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Field experiments were conducted at Shalimar Campus of Sher-e-Kashmir University of Agricultural Sciences and Technology of Kashmir during 2015 and 2016 to study the growth and yield of maize at different planting dates and Nitrogen levels with further simulating Maize growth and yield at different agro-ecological zones (altitudes) of Kashmir valley at fixed dates with varied nitrogen levels and at fixed nitrogen level with varied dates of sowing. Experiment was laid in split plot design with three dates of sowing i.e. 22nd May, 30th May and 8th June assigned to main plot and four levels of nitrogen i.e. 80 kg N ha-1 (N1), 120 kg N ha-1(N2), 160 kg N ha-1 (N3) and 200 kg N ha-1 assigned to sub plot. Genetic coefficients of maize crop variety (Shalimar Maize composite-4) were generated for calibrated and validation of CERES Maize model using DSSAT 4.5. Simulation was carried out for different dates of sowing viz, 1st May, 10th May, 22nd May, 30th May, 8th June, 20th June and 30th June at same fertility levels of 160 kg N ha-1, 60 kg P2O5 ha-1 and 30 kg K2O ha-1 for all the locations. Simulated studies carried at different locations indicated that sowing of Maize on 30th May (D2) with 200 kg N ha-1 (N4) predicted highest grain yield in location Kokernag which was followed by location Srinagar on same date 30th May (D2) with 160 kg N ha-1(N3) and lowest yield was recorded in district Kupwara. Maximum Biological yield was also recorded at 30th May with 160 kg N ha-1 (N3). Among the district Kokernag recorded maximum biological yield with delayed maturity (160 days) simulation studies were carried out with 7 dates of sowing at fixed level of Nitrogen in all the districts. Simulated studies of maize showed that sowing on 30th May with 160 kg N ha-1 (N3) recorded maximum LAI, Biological yield and grain yield. However, highest grain yield was recorded with location Kokernag and lowest was recorded with location Kupwara.

The Italian Space Agency (ASI) is supporting the calibration/validation (CAL/VAL) activities of the PRISMA hyperspectral mission with the PRISMA Calibration Validation project (PRISCAV). PRISCAV provides, a network of reference test sites to support the PRISMA validation of the L1 and L2 processing chain performance. Among the PRISCAV test sites, representing the different Italian territory, the Pignola test site depicts an agricultural scenario pertaining to the Southern Apennines in the Basilicata Region (Italy). On this site, contemporary to PRISMA acquisitions, a set of ground measurements have been collected from October 2019 to December 2020 to characterize the atmosphere and the ground optical properties and validate the PRISMA radiometry and the L2 reflectance products. Measures are still ongoing on the base of the PRISMA acquisition plan. The comparison of the Modtran simulated radiance with the PRISMA L1 radiance data show the same magnitude and shape. RMSE for the full range of wavelengths vary from 0.000153 to 0.000995 [W/m−2 sr−1 nm−1]. Further analyses will include the new PRISMA acquisitions and the possible matchups with Sentinel-2, to assure the full exploitation of the PRISMA data for the agricultural monitoring in the Southern Apennines.

INRAPORC® is a mechanistic, dynamic, and deterministic model system that is used in commercial pig production. However, its use is limited as it requires performance information for animals under ad libitum (AL) feed management, which is not provided at all stages of production. Verification of the INRAPORC® calibrations were conducted in this investigation using data from a small group of animals fed with AL in a laboratory situation, to simulate the mean kinetics of a larger commercial population and generate the correction equations for the predicted body weight (BW), and backfat thickness (BT). Analyses were performed by comparing the predicted and observed data, and by submitting them to prediction calibration curve tests (b0 = 0, and b1 = 1). The obtained curves presented a systematic, fixed effect error (+2.37 mm) for BT. The predicted BW and BT values were corrected using the values of the systematic errors obtained. As a result, 100% of the BW averages observed were contained in the confidence intervals (CI) of the INRAPORC® predicted averages, without the need for corrections, and 78.5% of the actual BT averages were contained in the CI of the averages predicted by the system, after corrections. The INRAPORC® calibrations, based on a small population of animals in laboratory conditions could thus be utilized to make predictions for commercial pig production systems and for value correction procedures for the BW and BT of pig populations that have systematic errors in their prediction validations. RESUMO: O INRAPORC® é um sistema de modelos mecanicista, dinâmico e determinista. Seu uso em sistemas comerciais de produção de suínos é restrito, pois necessita de informações de desempenho de animais sob manejo alimentar à vontade (AV), uma vez que este manejo não é utilizado em todas as fases de produção. Por isso é interessante verificar se a calibração do INRAPORC® baseada em um pequeno grupo de animais AV em situação laboratorial é capaz de simular a cinética média de uma população comercial maior e de subsidiar equações de correção de dados preditos de peso vivo (PV) e espessura de toucinho (ET). As análises foram realizadas comparando os dados preditos e observados sob o teste da curva de calibração da predição (b0=0 e b1=1), as curvas obtidas apresentaram erro sistemático de efeito fixo para a ET de +2,37mm. Os valores preditos de PV e ET foram corrigidos utilizando os valores dos erros sistemáticos obtidos. Como resultado, 100% das médias observadas de PV, estavam contidas nos intervalos de confiança (IC) das médias preditas pelo INRAPORC®, sem necessidade de correções e 78,5% das médias reais de ET estavam contidas nos IC das médias preditas pelo sistema, após as correções. A calibração do INRAPORC® baseada em uma pequena população de animais em situação laboratorial pode ser aplicada para predições de um sistema comercial de criação de suínos, bem como a aplicação do procedimento de correção dos dados preditos de PV e ET nas populações suínas que apresentem erros sistemáticos nas validações preditivas do sistema.

As the cooling energy demand in buildings is set to increase dramatically in the future, the exploitation of passive solutions like natural ventilation could prove vital in reducing the reliance on mechanical systems. Models that can predict air temperature accurately in naturally ventilated mode are key to understanding the potential of natural ventilation now and in the future. This article presents a simulation based case study of a retrofitted nearly zero energy test-bed university building, in naturally ventilated mode only. The study had three aims: (1) calibration and validation of a whole building energy model, (2) a comparative analysis of occupancy schedules and opening control strategies, and (3) a comparison of researcher and practitioner approaches. Results showed the detailed building model was capable of predicting room level air temperature with a low level of error (0.27 °C ≤ RMSE ≤ 1.50 °C) that was well within the limits of existing calibration standards (MBE ±10%, CVRMSE

Optical remote sensing and in particular hyperspectral or imaging spectroscopy remote sensing has been long proved to be an adequate method to predict topsoil organic carbon (Corg) content with good accuracy when the soils are well exposed and undisturbed. Several recent studies demonstrated further in science cases the potential of multispectral Copernicus Sentinel-2 data for bare soils Corg prediction, although challenges were reported related to the impact of disturbing factors. Disturbing factors that can affect the prediction and performances of soil surface properties from optical remote sensing are several and can be e.g. due to mixing in the field-of-view with partial vegetation cover depending on the landscape fragmentation. Most pixels at the remote sensing level are composites and in croplands, mixtures of soils with trees or green plants, or mixture with crop residues after harvest are likely. Another factor might be the presence of residual soil moisture or standing water after rain events. Soil reflectance decreases with increasing soil moisture and increasing soil roughness. Soil Surface roughness changes are observed due to variations in soil texture and to variable microtopography. Possible angular and solar illumination changes may affect the soil reflectance as well.In the frame of the ESA WORLDSOILS Project (https://www.world-soils.com) aiming at developing a pre-operational Soil Monitoring System to provide yearly estimations of soil organic carbon at global scale based on space-based EO data, we are working on the development of a spatially upscaled soil spectral library (SUSSL). The SUSSL is based on a sub-selection of the European LUCAS soil database, and includes simulation of realistic scenarios of ‘landscape-like’ cropland reflectance data with effect of mixture with green and dry vegetation, effect of varying soil moisture content, and effect of variable soil roughness. This database is further convoluted to the different spectral response functions of several EO sensors to simulate EO view of surface reflectances in croplands. In a next step, the SUSSL shall be used for the test and validation of different correction, disaggregation and unmixing techniques to assess the capabilities of the retrieval of undisturbed surface reflectance, to which soil prediction models can be applied with increased accuracy. In this talk, we will present the database developed, including methodological choices and parameter selections for the simulation of the different disturbing effects. Further, preliminary assessments will be shown on the uncertainties of the undisturbed vs. disturbed signal and impact on soil properties prediction.

The NASA/ESA Joint Program Planning Group (JPPG) subgroup on satellite calibration/validation was created to facilitate coordinated efforts between ESA, NASA, and their respective investigator communities to enhance calibration and/or validation activities for current and/or future satellite missions. The cooperation enabled through this activity includes airborne campaigns, use of surface-based measurements, and satellite-to-satellite intercomparisons. Numerous examples of such activities exist over the ten years of the JPPG. In this talk, examples of calibration/validation focused activities, accomplishments, and future plans will be presented. A particular focus will be on how the COVID-19 pandemic has affected field work planned for 2020 and 2021. The JPPG subgroup also includes joint European-US studies of satellite results that integrate the results of both parties’ observational capabilities, and the status of those activities will be presented as well.

Due to climate changes, the stationary assumption in hydrology has become obsolete. Moreover, the uncertainty regarding the future evolution of the Earth's climate and its impact on flow regimes is still large. Over the last decade, new risk management approaches have been proposed to support water resources planning under deep uncertainty. Those approaches rely at some point on a hydrological model to derive time series of streamflows for various hydro-climatic scenarios. One of the key issue is to make sure that the hydrological model is robust, i.e. that it performs well over contrasted hydro-climatic conditions. The differential split-sample test principle proposed by Klemes in 1986 recommends partitioning the time series into numerous and independent subperiods with different stationary climate features. Then, the hydrological model calibration is achieved on a specific climate period, and the validation on other(s). Classical detection methods commonly used to partition the times series, such as Mann-Kendall test or Pettitt test, can only detect a single change point, and thus are unable to handle complex climate sequences with multiple change points. We propose a calibration/validation protocol of hydrological models which rely on both the differential split-sample test and on an Hidden Markov Model to identify a succession of subsequences in a time series based on the state of the underlying process. We applied the proposed protocol on the Senegal River (West Africa). The hydrological model used is the conceptual GR2M model. Results show that (i) when the river discharges time series does not display a clear climate trend, and have multiple change points, classical rupture tests are not suitable. Hidden Markov Models are a good alternative as long as the climate sub-sequences are long enough (typically around 30 years or more); (ii) including a Hidden Markov Models in such protocol open up the range of possibilities for calibrate/validate, which can lead to an enhancement of the criterion function (but not necessarily).Klemes, V.: Operational testing of hydrological simulation models, Hydrological Sciences Journal, 31, 13-24, 415 https://doi.org/10.1080/02626668609491024, 1986.

Similar to the Reflective Solar Band (RSB) calibration, Suomi-National Polar-orbiting Partnership (S-NPP) Visible Infrared Imaging Radiometer Suite (VIIRS) on-board calibration of Day Night Band (DNB) is based on the Solar Diffuser (SD) observations in the Low Gain State (LGS). DNB has a broad spectral response covering a wavelength range roughly from 500 nm to 900 nm with a large dynamic range from three different gain states called High Gain State (HGS), Mid Gain State (MGS), and LGS. The calibration of MGS and HGS is also dependent on the LGS gain estimation with the gain ratios for each gain state. Over the lifetime of S-NPP VIIRS operations, the LGS gains have been derived from the on-board SD observations since its launch in October 2011. In this study, the lifetime LGS gains are validated by the lunar calibration coefficients (defined as F-factors) using a lunar irradiance model called Global Space-based Inter-Calibration System (GSICS) Implementation of RObotic Lunar Observatory (ROLO) (GIRO). Using the moon as an independent on-orbit calibration source, the S-NPP VIIRS DNB on-board SD based radiometric calibration is validated by the lunar F-factors within two percent of the lunar F-factors in terms of the standard deviation in the long-term trends over nine years of the S-NPP VIIRS operation.

A surface-based kinetic model for the anaerobic digestion of potato waste was proposed. The model was calibrated and validated, and a local sensitivity analysis was performed to investigate the most sensitive parameters. The model consisted of a modified Anaerobic Digestion Model n.1 where the disintegration process was defined through a surface-based approach able to account for the influence of the particle size on the process development. Ad-hoc experiments were carried out to calibrate and validate the model at a laboratory scale. The calibration and validation procedures accounted for the methane production and organic acid concentrations observed during experimental tests. The quality of model fitting with lab-scale data was evaluated by the Modeling Efficiency, the Index of Agreement, and the Root Mean Square Error methods. Results confirmed the high accuracy of the model for the bio-methane and organic by-products prediction during the anaerobic conversion of potato waste.

The impacts of climate and land-use changes make the stationary assumption in hydrology obsolete. Moreover, there is still considerable uncertainty regarding the future evolution of the Earth's climate and the extent of the alteration of flow regimes. In that context, it is crucial to assess the performance of a hydrologic model over a wide range of climates and their corresponding hydrologic conditions. We propose a calibration/validation protocol based on the differential split-sample test and numerous, contrasted, climate sequences identified through a Hidden Markov Model (HMM) classification. The proposed protocol is tested on the Senegal River in West Africa. Results show that when the time series of river discharges does not exhibit a clear climate trend, or when it has multiple change points, classical rupture tests are useless and HMM classification is a viable alternative as long as the climate sub-sequences are long enough.

Since 2007, a series of ESA supported airborne campaigns have been essential to the development of the Aeolus Doppler Wind Lidar satellite mission, which was successfully launched on 22 September 2018 and is providing a novel wind and aerosol profile data.A core element of the Aeolus Cal/Val activities is DLR’s A2D wind lidar on-board the DLR Falcon aircraft, an airborne demonstrator for the Aeolus ALADIN satellite instrument flown in combination with the 2-µm Doppler Wind Lidar reference system. Following the pre-launch WindVal-I and –II campaigns in 2015 and 2016, a number of calibration and validation campaigns have been successfully implemented: WindVal-III providing early Cal/Val results in November 2018 only three months after the Aeolus launch, AVATAR-E in May 2019 focussing on the Cal/Val over Central Europe, and AVATAR-I in September 2019 providing Cal/Val information in the North Atlantic and Arctic flying from Iceland.The airborne validation is also being supported through balloon flights in the tropical UTLS and lower stratosphere in the frame of the CNES Stratéole-2 stratospheric balloon activities. In the frame of the ESA supported pre-Stratéole-2 campaign, eight stratospheric balloons have been launched from the Seychelles in November/December 2019 providing unique upper level wind data for the Aeolus validation.The largest impact of the Aeolus observations is expected in the Tropics, and in particular over the Tropical oceans, where only a limited number of wind profile information is provided by ground based observations. Aeolus provides key direct measurements which are of importance to correctly constrain the wind fields in models. In addition, Aeolus observations have the potential to further enhance our current knowledge on aerosols and clouds by globally providing optical properties products that include atmospheric backscatter and extinction coefficient profiles, lidar ratio profiles and scene classification. In the tropics, a particularly interesting case is the outflow of Saharan dust and its impact on micro-physics in tropical cloud systems. The region off the coast of West Africa allows the study of the Saharan Aerosol layer, African Easterly Waves and Jets, Tropical Easterly Jet, as well as the deep convection in ITCZ. Together with international partners, ESA is currently implementing a Tropical campaign in July 2020 with its base in Cape Verde that comprises both airborne and ground-based activities addressing the tropical winds and aerosol validation, as well as science objectives. The airborne component includes the DLR Falcon-20 carrying the A2D and 2-µm Doppler Wind lidars, the NASA P-3 Orion with the DAWN and HALO lidar systems, the APR Ku-, Ka- and W-band Doppler radar and drop sondes, and a Slovenian small aircraft providing in-situ information from aethalometers, nephelometers and optical particle counters. The ground-based component led by the National Observatory of Athens is a collaboration of European teams providing aerosol and cloud measurements with a range of lidar, radar and radiometer systems, as well as a drone providing in-situ aerosol observations. In addition, the participation airborne capabilities by NOAA and LATMOS/Meteo France are currently being investigated.This paper will provide a summary of the Aeolus campaign focussing on the planned tropical

Clouds and aerosols play an important role in the Earth’s energy budget through a complex interaction with solar, atmospheric, and terrestrial radiation, and air humidity. Optically thick clouds efficiently reflect the incoming solar radiation and, globally, clouds are responsible for about two thirds of the planetary albedo. Thin cirrus trap the outgoing longwave radiation and keep the planet warm. Aerosols scatter or absorb sunlight depending on their size and shape and interact with clouds in various ways.Due to the importance of clouds and aerosols for the Earth’s energy budget, global satellite observations of their properties are essential for climate studies, for constraining climate models, and for evaluating cloud parameterizations. Active sounding from space by lidars and radars is advantageous since it provides the vertically resolved information. This has been proven by CALIOP lidar which has been observing the Earth’s atmosphere since 2006. Another instrument of this kind, CATS lidar on-board ISS provided measurements for over 33 months starting from the beginning of 2015. The ALADIN lidar on-board ADM/Aeolus has been measuring horizontal winds and aerosols/clouds since August 2018. More lidars are planned – in 2021, the ATLID/EarthCare lidar will be launched and other space-borne lidars are currently in the development phase.Needless to say that the quality of the retrieved data strongly depends on the quality of the calibration of the lidar system and its components. Besides “classical” calibration methods (laboratory calibration, calibration in space using on-board sources and/or known external sources and calibration through collocation, which involves comparisons with ground-based station-, balloon-, and aircraft measurements), one can also make use of a vicarious calibration, where the sites with known properties are used. In this work, we use the whole atmosphere for quality control of the space-borne lidar system, which includes the laser, the sending optics, the receiver with its telescope, and the detection system.We describe the quality control approach based on a set of several indicators, which characterize the behavior of the lidar system on a day-to-day basis using the L1 (and even L2) data as an input. With the help of this set one can trace:(a) the stability of the detection chain for the lidar channels (Rayleigh, Mie);(b) the drift of crosstalk coefficients;(c) the stability of day- and nighttime stratospheric noise;(d) the stability of the radiation detection for all atmospheric scenarios and over the whole globe using a clustering algorithm applied to the scattering ratio (SR) histograms.We demonstrate the results using the L1B and L2A data flow of Aeolus obtained from the first days of its operation and up to now, compare them with the results obtained for CALIOP, and discuss the applications of the approach.

International audience; In the frame of the monitoring of Sentinel 2 MSI sensors in orbit, a method for inter-band calibration validation, using Antarctic site, has been developed and tested. The method relies on the directional hemispheric reflectance found in ASTER data base. In order to consider that the spectral shape of the directional directional reflectance is the same as the directional hemispheric one, wavelengths have to be limited to the spectral range below 0.9 µm. Thus this method cannot perform the inter-band calibration for the whole set of spectral bands of MSI but only from B1 to B8A. Results for B9 seem also correct although the central wavelength is a little bit beyond 0.9 µm. Given a clear day acquisition on DomeC site, a first set of computations of Top Of Atmosphere (TOA) reflectances for MSI spectral bands is performed using the three ASTER reflectances corresponding respectively to small, medium and coarse grain size for snow. The spectral reflectances are normalized by the B2 value. The comparison of the three normalized TOA reflectances to the actual one given by MSI enables to select the right snow grain size. Then the second step is the atmospheric parameters adjustment. Water content is assessed thanks to B9 band centered at 0.945 µm and ozone is assessed thanks to mainly B3 band centered at 0.56 µm. For a clear day, the aerosol optical thickness is weak and thus the TOA reflectance is not very sensitive to the more or less low abundance of continental aerosols. The ratio of normalized reflectance given by MSI to the normalized reflectance given by the computation is assessed for each spectral band between B1 and B9 and is compared to the inter-band calibration validations performed with other methods.; Dans le cadre du suivi en orbite du capteur MSI de Sentinel 2, une méthode de validation de l'étalonnage inter-bande a été développée et testée. La méthode s'appuie sur la réflectance hémisphérique - directionnelle que l'on trouve dans la base de données ASTER. Pour pouvoir considérer que la réflectance directionnelle - directionnelle est similaire à la réflectance hémisphérique - directionnelle, il faut se limiter aux longueurs d'onde inférieures à 0,9 µm. De ce fait, la méthode ne peut pas assurer la validation de l'étalonnage inter-bande pour l'ensemble des bandes spectrales de MSI mais uniquement pour les bandes B1 à B8A. Les résultats pour la bande B9 semblent également corrects bien que la longueur d'onde centrale soit un peu au delà de 0,9 µm.Pour une acquisition en ciel clair sur Dome C, un premier ensemble de réflectance en haut de l'atmosphère (TOA pour Top Of Atmosphère) pour les bandes spectrales de MSI est calculé en utilisant les trois réflectances ASTER correspondant à une taille de grain de neige respectivement petite, moyenne ou grosse. Les réflectances spectrales sont normalisées par leur valeur en B2. La comparaison des trois réflectances ainsi obtenues avec la réflectance réelle donnée par MSI permet de sélectionner la bonne taille de grain de neige. La seconde étape correspond à l'ajustement des paramètres atmosphériques. Le contenu en vapeur d'eau est estimé à partir de la bande B9 centrée à 0,945 µm et l'ozone est estimée à partir de la bande B3 centrée à 0,56 µm. En condition de ciel clair, l'épaisseur optique des aérosol est faible et de ce fait, la reflectance TOA n'est pas très sensible à l'abondance plus ou moins grande des aérosols continentaux. Le rapport de la réflectance normalisée fournie par MSI à la réflectance normalisée calculée est estimé pour chaque bande spectrale entre B1 et B9 et est comparé aux validations de l'étalonnage inter-bande réalisées avec d'autres méthodes.

International audience; Recent research on the Land Surface Temperature (LST) signal reveals that attention must be paid to reference in-situ measurements used for validating thermal infrared (TIR) data collected from space. Here we focus on the effects of directional anisotropy of LST and atmospheric turbulence-induced temporal fluctuations. Adaptations to ground measurement protocols and new experimental set-ups are suggested to improve the confidence in validation activities.

This chapter examines the role of parameter calibration in the confirmation and validation of complex computer simulation models. I examine the question to what extent calibration data can confirm or validate the calibrated model, focusing in particular on Bayesian approaches to confirmation. I distinguish several different Bayesian approaches to confirmation and argue that complex simulation models exhibit a predictivist effect: Complex computer simulation models constitute a case in which predictive success, as opposed to the mere accommodation of evidence, provides a more stringent test of the model. Data used in tuning do not validate or confirm a model to the same extent as data successfully predicted by the model do.

Human-based modelling and simulations are becoming ubiquitous in biomedical science due to their ability to augment experimental and clinical investigations. Cardiac electrophysiology is one of the most advanced areas, with cardiac modelling and simulation being considered for virtual testing of pharmacological therapies and medical devices. Current models present inconsistencies with experimental data, which limit further progress. In this study, we present the design, development, calibration and independent validation of a human-based ventricular model (ToR-ORd) for simulations of electrophysiology and excitation-contraction coupling, from ionic to whole-organ dynamics, including the electrocardiogram. Validation based on substantial multiscale simulations supports the credibility of the ToR-ORd model under healthy and key disease conditions, as well as drug blockade. In addition, the process uncovers new theoretical insights into the biophysical properties of the L-type calcium current, which are critical for sodium and calcium dynamics. These insights enable the reformulation of L-type calcium current, as well as replacement of the hERG current model., eLife digest Decades of intensive experimental and clinical research have revealed much about how the human heart works. Though incomplete, this knowledge has been used to construct computer models that represent the activity of this organ as a whole, and of its individual chambers (the atria and ventricles), tissues and cells. Such models have been used to better understand life-threatening irregular heartbeats; they are also beginning to be used to guide decisions about the treatment of patients and the development of new drugs by the pharmaceutical industry. Yet existing computer models of the electrical activity of the human heart are sometimes inconsistent with experimental data. This problem led Tomek et al. to try to create a new model that was consistent with established biophysical knowledge and experimental data for a wide range of conditions including disease and drug action. Tomek et al. designed a strategy that explicitly separated the construction and validation of a model that could recreate the electrical activity of the ventricles in a human heart. This model was able to integrate and explain a wide range of properties of both healthy and diseased hearts, including their response to different drugs. The development of the model also uncovered and resolved theoretical inconsistencies that have been present in almost all models of the heart from the last 25 years. Tomek et al. hope that their new human heart model will enable more basic, translational and clinical research into a range of heart diseases and accelerate the development of new therapies.

A summary of the GOES-16 Advanced Baseline Imager (ABI) radiometric calibration validation assessment conducted using the aircraft-based Scanning High-resolution Interferometer Sounder (S-HIS) during the 2017 GOES-16 calibration validation campaign is presented.

The parameters involved in AQUATOX of the relatively large lakes do not have widespread applicability in landscape lakes. To get a series of model parameters of landscape lakes, an AQUATOX model was developed for SH landscape lake in Tianjin. The parameter sensitivity analysis, calibration and validation were carried out in this study. Moreover, the model was applied to predict the changes of nutrient with different species in SH. The results of parameter sensitivity analysis indicated that the model is highly sensitive to the optimal temperature, maximum photosynthetic rate and respiration rate. The results from calibration and validation have revealed that the AQUATOX model did a good simulating for SH. Furthermore, the AQUATOX provided necessary information for management of SH. Based on the prognosis under different scenarios, the biomass of Bullhead and plants should be controlled to remain the water quality of SH in a good condition.

본 연구에서는 수원 성균관대학교 내 Frequency Domain Reflectometry (FDR) 토양수분 측정 장비 및 COSMIC-ray 중성자 측정 장비를 통한 토양수분 지점 관측 사이트를 확립하였다. 또한 양질의 토양수분 데이터 확보를 위해 연구지역 내 토질실험, 토질별 FDR 토양수분 데이터 및 COSMIC-ray 중성자 개수의 시계열 분석, 관측한 토양수분 데이터와 위성 기반 토양수분 데이터와의 비교분석을 실시하였다. 2014년도부터 6개 지점에서 표층으로부터 5 cm에서 40 cm까지 총 24개의 FDR 센서를 5~10 cm 깊이별로 설치하여 토양수분 데이터를 측정하였다. 해당 지점들의 토질 분석결과, Sand에서 Loamy Sand까지의 다양한 토질이 불균질한 층을 이루어 분포되어 있는 것으로 판단되었다. 측정된 토양수분 데이터는 강우 데이터와 높은 상관성을 보이며, 위성 산출 토양수분 데이터와의 비교에서도 상대적으로 높은 상관관계와 낮은 평균제곱근편차(Root mean square deviation, RMSD)값을 보여주었다. 2014년도 설치 지역 토양수분 데이터의 신뢰도가 확보됨에 따라 2015년도에는 10개의 FDR 토양수분 측정 장비 및 COSMIC-ray 중성자 측정 장비가 추가로 설치되어 성균관대학교의 Soil Moisture site with FDR and COSMIC-ray(SM-FC) 연구지역이 구축되었다. SM-FC에 설치된 COSMIC-ray 중성자 측정 장비의 최초 검증을 위해 2015년 8~11월의 COSMIC-ray 중성자 데이터 및 FDR 토양수분 데이터가 활용되었다. 중성자기반 토양수분 값과 전체 지점 FDR 토양수분 평균값을 비교한 결과 매우 높은 상관관계를 볼 수 있었다 (상관계수 0.95). 이러한 연구를 통해 성균관대학교 SM-FC는 향후 한반도 지역 위성 및 모델 토양수분 데이터를 검증하는 대표 연구지역이 될 것으로 기대된다. 【In this study, Frequency Domain Reflectometry (FDR) and COSMIC-ray soil moisture (SM) stations were installed at Sungkyunkwan University in Suwon, South Korea. To provide reliable information about SM, soil property test, time series analysis of measured soil moisture, and comparison of measured SM with satellite-based SM product are conducted. In 2014, six FDR stations were set up for obtaining SM. Each of the stations had four FDR sensors with soil depth from 5 cm to 40 cm at 5~10 cm different intervals. The result showed that study region had heterogeneous soil layer properties such as sand and loamy sand. The measured SM data showed strong coupling with precipitation. Furthermore, they had a high correlation coefficient and a low root mean square deviation (RMSD) as compared to the satellite-based SM products. After verifying the accuracy of the data in 2014, four FDR stations and one COSMIC-ray station were additionally installed to establish the Soil Moisture site with FDR and COSMIC-ray, called SM-FC. COSMIC-ray-based SM had a high correlation coefficient of 0.95 compared with mean SM of FDR stations. From these results, the SM-FC will give a valuable insight for researchers into investigate satellite- and model-based SM validation study in South Korea.】

The increasing use of hydrologic and water quality (H/WQ) models for technical, policy, and legal decision making calls for greater transparency in communicating the methods used and decisions made when using H/WQ models. The objectives of this article are to: (1) provide guidelines to properly document H/WQ model calibration, validation, and use, and (2) raise issues about how to improve model documentation and reproducibility, and encourage open discussion of these topics in the H/WQ modeling community. First, eight recommended elements of model documentation and reporting are identified and described, and good examples are provided for each. Next, steps to move the H/WQ modeling community toward a culture of full model reproducibility are discussed. The use by model practitioners of the consistent and comprehensive elements described herein for documentation and reporting of H/WQ model calibration, validation, and use will allow better interpretation of published modeling studies, improve the utility of modeling studies, and allow more systematic advancement of H/WQ models.

This paper evaluates uncertainties in the ocean permittivity model needed to compute surface emission and reflection, at frequencies of 6 - 90 GHz. The study transforms permittivities and laboratory measurement uncertainties into typical brightness temperatures for microwave imagers. It is established that the permittivity model used by FASTEM agrees with the laboratory measurements to within their published uncertainties at frequencies of 23 GHz, 37 GHz and 89 GHz, but this is not the case at 6.8, 10.65 and 18.7 GHz, particularly at low temperatures. The laboratory measurement uncertainties transformed into typical brightness temperatures are reported and compared to observation minus short-range forecast (O - B) differences for microwave imagers for a range of ocean surface skin temperatures. For GMI and AMSR-2 these differences show very little skin-temperature dependent bias when calculated using FASTEM. However, when a new version of the permittivity model is used in FASTEM, that fits the low-frequency laboratory data better, an increase in skin temperature dependent bias is observed for channels at 10 - 23 GHz that is larger than the stated measurement uncertainties. This suggests that either the laboratory measurements have additional sources of systematic error at low frequencies, or there are additional sources of bias in the mean O - B calculations for microwave imagers that are currently masked by biases in FASTEM. To resolve this, reference-quality measurements of seawater permittivity are needed at all relevant microwave frequencies and temperatures, particularly at low temperatures and low frequencies.

Vicarious calibration refers to methods that make use of “invariant” natural targets of the Earth for the post-launch calibration of sensors. This process of calibration is useful for initial phase orbit operation of sensor and thereafter for validation during its operational span. This method of periodic evaluation of radiometric and geometric performance of the space-borne optical sensors and validation of derived radiance/reflectance is to ensure availability of consistent and accurate data products to the user community. This paper describes one such Cal/Val facility’s design, engineering aspects and realization at National Remote Sensing Centre (NRSC), Shadnagar. The target materials used for filling the Cal/Val site are studied for its reflectance. These targets have reflectance ranging from 9% to 53%, in the VNIR (Visible and Near Infra-Red) region. This paper also describes instruments used for calibration and homogeneity studies of targets for its invariance since operationalisation (January 2016). The last section of this paper is about reflectance based absolute radiometric calibration of medium resolution Indian Remote Sensing (IRS) sensor (Resourcesat and Cartosat series) using RT model and edge based LSF/MTF estimation of high resolution sensor carried out using the Cal/Val facility.

Calibration and validation metrics that involve decomposition of simulation and test data have been developed for potential use in the quantification of margin and uncertainty. The uniqueness of these validation metrics allows for nearly full-field, simulation and test data over a wide range of spatial realizations (three-dimensional responses over multiple input conditions) and temporal (time or frequency) information, as needed. Currently, no other calibration/validation metrics have been developed that span multiple spatial realizations and temporal information simultaneously. A demonstration example utilizing two datasets explains how the calibration/validation metrics are formed and how they can be used to quantify the margin between the simulation and the test data as well as how it can quantify the uncertainty. The primary advantage of a proposed principal component analysis validation metric is that it preserves the engineering units of the original data so that the quantifications of margin and uncertainty can be made in engineering unit. A second advantage of the principal component analysis validation metric is that it can be used over a wide range of temporal information. The potential case of using sets of data with mismatched degree of freedom information is also explored. The general approach of using decomposition methods as the basis for calibration/validation metrics is extended to image decomposition methods. All decomposition methods successfully quantify margin and uncertainty in this general calibration/validation metric approach.

The CNES/ISRO mission SARAL/AltiKa was successfully launched on 25 February 2013. It reached its nominal orbit on 13 March 2013. AltiKa is the first altimeter using the Ka-band frequency. This article presents the results of the calibration and validation activities perfromed on the first year of the SARAL/AltiKa mission. The main objective of the article is to assess the SARAL/AltiKa data quality and to estimate the altimeter system performance using GDR products. To achieve this goal, we present mono-mission metrics and compare them with Jason-2 over the same period. Even if these missions do not have the same ground track, precise comparisons are still possible. They allow assessing parameter discrepancies and SSH consistency between both missions in order to detect geographically correlated biases, jumps or drifts. These results show that SARAL/AltiKa data quality is excellent: ocean data coverage is greater than 99.5%, standard deviation at cross-overs is 5.4 cm. The mission therefore fulfills the re...

Model calibration and validation are important processes in the development of stochastic computer models of real complex systems. This article introduces an integrated approach for model calibration, validation, and prediction based on Gaussian process metamodels and a Bayesian approach. Within this integrated approach, a sequential approach is further proposed for stochastic computer model calibration. Several design criteria for this sequential stage are proposed and studied, including an entropy-based criterion and one based on minimizing prediction error. To further use the data resources to improve the performance of both calibration and prediction, an adaptive procedure that combines these criteria is introduced to balance the resource allocation between the calibration and prediction. The accuracy and efficiency of the proposed sequential calibration approach and the integrated approach are illustrated with several numerical examples.

The papers in this special section focus on the Aquarius/SAC-D observatory, a partnership between the USA Space Agency, NASA, and the Argentine Space Agency, CONAE (Comision Nacional de Actividades Espaciales). Aquarius/SAC-D observatory was launched on June 10, 2011 from Vandenberg Air Force Base, Vandenberg, CA, USA. The observatory included several instruments and two of them are related to the primary goal of the mission. The primary goal of the mission was mapping ocean surface salinity, and mission parameters (e.g., the orbit and repeat cycle) were tuned to this objective. From the NASA perspective, this was a pathfinder mission to demonstrate the technology of remote sensing of sea urface salinity from space and to provide data for understanding the role of salinity in Earth’s water cycle, and Aquarius was the primary instrument. But the mission was international in scope and also fit the needs of the Argentine plans for space applications.

The first satellite of the Geostationary Operational Environmental Satellite-R series (GOES-R), the next generation of NOAA geostationary environmental satellites, was launched November 19, 2016. This satellite, GOES-16, carries six instruments dedicated to the study of the Earth’s weather (ABI), lightning mapping (GLM), solar observations (EXIS and SUVI), and space weather monitoring (SEISS and MAG). Each of these six instruments are in the process of going through a series of specialized calibration plans to achieve their product quality requirements. In this review paper we will describe the overall status of the on-orbit calibration program, the path forward to Full product validation status, and any changes that may occur for the cal/val plans for GOES-S, which is planned for launch in early 2018.

Spatially explicit agent-based models (ABM) of innovation diffusion have experienced growing attention over the last few years. The ABM presented in this paper investigates the adoption of green electricity tariffs by German households. The model represents empirically characterised household types as agent types which differ in their decision preferences regarding green electricity and other psychological properties. Agent populations are initialised based on spatially explicit socio demographic data describing the sociological lifestyles found in Germany. For model calibration and validation we use historical data on the German green electricity market including a rich dataset of spatially explicit customer data of one of the major providers of green electricity. In order to assess the similarity of the simulation results to historical observations we introduce two validation measures which capture different aspects of the green electricity diffusion. One measure is based on the residuals of spatially-aggregated time series of model indicators and the other measure considers a temporally aggregated but spatially disaggregated indicator of spatial spread. Finally, we demonstrate the descriptive richness of the model by investigating simulation outputs of the calibrated model in more detail. In particular, the results provide insights into the dynamics of the spatial and lifestyle heterogeneity “underneath†the diffusion curve of green electricity in Germany.

The Suomi National Polar-orbiting Partnership (S-NPP) satellite, launched in October 2011, initiated a series of the next-generation weather satellites for the National Oceanic and Atmospheric Administration (NOAA) Joint Polar Satellite System (JPSS) program. The JPSS program at the Center for Satellite Applications and Research (JSTAR) leads the development of the algorithms, the calibration and validation of the products to meet the specified requirements, and long-term science performance monitoring and maintenance. All of the S-NPP products have been validated and are in successful operation. The recently launched JPSS-1 (renamed as NOAA-20) satellite is producing high-quality data products that have been available from S-NPP, along with additional products, as a direct result of the instrument upgrades and science improvements. This paper presents an overview of the JPSS product suite, the performance metrics achieved for the S-NPP, and the utilization of the products by NOAA stakeholders and user agencies worldwide. The status of NOAA-20 science data products and ongoing calibration/validation (Cal/Val) efforts are discussed for user awareness. In addition, operational implementation statuses of JPSS enterprise (multisensor and multiplatform) science algorithms for product generation and science product reprocessing efforts for the S-NPP mission are discussed.

New procedures have been developed to help identify snow and sea ice with the Suomi-National Polar-orbiting Partnership (S-NPP) Visible Infrared Imager Radiometer Suite (VIIRS) Cloud Mask (VCM) algorithm. The accurate detection of snow and sea ice is necessary in order to apply the correct spectral tests needed to detect clouds and make accurate cloud confidence classifications. During the VCM Calibration Validation activity, it was found that the procedures in place at the time of the satellite launch occasionally produced four types of misclassifications: (1) snow and/or ice surfaces in dry atmospheric regions misclassified as clouds, (2) multi-layered clouds in humid regions misclassified as snow, (3) low-level clouds with glaciated tops misclassified as sea ice, and (4) frozen lakes not classified as ice. The new procedures presented in this article use data collected in the VIIRS mid-wavelength region, i.e. both the 3.7 μm and 4.0 μm bands, as well as the 12.0 μm IR band to eliminate all four types o...

In a previous study by the same authors, a new modified Jarvis model (NMJ-model) was developed, calibrated, and validated to estimate stomatal resistance (r s ) for maize canopy on an hourly time step. The NMJ-model’s unique subfunctions, different from the original Jarvis model (J-model), include a photosynthetic photon flux density (PPFD)-r s response subfunction developed from field measurements and a new physical term, A exp(1/LAI) , where A is the minimum stomatal resistance and LAI is the green leaf area index, to account for the influence of canopy development on r s , especially during partial canopy stage in the early season and in late-season stage during leaf aging and senescence. This study evaluated the transferability of the J-model and NMJ-models that were re-parameterized and calibrated for maize canopy to estimate soybean r s . Due to the differences in physiological and photosynthetic pathway differences between the two crops, the r s response to the same environmental variables, i.e., PPFD, vapor pressure deficit (VPD), and air temperature (T a ), were substantially different. Thus, this study demonstrated the inherent limitation in applying the Jarvis-type models that were calibrated for maize to soybean without re-calibration. Maize-calibrated models performed poorly in estimating soybean r s , with the coefficient of determination (r 2 ) ranging from 0.30 to 0.38 and the root mean square difference (RMSD) between the estimated and measured r s ranging from 94.4 to 166 s m -1 . The J-model and NMJ-model were re-calibrated by parameter optimization method for soybean. The J-model calibrated well; however, the validation had poor performance results. The NMJ-model had a good calibration, resulting in a good r 2 (0.71) and a small RMSD (13.7 s m -1 ). The NMJ-model validation produced superior results to the J-model, explaining more than 80% of the variation in the measured r s (RMSD = 38.4 s m -1 ). These results show the robustness and practical accuracy of the NMJ-model in estimating r s over different canopies if well calibrated for a specific crop. In terms of sensitivity and elasticity analyses, among all parameters, r s estimates were most sensitive to uncertainties introduced in parameter a 1 of the PPFD subfunction due to its exponential impact on r s in the NMJ-model. Therefore, for accurate estimates of r s , uncertainties in parameter a 1 should not exceed the range of -2% and 2% so that the error in estimated r s is kept between -3.5% and 3.6%. The study observed that the relative change in r s due to uncertainties in parameters a 2 and a 3 of the VPD subfunction was a linear function and less sensitive than the PPFD subfunction. The sensitivity of r s to uncertainties in temperature subfunction parameters (a 4 and a 5 ) was higher than that of VPD subfunction parameters, but less than that of PPFD subfunction parameters. The uncertainty in parameters a 4 and a 5 should range within -10% and 10%, and the calibration of these parameters should be determined with greater precision as compared with the VPD subfunction parameters. The study confirmed that the addition of the r s_min and the A exp(1/LAI) terms, which were not accounted for in the original J-model, improved the model accuracy for estimating soybean r s .

The Global Change Unit (GCU) at the University of Valencia has been involved in several calibration/validation (cal/val) activities carried out in dedicated field campaigns organized by ESA and other organisms. However, permanent stations are required in order to ensure a long-term and continuous calibration of on-orbit sensors. In the framework of the CEOS-Spain project, the GCU has managed the set-up and launch of experimental sites in Spain for the calibration of thermal infrared sensors and the validation of Land Surface Temperature (LST) products derived from those data. Currently, three sites have been identified and equipped: the agricultural area of Barrax (39.05 N, 2.1 W), the marshland area in the National Park of Donana (36.99 N, 6.44 W), and the semi-arid area of the National Park of Cabo de Gata (36.83 N, 2.25 W). This work presents the performance of the permanent stations installed over the different test areas, as well as the cal/val results obtained for a number of Earth Observation sensors: SEVIRI, MODIS, and TIRS/Landsat-8.

This paper presents the recent progress in the cal/val of the Visible Infrared Imaging Radiometer Suite (VIIRS) on Suomi National Polar Orbiting Partnership (NPP) since launch, and Joint Polar Satellite System (JPSS) J1 which will be launched in 2017. The Suomi NPP VIIRS instrument continues to perform well with a very stable calibration according to extensive comparisons with other instruments and at vicarious validation sites. The calibration accuracy for most bands meet the specification and user needs, although additional efforts are being made to meet more stringent needs such as ocean color applications. The VIIRS Sensor Data Records (SDR) have been operationally used at the Alaska National Weather Service, and have been used to generate a large number of environmental data records ranging from aerosols, fire, to ocean color, sea surface temperature, vegetation, and many others. However, since many updates have been made to the operational processing of VIIRS since launch, inconsistency exists in the long term data records. To address this issue, the VIIRS SDR team is preparing for the reprocessing of the VIIRS historical data using the new automated calibration module RSBAutocal, with the latest calibration coefficients. The reprocessed data will be more consistent over the entire history of VIIRS with all known corrections. At the same time, significant efforts are devoted to the prelaunch calibration of the JPSS J1 VIIRS. While in general the J1 VIIRS performance meets specifications, there are performance waivers and as a result, mitigations have to be developed. This includes the implementation of a new aggregation scheme to address the nonlinear response of the VIIRS Day/Night Band (DNB). Other waivers include the larger than expected polarization sensitivity which will impact the ocean color bands and additional corrections will be needed postlaunch. In addition, the team has also been performing a trade study for adding a water vapor band to future VIIRS. Many of these efforts have been documented in the VIIRS special issue in the remote sensing open access journal which is being finalized to reach out to a broader user community.

Knowledge of uncertainties and errors are essential for comparisons of remote sensing data across time, space, and spectral domains. Vicarious radiometric calibration is used to demonstrate the need for uncertainty knowledge and to provide an example error budget. The sample error budget serves as an example of the questions and issues that need to be addressed by the calibration/validation community as accuracy requirements for imaging spectroscopy data will continue to become more stringent in the future. Error budgets will also be critical to ensure consistency between the range of imaging spectrometers expected to be launched in the next five years.