Modélisation spatialisée de la production des flux et des bilans de carbone et d'eau des cultures de blé à l'aide de données de télédétection : application au sud-ouest de la France

Les terres agricoles, qui occupent plus d'un tiers de la surface continentale de la Terre, contribuent au changement climatique et sont aussi affectées par ces changements puisque leur production est contrainte par les conditions climatiques et les ressources en eau. L'objectif principal de cette thèse est donc de quantifier et d'analyser la production et aussi les principales composantes des cycles biogéochimiques du carbone et de l'eau des agrosystèmes, pour des années climatiques contrastées, afin d'identifier les meilleures stratégies pour maintenir la production et réduire les impacts environnementaux. Ce travail a été focalisé sur les cultures de blé du sud-ouest de la France. Pour répondre à cet objectif nous proposons une approche de modélisation spatialisée qui combine : i) des données de télédétection optique à hautes résolutions spatiale et temporelle, ii) des modèles de culture semi-empiriques et iii) un ample dispositif de mesures in-situ pour la calibration et la validation des modèles. L'utilisation combinée de ces trois outils offre de nouvelles perspectives pour la modélisation et le suivi des agrosystèmes à l'échelle régionale et globale.The agricultural lands that occupy more than one third of Earth's terrestrial surface contribute to climate change and are also impacted by those changes, since their production is conditioned by climatic conditions and water resources. The main objective of this thesis is therefore to quantify and analyze the production and also the main components of the carbon and water biogeochemical cycles for crop ecosystems in contrasted climatic years, focusing specifically on the winter wheat crop, in order to identify the best strategies for maintaining crop production and reducing environmental impacts. The study area is located in southwest France. We propose a regional modeling approach that combines: i) high spatial and temporal resolutions optical remote sensing data, ii) simple crop models and iii) an extensive set of in-situ measurements for models' calibration and validation. The combined use of these three 'tools' opens new perspectives for advanced agro-ecosystems modeling and monitoring at regional or global scales

Inland Valley Wetland Cultivation and Preservation for Africa’s Green and Blue Revolution Using Multi-Sensor Remote Sensing

Africa is the second largest continent after Asia with a total area of 30.22 million km2 (including the adjacent islands). It has great rivers such as the River Nile, which is the longest in the world and flows a distance of 6650 km, and the River Congo, which is the deepest in the world, as well as the second largest in the world in terms of water availability. Yet, Africa also has vast stretches of arid, semiarid, and desert lands with little or no water. Further, Africa’s population is projected to increase by four times by the year 2100, reaching about four billion from the current population of little over one billion. Food insecurity and malnutrition are already highest in Africa (Heidhues et al., 2004) and the challenge of meeting the food security needs of the fastest-growing continent in the twenty-first century is daunting. So, many solutions are thought of to ensure food security in Africa. These ideas include such measures as increasing irrigation in a continent that currently has just about 2% of the global irrigated areas (Thenkabail et al., 2009a, 2010), improving crop productivity (kg m−2), and increasing water productivity (kg m−3). However, an overwhelming proportion of Africa’s agriculture now takes place on uplands that have poor soil fertility and water availability (Scholes, 1990). Thereby, the interest in developing sustainable agriculture in Africa’s lowland wetlands, considered by some as the “new frontier” in agriculture, has swiftly increased in recent years. The lowland wetland systems include the big wetland systems that are prominent and widely recognized (Figure 9.1) as well as the less prominent, but more widespread, inland valley (IV) wetlands (Figures 9.2 through 9.8) that are all along the first to highest order river systems..

Innovation Issues in Water, Agriculture and Food

In a worldwide context of ever-growing competition for water and land, climate change, droughts and man-made water scarcity, and less-participatory water governance, agriculture faces the great challenge of producing enough food for a continually increasing population. In this line, this book provides a broad overview of innovation issues in the complex water–agriculture–food nexus, thus also relative to their interconnections and dependences. Issues refer to different spatial scales, from the field or the farm to the irrigation system or the river basin. Multidisciplinary approaches are used when analyzing the relationships between water, agriculture, and food security. The covered issues are quite diverse and include: innovation in crop evapotranspiration, crop coefficients and modeling; updates in research relative to crop water use and saving; irrigation scheduling and systems design; simulation models to support water and agricultural decisions; issues to cope with water scarcity and climate change; advances in water resource quality and sustainable uses; new tools for mapping and use of remote sensing information; and fostering a participative and inclusive governance of water for food security and population welfare. This book brings together a variety of contributions by leading international experts, professionals, and scholars in those diverse fields. It represents a major synthesis and state-of-the-art on various subjects, thus providing a valuable and updated resource for all researchers, professionals, policymakers, and post-graduate students interested in the complex world of the water–agriculture–food nexus

Implementing an Agro-Environmental Information System (AEIS) Based on GIS, Remote Sensing, and Modelling -- A case study for rice in the Sanjiang Plain, NE-China

Information on agro-ecosystems is crucial for understanding the agricultural production and its impacts on the environment, especially over large agricultural areas. The Sanjiang Plain (SJP), covering an area of 108 829 km², is a critical food base located in NE-China. Rice, soya bean and maize are the major crops in the SJP which are sold as commercial grain throughout China. The aim of this study is to set up an Agro-Environmental Information System (AEIS) for the SJP by employing the technologies of geographic information systems (GIS), remote sensing (RS), and agro-ecosystem modelling. As the starting step, data carrying interdisciplinary information from multiple sources are organized and processed. For an AEIS, geospatial data have to be acquired, organized, operated, and even regenerated with good positioning conditions. Georeferencing of the multi-source data is mandatory. In this thesis, high spatial accuracy TerraSAR-X imagery was used as a reference for georeferencing raster satellite data and vector GIS topographic data. For the second step, the georeferenced multi-source data with high spatial accuracy were integrated and categorized using a knowledge-based classifier. Rice was analysed as an example crop. A rice area map was delineated based on a time series of three high resolution FORMOSAT-2 (FS-2) images and field observed GIS topographic data. Information on rice characteristics (i.e., biomass, leaf area index, plant nitrogen concentration and plant nitrogen uptake) was derived from the multi-temporal FS-2 images. Spatial variability of rice growing status on a within-field level was well detected. As the core part of the AEIS, an agro-ecosystem modelling was then applied and subsequently crops and the environmental factors (e.g., climate, soil, field management) are linked together through a series of biochemical functions inherent in the modelling. Consequently, the interactions between agriculture and the environment are better interpreted. In the AEIS for the SJP, the site-specific mode of the DeNitrification-DeComposition (DNDC) model was adapted on regional scales by a technical improvement for the source code. By running for each pixel of the model input raster files, the regional model assimilates raster data as model inputs automatically. In this study, detailed soil data, as well as the accurate field management data in terms of crop cultivation area (i.e. rice) were used as model inputs to drive the regional model. Based on the scenario optimized from field observation, rice yields over the Qixing Farm were estimated and the spatial variability was well detected. For comparison, rice yields were derived from multi-temporal FS-2 images and the spatial patterns were analysed. As representative environmental effects, greenhouse gas of nitrous oxide (N2O) and carbon dioxide (CO2) emitted from the paddy rice fields were estimated by the regional model. This research demonstrated that the AEIS is effective in providing information about (i) agriculture on the region, (ii) the impacts of agricultural practices on the environment, and (iii) simulation scenarios for sustainable strategies, especially for the regional areas (e.g. the SJP) that is lacking of geospatial data

Advancing agricultural monitoring for improved yield estimations using SPOT-VGT and PROBA-V type remote sensing data

Accurate and timely crop condition monitoring is crucial for food management and the economic development of any nation. However, accurately estimating crop yield from the field to global scales is a challenge. According to the global strategy of the World Bank, in order to improve national agricultural statistics, crop area, crop production, and crop yield are key variables that all countries should be able to provide. Crop yield assessment requires that both an estimation of the quantity of a product and the area provided for that product should be available. The definition seems simple; however, these measurements are time consuming and subject to error in many circumstances. Remote sensing is one of several methods used for crop yield estimation. The yield results from a combination of environmental factors, such as soil, weather, and farm management, which are responsible for the unique spectral signature of a crop captured by satellite images. Additionally, yield is an expression of the state, structure, and composition of the plant. Various indices, crop masks, and land observation sensors have been developed to remotely observe and control crops in different regions. This thesis focuses on how much low spatial resolution satellites, such as Project for On Board Autonomy Vegetation (PROBA V), can contribute to global crop monitoring by aiding the search for improved methods and datasets for better crop yield estimation. This thesis contains three chapters. The first chapter explores how an existing product, Dry Matter Productivity (DMP), that has been developed for Satellites Pour l’Observation de la Terre or Earth observing Satellites VeGeTation (SPOT VGT), and transferred to PROBA V, can be improved to more closely relate to yield anomalies across selected regions. This chapter also covers the testing of the contribution of stress factors to improve wheat and maize yield estimations. According to Monteith’s theory, crop biomass linearly correlates with the amount of Absorbed Photosynthetically Active Radiation (APAR) and constant Radiation Use Efficiency (RUE) downregulated by stress factors such as CO2, fertilization, temperature, and water stress. The objective of this chapter is to investigate the relative importance of these stress factors in relation to the regional biomass production and yield. The production efficiency model Copernicus Global Land Service Dry Matter Productivity (CGLS DMP), which follows Monteith’s theory, is modified and evaluated for common wheat and silage maize in France, Belgium, and Morocco using SPOT VGT for the 1999–2012 period. The correlations between the crop yield data and the cumulative modified DMP, CGLS DMP, Fraction of APAR (fAPAR), and Normalized Difference Vegetation Index (NDVI) values are analyzed for different crop growth stages. The best results are obtained when combinations of the most appropriate stress factors are included for each selected region, and the modified DMP during the reproductive stage is accumulated. Though no single solution can demonstrate an improvement of the global product, the findings support an extension of the methodology to other regions of the world. The second chapter demonstrates how PROBA V can be used effectively for crop identification mapping by utilizing spectral matching techniques and phenological characteristics of different crop types. The study sites are agricultural areas spread across the globe, located in Flanders (Belgium), Sria (Russia), Kyiv (Ukraine), and Sao Paulo (Brazil). The data are collected for the 2014–2015 season. For each pure pixel within a field, the NDVI profile of the crop type for its growing season is matched with the reference NDVI profile. Three temporal windows are tested within the growing season: green up to senescence, green up to dormancy, and minimum NDVI at the beginning of the growing season to minimum NDVI at the end of the growing season. In order of importance, the crop phenological development period, parcel size, shorter time window, number of ground truth parcels, and crop calendar similarity are the main reasons behind the differences between the results. The methodology described in this chapter demonstrates the potentials and limitations of using 100 m PROBA V with revisiting frequency every 5 days in crop identification across different regions of the world. The final chapter explores the trade off between the different spatial resolutions provided by PROBA V products versus the temporal frequency and, additionally, explores the use of thermal time to improve statistical yield estimations. The ground data are winter wheat yields at the field level for 39 fields across Northern France during one growing season 2014–2015. An asymmetric double sigmoid function is fitted, and the NDVI values are integrated over thermal time and over calendar time for the central pixel of the field, exploring different thresholds to mark the start and end of the cropping season. The integrated NDVI values with different NDVI thresholds are used as a proxy for yield. In addition, a pixel purity analysis is performed for different purity thresholds at the 100 m, 300 m, and 1 km resolutions. The findings demonstrate that while estimating winter wheat yields at the field level with pure pixels from PROBA V products, the best correlation is obtained with a 100 m resolution product. However, several fields must be omitted due to the lack of observations throughout the growing season with the 100 m resolution dataset, as this product has a lower temporal resolution compared to 300 m and 1 km. This thesis is a modest contribution to the remote sensing and data analysis field with its own merits, in particular with respect to PROBA V. The experiments provide interesting insight into the PROBA V dataset at 1 km, 300 m, and 100 m resolutions. Specifically, the results show that 100 m spatial resolution imagery could be used effectively and advantageously in agricultural crop monitoring and crop identification at local – field level – and regional – the administrative regions defined by the national governments – levels. Furthermore, this thesis discusses the limitations of using a low resolution satellite, such as the PROBA V 100 m dataset, in crop monitoring and identification. Also, several recommendations are made for space agencies that can be used when designing the new generation of satellites

Estimation de l'humidité du sol à haute résolution spatio-temporelle : une nouvelle approche basée sur la synergie des observations micro-ondes actives/passives et optiques/thermiques

Les capteurs micro-ondes passifs SMOS et SMAP fournissent des données d'humidité du sol (SM) à une résolution d'environ 40 km avec un intervalle de 2 à 3 jours à l' échelle mondiale et une profondeur de détection de 0 à 5 cm. Ces données sont très pertinentes pour les applications cli- matiques et météorologiques. Cependant, pour les applications à échelle régionales (l'hydrologie) ou locales (l'agriculture), des données de SM à une haute résolution spatiale (typiquement 100 m ou plus fine) seraient nécessaires. Les données collectées par les capteurs optiques/thermiques et les radars peuvent fournir des indicateurs de SM à haute résolution spatiale, mais ces deux approches alternatives ont des limites. En particulier, les données optiques/thermiques ne sont pas disponibles sous les nuages et sous les couverts végétaux. Quant aux données radar, elles sont sensibles à la rugosité du sol et à la structure de la végétation, qui sont tous deux difficiles à caractériser depuis l'espace. De plus, la résolution temporelle de ces données est d'environ 6 jours. Dans ce contexte, la ligne directrice de la thèse est de proposer une nouvelle approche qui combine pour la première fois des capteurs passifs micro-ondes, optiques/thermiques et actifs micro-ondes (radar) pour estimer SM sur de grandes étendues à une résolution de 100 m chaque jour. Notre hypothèse est d'abord de nous appuyer sur une méthode de désagrégation existante (DISPATCH) des données SMOS/SMAP pour atteindre la résolution cible obtenue par les radars. A l'origine, DISPATCH est basé sur l'efficacité d' évaporation du sol (SEE) estimée sur des pixels partiellement végétalisés à partir de données optiques/thermiques (généralement MODIS) de température de surface et de couverture végétale à résolution de 1 km. Les données désagrégées de SM sont ensuite combinées avec une méthode d'inversion de SM basée sur les données radar afin d'exploiter les capacités de détection des radars Sentinel-1. Enfin, les capacités de l'assimilation des donnés satellitaires de SM dans un modèle de bilan hydrique du sol sont évaluées en termes de prédiction de SM à une résolution de 100 m et à une échelle temporelle quotidienne.Dans une première étape, l'algorithme DISPATCH est amélioré par rapport à sa version actuelle, principalement 1) en étendant son applicabilité aux pixels optiques entièrement végétalisés en utilisant l'indice de sécheresse de la végétation basé sur la température et un produit de couverture végétale amélioré, et 2) en augmentant la résolution de désagrégation de 1 km à 100 m en utilisant les données optiques/thermiques de Landsat (en plus de MODIS). Le produit de SM désagrégé à la résolution de 100 m est validé avec des mesures in situ collectées sur des zones irriguées au Maroc, indiquant une corrélation spatiale quotidienne variant de 0,5 à 0,9. Dans un deuxième étape, un nouvel algorithme est construit en développant une synergie entre les données DISPATCH et radar à 100 m de résolution. En pratique, le produit SM issu de DISPATCH les jours de ciel clair est d'abord utilisé pour calibrer un modèle de transfert radiatif radar en mode direct. Ensuite, le modèle de transfert radiatif radar ainsi calibré est utilisé en mode inverse pour estimer SM à la résolution spatio-temporelle de Sentinel-1. Sur les sites de validation, les résultats indiquent une corrélation entre les mesures satellitaires et in situ, de l'ordre de 0,66 à 0,81 pour un indice de végétation inférieur à 0,6. Dans une troisième et dernière étape, une méthode d'assimilation optimale est utilisée pour interpoler dans le temps les données de SM à la résolution de 100 m. La dynamique du produit SM dérivé de l'assimilation de SM DISPATCH à 100 m de résolution est cohérente avec les événements d'irrigation. Cette approche peut être facilement appliquée sur de grandes zones, en considérant que toutes les données (télédétection et météorologique) requises en entrée sont disponibles à l' échelle globale.SMOS and SMAP passive microwave sensors provide soil moisture (SM) data at 40 km resolution every 2-3 days globally, with a 0-5 cm sensing depth relevant for climatic and meteorological applications. However, SM data would be required at a higher (typically 100 m or finer) spatial resolution for many other regional (hydrology) or local (agriculture) applications. Optical/thermal and radar sensors can be used for retrieving SM proxies at such high spatial resolution, but both techniques have limitations. In particular, optical/thermal data are not available under clouds and under plant canopies. Moreover, radar data are sensitive to soil roughness and vegetation structure, which are challenging to characterize from outer space, and have a repeat cycle of at least six days, limiting the observations' temporal frequency. In this context, the leading principle of the thesis is to propose a new approach that combines passive microwave, optical/thermal, and active microwave (radar) sensors for the first time to retrieve SM data at 100 m resolution on a daily temporal scale. Our assumption is first to rely on an existing disaggregation method (DISPATCH) of SMOS/SMAP SM data to meet the target resolution achieved by radars. DISPATCH is originally based on the soil evaporative efficiency (SEE) retrieved over partially vegetated pixels from 1 km resolution optical/thermal (typically MODIS) surface temperature and vegetation cover data. The disaggregated SM data is then combined with a radar-based SM retrieval method to exploit the sensing capabilities of the Sentinel-1 radars. Finally, the efficacy of the assimilation of satellite-based SM data in a soil water balance model is assessed in terms of SM predictions at the 100 m resolution and daily temporal scale. As a first step, the DISPATCH algorithm is improved from its current version by mainly 1) extending its applicability to fully vegetated optical pixels using the temperature vegetation dryness index and an enhanced vegetation cover product, and 2) increasing the targeted downscaling resolution from 1 km to 100 m using Landsat (in addition to MODIS) optical/thermal data. The 100 m resolution disaggregated SM product is validated with in situ measurements collected over irrigated areas in Morocco, showing a daily spatial correlation in the range of 0.5-0.9. As a second step, a new algorithm is built on a synergy between DISPATCH and radar 100 m resolution data. In practice, the DISPATCH SM product available on clear sky days is first used to calibrate a radar radiative transfer model in the direct mode. Then the calibrated radar radia- tive transfer model is used in the inverse mode to estimate SM at the spatio-temporal resolution of Sentinel-1. Results indicate a positive correlation between satellite and in situ measurements in the range of 0.66 to 0.81 for a vegetation index lower than 0.6. As a third and final step, an optimal assimilation method is used to interpolate 100 m resolution SM data in time. The assimilation exercise is undertaken over irrigated crop fields in Spain. The analyzed SM product derived from the assimilation of 100 m resolution DISPATCH SM is consistent with irrigation events. This approach can be readily applied over large areas, given that all the required input (remote sensing and meteorological) data are available globally

Remote Sensing Monitoring of Land Surface Temperature (LST)

This book is a collection of recent developments, methodologies, calibration and validation techniques, and applications of thermal remote sensing data and derived products from UAV-based, aerial, and satellite remote sensing. A set of 15 papers written by a total of 70 authors was selected for this book. The published papers cover a wide range of topics, which can be classified in five groups: algorithms, calibration and validation techniques, improvements in long-term consistency in satellite LST, downscaling of LST, and LST applications and land surface emissivity research

Modélisation de l’évolution hydroclimatique des flux et stocks d’eau verte et d’eau bleue du bassin versant de la Garonne

La gestion intégrée de la ressource en eau implique de distinguer les parcours de l’eau qui sont accessibles aux sociétés de ceux qui ne le sont pas. Les cheminements de l’eau sont nombreux et fortement variables d’un lieu à l’autre. Il est possible de simplifier cette question en s’attardant plutôt aux deux destinations de l’eau. L’eau bleue forme les réserves et les flux dans l’hydrosystème : cours d’eau, nappes et écoulements souterrains. L’eau verte est le flux invisible de vapeur d’eau qui rejoint l’atmosphère. Elle inclut l’eau consommée par les plantes et l’eau dans les sols. Or, un grand nombre d’études ne portent que sur un seul type d’eau bleue, en ne s’intéressant généralement qu’au devenir des débits ou, plus rarement, à la recharge des nappes. Le portrait global est alors manquant. Dans un même temps, les changements climatiques viennent impacter ce cheminement de l’eau en faisant varier de manière distincte les différents composants de cycle hydrologique. L’étude réalisée ici utilise l’outil de modélisation SWAT afin de réaliser le suivi de toutes les composantes du cycle hydrologique et de quantifier l’impact des changements climatiques sur l’hydrosystème du bassin versant de la Garonne. Une première partie du travail a permis d’affiner la mise en place du modèle pour répondre au mieux à la problématique posée. Un soin particulier a été apporté à l’utilisation de données météorologiques sur grille (SAFRAN) ainsi qu’à la prise en compte de la neige sur les reliefs. Le calage des paramètres du modèle a été testé dans un contexte differential split sampling, en calant puis validant sur des années contrastées en terme climatique afin d’appréhender la robustesse de la simulation dans un contexte de changements climatiques. Cette étape a permis une amélioration substantielle des performances sur la période de calage (2000-2010) ainsi que la mise en évidence de la stabilité du modèle face aux changements climatiques. Par suite, des simulations sur une période d’un siècle (1960-2050) ont été produites puis analysées en deux phases : i) La période passée (1960-2000), basée sur les observations climatiques, a servi de période de validation à long terme du modèle sur la simulation des débits, avec de très bonnes performances. L’analyse des différents composants hydrologiques met en évidence un impact fort sur les flux et stocks d’eau verte, avec une diminution de la teneur en eau des sols et une augmentation importante de l’évapotranspiration. Les composantes de l’eau bleue sont principalement perturbées au niveau du stock de neige et des débits qui présentent tous les deux une baisse substantielle. ii) Des projections hydrologiques ont été réalisées (2010-2050) en sélectionnant une gamme de scénarios et de modèles climatiques issus d’une mise à l’échelle dynamique. L’analyse de simulation vient en bonne part confirmer les conclusions tirées de la période passée : un impact important sur l’eau verte, avec toujours une baisse de la teneur en eau des sols et une augmentation de l’évapotranspiration potentielle. Les simulations montrent que la teneur en eau des sols pendant la période estivale est telle qu’elle en vient à réduire les flux d’évapotranspiration réelle, mettant en évidence le possible déficit futur des stocks d’eau verte. En outre, si l’analyse des composantes de l’eau bleue montre toujours une diminution significative du stock de neige, les débits semblent cette fois en hausse pendant l’automne et l’hiver. Ces résultats sont un signe de l’«accélération» des composantes d’eau bleue de surface, probablement en relation avec l’augmentation des évènements extrêmes de précipitation. Ce travail a permis de réaliser une analyse des variations de la plupart des composantes du cycle hydrologique à l’échelle d’un bassin versant, confirmant l’importance de prendre en compte toutes ces composantes pour évaluer l’impact des changements climatiques et plus largement des changements environnementaux sur la ressource en eau

Remote sensing analysis of croplands, woody plant encroachment and carbon fluxes of woody savanna

Since 1990s, much attention has been paid to Land use/land cover change (LULCC) studies because it is an important component of global change. The vegetation change is a critical factor of land cover changes, which interacts with climate, ecosystem processes, biogeochemical cycles and biodiversity. Remote sensing is a good tool to detect the changes of land use and land cover. To date, most of studies on vegetation changes have been conducted at biome scales, but have not examined changes at the species level. This lack of studies on species inhibits analysis of ecosystem functions caused by the shifts of vegetation types. This dissertation aims to explore the potential of remote sensing images to produce long-term products on specific vegetation type and study the interactions between vegetation type, climate and gross primary production. In Chapter 2, a simple algorithms was developed to identify paddy rice by selecting a unique temporal window (flooding/transplanting period) at regional scale using time series Landsat-8 images. A wheat-rice double-cropped area from China was selected as the study area. The resultant paddy rice map had overall accuracy and Kappa coefficient of 89.8% and 0.79, respectively. In comparison with the National Land Cover Data (China) from 2010, the resultant map had a better detection of the changes in the paddy rice fields. These results demonstrate the efficacy of using images from multiple sources to generate paddy rice maps for two-crop rotation systems. Chapter 3 developed a pixel and phenology-based mapping algorithm, and used it to analyze PALSAR mosaic data in 2010 and all the available Landsat 5/7 data during 1984-2010. This study analyzed 4,233 images covering more than 10 counties in the central region of Oklahoma, and generated eastern redcedar forest maps for 2010 and five historical time periods: the late 1980s (1984-1989), early 1990s (1990-1994), late 1990s (1995-1999), early 2000s (2000-2004), and late 2000s (2005-2010). The resultant maps clearly illustrated an increase in red cedar encroachment within the study area at an annual rate of ~8% during 1984-2010. Chapter 4 investigates the dynamics of juniper encroachment on the grasslands of Oklahoma by generating multi-period maps of juniper encroachment from 1984 to 2010 at a 30-m spatial resolution. The juniper forest maps in 1984 to 2010 were produced by the algorithms developed in Chapter 3. The resultant maps revealed the spatio-temporal dynamics of juniper forest encroachment at state and county scales. This study also characterized the juniper forest encroachment by geographical pattern and soil conditions. The resultant maps can be used to support studies on ecosystem processes, sustainability, and ecosystem services. Chapter 5 compared dynamics of major climatic variables, eddy covariance tower-based GPP, and vegetation indices (VIs) over the last decade in a deciduous savanna and an evergreen savanna under a Mediterranean climate. The relationships were also examined among VIs, GPP, and major climatic variables in dry, normal, and wet hydrological years. GPP of these two savanna sites were also simulated using a light-use efficiency based Vegetation Photosynthesis Model (VPM). The results of this study help better understanding the eco-physiological response of evergreen and deciduous savannas, and also suggest the potential of VPM to simulate interannual variations of GPP in different types of Mediterranean-climate savannas