Measurements of the energy balances and net photosynthesis rates of two low productivity coniferous forest canopies (12 and 22 years old), were made successfully during both wet and dry growing seasons, using a modified Bowen Ratio method. Canopy conductances (gc) were calculated from canopy evaporation rates (E) using the Penman-Monteith equation. A model was developed to predict canopy growth and evaporation rates from basic soil and weather data, and compared with the measured data. The photosynthesis model was physiologically based, derived from recent work of Farquhar and coworkers. The canopy conductance model used an empirical approach, based on simple relationships with recorded environmental variables, while canopy E was predicted from the Penman-Monteith equation. Findings were: (1) Daytime E and canopy net photosynthesis rates (Fc) were generally lower in the younger canopy. (2) In the old canopy, E was more strongly decoupled from net irradiance (Rn) and more dependent on the atmospheric vapour pressure deficit (D) in accordance with the predictions of McNaughton and Jarvis (1983). (3) In the old canopy, Fc was significantly reduced by low soil water potential (Ψs) within the range of soil water storages at which measurements were made, while gc was less dependent on Ψs. From consideration of changes in intercellular C0₂ concentration, gc was not found normally limiting to Fc. (4) No simple relationship was apparent between solar irradiance (S) and F at the canopy level. However highest Fc and canopy water use efficiency ratios occurred on cloudy days with low air temperature and low D. (5) Night-time Fc measurements indicated that canopy respiration rates are generally very high and hence air temperature was a major factor limiting overall forest productivity. (6) The computer model could predict gc from four variables (D, S, root-zone soil water storage, W and time since dawn, t) with reasonable 2 success (r² 0.75). However, on days when gc was low, due to high D, E was occasionally significantly in error, because the Penman-Monteith equation is more sensitive to gc when the latter is low. Best agreement between measured and modelled E occurred on cloudy days when D was low and gc consequently high. (7) Values for the maximum rates of carboxylation, as limited by foliar carboxylase activity and electron transport rate, were set at one third of those reported by Farquhar and coworkers, in order to obtain best overall agreement between measured and modelled data. This requirement indicated that poor nutrition was also limiting to stand productivity. (8) Model prediction of canopy net photosynthesis was not satisfactory (r² 0.50), attributed mainly to using too simple an approach to estimating irradiance at the individual leaf level, and partly to unexplained variation in the measurements of Fc. In spite of its limitations, the model was found to respond realistically to changes in weather and Ψs, suggesting the approach was valid, and might be more successful with further development. / Forestry, Faculty of / Graduate

L'humidité du sol (SM) contrôle les bilans d’eau et d’énergie des surfaces continentales et joue ainsi un rôle clé dans les domaines de la météorologie, l'hydrologie et l'écologie. La communauté scientifique en télédétection micro-ondes a fait des efforts considérables pour établir des bases de données globales de l’humidité du sol en surface (SSM) découlant d'instruments micro-ondes actifs et passifs. Parmi ces instruments, SMOS (Soil Moisture and Ocean Salinity), lancé en 2009, est le premier satellite passif conçu spécifiquement pour mesurer SSM à partir d’observations en bande L (1.4 GHz) à l'échelle globale. La validation des données SMOS SSM sur différentes régions climatiques et pour des conditions environnementales variées est une étape indispensable avant qu’elles soient utilisées de manière opérationnelle. En effet, une meilleure connaissance de la précision des estimations de SSM et des incertitudes associées permettra non seulement d'améliorer les produits SMOS SSM, mais aussi d'optimiser les approches de fusion de données utilisées pour créer des produits multi-capteurs long terme. De tels produits sont développés dans le cadre du programme Climate Change Initiative (CCI) de l'Agence spatiale européenne (ESA) pour l’ensemble des variables climatiques essentielles (ECV), dont SSM. A la suite des chapitres d'introduction I à III, les résultats de cette thèse sont présentés en trois chapitres. Le chapitre IV présente une comparaison des produits SSM issus des capteurs passifs SMOS (bande L) et AMSR-E (bande C) en prenant pour référence les estimations SSM du système d'assimilation SM-DAS-2 du Centre Européen pour les Prévisions Météorologiques à Moyen Terme (CEPMMT). Cette évaluation est menée sur la période d’observation commune à SMOS et AMSR-E (2010- 2011), en utilisant des indicateurs classiques (corrélation, RMSD, Biais). En parallèle, le chapitre V présente une comparaison des produits SMOS SSM avec les produits SSM issus du capteur actif ASCAT en bande C en utilisant comme référence les simulations SSM d’un modèle des surfaces continentales (MERRA-Land), et en utilisant des indicateurs classiques, des méthodes statistiques avancées (triple collocation), et des diagrammes de Hovmöller sur la période 2010-2012. Ces deux évaluations ont montré que la densité de la végétation (paramétrée ici par l’indice foliaire LAI) est un facteur clé pour interpréter la cohérence entre le produit SMOS et les produits AMSR-E et ASCAT. Cet effet de la végétation a été quantifié pour la première fois à l’échelle globale pour les trois capteurs micro-ondes. Ces deux chapitres ont également montré que les trois capteurs SMOS, AMSR-E et ASCAT ont des performances complémentaires selon la densité de végétation et qu’il y a ainsi un potentiel intéressant en terme de fusion des jeux de données micro-ondes passifs et actifs. Dans le chapitre VI, avec l’objectif général d’étendre vers le passé les séries de données SSM de SMOSL3 et de développer un jeu de données SSM homogène sur 2003-2014, nous avons évalué l’utilisation d’une approche de régression linéaire multiple appliquée aux mesures de températures de brillance de AMSR-E (2003 - 2011). Les coefficients de régression ont été calibrés avec les produits SSM issus de SMOS sur 2010-2011. Le produit SSM résultant, qui fusionne les observations SMOS et AMSR-E, a été évalué par comparaison avec un produit SSM AMSR-E et les produits SSM MERRA-Land sur 2007-2009. Ces résultats préliminaires montrent que la méthode de régression linéaire est une approche simple et robuste pour construire un produit SSM réaliste en termes de variations temporelles et de valeurs absolues. En conclusion, cette thèse a montré que le potentiel de synergie entre les systèmes micro-ondes passifs (AMSR-E et SMOS) et actifs (ASCAT) est très prometteur pour le développement et l'amélioration de longues séries temporelles SSM à l'échelle mondiale, telles que celles produites dans le cadre du programme CCI de l'ESA. / Soil moisture (SM) plays a key role in meteorology, hydrology, and ecology as it controls the evolution of various hydrological and energy balance processes. The community of scientists involved in the field of microwave remote sensing has made considerable efforts to build accurate estimates of surface SM (SSM), and global SSM datasets derived from active and passive microwave instruments have recently become available. Among them, SMOS (Soil Moisture and Ocean Salinity), launched in 2009, was the first ever passive satellite specifically designed to measure the SSM, at L-band (1.4 GHz), at the global scale. Validation of the SMOS SSM datasets over different climatic regions and environmental conditions is extremely important and a necessary step before they can be used. A better knowledge of the skill and uncertainties of the SSM retrievals will help not only to improve the individual products, but also to optimize the fusion schemes required to create long-term multi-sensor products, like the essential climate variable (ECV) SSM product generated within the European Space Agency’s (ESA's) Climate Change Initiative (CCI) program. After the introductory Chapters I to III, this dissertation consists of three main parts. Chap. IV of the dissertation evaluates the passive SMOS level 3 (SMOSL3) SSM products at L-band against the passive AMSR-E SSM at C-band by comparing them with a Land Data Assimilation System estimates (SM-DAS-2) produced by the European Centre for Medium Range Weather Forecasts (ECMWF). This was achieved over the common period 2010-2011 between SMOS and AMSR-E, using classical metrics (Correlation, RMSD, and Bias). In parallel, Chap. V of the dissertation evaluates the passive SMOSL3 products against the active ASCAT SSM at C-band by comparing them with land surface model simulations (MERRA-Land) using classical metrics, advanced statistical methods (triple collocation), and the Hovmöller diagram over the period 2010-2012. These two evaluations indicated that vegetation density (parameterized here by the leaf area index LAI) is a key factor to interpret the consistency between SMOS and the other remotely sensed products. This effect of the vegetation has been quantified for the first time at the global scale for the three microwave sensors. These two chapters also showed that both SMOS and ASCAT (AMSR-E) had complementary performances and, thus, have a potential for datasets fusion into long-term SSM records. In Chap. VI of the dissertation, with the general purpose to extend back the SMOSL3 SSM time series and to produce an homogeneous SM product over 2003-2014 based on SMOS and AMSR-E, we investigated the use of a multiple linear regression model based on bi-polarization (horizontal and vertical) brightness temperatures (TB) observations obtained from AMSR-E (2003 - 2011). The regression coefficients were calibrated using SMOSL3 SSM as a reference over the 2010-2011 period. The resulting merged SSM dataset was evaluated against an AMSR-E SSM retrievals and modelled SSM products (MERRA-Land) over 2007-2009. These first results show that the multi-linear regression method is a robust and simple approach to produce a realistic SSM product in terms of temporal variation and absolute values. In conclusion, this PhD showed that the potential synergy between the passive (AMSR-E and SMOS) and active (ASCAT) microwave systems at global scale is very promising for the development of improved, long-term SSM time series at global scale, such as those pursued by the ESA’s CCI program. It also provides new ideas on the way to merge the different SSM datasets with the aim of producing the CCI (phase 2) long-term series (a coherent "SMOS-AMSR-E" SSM time series for the period 2003 -2014), that will be evaluated further in the framework of on-going ESA projects.

One can use unlicensed and often very inexpensive radios for unconventional communication (underwater- and underground) links. However, one can go further, and use these radios as sensors rather than communication links. Such communication links and sensors can have important application in hydroscience. While the attenuation of RF signals is high in these mediums, by using the wireless sensor network (WSN) paradigm of multi-hop and retransmission, reliable networks can be formed underwater and underground. One no longer needs to think of RF modules as only a source of data transmission. This revelation lends itself to thinking of these modules as inexpensive RF wave generators at prescribed unlicensed frequencies. Analyzing the received signal strength indicator (RSSI) of a link over time, one can infer changes in the medium from the changes in RSSI. In this thesis, I develop a simple mathematical model to relate changes in RSSI to changes in the medium. Additionally, five experimentally validated examples demonstrate the possibility of non-traditional uses for RF modules. Demonstrated sensor possibilities include soil moisture estimation, leaf wetness measurement, and vegetation water content estimation. This thesis served to validate the use of inexpensive unlicensed RF modules as more than just communication links through air, but as links in unconventional media, and more importantly as measurement instruments.

The American Cranberry (Vaccinium macrocarpon Ait.) represents a vital sector of the economy of southeastern Massachusetts. Due to the hydrogeological and edaphic characteristics of peatlands, variations in soil drainage and soil moisture represent major management challenges for growers in Massachusetts. An emerging trend of upland (mineral soil) cranberry farms planted with new hybrid cultivars has the potential to enhance the profitability and long-term viability of cranberry production in Massachusetts. However, sparse data exist on soil moisture characteristics of peatland and upland cranberry farms. The purpose of this research was to elucidate the differences in soil moisture between upland and peatland cranberry farms, to evaluate the soil temperature-moisture relationship and its use for inferring soil moisture, and to explore the use of unmanned aircraft systems (UAS) as a soil moisture management tool in cranberry agriculture.In this thesis, we found that volumetric soil water content (qv) in upland farms ranged from 5-15%, contrasting with values of 10-40% for peatland farms. In general, soil moisture in upland farms was two times drier and four times more uniform than peatlands farms. Our results suggest that open ditches should be dredged to at least 50 cm to obtain irrigation setpoints of -5 to -2 kPa for Massachusetts cranberry farms. We found that soil temperature and near-surface temperature were accurate predictors of soil moisture but were also strongly dependent on the magnitude of differences between air and water temperature. Soil and near-surface temperatures were also better predictors of moisture in soils with lower vegetation coverage and organic matter content. Near-surface temperature collected with a UAS was consistent with field measurements of qv, suggesting that UAS could be used to assist Massachusetts cranberry farmers by predicting large-scale variation in q v and offering management insights.

Eastern North Dakota has received excessive rainfall events since 1995, and soils are prone to waterlogging. This research evaluated the effects of subsurface tile drainage, raised beds, and iron-chelate (Fe-EDDHA) seed-application on iron-deficiency chlorosis (IDC) incidence in soybean [Glycine max (L.) Merr.], soybean growth, and yield, across six environments during 2013 and 2014. Tile drainage without beds increased soybean yield and reduced IDC by 11%. Beds resulted in more vigorous plants with 9% more biomass and increased soybean yield by 6%. There was no yield advantage to using both tile and raised beds within the same field. The Fe-EDDHA reduced plant population and IDC expression, increased plant biomass, but did not result in a yield increase. Farmers are encouraged to consider utilizing raised beds as a means to mitigate excess water. Additional research is needed to determine the cause of lower established plant density after seed application with Fe-EDDHA. / Minnesota Soybean Research and Promotion Council / North Dakota Soybean Council / DuPont Pioneer

Experimental studies on infiltration/soil-water movement processes are vital to better understanding movement of soil-water in the vadose zone. The objective of this experimental research was to investigate infiltration/soil-water movement processes utilizing laboratory experiments and computer modeling. Small scale laboratory soil box infiltration experiments were conducted and utilized for the improved parameterization of the Green-Ampt (GA) saturated moisture content parameter to produce an effective moisture content parameter (Be) for utilization in a modified GA model. By incorporating ⊖e values into GA modeling, modeling results showed greatly improved wetting front prediction across different soil conditions. A new soil packing method was proposed for replicating complex microtopographical surfaces with uniform bulk densities in laboratory soil box experiments which proved efficient and effective at accomplishing both objectives. A rainfall simulator and an instantaneous-profile laser scanner were used to simulate rainfall and quantify surface microtopography for experiments. The results clearly show the effect of microtopography on infiltration and soil-water movement characteristics. This offers valuable insight into infiltration/soil-water movement processes as affected by different soil and surface microtopographic conditions. / National Science Foundation (Grant No. EAR-0907588)

Soil moisture influences the water and energy cycles of terrestrial environments, and thus plays an important climatic role. However, the behavior of soil moisture at large scales, including its impact on atmospheric processes such as precipitation, is not well characterized. Satellite remote sensing allows for indirect observation of large-scale soil moisture, but validation of these data is complicated by the difference in scales between remote sensing footprints and direct ground-based measurements. To address this problem, a method, based on information theory (specifically, mutual information), was developed to determine the useful information content of satellite soil moisture records using precipitation observations. This method was applied to three soil moisture datasets derived from Advanced Microwave Scanning Radiometer for EOS (AMSR-E) measurements over the contiguous U.S., allowing for spatial identification of the algorithm with the least inferred error. Ancillary measures of biomass and topography revealed a strong dependence between algorithm performance and confounding surface properties. Next, statistical causal identification methods (i.e. Granger causality) were used to examine the link between AMSR-E soil moisture and the occurrence of next day precipitation, accounting for long term variability and autocorrelation in precipitation. The probability of precipitation occurrence was modeled using a probit regression framework, and soil moisture was added to the model in order to test for statistical significance and sign. A contrasting pattern of positive feedback in the western U.S. and negative feedback in the east was found, implying a possible amplification of drought and flood conditions in the west and damping in the east. Finally, observations and simulations were used to demonstrate the pitfalls of determining causality between soil moisture and precipitation. It is shown that ignoring long term variability and precipitation autocorrelation can result in artificial positive correlation between soil moisture and precipitation, unless explicitly accounted for in the analysis. In total, this dissertation evaluates large-scale soil moisture measurements, outlines important factors that can cloud the determination of land surface-atmosphere hydrologic feedback, and examines the causal linkage between soil moisture and precipitation at large scales.

Soil moisture is an important element in hydrological land-surface processes as well as land atmosphere interactions and has proven useful in numerous agronomical, climatological and meteorological studies. Since hydrological soil moisture estimates are usually point-based measurements at a specific site and time, spatial and temporal dynamics of soil moisture are difficult to capture. Soil moisture retrieval techniques in remote sensing present possibilities to overcome the abovementioned limitations by continuously providing distributed soil moisture data atdifferent scales and varying temporal resolutions. The main purpose of this study is to derive soil moisture estimates for the Stockholm region by means of two different approaches from a hydrological and a remote sensing point of view and the comparison of both methods. Soil moisture is both modelled with the Topographic Wetness Index (TWI) based on digital elevation data and with the Temperature‐Vegetation Dryness Index (TVDI) as a representation of land surface temperature and Normalized Difference Vegetation Index (NDVI) ratio. Correlations of both index distributions are investigated. Possible index dependencies onvegetation cover and underlying soil types are explored. Field measurements of soil moistureare related to the derived indices. The results indicate that according to a very low Pearson correlation coefficient of 0.023, nolinear dependency between the two indices existed. Index classification in low, medium and high value categories did not result in higher correlations. Neither index distribution is found to berelated to soil types and only the TVDI correlates alongside changes in vegetation cover distribution. In situ measured values correlate better with TVDIs, although neither index is considered to give superior results in the area due to low correlation coefficients. The decision which index to apply is dependent on available data, intent of usage and scale. The TWI surface is considered to be a more suitable soil moisture representation for analyses on smaller scaleswhereas the TVDI should prove more valuable on a larger, regional scale. The lack of correlation between the indices is attributed to the fact that they differ greatly in their underlying theories. However, the synthesis of hydrologic modelling and remote sensing is a promising field of research. The establishment of combined effective models for soil moisture determination over large areas requires more extensive in situ measurements and methods to fully assess the models’ capabilities, limitations and value for hydrological predictions.

Satellite radiometers are used to remotely measure properties of the Earth's surface. Radiometers enable wide spatial coverage and daily temporal coverage. Radiometer measurements are used in a wide array of applications, including freeze/thaw states inference, vegetation index calculations, rainfall estimation, and soil moisture estimation. Resolution enhancement of these radiometer measurements enable finer details to be resolved and improve our understanding of Earth. The Soil Moisture Active Passive (SMAP) radiometer was launched in April 2014 with a goal to produce high resolution soil moisture estimates. However, due to hardware failure of the radar channels, prepared algorithms could no longer be used. Current algorithms utilize a narrow spatial and temporal overlap between the SMAP radiometer and the SENTINEL-1 radar to produce high resolution soil moisture estimates that are spatially and temporally limited. This thesis explores the use of resolution enhancing algorithms to produce high resolution soil moisture estimates without the spatial coverage limitations caused by using multiple sensors. Two main approaches are considered: calculating the iterative update in brightness temperature and calculating the update in soil moisture. The best performing algorithm is the Soil Moisture Image Reconstruction (SMIR) algorithm that is a variation of the Radiometer form of the Scatterometer Image Reconstruction (rSIR) algorithm that has been adapted to operate in parameter space. This algorithm utilizes a novel soil moisture measurement response function (SMRF) in the reconstruction. It matches or exceeds the performance of other algorithms and allows for wide spatial coverage.

Cotton (<i>Gossypium hirsutum</i> L.) is a low residue crop, that may not provide sufficient surface residue to reduce erosion and protect the soil. A winter annual cover crop could alleviate erosion between cotton crops. Field experiments were conducted to evaluate selected winter annual cover crops for biomass production, ground cover, and N assimilation. The cover crop treatments were monitored under no-till and conventional tillage systems for the effects on soil moisture, cotton yield and quality. Six cover crop treatments, crimson clover (<i>Trifolium incarnatum</i> L.), hairy vetch (<i>Vicia vilosa</i> L.), hairy vetch and rye (<i>Secale cereale</i> L.), rye, wheat (<i>Triticum aestivum</i> L. amend. Thell.), and white lupin (<i>Lupinus albus</i> L.), and two tillage systems (conventional and no-till) were arranged in a split-plot design with four replications. Cover crop biomass production depended on climate conditions. Ground cover percent and N assimilation by cover crops were directly correlated with the amount of biomass produced within cover crop treatments. Within a range of near average winter temperatures, all cover crops except lupin provided enough ground cover to comply with federal conservation tillage standards. More ground cover remained on the soil surface further into the cotton growing season following the small grain treatments compared to the legume cover crop treatments. Soil moisture was higher (P < 0.05) under no-till compared to conventional tillage during the periods of drought in 1997. Tillage system had no effect on cotton yield and quality in 1995 and 1996. High cover crop biomass production coupled with an extended cotton growing season in 1995 resulted in higher lint yield for cotton grown following the hairy vetch + rye treatment compared with cotton grown following the wheat treatment. High heat unit accumulation in October 1995 led to the over maturity of cotton fiber and high micronaire values for cotton grown following all cover crop treatments. The high micronaire values (5.0 - 5.2) for cotton grown in all cover crop treatments except hairy vetch + rye (4.9), resulted in a market price deduction of 1.4 cents per kilogram of lint in 1995. All cover crops used in this experiment, with the exception of lupin, provided enough ground cover within a range of average winter temperatures to meet federal conservation requirements. The winter annual cover crops in a no-till cotton production system provided greater soil moisture conservation during periods of drought, and produced cotton yields and quality comparable to conventional tillage. / Master of Science