we reviewed drought-related studies based on Earth observation (EO) products in Southeast Asia between 2000 and 2021. The results of this review indicated that drought publications in the region are on the increase, with a majority (70%) of the studies being undertaken in Vietnam, Thailand, Malaysia and Indonesia. These countries also accounted for nearly 97% of the economic losses due to drought extremes. Vegetation indices from multispectral optical remote sensing sensors remained a primary source of data for drought monitoring in the region. Many studies (~21%) did not provide accuracy assessment on drought mapping products, while precipitation was the main data source for validation. We observed a positive association between spatial extent and spatial resolution, suggesting that nearly 81% of the articles focused on the local and national scales. Although there was an increase in drought research interest in the region, challenges remain regarding large-area and long time-series drought measurements, the combined drought approach, machine learning-based drought prediction, and the integration of multi-sensor remote sensing products (e.g., Landsat and Sentinel-2). Satellite EO data could be a substantial part of the future efforts that are necessary for mitigating drought-related challenges, ensuring food security, establishing a more sustainable economy, and the preservation of the natural environment in the region

Risodling i Mekongdeltat

Uppsatsen redogör för hur klimatförändringen och vattenkraften i Mekongregionen påverkar risodling i Mekongdeltat samt vad det betyder för livsmedelssäkerheten och fattigdomsbekämpningen i Vietnam. Cirka 40% av Vietnams landyta används till jordbruk och Mekongdeltat är ett av världens största jordbrukssystem för risodling (Food and Agriculture Organization of the United Nations, 2018). Produktionen hotas av klimatförändringen och den ökande användning och utbyggnaden av vattenkraft uppströms i Mekongfloden. Klimatförändringen förutses minska möjligheten för risodling på grund av havsnivåhöjning, översvämning, förändrade vattenflöden samt saltvatteninvasion. Vattenkraften i Mekongfloden påverkar främst strömmarna vilket leder till mindre sediment i Mekongdeltat och förändrade förutsättningar för risproduktion i deltat. Flera lösningar för att skydda odlingen finns presenterade idag, men att odla andra grödor och alternativ djuruppfödning samt salt- och vattentåliga rissorter framhålls som viktiga anpassningsåtgärder för att bibehålla jordbruket i deltat. Det är även väldigt viktigt med skydd av landet mot de ökade översvämningarna som kommer ske av högre havsnivå, för att förhindra omlokalisering av deltats befolkning. Framtiden för vattenkraften i Mekongregionen är osäker och samtidigt ökar användandet av solenergi i Sydostasien. Forskare och intressenter har delade meningar om hur livsmedelssäkerheten påverkas av försämrad risodling, men alla instämmer om att det handlar om en ekonomisk förlust för landet och risk för ökad fattigdom. Att förbättra risodlingen kommer inte vara avgörande för Vietnams välfärd utan lösningen framhålls vara att diversifiera odlingen för att säkerställa inkomster.The thesis describes how climate change and hydropower in the Mekong region affect rice cultivation in the Mekong Delta and what it means for food security and poverty reduction in Vietnam. About 40% of Vietnam's land is used for agriculture and the Mekong Delta is one of the world's largest agricultural systems for rice cultivation (Food and Agriculture Organization of the United Nations, 2018). The production is threatened by climate change and the increasing use and expansion of hydropower upstream in the Mekong River. Climate change is predicted to deteriorate rice cultivation due to sea level rise, floods, changes in water flows and saltwater invasion. Hydropower in the Mekong River affects the main currents which leads to less sediment in the Mekong Delta and changing conditions for rice production in the Delta. Several solutions to protect cultivation are presented today but cultivating other crops and alternative animal husbandry as well as salt- and water-resistant rice varieties are emphasized as important adaptation measures to maintain agriculture in the delta. It is also very important to protect the country from the increased floods that will occur from higher sea levels, to prevent the relocation of the delta population. The future of hydropower in the Mekong region is uncertain and at the same time is the use of solar energy in Southeast Asia increasing. The interviewed researchers have different opinions on how food security is affected by deteriorated rice cultivation, but both agrees that it is a matter of an economic loss for the country and a risk of increased poverty. Rice cultivation will however not be decisive for Vietnam's welfare, but the solution is emphasized to be to diversify cultivation to ensure income

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