Universitätsbibliothek

The Antarctic Ice Sheet stores ~91% of the global ice volume which is equivalent to a sea-level rise of 58.3 meters. Recent disintegration events of ice shelves and retreating glaciers along the Antarctic Peninsula and West Antarctica indicate the current vulnerable state of the Antarctic Ice Sheet. Glacier tongues and ice shelves create a safety band around Antarctica with buttressing effects on ice discharge. Current decreases in glacier and ice shelf extent reduce the effective buttressing forces and increase ice discharge of grounded ice. The consequence is a higher contribution to sea-level rise from the Antarctic Ice Sheet. So far, it is unresolved which proportion of Antarctic glacier retreat can be attributed to climate change and which part to the natural cycle of growth and decay in the lifetime of a glacier. The quantitative assessment of the magnitude, spatial extent, distribution, and dynamics of circum-Antarctic glacier and ice shelf retreat is of utmost importance to monitor Antarctica’s weakening safety band. In remote areas like Antarctica, earth observation provides optimal properties for large-scale mapping and monitoring of glaciers and ice shelves. Nowadays, the variety of available satellite sensors, technical advancements regarding spatial resolution and revisit times, as well as open satellite data archives create an ideal basis for monitoring calving front change. A systematic review conducted within this thesis revealed major gaps in the availability of glacier and ice shelf front position measurements despite the improved satellite data availability. The previously limited availability of satellite imagery and the time-consuming manual delineation of calving fronts did neither allow a circum-Antarctic assessment of glacier retreat nor the assessment of intra-annual changes in glacier front position. To advance the understanding of Antarctic glacier front change, this thesis presents a novel automated approach for calving front extraction and explores drivers of glacier retreat. A comprehensive review of existing methods for glacier front extraction ascertained the lack of a fully automatic approach for large-scale monitoring of Antarctic calving fronts using radar imagery. Similar backscatter characteristics of different ice types, seasonally changing backscatter values, multi-year sea ice, and mélange made it challenging to implement an automated approach with traditional image processing techniques. Therefore, the present abundance of satellite data is best exploited by integrating recent developments in big data and artificial intelligence (AI) research to derive circum-Antarctic calving front dynamics. In the context of this thesis, the novel AI-based framework “AntarcticLINES” (Antarctic Glacier and Ice Shelf Front Time Series) was created which provides a fully automated processing chain for calving front extraction from Sentinel-1 imagery. Open access Sentinel-1 radar imagery is an ideal data source for monitoring current and future changes in the Antarctic coastline with revisit times of less than six days and all-weather imaging capabilities. The developed processing chain includes the pre-processing of dual-polarized Sentinel-1 imagery for machine learning applications. 38 Sentinel-1 scenes were used to train the deep learning architecture U-Net for image segmentation. The trained weights of the neural network can be used to segment Sentinel-1 scenes into land ice and ocean. Additional post-processing ensures even more accurate results by including morphological filtering before extracting the final coastline. A comprehensive accuracy assessment has proven the correct extraction of the coastline. On average, the automatically extracted coastline deviates by 2-3 pixels (93 m) from a manual delineation. This accuracy is in range with deviations between manually delineated coastlines from different experts. For the first time, the fully automated framework AntarcticLINES enabled the extraction of intra-annual glacier front fluctuations to assess seasonal variations in calving front change. Thereby, for example, an increased calving frequency of Pine Island Glacier and a beginning disintegration of Glenzer Glacier were revealed. Besides, the extraction of the entire Antarctic coastline for 2018 highlighted the large-scale applicability of the developed approach. Accurate results for entire Antarctica were derived except for the Western Antarctic Peninsula where training imagery was not sufficient and should be included in future studies. Furthermore, this dissertation presents an unprecedented record of circum-Antarctic calving front change over the last two decades. The newly extracted coastline for 2018 was compared to previous coastline products from 2009 and 1997. This revealed that the Antarctic Ice Sheet shrank 29,618±1193 km2 in extent between 1997-2008 and gained an area of 7,108±1029 km2 between 2009-2018. Glacier retreat concentrated along the Antarctic Peninsula and West Antarctica. The only East Antarctic coastal sector primarily experiencing calving front retreat was Wilkes Land in 2009-2018. Finally, potential drivers of circum-Antarctic glacier retreat were identified by combining data on glacier front change with changes in climate variables. It was found that strengthening westerlies, snowmelt, rising sea surface temperatures, and decreasing sea ice cover forced glacier retreat over the last two decades. Relative changes in mean air temperature could not be identified as a driver for glacier retreat and further investigations on extreme events in air temperature are necessary to assess the effect of atmospheric forcing on frontal retreat. The strengthening of all identified drivers was closely connected to positive phases of the Southern Annular Mode (SAM). With increasing greenhouse gases and ozone depletion, positive phases of SAM will occur more often and force glacier retreat even further in the future. Within this thesis, a comprehensive review on existing Antarctic glacier and ice shelf front studies was conducted revealing major gaps in Antarctic calving front records. Therefore, a fully automated processing chain for glacier and ice shelf front extraction was implemented to track circum-Antarctic calving front fluctuations on an intra-annual basis. The large-scale applicability was certified by presenting two decades of circum-Antarctic calving front change. In combination with climate variables, drivers of recent glacier retreat were identified. In the future, the presented framework AntarcticLINES will greatly contribute to the constant monitoring of the Antarctic coastline under the pressure of a changing climate.

Climate change and associated Arctic amplification cause a degradation of permafrost which in turn has major implications for the environment. The potential turnover of frozen ground from a carbon sink to a carbon source, eroding coastlines, landslides, amplified surface deformation and endangerment of human infrastructure are some of the consequences connected with thawing permafrost. Satellite remote sensing is hereby a powerful tool to identify and monitor these features and processes on a spatially explicit, cheap, operational, long-term basis and up to circum-Arctic scale. By filtering after a selection of relevant keywords, a total of 325 articles from 30 international journals published during the last two decades were analyzed based on study location, spatio- temporal resolution of applied remote sensing data, platform, sensor combination and studied environmental focus for a comprehensive overview of past achievements, current efforts, together with future challenges and opportunities. The temporal development of publication frequency, utilized platforms/sensors and the addressed environmental topic is thereby highlighted. The total number of publications more than doubled since 2015. Distinct geographical study hot spots were revealed, while at the same time large portions of the continuous permafrost zone are still only sparsely covered by satellite remote sensing investigations. Moreover, studies related to Arctic greenhouse gas emissions in the context of permafrost degradation appear heavily underrepresented. New tools (e.g., Google Earth Engine (GEE)), methodologies (e.g., deep learning or data fusion etc.)and satellite data (e.g., the Methane Remote Sensing LiDAR Mission (Merlin) and the Sentinel-fleet)will thereby enable future studies to further investigate the distribution of permafrost, its thermal state and its implications on the environment such as thermokarst features and greenhouse gas emission rates on increasingly larger spatial and temporal scales.

Plant phenology is well known to be affected by meteorology. Observed changes in the occurrence of phenological phases arecommonly considered some of the most obvious effects of climate change. However, current climate models lack a representationof vegetation suitable for studying future changes in phenology itself. This study presents a statistical-dynamical modelingapproach for Bavaria in southern Germany, using over 13,000 paired samples of phenological and meteorological data foranalyses and climate change scenarios provided by a state-of-the-art regional climate model (RCM). Anomalies of severalmeteorological variables were used as predictors and phenological anomalies of the flowering date of the test plantForsythiasuspensaas predictand. Several cross-validated prediction models using various numbers and differently constructed predictorswere developed, compared, and evaluated via bootstrapping. As our approach needs a small set of meteorological observationsper phenological station, it allows for reliable parameter estimation and an easy transfer to other regions. The most robust andsuccessful model comprises predictors based on mean temperature, precipitation, wind velocity, and snow depth. Its averagecoefficient of determination and root mean square error (RMSE) per station are 60% and ± 8.6 days, respectively. However, theprediction error strongly differs among stations. When transferred to other indicator plants, this method achieves a comparablelevel of predictive accuracy. Its application to two climate change scenarios reveals distinct changes for various plants andregions. The flowering date is simulated to occur between 5 and 25 days earlier at the end of the twenty-first century comparedto the phenology of the reference period (1961–1990).

Accurate and detailed spatial soil information is essential for environmental modelling, risk assessment and decision making. The use of Remote Sensing data as secondary sources of information in digital soil mapping has been found to be cost effective and less time consuming compared to traditional soil mapping approaches. But the potentials of Remote Sensing data in improving knowledge of local scale soil information in West Africa have not been fully explored. This study investigated the use of high spatial resolution satellite data (RapidEye and Landsat), terrain/climatic data and laboratory analysed soil samples to map the spatial distribution of six soil properties–sand, silt, clay, cation exchange capacity (CEC), soil organic carbon (SOC) and nitrogen–in a 580 km2 agricultural watershed in south-western Burkina Faso. Four statistical prediction models–multiple linear regression (MLR), random forest regression (RFR), support vector machine (SVM), stochastic gradient boosting (SGB)–were tested and compared. Internal validation was conducted by cross validation while the predictions were validated against an independent set of soil samples considering the modelling area and an extrapolation area. Model performance statistics revealed that the machine learning techniques performed marginally better than the MLR, with the RFR providing in most cases the highest accuracy. The inability of MLR to handle non-linear relationships between dependent and independent variables was found to be a limitation in accurately predicting soil properties at unsampled locations. Satellite data acquired during ploughing or early crop development stages (e.g. May, June) were found to be the most important spectral predictors while elevation, temperature and precipitation came up as prominent terrain/climatic variables in predicting soil properties. The results further showed that shortwave infrared and near infrared channels of Landsat8 as well as soil specific indices of redness, coloration and saturation were prominent predictors in digital soil mapping. Considering the increased availability of freely available Remote Sensing data (e.g. Landsat, SRTM, Sentinels), soil information at local and regional scales in data poor regions such as West Africa can be improved with relatively little financial and human resources.

The contribution highlights the use of Landsat spectral-temporal metrics (STMs) for the detection of surface anomalies that are potentially related to buried near-surface paleogeomorphological deposits in the Nile Delta (Egypt), in particular for a buried river branch close to Buto. The processing was completed in the Google Earth Engine (GEE) for the entire Nile Delta and for selected seasons of the year (summer/winter) using Landsat data from 1985 to 2019. We derived the STMs of the tasseled cap transformation (TC), the Normalized Difference Wetness Index (NDWI), and the Normalized Difference Vegetation Index (NDVI). These features were compared to historical topographic maps of the Survey of Egypt, CORONA imagery, the digital elevation model of the TanDEM-X mission, and modern high-resolution satellite imagery. The results suggest that the extent of channels is best revealed when differencing the median NDWI between summer (July/August) and winter (January/February) seasons (ΔNDWI). The observed difference is likely due to lower soil/plant moisture during summer, which is potentially caused by coarser-grained deposits and the morphology of the former levee. Similar anomalies were found in the immediate surroundings of several Pleistocene sand hills (“geziras”) and settlement mounds (“tells”) of the eastern delta, which allowed some mapping of the potential near-surface continuation. Such anomalies were not observed for the surroundings of tells of the western Nile Delta. Additional linear and meandering ΔNDWI anomalies were found in the eastern Nile Delta in the immediate surroundings of the ancient site of Bubastis (Tell Basta), as well as several kilometers north of Zagazig. These anomalies might indicate former courses of Nile river branches. However, the ΔNDWI does not provide an unambiguous delineation.

Städte sehen sich in der Entwicklung ihres Einzelhandelsangebots zunehmend Konkurrenzsituationen zwischen traditionellen Innenstadt- und neu entstehenden Stadtrandlagen ausgesetzt, die einerseits die gestiegenen Flächen- und Produktivitätsansprüche der Unternehmen eher erfüllen, während andererseits Bürger, Politik und etablierter Handel ein ‚Aussterben’ der Innenstädte befürchten. Die Konsequenzen planerischer Entscheidungen in dieser Hinsicht abzuschätzen, wird zunehmend komplexer. Dafür sind ebenso eine stärkere Individualisierung des Konsumverhaltens verantwortlich, wie eine gestiegene Sensibilität gegenüber Verkehrs- und Emissionsbelastungen. Modellierungen und Simulationen können einen Beitrag zu fundierter Entscheidungsfindung leisten, indem sie durch Prognosen von Szenarien mit unterschiedlichen Rahmenbedingungen solche Auswirkungen aufzeigen. In der Vergangenheit wurden Kaufkraftströme durch Modelle abgebildet, die auf aggregierten Ausgangsdaten und Analogieschlüssen zu Naturgesetzen (Gravitations-, Potenzialansatz) oder nutzentheoretischen Annahmen (Diskreter Entscheidungsansatz) beruhten. In dieser Arbeit wird dafür erstmals ein agentenbasierter Ansatz angewendet, da sich so individuelle Ausdifferenzierungen des Konsumentenhandelns wesentlich leichter integrieren und Ergebnisse anschaulicher präsentieren lassen. Ursprünglich entstammt die Idee zur Agententechnologie einem Forschungsfeld der Informatik, der Künstlichen Intelligenz. Ziel war hier, Algorithmen zu entwickeln, die aus einer Menge von kleinen Softwarebausteinen bestehen, die zur Lösung eines Problems miteinander in Kommunikation treten und sich selbst zielbezogen anordnen. Somit schreibt sich der Algorithmus im Grunde selbst. Dieses Konzept kann in den Sozialwissenschaften als Modellierungsparadigma genutzt werden, insofern als dass sie der Idee der Selbstorganisation von Gesellschaften recht nahe kommt. Insbesondere zeichnen sich Multiagentensysteme durch eine dezentrale Kontrolle und Datenvorhaltung aus, die es darüber hinaus ermöglichen, auch komplexe Systeme von Entscheidungsprozessen mit wenigen Spezifikationen darzustellen. Damit begegnet der Agentenansatz vielen Einwänden gegen Analogie- und Entscheidungsmodelle. Durch die konsequente Einnahme einer individuenbezogenen Sichtweise ist die individuelle Ausdifferenzierung von Entscheidungsprozessen viel eher abbildbar. Für das Forschungsprojekt konnten für einenm ntersuchungsraum in Nordschweden (Funktionalregion Umeå, ca. 140.000 Einwohner) individuenbezogene Einwohnerdaten verfügbar gemacht werden. Diese enthielten u.a. Lagekoordinaten des Wohn- und Arbeitsorts, Alter, Geschlecht, verfügbares Einkommen und Angaben zur Haushaltsstruktur. Verbunden mit Erkenntnissen aus empirischen Untersuchungen (Konsumentenbefragung, Geschäftskartierung) stellten sie die Eingabegrößen für ein agentenbasiertes Modell der Einkaufsstättenwahl bei der Lebensmittelversorgung dar. Die Konsumentenbefragung stellte regressionsanalytische Abhängigkeiten zwischen sozioökonomischen Daten und Konsumpräferenzen bezüglich einzelner Geschäftsattribute (Preisniveau, Produktqualität, Sortimentsbreite, Service etc.) her, die gleichen Attribute wurden für die Geschäfte erhoben. Somit können Kaufkraftströme zwischen Einzelelementen der Nachfrage (individuelle Konsumenten) und des Angebots (einzelne Geschäftsstandorte) als individuell variierende Bewertung der Geschäfte durch die Agenten dargestellt werden, gemäß derer die Agenten ihre lebensmittelrelevante Kaufkraft auf die Geschäfte verteilen. Für die Geschäfte der gesamten Region konnten Gütemaßwerte bis 0,7 erreicht werden, für einzelne Betriebsformate auch über 0,9. Dies zeigt, dass auch bei der Verwendung individuenbezogener Modelle, die mit einer deutlich höheren Anzahl Freiheitsgraden behaftet sind als ihre aggregierten Gegenstücke, hohe Prognosequalitäten für Umsatzschätzungen von Standorten erreicht werden können. Gleichzeitig bietet der Agentenansatz die Möglichkeit, einzelne Simulationsobjekte bei ihrer Entscheidungsfindung und ihren Aktivitäten zu verfolgen. Dabei konnten ebenfalls plausible Einkaufsmuster abgebildet werden. Da die Distanz vom Wohn- bzw. Arbeitsort zum Geschäft Bestandteil des Modells ist, können auch die von den Einwohnern zum Zweck der Grundversorgung zu leistenden Distanzaufwände in verschiedenen Angebotssituationen analysiert werden. Als Fallstudie wurde ein Vergleich von zwei Situationen 1997 und 2004 vorgenommen. Während dieses Zeitraums haben im Untersuchungsgebiet grundlegende Veränderungen der Einzelhandelsstruktur stattgefunden, die zu einem weitgehenden Rückzug des Angebots aus den peripheren ländlichen Gebieten geführt haben. Die Ergebnisse zeigteneine hohe Übereinstimmung mit den auf nationaler Ebene erhobenen Mobilitätsdaten, ließen aber auch einen differenzierten Blick auf die unterschiedliche Betroffenheit der Einwohner der Region zu. An agentenbasierte Simulationen werden in den Sozialwissenschaften große Erwartungen geknüpft, da sie erstmals ermöglichen, gesellschaftliche Phänomene auf der Ebene ihres Zustandekommens, dem Individuum, zu erfassen, sowie komplexe mentale Vorgänge des Handelns, Lernens und Kommunizierens auf einfache Weise in ein Modell zu integrieren. Mit der vorliegenden Arbeit wurde im Bereich der Konsumentenforschung erstmals ein solcher Ansatz auf regionaler Ebene angewendet, um zu planungsrelevanten Aussagen zu gelangen. In Kombination mit anderen Anwendungen im Bereich der Bevölkerungsprognose, des Verkehrs und der innerstädtischen Migration haben Agentensimulationen alle Voraussetzungen zu einem zukunftsweisenden Paradigma für die Raum- und Fachplanung.

Mit der vorliegenden Arbeit werden konventionelle thermische Kraftwerke an deutschen Flüssen identifiziert, bei denen aufgrund hoher Flusswassertemperaturen im Zusammenhang mit wasserrechtlichen Grenzwerten Leistungseinschränkungen auftraten. Weiterhin wird aufgezeigt, wie sich die Wassertemperaturen der Flüsse in der Vergangenheit (rezent) entwickelt haben und wie sie sich zukünftig im Kontext des Klimawandels entwickeln könnten. Mittels Literaturrecherche, Medienanalyse und schriftlicher Befragung wurden konventionelle thermische Kraftwerke identifiziert, welche wassertemperaturbedingte Leistungseinschränkungen verzeichneten. Die meisten dieser Leistungseinschränkungen zwischen 1976 und 2007 zeigen sich bei großen Kraftwerken mit einer elektrischen Bruttoleistung über 300 Megawatt, bei Steinkohle- und Kernkraftwerken, bei Kraftwerken mit Durchlaufkühlung und bei solchen, die zwischen 1960 und 1990 in Betrieb gingen. Trendanalysen interpolierter und homogenisierter, rezenter Wassertemperaturzeitreihen deutscher Flüsse ergeben positive Trends v. a. im Frühjahr und Sommer. Die Zählstatistik zeigt in den Jahren 1994, 2003 und 2006 die meisten Tage mit sehr hohen und extrem hohen Wassertemperaturen in den Sommermonaten. In diesen Jahren traten gleichzeitig 63 % aller identifizierter wassertemperaturbedingter Leistungseinschränkungen bei Kraftwerken, meist zwischen Juni und August, auf. Für die Trendanalysen und den Mittelwertvergleich simulierter zukünftiger Wassertemperaturzeitreihen wurden drei Szenarien – B1, A1B und A2 sowie drei Zukunftsperioden 2011-2040, 2011/2041-2070, 2011/2071-2100 betrachtet. Es ergeben sich für die Zukunftsperiode 2011-2040 des A1B- oder A2-Szenarios in mindestens einem der Sommermonate eine Erwärmung und für das B1-Szenario negative oder keine Trends. Die mittleren Wassertemperaturen der Zukunftsperiode 2011-2040 zeigen in allen drei Szenarien gegenüber denen der Klimanormalperiode 1961-1990 positive Unterschiede in mindestens einem der Sommermonate. Für die beiden späteren Zukunftsperioden bis 2070 bzw. bis 2100 liegen in allen Wassertemperaturzeitreihen der drei Szenarien im Sommer positive Trends bzw. Differenzen gegenüber den mittleren Wassertemperaturen der Klimanormalperiode vor. Durch die Synthese der drei Analysen ist erkennbar, dass Isar, Rhein, Neckar, Saar, Elbe und Weser die meisten Kraftwerksstandorte mit wassertemperaturbedingten Leistungseinschränkungen verzeichnen. Es zeigen sich hier positive Trends sowohl in den rezenten als auch zukünftigen Wassertemperaturen für die Zukunftsperiode 2011-2040 des A1B- und A2-Szenarios in jeweils mindestens einem der Sommermonate. Gegenüber den mittleren Wassertemperaturen der Klimanormalperiode liegen für alle drei Szenarien positive Unterschiede der Wassertemperaturen vor. Bei einer Kraftwerkslaufzeit von 40-50 Jahren und einem Kernenergieausstieg 2022 bzw. 2034, werden 48-64 % bzw. 67-91 % der Kraftwerke mit wassertemperaturbedingten Leistungseinschränkungen bis 2022 bzw. 2034 außer Betrieb gehen. Bei einer Laufzeitverlängerung würden nach 2022 fünf der elf betroffenen Kernkraftwerke weiter am Netz bleiben. Somit kann es wieder zu wassertemperaturbedingten Leistungseinschränkungen kommen. In Deutschland sind nach wie vor große Kraftwerke an Flüssen geplant. Deren Kühlsysteme müssen entsprechend ausgewählt und konstruiert werden, um der zu erwartenden Erhöhung der Flusstemperaturen Rechnung zu tragen.

Monitoring the spatio-temporal development of vegetation is a challenging task in heterogeneous and cloud-prone landscapes. No single satellite sensor has thus far been able to provide consistent time series of high temporal and spatial resolution for such areas. In order to overcome this problem, data fusion algorithms such as the Enhanced Spatial and Temporal Adaptive Reflectance Fusion Model (ESTARFM) have been established and frequently used in recent years to generate high-resolution time series. In order to make it applicable to larger scales and to increase the input data availability especially in cloud-prone areas, an ESTARFM framework was developed in this study introducing several enhancements. An automatic filling of cloud gaps was included in the framework to make best use of available, even partly cloud-covered Landsat images. Furthermore, the ESTARFM algorithm was enhanced to automatically account for regional differences in the heterogeneity of the study area. The generation of time series was automated and the processing speed was accelerated significantly by parallelization. To test the performance of the developed ESTARFM framework, MODIS and Landsat-8 data were fused for generating an 8-day NDVI time series for a study area of approximately 98,000 km\(^{2}\) in West Africa. The results show that the ESTARFM framework can accurately produce high temporal resolution time series (average MAE (mean absolute error) of 0.02 for the dry season and 0.05 for the vegetative season) while keeping the spatial detail in such a heterogeneous, cloud-prone region. The developments introduced within the ESTARFM framework establish the basis for large-scale research on various geoscientific questions related to land degradation, changes in land surface phenology or agriculture

West African summer monsoon precipitation is characterized by distinct decadal variability. Due to its welldocumented link to oceanic boundary conditions in various ocean basins it represents a paradigm for decadal predictability. In this study, we reappraise this hypothesis for several sub-regions of sub-Saharan West Africa using the new German contribution to the coupled model intercomparison project phase 5 (CMIP5) near-term prediction system. In addition, we assume that dynamical downscaling of the global decadal predictions leads to an enhanced predictive skill because enhanced resolution improves the atmospheric response to oceanic forcing and landsurface feedbacks. Based on three regional climate models, a heterogeneous picture is drawn: none of the regional climate models outperforms the global decadal predictions or all other regional climate models in every region nor decade. However, for every test case at least one regional climate model was identified which outperforms the global predictions. The highest predictive skill is found in the western and central Sahel Zone with correlation coefficients and mean-square skill scores exceeding 0.9 and 0.8, respectively.

Rice is an important food crop and a large producer of green-house relevant methane. Accurate and timely maps of paddy fields are most important in the context of food security and greenhouse gas emission modelling. During their life-cycle, rice plants undergo a phenological development that influences their interaction with waves in the visible light and infrared spectrum. Rice growth has a distinctive signature in time series of remotely-sensed data. We used time series of MODIS (Moderate Resolution Imaging Spectroradiometer) products MOD13Q1 and MYD13Q1 and a one-class support vector machine to detect these signatures and classify paddy rice areas in continental China. Based on these classifications, we present a novel product for continental China that shows rice areas for the years 2002, 2005, 2010 and 2014 at 250-m resolution. Our classification has an overall accuracy of 0.90 and a kappa coefficient of 0.77 compared to our own reference dataset for 2014 and correlates highly with rice area statistics from China’s Statistical Yearbooks (R2 of 0.92 for 2010, 0.92 for 2005 and 0.90 for 2002). Moderate resolution time series analysis allows accurate and timely mapping of rice paddies over large areas with diverse cropping schemes.