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

PEMANFAATAN DATA ENHANCED VEGETATION INDEX VIIRS DAN PERBANDINGAN DENGAN MODIS UNTUK PEMANTAUAN PERTUMBUHAN PADI DI PULAU JAWA

Beras merupakan salah satu makanan pokok masyarakat Indonesia yang banyak diproduksi di dalam negeri. Karena tingginya tingkat konsumsi beras, pemerintah perlu memprediksi produksi tanaman padi dalam negeri untuk membuat kebijakan. Prediksi produktifitas padi ini dapat dilakukan menggunakan data penginderaan jauh. Di Indonesia telah dibuat pedoman pengolahan prediksi padi oleh Pusat Pemanfaatan Penginderaan Jauh, LAPAN menggunakan enhanced vegetation index (EVI) yang berasal dari sensor Moderate Resolution Imaging Spectroradiometer (MODIS) satelit Terra. Selain itu, data MODIS juga banyak digunakan di bidang pertanian, khususnya padi. Tetapi data MODIS hampir berakhir masa berlakunya sehingga diperlukan data pengganti. Data Visible Infrared Imaging Radiometer Suite (VIIRS) didesain sebagai pengganti MODIS. Untuk itu, penelitian ini dilakukan untuk mengetahui hubungan EVI data dari VIIRS dan MODIS dalam tujuannya menggantikan data MODIS dalam pemantauan padi. Dan hasil yang didapatkan menunjukkan tingkat korelasi tinggi dengan R2 sebesar 0.84 antara kedua EVI tersebut. Oleh karena itu, EVI VIIRS memiliki potensi yang sangat baik untuk menggantikan EVI MODIS

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

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

Convolutional Neural Networks Facilitate River Barrier Detection and Evidence Severe Habitat Fragmentation in the Mekong River Biodiversity Hotspot

Construction of river infrastructure, such as dams and weirs, is a global issue for ecosystem protection due to the fragmentation of river habitat and hydrological alteration it causes. Accurate river barrier databases, increasingly used to determine river fragmentation for ecologically sensitive management, are challenging to generate. This is especially so in large, poorly mapped basins where only large dams tend to be recorded. The Mekong is one of the world's most biodiverse river basins but, like many large rivers, impacts on habitat fragmentation from river infrastructure are poorly documented. To demonstrate a solution to this, and enable more sensitive basin management, we generated a whole‐basin barrier database for the Mekong, by training Convolutional Neural Network (CNN)–based object detection models, the best of which was used to identify 10,561 previously unrecorded barriers. Combining manual revision and merged with the existing barrier database, our new barrier database for the Mekong Basin contains 13,054 barriers. Existing databases for the Lower Mekong documented under ∼3% of the barriers recorded by CNN combined with manual checking. The Nam Chi/Nam Mun region, eastern Thailand, is the most fragmented area within the basin, with a median [95% CI] barrier density of 15.53 [0.00–49.30] per 100 km, and Catchment Area‐based Fragmentation Index value, calculated in an upstream direction, of 1,178.67 [0.00–6,418.46], due to the construction of dams and sluice gates. The CNN‐based object detection framework is effective and potentially can transform our ability to identify river barriers across many large river basins and facilitate ecologically‐sensitive management

Deriving wheat crop productivity indicators using Sentinel-1 time series

High-frequency Earth observation (EO) data have been shown to be effective in identifying crops and monitoring their development. The purpose of this paper is to derive quantitative indicators of crop productivity using synthetic aperture radar (SAR). This study shows that the field-specific SAR time series can be used to characterise growth and maturation periods and to estimate the performance of cereals. Winter wheat fields on the Rothamsted Research farm in Harpenden (UK) were selected for the analysis during three cropping seasons (2017 to 2019). Average SAR backscatter from Sentinel-1 satellites was extracted for each field and temporal analysis was applied to the backscatter cross-polarisation ratio (VH/VV). The calculation of the different curve parameters during the growing period involves (i) fitting of two logistic curves to the dynamics of the SAR time series, which describe timing and intensity of growth and maturation, respectively; (ii) plotting the associated first and second derivative in order to assist the determination of key stages in the crop development; and (iii) exploring the correlation matrix for the derived indicators and their predictive power for yield. The results show that the day of the year of the maximum VH/VV value was negatively correlated with yield (r = −0.56), and the duration of “full” vegetation was positively correlated with yield (r = 0.61). Significant seasonal variation in the timing of peak vegetation (p = 0.042), the midpoint of growth (p = 0.037), the duration of the growing season (p = 0.039) and yield (p = 0.016) were observed and were consistent with observations of crop phenology. Further research is required to obtain a more detailed picture of the uncertainty of the presented novel methodology, as well as its validity across a wider range of agroecosystem

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

Uso de séries temporais Sentinel 1 na identificação de culturas agrícolas utilizando modelos de machine learning

Na área agrícola o Sensoriamento Remoto vem sendo uma opção de baixo custo, no entanto o aumento da disponibilidade de imagens com alta resolução espacial e temporal gratuitas, veio para contribuir de modo significativo para com esses estudos. Mais especificamente as imagens do RADAR/SAR Sentinel-1A e 1B, o qual é capaz de alcançar uma resolução temporal de até 6 dias. As imagens de RADAR são de fundamental importância para compreensão do comportamento de culturas agrícolas e sua identificação, uma vez que independem das condições atmosféricas, favorecendo a aquisição de imagens em quaisquer situações, resultando em séries temporais mais completas e refinadas. Neste estudo buscou-se avaliar o desempenho de três modelos de classificadores de Machine Learning, Random Forest (RF), Support Vector Machine (SVM) e K-Nearest Neighbor (KNN), utilizando séries temporais Sentinel-1/SAR, com a finalidade de identificar os tipos de culturas presentes na região do Panambi, Bahia, no período de safras que compreendem a 2016/2017, 2017/2018. Os procedimentos metodológicos consistiram no pré-processamento das imagens no Software Sentinel’s Application Platform (SNAP); empilhamento de imagens para construção do cubo temporal; filtragem espacial utilizando o método de Análise de Componentes de Densidade de Probabilidade (ACDP); técnicas de Transformação Minimum Noise Fraction (MNF) e MNF Invertido para extração do ruído na frequência das imagens e reconstrução da mesma; e classificação do cubo temporal. Os melhores resultados foram obtidos na filtragem para a polarização VH, com capacidade de melhor separar os alvos agrícolas e para o classificador KNN, alcançando um Kappa de 0,85 e um índice de Exatidão Global de 0,88, seguido do RF com 0,78 e 0,83 e então o SVM com o menor Kappa, 0,59 e 0,67 respectivamente, com melhores respostas na polarização VV. A imagens SAR possuem um alto potencial para identificação de culturas utilizando os modelos propostos em ambas as polarizações, com destaque para o KNN, alcançando uma acurácia geral neste estudo de 96,7%. Entretanto, mais estudos devem ser direcionados para estes fins utilizando imagens Sentinel-1/SAR, fazendo ainda, uso da junção de ambas as polarizações, VV e VH, para alcançar uma maior precisão nas classificações.CAPESIn agricultural field, Remote Sensing has been a low-cost option, however the increase in the availability of free and temporal high-resolution imagery has contributed significantly to these studies. For instance, the images from Sentinel-1A and 1B Synthetic-aperture radar (SAR) are capable of achieving a temporal resolution of up to 6 days. SAR images are pivotal for understanding the behavior of agricultural crops and their identification, since they are independent of atmospheric conditions, favoring the acquisition of images in any situation, resulting in more complete and refined time series. In this study, we evaluate the performance of three models of Machine Learning classifiers, Random Forest (RF), Support Vector Machine (SVM) and K-Nearest Neighbor (KNN), using Sentinel-1 time series, aiming to identify the types of crops present in the region of Panambi, Bahia, during the harvesting (2016/2017 and 2017/2018). We adopted the following methodological procedures: pre-processing the images in the Sentinel’s Application Platform (SNAP) Software; stacking images for the construction of the temporal cube; spatial filtering using the Probability Density Component Analysis (ACDP) method; Minimum Noise Fraction (MNF) and Inverted MNF Transformation techniques for extracting noises in the image frequency and reconstructing them; and classification of the temporal cube. Our best result was obtained in the filtering for the VH polarization, with the ability to better separate the agricultural targets and for the KNN classifier, reaching a Kappa coefficient of 0.85 and a Global Accuracy index of 0.88, followed by the RF with 0.78 and 0.83 and then the SVM with the lowest Kappa coefficient, 0.59 and 0.67 respectively, with better responses in the VV polarization. SAR images have a high potential for identifying cultures using the models proposed in both polarizations, with emphasis on KNN, reaching a general accuracy in this study of 96.7%. However, further studies should focus on these purposes, using Sentinel-1/SAR images and combining both polarizations (VV and VH) as a means to achieve greater accuracy in the classifications

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

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