473 research outputs found

    Rice monitoring using ENVISAT-ASAR data: preliminary results of a case study in the Mekong River Delta, Vietnam

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    Vietnam is one of the world’s largest rice exporting countries, and the fertile Mekong River Delta at the southern tip of Vietnam accounts for more than half of the country’s rice production. Unfortunately, a large part of rice crop growing time coincides with a rainy season, resulting in a limited number of cloud-free optical remote sensing images for rice monitoring. Synthetic aperture radar (SAR) data allows for observations independent of weather conditions and solar illumination, and is potentially well suited for rice crop monitoring. The aim of the study was to apply new generation Envisat ASAR data with dual polarization (HH and VV) to rice cropping system mapping and monitoring in An Giang province, Mekong River Delta. Several sample areas were established on the ground, where selected rice parameters (e.g. rice height and biomass) are periodically being measured over a period of 12 months. A correlation analysis of rice parameters and radar imagery values is then being conducted to determine the significance and magnitude of the relationships. This paper describes a review of the previous research studies on rice monitoring using SAR data, the context of this on-going study, and some preliminary results that provide insights on how ASAR imagery could be useful for rice crop monitoring. More work is being done to develop algorithms for mapping and monitoring rice cropping systems, and to validate a rice yield prediction model for one year cycle using time-series SAR imagery

    Effects of changing cultural practices on C-band SAR backscatter using Envisat ASAR data in the Mekong River Delta

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    International audienceChanges in rice cultivation systems have been observed in the Mekong River Delta, Vietnam. Among the changes in cultural practices, the change from transplanting to direct sowing, the use of water-saving technology, and the use of high production method could have impacts on radar remote sensing methods previously developed for rice monitoring. Using Envisat (Environmental Satellite) ASAR (Advanced Synthetic Aperture Radar) data over the province of An Giang, this study showed that the radar backscattering behaviour is much different from that of the reported traditional rice. At the early stage of the season, direct sowing on fields with rough and wet soil surface provides very high backscatter values for HH (Horizontal transmit - Horizontal receive polarisation) and VV (Vertical transmit - Vertical receive polarisation) data, as a contrast compared to the very low backscatter of fields covered with water before emergence. The temporal increase of the backscatter is therefore not observed clearly over direct sowing fields. Hence, the use of the intensity temporal change as a rice classifier proposed previously may not apply. Due to the drainage that occurs during the season, HH, VV and HH/VV are not strongly related to biomass, in contrast with past results. However, HH/VV ratio could be used to derive the rice/non-rice classification algorithm for all conditions of rice fields in the test province. The mapping results using the HH/VV polarization ratio at a single date in the middle period of the rice season were assessed using statistical data at different districts in the province, where very high accuracy was found. The method can be applied to other regions, provided that the synthetic aperture radar data are acquired during the peak period of the rice season, and that few training fields provide adjusted threshold values used in the method

    Advances in Radar Remote Sensing of Agricultural Crops: A Review

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    There are enormous advantages of a review article in the field of emerging technology like radar remote sensing applications in agriculture. This paper aims to report select recent advancements in the field of Synthetic Aperture Radar (SAR) remote sensing of crops. In order to make the paper comprehensive and more meaningful for the readers, an attempt has also been made to include discussion on various technologies of SAR sensors used for remote sensing of agricultural crops viz. basic SAR sensor, SAR interferometry (InSAR), SAR polarimetry (PolSAR) and polarimetric interferometry SAR (PolInSAR). The paper covers all the methodologies used for various agricultural applications like empirically based models, machine learning based models and radiative transfer theorem based models. A thorough literature review of more than 100 research papers indicates that SAR polarimetry can be used effectively for crop inventory and biophysical parameters estimation such are leaf area index, plant water content, and biomass but shown less sensitivity towards plant height as compared to SAR interferometry. Polarimetric SAR Interferometry is preferable for taking advantage of both SAR polarimetry and SAR interferometry. Numerous studies based upon multi-parametric SAR indicate that optimum selection of SAR sensor parameters enhances SAR sensitivity as a whole for various agricultural applications. It has been observed that researchers are widely using three models such are empirical, machine learning and radiative transfer theorem based models. Machine learning based models are identified as a better approach for crop monitoring using radar remote sensing data. It is expected that the review article will not only generate interest amongst the readers to explore and exploit radar remote sensing for various agricultural applications but also provide a ready reference to the researchers working in this field

    A MODIS-Based Automated Flood Monitoring System for Southeast Asia

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    Flood disasters in Southeast Asia result in significant loss of life and economic damage. Remote sensing information systems designed to spatially and temporally monitor floods can help governments and international agencies formulate effective disaster response strategies during a flood and ultimately alleviate impacts to population, infrastructure, and agriculture. Recent destructive flood events in the Lower Mekong River Basin occurred in 2000, 2011, 16 2013, and 2016 (http://ffw.mrcmekong.org/historical_rec.htm, April 24, 2017). The large spatial distribution of flooded areas and lack of proper gauge data in the region makes accurate monitoring and assessment of impacts of floods difficult. Here, we discuss the utility of applying satellite-based Earth observations for improving flood inundation monitoring over the flood-prone Lower Mekong River Basin. We present a methodology for determining near real-time surface water extent associated with current and historic flood events by training surface water classifiers from 8-day, 250-meter Moderate-resolution Imaging Spectroradiometer (MODIS) data spanning the length of the MODIS satellite record. The Normalized Difference Vegetation Index (NDVI) signature of permanent water bodies (MOD44W; Carroll et al., 2009) is used to train surface water classifiers which are applied to a time period of interest. From this, an operational nowcast flood detection component is produced using twice daily imagery acquired at 3-hour latency which performs image compositing routines to minimize cloud cover. Case studies and accuracy assessments against radar-based observations for historic flood events are presented. The customizable system has been transferred to regional organizations and near real-time derived surface water products are made available through a web interface platform. Results highlight the potential of near real-time observation and impact assessment systems to serve as effective decision support tools for governments, international agencies, and disaster responders

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

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    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

    Land Use Based Flood Hazard Analysis for the Mekong Delta

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    Das Mekong-Delta nimmt für die Republik Vietnam einen sehr hohen Stellenwert in Bezug auf Natur, Wirtschaft, Politik, Menschen, Landwirtschaft, Fischerei, Geopolitik und vielen anderen Bereichen ein. Der sogenannte Dreifachreis (auch Herbst-Winter-Ernte oder Third Crop genannt) wurde in den letzten Jahren für das Mekong-Delta in den stark überfluteten Gebiete durch umschlossene Kompartimenten wie Halbdeichstrukturen (zum Schutz der Reisfelder vor Hochwasser (von Juli bis Mitte August) sowie Volldeichmessungen (zum vollständigen Schutz der Reisfelder während der Hochwassersaison) schnell ausgebaut. Der Reisanbau hat daher Auswirkungen auf die Hochwassersituation in den flußabwärts gelegenen Gebieten. Diese Studie zielt darauf ab, die Auswirkungen von Deichmessungen auf Hochwasser in den Mekong-Flüssen zu analysieren, indem das 1D-Hydraulikmodell MIKE11 sowie Fernerkundungsprodukte (MODIS-Satellit) verwendet werden. Um diese Einflüsse umfassend zu erforschen, wurde mit dem Hydraulikmodell MIKE11 die Auswirkungen von mehreren Volldeichkompartimenten auf das Hochwasser entlang der Hauptflüsse basierend auf einem Geographical Impact Factor (GIF) analysiert. Der Autor fand heraus, daß verschiedene geografische Kompartimente unterschiedliche Einflüsse auf das Hochwasserniveau entlang des Mekong haben. Fernerkundungsprodukte wurden eingesetzt, um die Veränderung der Landnutzungsgebiete im Mekong-Delta von 2000 bis 2017 zu analysieren. Außerdem wurde von MODIS Satellitenprodukte eine komplette Datenbank von Hochwasserverteilungskarten (476 Karten) im Mekong-Delta während der Hochwassersaison 2000 bis 2017 interpretiert. Darüber hinaus wurden die Satellitenprodukte einschließlich Landnutzung und Hochwasserkarten in MD zu weiteren Untersuchungen des Mekong Delta für die Öffentlichkeit online zur Verfügung gestellt. Die Simulation funktioniert für ein großes und komplexes Flussnetz, da das Mekong-System viele Anstrengungen und Erfahrungen der Ingenieure erfordert, die nicht leicht zu bewältigen sind. Daher wurde eine einfache Methode zur Interpretation des Hochwasserstandes entlang der Mekong Flüsse entwickelt, um Ingenieuren ein schnelles Werkzeug zur Bewertung der Auswirkungen von Deichkonstruktionen für Landnutzungszwecke auf Hochwasserregime zur Verfügung zu stellen. Im Bereich Hydraulik wurde ebenfalls eine Empfehlung zum Reisanbau in den Gebieten vom Mekong-Delta abgegeben, welche den Anwendern die Möglichkeit bieten soll, die Ausrichtung der landwirtschaftlichen Entwicklung gegenüber dem Hochwassermanagement zu steuern

    A low-cost rice mapping remote sensing based algorithm

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    Egypt faces a great challenge, limited water resources and increasing water demand. The agriculture sector consumes about 83% of the available water resources. The main water-consuming crop planted in summer is rice. Thus for any better water resources management, rice mapping is required. Remote sensing can be utilized for rice mapping. This will potentially save money and effort. The most differentiating feature of rice is being flooded in the transplanting period. Xiao (2005) developed a rice mapping algorithm by studying the dynamics of three vegetation indices, the Land surface water index (LSWI), the normalized difference vegetation index (NDVI), and the Enhanced vegetation Index (EVI). The key assumption is that a moisture sensitive index, as LSWI, will capture the flooding of rice and will temporal lily exceeds or approaches NDVI, or EVI, thus signaling rice transplanting. Xiao utilized MODIS (500 m spatial resolution, twice a day temporal resolution) free satellite imagery. However, its coarse resolution combined with Egypt heterogeneous and fragmented land ownership raised the need for the algorithm modification. In the current research a low-cost rice-mapping algorithm was developed. The accuracy of rice mapping from MODIS satellite imagery was enhanced by making use of LANDSAT imagery. This was achieved by developing a novel decision tree classifier that classifies land cover into its four main classes namely: vegetation, desert, bare land or urban, and water utilizing LANDSAT imagery. The non-vegetation area is then used to refine the rice area calculated from MODIS. Another challenge of rice mapping from MODIS is that in rice fields the reflectance is a combination of water, vegetation, soil, and ditches thus not always the LSWI will exceed the EVI or the NDVI as proposed in the literature, but instead it will approach it in the transplanting period. In order to reflect this, a ∆-parameter was introduced. The adopted criteria for rice mapping was LSWI + ∆\u3e NDVI or LSWI + ∆\u3e EVI. The ∆-parameter was obtained as best fit for each rice-growing region. The ∆-parameter is different for EVI and NDVI. The ∆EVI for Kafrelsheikh and Dumyat was found to be 0.04. Daqehleya, Gharbeya and Sharqeya ∆-parameter was calculated as 0.05. While Behera governorate ∆-parameter was estimated to be 0.07. While ∆--NDVI parameter for KafrElsheikh was 0.174, for Dumyat was 0.178, for Sharqeya was 0.18, for Gharbeya was 0.197, for Behera was 0.23, and for Daqhleya the ∆- NDVI parameter was 0.155. The developed rice-mapping algorithm was applied to the Delta region in Egypt to predict the rice cultivated areas in the year 2009. The resultant rice areas map was validated using randomly selected points, and local knowledge of rice planting practices, against very high-resolution (60 cm) imagery. The overall accuracy of the main land cover mapping was 90%. The rice areas map and probable transplanting dates conforms to local knowledge of rice planting practices. The results of this study indicate that the developed rice-mapping algorithm can be applied as an economic way for rice area mapping on a timely and frequent basis. However mapping rice fields prior to flooding would have been revealed more information for water management. More research should be directed to the early mapping of rice transplanting in the future

    Crop Growth Monitoring by Hyperspectral and Microwave Remote Sensing

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    Methoden und Techniken der Fernerkundung fungieren als wichtige Hilfsmittel im regionalen Umweltmanagement. Um diese zu optimieren, untersucht die folgende Arbeit sowohl die Verwendung als auch Synergien verschiedener Sensoren aus unterschiedlichen Wellenlängenbereichen. Der Fokus liegt auf der Modellentwicklung zur Ableitung von Pflanzenparametern aus fernerkundlichen Bestandsmessungen sowie auf deren Bewertung. Zu den verwendeten komplementären Fernerkundungssystemen zählen die Sensoren EO-1 Hyperion und ALI, Envisat ASAR sowie TerraSAR-X. Für die optischen Hyper- und Multispektralsysteme werden die Reflexion verschiedener Spektralbereiche sowie die Performanz der daraus abgeleiteten Vegetationsindizes untersucht und bewertet. Im Hinblick auf die verwendeten Radarsysteme konzentriert sich die Untersuchung auf Parameter wie Wellenlänge, Einfallswinkel, Radarrückstreuung und Polarisation. Die Eigenschaften verschiedener Parameterkombinationen werden hierbei dargestellt und der komplementäre Beitrag der Radarfernerkundung zur Wachstumsüberwachung bewertet. Hierzu wurden zwei Testgebiete, eines für Winterweizen in der Nordchinesischen Tiefebene und eines für Reis im Nordosten Chinas ausgewählt. In beiden Gebieten wurden während der Wachstumsperioden umfangreiche Feldmessungen von Bestandsparametern während der Satellitenüberflüge oder zeitnah dazu durchgeführt. Mit Hilfe von linearen Regressionsmodellen zwischen Satellitendaten und Biomasse wird die Sensitivität hyperspektraler Reflexion und Radarrückstreuung im Hinblick auf das Wachstum des Winterweizens untersucht. Für die optischen Daten werden drei verschiedene Modelvarianten untersucht: traditionelle Vegetationsindices berechnet aus Multispektraldaten, traditionelle Vegetationsindices berechnet aus Hyperspektraldaten sowie die Berechnung von Normalised Ratio Indices (NRI) basierend auf allen möglichen 2-Band Kombinationen im Spektralbereich zwischen 400 und 2500 nm. Weiterhin wird die gemessene Biomasse mit der gleichpolarisierten (VV) C-Band Rückstreuung des Envisat ASAR Sensors linear in Beziehung gesetzt. Um den komplementären Informationsgehalt von Hyperspektral und Radardaten zu nutzen, werden optische und Radardaten für die Parameterableitung kombiniert eingesetzt. Das Hauptziel für das Reisanbaugebiet im Nordosten Chinas ist das Verständnis über die kohärente Dualpolarimetrische X-Band Rückstreuung zu verschiedenen phänologischen Wachstumsstadien. Hierfür werden die gleichpolarisierte TerraSAR-X Rückstreuung (HH und VV) sowie abgeleitete polarimetrische Parameter untersucht und mit verschiedenen Ebenen im Bestand in Beziehung gesetzt. Weiterhin wird der Einfluss der Variation von Einfallswinkel und Auflösung auf die Bestandsparameterableitung quantifiziert. Neben der Signatur von HH und VV ermöglichen vor allem die polarimetrischen Parameter Phasendifferenz, Ratio, Koherenz und Entropy-Alpha die Bestimmung bestimmter Wachstumsstadien. Die Ergebnisse der Arbeit zeigen, dass die komplementären Fernerkundungssysteme Optik und Radar die Ableitung von Pflanzenparametern und die Bestimmung von Heterogenitäten in den Beständen ermöglichen. Die Synergien diesbezüglich müssen auch in Zukunft weiter untersucht werden, da neue und immer variablere Fernerkundungssysteme zur Verfügung stehen werden und das Umweltmanagement weiter verbessern können
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