An Efficient Method of Estimating Downward Solar Radiation Based on the MODIS Observations for the Use of Land Surface Modeling

Solar radiation is a critical variable in global change sciences. While most of the current global datasets provide only the total downward solar radiation, we aim to develop a method to estimate the downward global land surface solar radiation and its partitioned direct and diffuse components, which provide the necessary key meteorological inputs for most land surface models. We developed a simple satellite-based computing scheme to enable fast and reliable estimation of these variables. The global Moderate Resolution Imaging Spectroradiometer (MODIS) products at 1° spatial resolution for the period 2003–2011 were used as the forcing data. Evaluations at Baseline Surface Radiation Network (BSRN) sites show good agreement between the estimated radiation and ground-based observations. At all the 48 BSRN sites, the RMSE between the observations and estimations are 34.59, 41.98 and 28.06 W∙m−2 for total, direct and diffuse solar radiation, respectively. Our estimations tend to slightly overestimate the total and diffuse but underestimate the direct solar radiation. The errors may be related to the simple model structure and error of the input data. Our estimation is also comparable to the Clouds and Earth’s Radiant Energy System (CERES) data while shows notable improvement over the widely used National Centers for Environmental Prediction and National Center for Atmospheric Research (NCEP/NCAR) Reanalysis data. Using our MODIS-based datasets of total solar radiation and its partitioned components to drive land surface models should improve simulations of global dynamics of water, carbon and climate

ESTIMATING LAND SURFACE ALBEDO FROM SATELLITE DATA

Land surface albedo, defined as the ratio of the surface reflected incoming and outgoing solar radiation, is one of the key geophysical variables controlling the surface radiation budget. Surface shortwave albedo is widely used to drive climate and hydrological models. During the last several decades, remotely sensed surface albedo products have been generated through satellite-acquired data. However, some problems exist in those products due to instrument measurement inaccuracies and the failure of current retrieving procedures, which have limited their applications. More significantly, it has been reported that some albedo products from different satellite sensors do not agree with each other and some even show the opposite long term trend regionally and globally. The emergence of some advanced sensors newly launched or planned in the near future will provide better capabilities for estimating land surface albedo with fine resolution spatially and/or temporally. Traditional methods for estimating the surface shortwave albedo from satellite data include three steps: first, the satellite observations are converted to surface directional reflectance using the atmospheric correction algorithms; second, the surface bidirectional reflectance distribution function (BRDF) models are inverted through the fitting of the surface reflectance composites; finally, the shortwave albedo is calculated from the BRDF through the angular and spectral integration. However, some problems exist in these algorithms, including: 1) "dark-object" based atmospheric correction methods which make it difficult to estimate albedo accurately over non-vegetated or sparsely vegetated area; 2) the long-time composite albedo products cannot satisfy the needs of weather forecasting or land surface modeling when rapid changes such as snow fall/melt, forest fire/clear-cut and crop harvesting occur; 3) the diurnal albedo signature cannot be estimated in the current algorithms due to the Lambertian approximation in some of the atmospheric correction algorithms; 4) prior knowledge has not been effectively incorporated in the current algorithms; and 5) current observation accumulation methods make it difficult to obtain sufficient observations when persistent clouds exist within the accumulation window. To address those issues and to improve the satellite surface albedo estimations, a method using an atmospheric radiative transfer procedure with surface bidirectional reflectance modeling will be applied to simultaneously retrieve land surface albedo and instantaneous aerosol optical depth (AOD). This study consists of three major components. The first focuses on the atmospheric radiative transfer procedure with surface reflectance modeling. Instead of executing atmospheric correction first and then fitting surface reflectance in the previous satellite albedo retrieving procedure, the atmospheric properties (e.g., AOD) and surface properties (e.g., BRDF) are estimated simultaneously to reduce the uncertainties produced in separating the entire radiative transfer process. Data from the Moderate Resolution Imaging Spectroradiometer (MODIS) onboard Terra and Aqua are used to evaluate the performance of this albedo estimation algorithm. Good agreement is reached between the albedo estimates from the proposed algorithm and other validation datasets. The second part is to assess the effectiveness of the proposed algorithm, analyze the error sources, and further apply the algorithm on geostationary satellite - the Spinning Enhanced Visible and InfraRed Imager (SEVIRI) onboard Meteosat Second Generation (MSG). Extensive validations on surface albedo estimations from MSG/SEVIRI observations are conducted based on the comparison with ground measurements and other satellite products. Diurnal changes and day-to-day changes in surface albedo are accurately captured by the proposed algorithm. The third part of this study is to develop a spatially and temporally complete, continuous, and consistent albedo maps through a data fusion method. Since the prior information (or climatology) of albedo/BRDF plays a vital role in controlling the retrieving accuracy in the optimization method, currently available multiple land surface albedo products will be integrated using the Multi-resolution Tree (MRT) models to mitigate problems such as data gaps, systematic bias or low information-noise ratio due to instrument failure, persistent clouds from the viewing direction and algorithm limitations. The major original contributions of this study are as follows: 1) this is the first algorithm for the simultaneous estimations of surface albedo/reflectance and instantaneous AOD by using the atmospheric radiative transfer with surface BRDF modeling for both polar-orbiting and geostationary satellite data; 2) a radiative transfer with surface BRDF models is used to derive surface albedo and directional reflectance from MODIS and SEVIRI observations respectively; 3) extensive validations are made on the comparison between the albedo and AOD retrievals, and the satellite products from other sensors; 4) the slightly modified algorithm has been adopted to be the operational algorithm of Advanced Baseline Imager (ABI) in the future Geostationary Operational Environmental Satellite-R Series (GOES-R) program for estimating land surface albedo; 5) a framework of using MRT is designed to integrate multiple satellite albedo products at different spatial scales to build the spatially and temporally complete, continuous, and consistent albedo maps as the prior knowledge in the retrieving procedure

Regional estimation of daily to annual regional evapotranspiration with MODIS data in the Yellow River Delta wetland

Evapotranspiration (ET) from the wetland of the Yellow River Delta (YRD) is one of the important components in the water cycle, which represents the water consumption by the plants and evaporation from the water and the non-vegetated surfaces. Reliable estimates of the total evapotranspiration from the wetland is useful information both for understanding the hydrological process and for water management to protect this natural environment. Due to the heterogeneity of the vegetation types and canopy density and of soil water content over the wetland (specifically over the natural reserve areas), it is difficult to estimate the regional evapotranspiration extrapolating measurements or calculations usually done locally for a specific land cover type. Remote sensing can provide observations of land surface conditions with high spatial and temporal resolution and coverage. In this study, a model based on the Energy Balance method was used to calculate daily evapotranspiration (ET) using instantaneous observations of land surface reflectance and temperature from MODIS when the data were available on clouds-free days. A time series analysis algorithm was then applied to generate a time series of daily ET over a year period by filling the gaps in the observation series due to clouds. A detailed vegetation classification map was used to help identifying areas of various wetland vegetation types in the YRD wetland. Such information was also used to improve the parameterizations in the energy balance model to improve the accuracy of ET estimates. This study showed that spatial variation of ET was significant over the same vegetation class at a given time and over different vegetation types in different seasons in the YRD wetlan

Amélioration de la capabilité de modélisation et de mitigation du gel radiatif au milieu agricole

Le gel radiatif est une des conditions météorologiques sévère affect la production agricole dans de nombreuses région du monde. Les objectives de cette étude inclut deux innovations scientifiques liées aux dégâts causés par le gel radiatif : (1) l'amélioration de la capacité de prédiction du gel local (température nocturne minimale à une résolution de 30 mètres) grâce à un modèle d’échange énergétique entre la végétation et l’atmosphère, et (2) une nouvelle méthode de diminution des risques et de protection des cultures agricoles pendant les périodes de gel. La première innovation a été réalisée en suivant plusieurs objectifs spécifiques visant à améliorer les capacités d'un modèle de répartition spatiale météorologique (Micro-Met) via quatre sous-modèles : (i) estimation journalière du gradient thermique adiabatique de l'air, (ii) modification de l’équation de rayonnement des grandes longueurs d'onde en l’absence de nuage dans l’atmosphère, (iii) quantification des effets de l’écoulement de l’air froid sur la température de l’air, et (iv) quantifier l’effet de haies brise–vent sur la vitesse du vent. La deuxième innovation a été réalisée en mettant en œuvre et en testant une nouvelle méthode active basée sur le cycle thermodynamique. Le site d'étude se localise dans la région de Vallée de Coaticook de l’Estrie (Québec) subit les conséquences désastreuses du gel. Le premier sous-modèle utilise une combinaison de profils de température provenant du satellite AIRS et de stations météorologiques afin d’estimer quotidiennement et régionalement le gradient thermique de l’air. L'utilisation de valeurs journalières, au lieu de valeurs fixes, permet d’estimer plus précisément les conditions atmosphériques. Les résultats ont démontré l’utilité de l’utilisation de la température de l'air obtenue par AIRS (850 hPa et 700 hPa) pour l’estimation du gradient thermique. Le second sous-modèle utilise les données associées aux conditions synoptiques du gel radiatif pour obtenir une équation du rayonnement descendant localement ajustée. Alors que l’erreur aux moindres carrés (RMSE) de Micro-Met était de 176.95 Wm-2 avec une erreur absolue (MAE) moyenne de 176.40 Wm-2, la nouvelle équation génère une RMSE de 4.90 Wm-2 et une MAE de 4.00 Wm-2. Le troisième sous-modèle contient trois parties :la détection des vallées fermées, l’estimation de la rapidité de drainage de l’air, et l’intégration de la perte de chaleur sensible ainsi que le refroidissement radiatif en vallée durant la nuit. La comparaison entre les simulations Micro-Met et les mesures de la température de l’air montrent une MAE de 1.11 (°C) et une RMSE de 1.66 (°C). La comparaison avec le modèle amélioré indique un gain avec une MAE de 0.68 (°C) et une RMSE de 1.08 (°C). Le quatrième sous-modèle était construit sur des résultats expérimentaux de vitesse du vent générés en laboratoire par des simulations. Trois équations ont été proposées pour estimer la vitesse du vent. Les résultats indiquent un coefficient de corrélation (R2) de 71% pour une vitesse de vent en dessous de 6 ms-1. La version améliorée de Micro-Net fournit une nouvelle plateforme pour des modèles d’énergie végétation-atmosphère et permet de prévoir la température minimale nocturne. Les résultats des tests de prédiction de cette température minimum concordent avec les mesures in-situ. Ces mesures ont été prises dans 5 secteurs topographiques différents afin d’améliorer les modèles de prédiction et engendrent des erreurs pour des vallées fermées (RMSE = 1.34, MAE = 1.03), pour différentes pentes (RMAE = 0.93, MAE = 0.73), crêtes (RMSE = 1.02, MAE = 0.88), plaines (RMSE = 0.44, MAE = 0.40), et aux orées des forêts (RMSE = 0.58, MAE = 0.53). En plus des objectifs spécifiques précédents, cette étude a proposé une nouvelle méthode d'atténuation du gel basée sur la thermodynamique du transport de la vapeur d'eau d'une source humide à un puits sec. Nous avons ajouté au Selective Inverse System (SIS) déjà utilisé dans le milieu, un contenant d'eau chaude à sa base pour diffuser la vapeur d'eau dans l'air ambiant. Cette opération a augmenté l’humidité de l'air ambiant et augmenté l'entropie humide. Cet essai a été réalisé dans un verger. La méthode d'atténuation la plus courante se concentre sur la température de l'air. La méthode proposée repose plutôt sur les principes physiques de l'entropie humide, qui combinait à la fois la température et l'humidité de l'air et le contenu thermique représenté. Dans l'ensemble, pour ce projet de recherche, un modèle couplé a été conçu pour prévision la température minimale nocturne de l'air dans des terrains agricoles vallonnés. En particulier, en améliorant la précision des prévisions, nous avons élaboré et ajouté des sous-modèles pour estimer les baisses de température dues à la stagnation du drainage de l'air froid et à l'effet des brise-vent forestiers sur la vitesse du vent. Pour réduire l'effet de gel, une nouvelle méthode de mitigation active respectueuse de l'environnement a été présentée. Cette étude a le potentiel d’aider les agriculteurs à réduire les dommages causés par le gel. De plus, elle peut être utile pour les services agricoles en termes de prise de décision, réduisant ainsi les dommages économiques.Abstract: The main objective of this study was related to radiation frost damage: (1) improving the forecasting capability of local frost, which was adapted to forecast nocturnal minimum temperature at a 30-meter resolution, using a vegetation atmosphere energy exchange framework, and (2) proposing a new mitigation approach to protect agricultural crops during frost periods. The first advance was achieved through several specific objectives to enhance the capabilities of a meteorological spatial distribution model (Micro-Met) on four sub-models: (i) estimating local air temperature lapse rate on a daily basis (ii) modifying downward longwave equation under clear sky condition, (iii) quantifying the effects of cold air drainage on air temperature, and (iv) quantifying the forest shelter effect on wind speed. The second advance advancement was accomplished by implementing and testing a new active method based on steam cycle thermodynamic. The first sub-model used AIRS (Atmosphere infrared sounder) air temperature profile and surface station data to estimate air temperature lapse rate on the daily and regional scale. The use of daily basis lapse rate, instead of the fixed value, allowed to present more accurate atmospheric condition. The results showed the potential of the AIRS air temperature profiles (850 hPa and 700 hPa) to estimate the temperature lapse rate. The second sub-model used observational data associated with synoptic conditions of radiation frost to present a locally adjusted downward longwave equation. The reported root means square error (RMSE) and mean absolute error (MAE) for the current version of Micro-Met were 176.95 (Wm-2) and 176.40 (Wm-2) respectively, while the results of the new equation led to an RMSE and MAE of 4.90 (Wm-2) and 4.00 (Wm-2) respectively. The third sub–model constituted three components: detected closed valley, estimated cold air drainage velocity, and integrated sensible heat loss and radiative cooling during the night on detected valleys. Comparison between the current Micro-Met simulation and the measured air temperature shows MAE of 1.11°C and RMSE of 1.66°C, while the comparison with the enhanced Micro-Met simulation indicated an improvement with MAE of 0.68 °C and RMSE of 1.08 °C. The fourth sub-model was based on experimental results of wind velocity produced in a laboratory with wind-tunnel models. Three separate equations were formulated for wind velocity estimation over the windward, through the shelterbelt, and leeward areas. The results indicated a coefficient of determination (R2) of 71% under the wind's velocity lower than 6ms-1. The Enhanced Micro-Met version provided a new platform to power vegetation-atmosphere energy model to forecast minimum nocturnal temperature. The performance test for forecasting minimum air temperatures indicated agreement with in-situ measurements. Measurements were taken on five topographic sectors in order to assess the improved modeled prediction and led to error assessment on closed valleys (RMSE=1.34, MAE = 1.03), different parts of slopes (RMAE = 0.93, MAE = 0.73), ridges (RMSE = 1.02, MAE = 0.88), flat areas (RMSE = 0.44, MAE = 0.40), and areas close to the forest (RMSE = 0.58, MAE = 0.53). In addition to previous specific objectives, this study proposed a new frost mitigation method based on the thermodynamics of water vapor transport from a moist source to dry sink. A vessel of warm water equipped with a Selective Inverted Sink (SIS) system was used to transport water vapor into the air, which ended up decreasing the air dryness and increasing moist entropy. This test was carried out in an orchard. The most common mitigation method focuses on air temperature. Instead, the proposed method was based on the physical principles of moist entropy, which combined both air temperature and humidity and depicted heat content. Overall, for this research project, a coupled model was designed to predict nocturnal minimum air temperature over hilly agricultural terrain. In particular, through improving prediction accuracy, we developed and added sub-models to estimate drops in temperature due to pooling and stagnation of cold air drainage and the effect of forest shelterbelt on wind velocity. To reduce frost effect, a new environmentally friendly active method was presented. This study served to help farmers reduce frost damages. Moreover, it can be useful for agricultural services in terms of decision-making, thereby, reducing economic damages

A physics-constrained machine learning method for mapping gapless land surface temperature

More accurate, spatio-temporally, and physically consistent LST estimation has been a main interest in Earth system research. Developing physics-driven mechanism models and data-driven machine learning (ML) models are two major paradigms for gapless LST estimation, which have their respective advantages and disadvantages. In this paper, a physics-constrained ML model, which combines the strengths in the mechanism model and ML model, is proposed to generate gapless LST with physical meanings and high accuracy. The hybrid model employs ML as the primary architecture, under which the input variable physical constraints are incorporated to enhance the interpretability and extrapolation ability of the model. Specifically, the light gradient-boosting machine (LGBM) model, which uses only remote sensing data as input, serves as the pure ML model. Physical constraints (PCs) are coupled by further incorporating key Community Land Model (CLM) forcing data (cause) and CLM simulation data (effect) as inputs into the LGBM model. This integration forms the PC-LGBM model, which incorporates surface energy balance (SEB) constraints underlying the data in CLM-LST modeling within a biophysical framework. Compared with a pure physical method and pure ML methods, the PC-LGBM model improves the prediction accuracy and physical interpretability of LST. It also demonstrates a good extrapolation ability for the responses to extreme weather cases, suggesting that the PC-LGBM model enables not only empirical learning from data but also rationally derived from theory. The proposed method represents an innovative way to map accurate and physically interpretable gapless LST, and could provide insights to accelerate knowledge discovery in land surface processes and data mining in geographical parameter estimation

Application Of A Remote Sensing Technique In Estimating Evapotranspiration For Nyazvidzi Sub- Catchment., Zimbabwe

The integration of Remote Sensing and ground data into hydrological and cropwater requirement models enables water resources managers to adequately quantify the availability of water for irrigation in space and time. The SEBS algorithm was used to derive actual evapotranspiration estimates using MODIS images to assess cropwater requirements in the Ruti irrigation scheme after validation with ground based evapotranspiration measurements. Results show that actual evapotranspiration computed using SEBS (EToS) were comparable to those obtained using Penman Monteith method (R2=0.96). The Kendall’s tau test showed that there is significant statistical association (α = 0.05) between Pan Coefficient (Kp) values determined using EToS and EToPM and Kp values from the Snyder equation. In conclusion, the study highlights the potential use of GIS and remote sensed data for catchment management, planning and irrigation scheduling at irrigation scheme level. Welch’s t test showed that there is no evidence to reject Ho: Kp determined from EToPM – Kp from EToS = 0. The above is crucial in the evaluation and comparison of performance of different irrigation systems in the country for food security and improvement of livelihoods in the light of integrated water resources management

Analysis of Long-Term Cloud Cover, Radiative Fluxes, and Sea Surface Temperature in the Eastern Tropical Pacific

Grant activities accomplished during this reporting period are summarized. The contributions of the principle investigator are reported under four categories: (1) AHVRR (Advanced Very High Resolution Radiometer) data; (2) GOES (Geostationary Operational Environ Satellite) data; (3) system software design; and (4) ATSR (Along Track Scanning Radiometer) data. The contributions of the associate investigator are reported for:(1) longwave irradiance at the surface; (2) methods to derive surface short-wave irradiance; and (3) estimating PAR (photo-synthetically active radiation) surface. Several papers have resulted. Abstracts for each paper are provided