Estimation of near-surface Air temperature during day and night-time from MODIS over Different LC/LU Using machine learning methods in Berlin

Urbanization is manifest by changes in the physical structure of the land surface, owing to extensive construction features such as buildings, street canyons, changes in the thermal structure because of materials of different thermal properties and also intensive human activities. Urban areas are generally also characterized by higher surface air temperatures as compared to the rural surroundings. This temperature excess can be up to 10-12°C and more and is referred to as the urban heat island(UHI)phenomenon. Since residents living in cities are especially affected by extreme temperature events, urban climate studies are gaining in importance. Currently, more than half of the world s population already lives in urban areas, which accentuates the major role agglomerations must play in mitigation and adaptation to climate change. Recommendations regarding behavioural patterns during heat stress situations and urban planning measures require a comprehensive understanding of the inner urban temperature distribution including the identification of thermal hot spots. Both very cold and very hot temperatures could affect the human health. Excessive exposure to heat is referred to as heat stress and excessive exposure to cold is referred to as cold stress. Urban temperature data (2 m temperature data) is very important for all investigations on the urban heat island (UHI) effect, human health. They are usually either based on remote sensing techniques or air temperature measurements or from models. Remote sensing data like infra-red surface temperature from airborne measuring instruments may have a very high spatial resolution and are presently available for many urban areas, but only in clear sky cases. This spatial resolution is appropriate to exhibit typical urban structures that are expected to cause the UHI effect. Nevertheless, information on surface temperature cannot replace air temperature data, since beside the problem that the former is typically only available for single days, there is no fixed relation between surface and air temperatures. Especially for systematic analyses of the relationship between urban structures and 2m temperatures for different weather situations a large data basis is desirable. Air temperature data can be obtained from mobile measurements and measurement at permanent or temporary weather stations. On the one hand, the use of weather stations provides high data accuracy using a well-known standard technology. On the other hand, the spatial representation of weather station data within the urban environment, which is characterized by the surface composition including buildings, infrastructure and different types of land use, is very limited. Consequently, since the beginning of the 20th century, many efforts have been made to identify temperature patterns in urban areas with high spatial resolution instead of only using single point information. In this regard, in this study Air temperature (T2m) or Tair measurements from 20 ground weather stations in Berlin were used to estimate the relationship between air temperature and the remotely sensed land surface temperature (LST) measured by Moderate Resolution Imaging Spectroradiometer over different land-cover types (LCT). Knowing this relationship enables a better understanding of the magnitude and pattern of Urban Heat Island (UHI), by considering the contribution of land cover in the formation of UHI. In order to understand the seasonal behaviour of this relationship, the influence of the normalized difference vegetation index (NDVI) as an indicator of degree of vegetation on LST over different LCT was investigated. Next to it, to evaluate the influence of LCT, a regression analysis between LST and NDVI was made. The results demonstrate that the slope of regression depends on the LCT. It depicts a negative correlation between LST and NDVI over all LCTs. Our analysis indicates that the strength of correlations between LST and NDVI depends on the season, time of day, and land cover. This statistical analysis can also be used to assess the variation of the LST– T2m relationship during day- and night-time over different land covers. The results show that LSTDay and LSTNight are correlated significantly (p = 0.0001) with T2mDay(daytime air temperature) and T2mNight(night-time air temperature). The correlation (r) between LSTDay and TDay is higher in cold seasons than in warm seasons. Moreover, during cold seasons over every LCT, a higher correlation was observed during daytime than during night-time. In contrast, a reverse relationship was observed during warm seasons. It was found that in most cases, during daytime and in cold seasons, LST is lower than T2m. In warm seasons, however, a reverse relationship was observed over all land-cover types. In every season, LSTNight was lower than or close to T2mNight . Air temperature (Tair or T2m) is an important climatological variable for forest biosphere processes and climate change research. Due to the low density and the uneven distribution of weather stations, traditional ground-based observations cannot accurately capture the spatial distribution of Tair. Therefore, it is necessary to develop a method for the estimation of air temperature with reasonable accuracy and spatial and temporal resolution in the urban areas with low temperature gauge density. But since the estimation of meteorological variables using various statistical techniques (such as linear regression models or combined regression and kriging techniques for T interpolation) have been examined by many researchers and they came to conclusion that an appropriate machine learning technique could be a robust computational technique which has been used for the estimation of meteorological data as a function of the corresponding data of one or more reference stations. In this research, Tair in Berlin is estimated during the day and night-time over six land cover/land use (LC/LU) types by satellite remote sensing data over a large domain and a relatively long period (7 years). Aqua and Terra MODIS (Moderate Resolution Imaging Spectroradiometer) data and meteorological data for the period from 2007 to 2013 were collected to estimate Tair. Twelve environmental variables (land surface temperature (LST), normalized difference vegetation index (NDVI), Julian day, latitude, longitude, Emissivity31, Emissivity32, altitude, albedo, wind speed, wind direction and air pressure) were selected as predictors. Moreover, a comparison between LST from MODIS Terra and Aqua with daytime and night-time air temperatures (TDay , TNight) was done respectively and in addition, the spatial variability of LST and Tair relationship by applying a varying window size on the MODIS LST grid was examined. An analysis of the relationship between the observed Tair and the spatially averaged remotely sensed LST, indicated that 3 × 3 and 1 × 1 pixel size was the optimal window size for the statistical model estimating Tair from MODIS data during the day and night time, respectively. Three supervised learning methods (Adaptive Neuro Fuzzy Inference system (ANFIS), Artificial Neural Network (ANN) and Support vector machine (SVR)) were used to estimate Tair during the day and night-time, and their performances were validated by cross-validation for each LC/LU. by applying each technique, a estimator model of air temperature had been generated. The comparison between these methods has been done and finally we evaluated the accuracy of each model and choose the best one for the high-resolution temperature estimation. Moreover, tuning the hyper parameters of some models like SVR and ANN were investigated. For tuning the hyper parameters of SVR, Simulated Annealing (SA) was applied (SA-SVR model) and a multiple-layer feed-forward (MLF) neural networks with three layers and variable nodes in hidden layers had been applied with Levenberg-Marquardt back-propagation (LM-BP), in order to achieve higher accuracy in the estimation of Tair . Results indicated that the ANN model achieved better accuracy (RMSE=2.16°C, MAE =1.69°C, R2 =0.95) than SA-SVR model (RMSE= 2.50°C, MAE =1.92°C, R2=0.91) and ANFIS model (RMSE=2.88°C, MAE=2.2°C, R2=0.89) over six LC/LU during the day and night time. The Q-Q diagram of SA-SVR, ANFIS and ANN show that all three models slightly tended to underestimate and overestimate the extreme and low temperatures for all LC/LU classes during the day and night-time. The weak performance in the extreme and low temperatures are a consequence of the small numbers of data in these temperatures. These satisfactory results indicate that this approach is proper for estimating air temperature and spatial window size is an important factor that should be considered in the estimation of air temperature. Moreover, for better understanding the relationship between LST and Tair in Berlin during day and night-time, over six land LC/LU types namely airport, agriculture, urban area, forest, industrial and needle leaf trees, two input variable selection methods were applied. Input variable selection is an essential step in environmental, biological, industrial and climatological applications. One approach which help us in better understanding data, decreasing computation effort, the impact of curse of dimensionality and improving the estimator performance. Through input variable selection the irrelevant or redundant variables will be to eliminated therefore a suitable subset of variables is identified as the input of a model. Meanwhile, the complexity of the model structure is simplified, and the computational efficiency is improved. In this work, the two input variable selection methods, including brute force search and greedy best search algorithm using artificial neural network (ANN) were considered for estimating of near surface air temperature from MODIS over six LC/LU types. The motivation behind this research was to formulate a more efficient way of choosing input variables using ANN models of environmental processes. Moreover, AIC, BIC and RMSE are considered for ranking the features and finding a subset of potential variables which improves the overall estimation performance. In this study, Aqua and Terra MODIS data and meteorological data for the period from 2007 to 2013 were collected to estimate Tair .Moreover, twelve environmental variables LST, normalized difference vegetation index (NDVI), Julian day, latitude, longitude, Emis31, Emis32, altitude, albedo, wind speed, wind direction and air pressure were selected as predictors. The results show that the LC/LU has a key factor in the relationship between Tair and LST. The results show that the effectiveness of optimal models in estimation Tair is varied in different LC/LU because of the specific heat capacities of different LC/LU. Air temperature mainly rely on the heat transfer process which was significantly affected by the local radiation budget. Generally, air is heated much quicker over barren land than forest because, barren land has lower heat capacity than forest. Vegetation can cause to latent heat flux, such as enhancing or reducing transpiration and cool the Tair in forests. In this study, the cooling effect was not taken into account because of roughly distribution of meteorological stations across different vegetation types. Therefore, it was difficult to consider the vegetation type in our models. However, land cover also affected land surface albedo, thus, the influence of LU/LC on estimating Tair was conditional and time dependent because different variables are selected for the same LU/LC during day and night-time. Moreover, another issue that we tried to find an answer was, what is the pitfall of using the global model and what is the advantage of features selection? It has been debated that inferencing from a model with all the features which thought to be important is simple and avoid the complications of model selection.Urbanisierung stellt eine Veränderungen in der physikalischen Struktur der Landoberfläche durch umfangreiche Konstruktionsmerkmale, wie Gebäude und Straßenschluchten, dar. Die damit verbundenen Änderungen der thermischen Struktur durch Verwendung von Materialien mit unterschiedlichen thermischen Eigenschaften sowie intensive menschliche Aktivität spielen hierbei eine wichtige Rolle. Urbane Gebiete sind im Allgemeinen durch eine höhere Oberflächentemperatur im Vergleich zur ländlichen Umgebung gekennzeichnet. Der Temperaturüberschuss kann bis zu 10-12°C und mehr betragen und wird als Phänomen der städtischen Wärmeinsel (UHI) bezeichnet. Da in Städten lebende Menschen besonders stark von extremen Temperaturereignissen betroffen sind, gewinnen Studien zum urbanen Klima vermehrt an Bedeutung. Derzeit lebt mehr als die Hälfte der Weltbevölkerung in urbanen Gebieten. Dies unterstreicht die wichtige Rolle die Ballungsräume in Bezug auf Minderung und Anpassung an den Klimawandel darstellt. Empfehlungen bezüglich des Verhaltens während Hitzestresssituationen sowie städtebauliche Maßnahmen erfordern ein umfangreiches Verständnis der innerstädtischen Temperaturverteilung einschließlich der Identifizierung von thermischen Hotspots. Sehr kalte wie auch sehr heiße Temperaturen können gleichermaßen die menschliche Gesundheit beeinträchtigen. Übermäßige Hitzebelastung wird als Hitzestress bezeichnet, übermäßige Kältebelastung als Kältestress. Urbane Temperaturdaten (2m Temperaturdaten) sind wichtig für alle Untersuchungen bezüglich des urbanen Wärmeinseleffekts (UHI), der menschlichen Gesundheit. Normalerweise basieren die Daten entweder auf Fernerkundungstechniken oder auf Messungen oder Simulationen der Lufttemperatur. Fernerkundungsdaten, wie die der Infrarot-Oberflächentemperatur von satellitengestützen Messinstrumenten können eine sehr hohe räumliche Auflösung haben und sind gegenwärtig für viele urbane Gebiete zugänglich, doch nur im Fall von wolkenfreiem Himmel. Die räumliche Auflösung ist dafür geeignet typische urbane Strukturen zu erkennen, die den UHI-Effekt auslösen. Dennoch können Informationen der Oberflächentemperatur, die Lufttemperaturdaten nicht ersetzen, da neben dem Problem, dass die Oberflächentemperatur in der Regel nur für einzelne Tage zur Verfügung steht, es keinen festen Zusammenhang zwischen Oberflächentemperatur und Lufttemperatur besteht. Besonders für systematische Analysen der Zusammenhänge zwischen urbanen Strukturen und der 2m-Temperatur unterschiedlicher Wettersituationen ist eine hohe Datenbasis wünschenswert. Daten der Lufttemperatur können von mobilen Messungen und permanenten Messstationen oder von temporären Wetterstationen erhalten werden. Einerseits bieten die Wetterstationen eine hohe Datenqualität durch Verwendung von bekannten Standard-Technologien; andererseits ist die räumliche Verteilung der Wetterstationsdaten in der urbanen Umgebung, die durch die oberflächliche Komposition von Gebäuden, Infrastruktur und verschiedenen Landnutzungsklassen charakterisiert ist, sehr eingeschränkt. Seit Beginn des 20. Jahrhunderts konnten somit viele Vorzüge bei der Identifizierung von Temperaturmustern in urbanen Gebieten mit hoher räumlicher Auflösung erzielt werden anstatt nur einzelne Punktinformationen zu nutzen. Folglich werden für diese Studie Lufttemperatur (T2m) oder Tair Messungen von 20 Bodenwetterstationen in Berlin verwendet, um den Zusammenhang zwischen Lufttemperatur und Fernerkundungsdaten der Oberflächentemperatur (LST) gemessen vom Moderate Resolution Imaging Spectroradiometer (MODIS) über verschiedene Landnutzungstypen (LCT). Die Kenntnis über diesen Zusammenhang ermöglicht ein besseres Verständnis der Stärke und Muster von urbanen Wärmeinseln (UHI) durch Beachtung der Verteilung der Oberflächenbeschaffenheit bei Ausbildung von UHI. Um das saisonale Verhalten dieses Zusammenhangs zu verstehen, wurde der Einfluss des normalisierten Differenzvegetationsindex (NDVI) als ein Indikator für den Vegetationsgrad auf LST über verschiedene LCT untersucht. Darüber hinaus wurde eine Regressionsanalyse zwischen der LST und dem NDVI durchgeführt, um den Einfluss der LCT zu bewerten. Die Ergebnisse zeigen, dass die Steigung der Regressionsgeraden von der LST abhängt. Es besteht eine negative Korrelation zwischen LST und NDVI über alle LCTs. Unsere Analyse signalisiert, dass die Stärke der Korrelation zwischen LST und NDVI von der Jahreszeit, der Tageszeit sowie der Landnutzung abhängig ist. Die statistische Analyse kann auch verwendet werden, um die Variation der LST-T2m-Beziegung während der Tages- und Nachtzeit über verschiedene Bodenbedeckungen zu bewerten. Die Ergebnisse zeigen eine signifikante Korrelation (p=0.0001) von LSTday und LSTnight mit der T2mDay (Lufttemperatur tagsüber) und der T2mNight (Lufttemperatur nachts). Zwischen LSTday und Tday ist die Korrelation (r) in der kalten Jahreszeit höher als in der warmen. Darüber hinaus wurde eine höhere Korrelation während der kalten Jahreszeit über alle LCTs am Tag beobachtet als in der Nacht. In der warmen Jahreszeit wurde im Gegensatz dazu ein umgekehrter Zusammenhang festgestellt. Es wurde beobachtet, dass in den meisten Fällen, tagsüber und in kalten Jahreszeiten, die LST niedriger ist als die T2m. In warmen Jahreszeiten wurde jedoch ein umgekehrter Zusammenhang über alle Landbedeckungsarten beobachtet. In jeder Saison war die LSTNight niedriger oder fast gleich wie die T2mNight. Die Lufttemperatur (Tair oder T2m) ist eine wichtige klimatologischen Variable für Prozesse der Waldbiosphäre und die Erforschung des Klimawandels. Aufgrund der geringen Dichte und der ungleichmäßigen Verteilung von Wetterstationen können herkömmliche bodengebundene Beobachtungen die räumliche Verteilung von Tair nicht genau erfassen. Daher ist es notwendig, eine Methode zur Abschätzung der Lufttemperatur mit angemessener Genauigkeit sowie räumlicher und zeitlicher Auflösung in urbanen Gebieten mit niedriger Temperaturmessdichte zu entwickeln. Da aber die Abschätzung meteorologischer Variablen mit verschiedenen statistischen Techniken (wie linearen Regressionsmodellen und kombinierten Regressions- und Krigingtechniken für die T-Interpolation) von vielen Forschern untersucht wurde, kamen sie zu dem Schluss, dass eine geeignete ‚machine learning‘ Technik eine robuste Rechentechnik sein könnte, die für die Abschätzung meteorologischer Daten in Abhängigkeit von entsprechenden Daten einer oder mehrerer Referenzstationen verwendete. In dieser Studie wird Tair in Berlin tagsüber sowie nachts über sechs Landbedeckungs/Landnutzungsarten (LC/LU) mittels Satelliten-Fernerkundungsdaten über einen großen Bereich und einem relativ langen Zeitraum (7Jahre) geschätzt. Daten des ‚Terra und Aqua MODIS‘ (Moderate Resolution Imaging Spectroradiometer) und meteorologische Daten für den Zeitraum von 2007 bis 2013 wurden gesammelt, um Tair zu bestimmen. Als Prädikatoren wurden zwölf Umweltvariablen (Landoberflächentemperatur (LST), normalisierter Differenzvegetationsindex (NDVI), Julianischer Tag, Breitengrad, Längengrad, Emissionsgrad 31, Emissionsgrad 32, Höhe, Albedo, Windgeschwindigkeit, Windrichtung und Luftdruck) ausgewählt. Drüber hinaus wurde ein Vergleich zwischen LST von MODIS Terra und Aqua mit Tages- und Nachtlufttemperaturen (TDay , TNight) durchgeführt bzw. zusätzlich die räumliche Variabilität des Zusammenhangs von LST und Tair durch Anwendung einer variierenden Fenstergröße auf das MODIS LST-Gitter untersucht. Eine Analyse der Beziehung zwischen der beobachteten Tair und dem räumlich gemittelten Fernerkundungs-LST ergab, dass die Größe 3 x 3 und 1 x 1 Pixel die optimale Fenstergröße für das statistische Modell war, das Tair aus den MODIS-Daten während Tages- bzw. Nachtzeit schätzte. Drei überwachte Lernmethoden (Adaptive Neuro Fuzzy Inference system (ANFIS), künstliches neuronales Netzwerk (ANN) und Support vector machine (SVR)) wurden verwendet, um Tair währen des Tages und der Nacht zu schätzen. Die Leistungen wurden durch Kreuzvalidierung für jede LC/LU validiert. Durch die Anwendung jeder Technik wurde ein Schätzmodell ausgewertet und das Beste für die hochauflösende Temperaturschätzung ausgewählt. Darüber hinaus wurde die Einstellung der Hyperparameter einiger Modelle wie SVR und ANN untersucht. Für die Einstellung der Hyperparameter von SVR wurde ‚Simulated Annealing‘ (SA) angewendet (SA-SVR Modell). Mit der Levenberg-Marquardt Backpropagation (LM-BP) wurde ein mehrschichtiges Feed-forward (MLF) neuronales Netzwerk mit drei Schichten und variablen Knoten in versteckten Schichten angewendet, um eine höhere Genauigkeit bei der Schätzung von Tair zu erreichen. Die Ergebnisse zeigten, dass das ANN-Modell über sechs LC/LU, tags sowie nachts, eine höhere Genauigkeit erreichte (RMSE=2.16°C, MAE =1.69°C, R2 =0.95) als das SA-SVR-Modell (RMSE= 2.50°C, MAE =1.92°C, R2=0.91) und das ANFIS-Modell (RMSE=2.88°C, MAE=2.2°C, R2=0.89). Das Q-Q-Diagramm von SA-SVR, ANFIS und ANN zeigt, dass alle drei Modelle die extrem hohen und niedrigen Temperaturen für alle LC/LU-Klassen tagsüber sowie nachts leicht unterschätzen und überschätzen. Die schwache Leistung bei extrem hohen und niedrigen Temperaturen ist eine Folge der geringen Datenmenge bei diesen Temperaturen. Um den Zusammenhang zwischen LST und Tair in Berlin bei Tag und Nacht besser verstehen zu können, wurden über sechs Land-LC/LU-Typen (Flughafen, Landwirtschaft, urbanes Gebiet, Wald-, Industrie- und Nadelblattbäume) zwei Auswahlmethoden für die Eingangsvariablen angewendet. Die Auswahl dieser Variablen ist ein wesentlicher Schritt in ökologischen, biologischen, industriellen und klimatologischen Anwendungen. Ein Ansatz, der uns hilft, Daten besser zu verstehen, den Rechenaufwand zu verringern, die Auswirkungen des Fluches der Dimensionalität und die Lei

Observational Characterization of the Downward Atmospheric Longwave Radiation at the Surface in the City of São Paulo

This work describes the seasonal and diurnal variations of downward longwave atmospheric irradiance (LW) at the surface in São Paulo, Brazil, using 5-min-averaged values of LW, air temperature, relative humidity, and solar radiation observed continuously and simultaneously from 1997 to 2006 on a micrometeorological platform, located at the top of a 4-story building. An objective procedure, including 2-step filtering and dome emission effect correction, was used to evaluate the quality of the 9-yr-long LW dataset. The comparison between LW values observed and yielded by the Surface Radiation Budget project shows spatial and temporal agreement, indicating that monthly and annual average values of LW observed in one point of São Paulo can be used as representative of the entire metropolitan region of São Paulo. The maximum monthly averaged value of the LW is observed during summer (389 ± 14 W m-2; January), and the minimum is observed during winter (332 ± 12 W m-2; July). The effective emissivity follows the LW and shows a maximum in summer (0.907 ± 0.032; January) and a minimum in winter (0.818 ± 0.029; June). The mean cloud effect, identified objectively by comparing the monthly averaged values of the LW during clear-sky days and all-sky conditions, intensified the monthly average LW by about 32.0 ± 3.5 W m-2 and the atmospheric effective emissivity by about 0.088 ± 0.024. In August, the driest month of the year in São Paulo, the diurnal evolution of the LW shows a minimum (325 ± 11 W m-2) at 0900 LT and a maximum (345 ± 12 W m-2) at 1800 LT, which lags behind (by 4 h) the maximum diurnal variation of the screen temperature. The diurnal evolution of effective emissivity shows a minimum (0.781 ± 0.027) during daytime and a maximum (0.842 ± 0.030) during nighttime. The diurnal evolution of all-sky condition and clear-sky day differences in the effective emissivity remain relatively constant (7% ± 1%), indicating that clouds do not change the emissivity diurnal pattern. The relationship between effective emissivity and screen air temperature and between effective emissivity and water vapor is complex. During the night, when the planetary boundary layer is shallower, the effective emissivity can be estimated by screen parameters. During the day, the relationship between effective emissivity and screen parameters varies from place to place and depends on the planetary boundary layer process. Because the empirical expressions do not contain enough information about the diurnal variation of the vertical stratification of air temperature and moisture in São Paulo, they are likely to fail in reproducing the diurnal variation of the surface emissivity. The most accurate way to estimate the LW for clear-sky conditions in São Paulo is to use an expression derived from a purely empirical approach

Technical Note: Determination of aerosol optical properties by a calibrated sky imager

The calibrated ground-based sky imager developed in the Marine Physical Laboratory, the Whole Sky Imager (WSI), has been tested with data from the Atmospheric Radiation Measurement Program (ARM) at the Southern Great Plain site (SGP) to determine optical properties of the atmospheric aerosol. Different neural network-based models calculate the aerosol optical depth (AOD) for three wavelengths using the radiance extracted from the principal plane of sky images from the WSI as input parameters. The models use data from a CIMEL CE318 photometer for training and validation and the wavelengths used correspond to the closest wavelengths in both instruments. The spectral dependency of the AOD, characterized by the A° ngstro¨m exponent in the interval 440–870 nm, is also derived using the standard AERONET procedure and also with a neural network-based model using the values obtained with a CIMEL CE318. The deviations between the WSI derived AOD and the AOD retrieved by AERONET are within the nominal uncertainty assigned to the AERONET AOD calculation (±0.01), in 80% of the cases. The explanation of data variance by the model is over 92% in all cases. In the case of , the deviation is within the uncertainty assigned to the AERONET (±0.1) in 50% of the cases for the standard method and 84% for the neural network-based model. The explanation of data variance by the model is 63% for the standard method and 77% for the neural network-based model.This work was supported by the Centro de Investigación Científica y Tecnológica (CICYT) of the Spanish Ministry of Science and Technology through projects CGL2007- 66477-C02-01 and CSD2007-00067 and the Andalusian Regional Government through project P06-RNM-01503 and P08-RNM-3568). First author has been funded by the Andalusian Regional Government and his research stay at University of California at San Diego has been also funded by the Andalusian Regional Government

Predicting rice (Oryza sativa L.) canopy temperature difference and estimating its environmental response in two rice cultivars, ‘Koshihikari’ and ‘Takanari’, based on a neural network

Canopy photosynthesis is an important component of biomass production in field-grown rice (Oryza sativa L.). Although canopy temperature differences (CTD) provide important information for evaluating canopy photosynthesis, the measurement of CTD is still a labor-intensive task. Therefore, we designed this study to establish a model for predicting CTD under different field conditions using meteorological data and evaluated the environmental response of CTD using the established model. Our study collected 2, 056, 264 CTD data points from two rice cultivars having different photosynthetic capacities, ‘Koshihikari’ and ‘Takanari’, and then used these data to create a novel model using a neural network (NN). The input variables were limited to meteorological data, and the output variable was set to CTD. The established NN model produced a prediction accuracy of R² = 0.792 and RMSE = 0.605°C. We then used this NN model to simulate the CTD response of the Koshihikari and Takanari cultivars in response to various environmental changes. These predictions revealed that Takanari had a lower CTD than Koshihikari when exposed to high relative humidity (RH) or low to moderate solar radiation (Rs). In contrast, the CTD of Koshihikari tended to be lower than that of Takanari under lower RH or higher Rs. This result implies that the advantages of the single-leaf gas exchange system in Takanari can be mitigated under extremely high-VPD conditions. Thus, our new method may provide a powerful tool to gain a better understanding of gas exchange, growth processes, and varietal differences in rice cultivated under field conditions

Gap-filling eddy covariance methane fluxes : Comparison of machine learning model predictions and uncertainties at FLUXNET-CH4 wetlands

Time series of wetland methane fluxes measured by eddy covariance require gap-filling to estimate daily, seasonal, and annual emissions. Gap-filling methane fluxes is challenging because of high variability and complex responses to multiple drivers. To date, there is no widely established gap-filling standard for wetland methane fluxes, with regards both to the best model algorithms and predictors. This study synthesizes results of different gap-filling methods systematically applied at 17 wetland sites spanning boreal to tropical regions and including all major wetland classes and two rice paddies. Procedures are proposed for: 1) creating realistic artificial gap scenarios, 2) training and evaluating gap-filling models without overstating performance, and 3) predicting halfhourly methane fluxes and annual emissions with realistic uncertainty estimates. Performance is compared between a conventional method (marginal distribution sampling) and four machine learning algorithms. The conventional method achieved similar median performance as the machine learning models but was worse than the best machine learning models and relatively insensitive to predictor choices. Of the machine learning models, decision tree algorithms performed the best in cross-validation experiments, even with a baseline predictor set, and artificial neural networks showed comparable performance when using all predictors. Soil temperature was frequently the most important predictor whilst water table depth was important at sites with substantial water table fluctuations, highlighting the value of data on wetland soil conditions. Raw gap-filling uncertainties from the machine learning models were underestimated and we propose a method to calibrate uncertainties to observations. The python code for model development, evaluation, and uncertainty estimation is publicly available. This study outlines a modular and robust machine learning workflow and makes recommendations for, and evaluates an improved baseline of, methane gap-filling models that can be implemented in multi-site syntheses or standardized products from regional and global flux networks (e.g., FLUXNET).Peer reviewe