Earth Observations and Integrative Models in Support of Food and Water Security

Global food production depends upon many factors that Earth observing satellites routinely measure about water, energy, weather, and ecosystems. Increasingly sophisticated, publicly-available satellite data products can improve efficiencies in resource management and provide earlier indication of environmental disruption. Satellite remote sensing provides a consistent, long-term record that can be used effectively to detect large-scale features over time, such as a developing drought. Accuracy and capabilities have increased along with the range of Earth observations and derived products that can support food security decisions with actionable information. This paper highlights major capabilities facilitated by satellite observations and physical models that have been developed and validated using remotely-sensed observations. Although we primarily focus on variables relevant to agriculture, we also include a brief description of the growing use of Earth observations in support of aquaculture and fisheries

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

Monitoring crops water needs at high spatio-temporal resolution by synergy of optical/thermal and radar observations

L'optimisation de la gestion de l'eau en agriculture est essentielle dans les zones semi-arides afin de préserver les ressources en eau qui sont déjà faibles et erratiques dues à des actions humaines et au changement climatique. Cette thèse vise à utiliser la synergie des observations de télédétection multispectrales (données radar, optiques et thermiques) pour un suivi à haute résolution spatio-temporelle des besoins en eau des cultures. Dans ce contexte, différentes approches utilisant divers capteurs (Landsat-7/8, Sentinel-1 et MODIS) ont été developpées pour apporter une information sur l'humidité du sol (SM) et le stress hydrique des cultures à une échelle spatio-temporelle pertinente pour la gestion de l'irrigation. Ce travail va parfaitement dans le sens des objectifs du projet REC "Root zone soil moisture Estimates at the daily and agricultural parcel scales for Crop irrigation management and water use impact: a multi-sensor remote sensing approach" (http://rec.isardsat.com/) qui visent à estimer l'humidité du sol dans la zone racinaire (RZSM) afin d'optimiser la gestion de l'eau d'irrigation. Des approches innovantes et prometteuses sont mises en place pour estimer l'évapotranspiration (ET), RZSM, la température de surface du sol (LST) et le stress hydrique de la végétation à travers des indices de SM dérivés des observations multispectrales à haute résolution spatio-temporelle. Les méthodologies proposées reposent sur des méthodes basées sur l'imagerie, la modélisation du transfert radiatif et la modélisation du bilan hydrique et d'énergie et sont appliquées dans une région à climat semi-aride (centre du Maroc). Dans le cadre de ma thèse, trois axes ont été explorés. Dans le premier axe, un indice de RZSM dérivé de LST-Landsat est utilisé pour estimer l'ET sur des parcelles de blé et des sols nus. L'estimation par modélisation de ET a été explorée en utilisant l'équation de Penman-monteith modifiée obtenue en introduisant une relation empirique simple entre la résistance de surface (rc) et l'indice de RZSM. Ce dernier est estimé à partir de la température de surface (LST) dérivée de Landsat, combinée avec les températures extrêmes (en conditions humides et sèches) simulée par un modèle de bilan d'énergie de surface piloté par le forçage météorologique et la fraction de couverture végétale dérivée de Landsat. La méthode utilisée est calibrée et validée sur deux parcelles de blé situées dans la même zone près de Marrakech au Maroc. Dans l'axe suivant, une méthode permettant de récupérer la SM de la surface (0-5 cm) à une résolution spatiale et temporelle élevée est développée à partir d'une synergie entre données radar (Sentinel-1) et thermique (Landsat) et en utilisant un modèle de bilan d'énergie du sol. L'approche développée a été validée sur des parcelles agricoles en sol nu et elle donne une estimation précise de la SM avec une différence quadratique moyenne en comparant à la SM in situ, égale à 0,03 m3 m-3. Dans le dernier axe, une nouvelle méthode est développée pour désagréger la MODIS LST de 1 km à 100 m de résolution en intégrant le SM proche de la surface dérivée des données radar Sentinel-1 et l'indice de végétation optique dérivé des observations Landsat. Le nouvel algorithme, qui inclut la rétrodiffusion S-1 en tant qu'entrée dans la désagrégation, produit des résultats plus stables et robustes au cours de l'année sélectionnée. Dont, 3,35 °C était le RMSE le plus bas et 0,75 le coefficient de corrélation le plus élevé évalués en utilisant le nouvel algorithme.Optimizing water management in agriculture is essential over semi-arid areas in order to preserve water resources which are already low and erratic due to human actions and climate change. This thesis aims to use the synergy of multispectral remote sensing observations (radar, optical and thermal data) for high spatio-temporal resolution monitoring of crops water needs. In this context, different approaches using various sensors (Landsat-7/8, Sentinel-1 and MODIS) have been developed to provide information on the crop Soil Moisture (SM) and water stress at a spatio-temporal scale relevant to irrigation management. This work fits well the REC "Root zone soil moisture Estimates at the daily and agricultural parcel scales for Crop irrigation management and water use impact: a multi-sensor remote sensing approach" (http://rec.isardsat.com/) project objectives, which aim to estimate the Root Zone Soil Moisture (RZSM) for optimizing the management of irrigation water. Innovative and promising approaches are set up to estimate evapotranspiration (ET), RZSM, land surface temperature (LST) and vegetation water stress through SM indices derived from multispectral observations with high spatio-temporal resolution. The proposed methodologies rely on image-based methods, radiative transfer modelling and water and energy balance modelling and are applied in a semi-arid climate region (central Morocco). In the frame of my PhD thesis, three axes have been investigated. In the first axis, a Landsat LST-derived RZSM index is used to estimate the ET over wheat parcels and bare soil. The ET modelling estimation is explored using a modified Penman-Monteith equation obtained by introducing a simple empirical relationship between surface resistance (rc) and a RZSM index. The later is estimated from Landsat-derived land surface temperature (LST) combined with the LST endmembers (in wet and dry conditions) simulated by a surface energy balance model driven by meteorological forcing and Landsat-derived fractional vegetation cover. The investigated method is calibrated and validated over two wheat parcels located in the same area near Marrakech City in Morocco. In the next axis, a method to retrieve near surface (0-5 cm) SM at high spatial and temporal resolution is developed from a synergy between radar (Sentinel-1) and thermal (Landsat) data and by using a soil energy balance model. The developed approach is validated over bare soil agricultural fields and gives an accurate estimates of near surface SM with a root mean square difference compared to in situ SM equal to 0.03 m3 m-3. In the final axis a new method is developed to disaggregate the 1 km resolution MODIS LST at 100 m resolution by integrating the near surface SM derived from Sentinel-1 radar data and the optical-vegetation index derived from Landsat observations. The new algorithm including the S-1 backscatter as input to the disaggregation, produces more stable and robust results during the selected year. Where, 3.35 °C and 0.75 were the lowest RMSE and the highest correlation coefficient assessed using the new algorithm

Monitoring 10-m LST from the Combination MODIS/Sentinel-2, Validation in a High Contrast Semi-Arid Agroecosystem

Downscaling techniques offer a solution to the lack of high-resolution satellite Thermal InfraRed (TIR) data and can bridge the gap until operational TIR missions accomplishing spatio-temporal requirements are available. These techniques are generally based on the Visible Near InfraRed (VNIR)-TIR variable relations at a coarse spatial resolution, and the assumption that the relationship between spectral bands is independent of the spatial resolution. In this work, we adopted a previous downscaling method and introduced some adjustments to the original formulation to improve the model performance. Maps of Land Surface Temperature (LST) with 10-m spatial resolution were obtained as output from the combination of MODIS/Sentinel-2 images. An experiment was conducted in an agricultural area located in the Barrax test site, Spain (39°03′35″ N, 2°06′ W), for the summer of 2018. Ground measurements of LST transects collocated with the MODIS overpasses were used for a robust local validation of the downscaling approach. Data from 6 different dates were available, covering a variety of croplands and surface conditions, with LST values ranging 300-325 K. Differences within ±4.0 K were observed between measured and modeled temperatures, with an average estimation error of ±2.2 K and a systematic deviation of 0.2 K for the full ground dataset. A further cross-validation of the disaggregated 10-m LST products was conducted using an additional set of Landsat-7/ETM+ images. A similar uncertainty of ±2.0 K was obtained as an average. These results are encouraging for the adaptation of this methodology to the tandem Sentinel-3/Sentinel-2, and are promising since the 10-m pixel size, together with the 3-5 days revisit frequency of Sentinel-2 satellites can fulfill the LST input requirements of the surface energy balance methods for a variety of hydrological, climatological or agricultural applications. However, certain limitations to capture the variability of extreme LST, or in recently sprinkler irrigated fields, claim the necessity to explore the implementation of soil moisture or vegetation indices sensitive to soil water content as inputs in the downscaling approach. The ground LST dataset introduced in this paper will be of great value for further refinements and assessments

Community Review of Southern Ocean Satellite Data Needs

This review represents the Southern Ocean community’s satellite data needs for the coming decade. Developed through widespread engagement, and incorporating perspectives from a range of stakeholders (both research and operational), it is designed as an important community-driven strategy paper that provides the rationale and information required for future planning and investment. The Southern Ocean is vast but globally connected, and the communities that require satellite-derived data in the region are diverse. This review includes many observable variables, including sea-ice properties, sea-surface temperature, sea-surface height, atmospheric parameters, marine biology (both micro and macro) and related activities, terrestrial cryospheric connections, sea-surface salinity, and a discussion of coincident and in situ data collection. Recommendations include commitment to data continuity, increase in particular capabilities (sensor types, spatial, temporal), improvements in dissemination of data/products/uncertainties, and innovation in calibration/validation capabilities. Full recommendations are detailed by variable as well as summarized. This review provides a starting point for scientists to understand more about Southern Ocean processes and their global roles, for funders to understand the desires of the community, for commercial operators to safely conduct their activities in the Southern Ocean, and for space agencies to gain greater impact from Southern Ocean-related acquisitions and missions.The authors acknowledge the Climate at the Cryosphere program and the Southern Ocean Observing System for initiating this community effort, WCRP, SCAR, and SCOR for endorsing the effort, and CliC, SOOS, and SCAR for supporting authors’ travel for collaboration on the review. Jamie Shutler’s time on this review was funded by the European Space Agency project OceanFlux Greenhouse Gases Evolution (Contract number 4000112091/14/I-LG)

시공간 해상도 향상을 통한 식생 변화 모니터링

학위논문(박사) -- 서울대학교대학원 : 환경대학원 협동과정 조경학, 2023. 2. 류영렬.육상 생태계에서 대기권과 생물권의 상호 작용을 이해하기 위해서는 식생 변화의 모니터링이 필요하다. 이 때, 위성영상은 지표면을 관측하여 식생지도를 제공할 수 있지만, 지표변화의 상세한 정보는 구름이나 위성 이미지의 공간 해상도에 의해 제한되었다. 또한 위성영상의 시공간 해상도가 식생지도를 통한 광합성 모니터링에 미치는 영향은 완전히 밝혀지지 않았다. 본 논문에서는 고해상도 식생 지도를 일단위로 생성하기 위성 영상의 시공간 해상도를 향상시키는 것을 목표로 하였다. 고해상도 위성영상을 활용한 식생 변화 모니터링을 시공간적으로 확장하기 위해 1) 정지궤도 위성을 활용한 영상융합을 통해 시간해상도 향상, 2) 적대적생성네트워크를 활용한 공간해상도 향상, 3) 시공간해상도가 높은 위성영상을 토지피복이 균질하지 않은 공간에서 식물 광합성 모니터링을 수행하였다. 이처럼, 위성기반 원격탐지에서 새로운 기술이 등장함에 따라 현재 및 과거의 위성영상은 시공간 해상도 측면에서 향상되어 식생 변화의 모니터링 할 수 있다. 제2장에서는 정지궤도위성영상을 활용하는 시공간 영상융합으로 식물의 광합성을 모니터링 했을 때, 시간해상도가 향상됨을 보였다. 시공간 영상융합 시, 구름탐지, 양방향 반사 함수 조정, 공간 등록, 시공간 융합, 시공간 결측치 보완 등의 과정을 거친다. 이 영상융합 산출물은 경작관리 등으로 식생 지수의 연간 변동이 큰 두 장소(농경지와 낙엽수림)에서 평가하였다. 그 결과, 시공간 영상융합 산출물은 결측치 없이 현장관측을 예측하였다 (R2 = 0.71, 상대 편향 = 5.64% 농경지; R2 = 0.79, 상대 편향 = -13.8%, 활엽수림). 시공간 영상융합은 식생 지도의 시공간 해상도를 점진적으로 개선하여, 식물 생장기동안 위성영상이 현장 관측을 과소 평가를 줄였다. 영상융합은 높은 시공간 해상도로 광합성 지도를 일간격으로 생성하기에 이를 활용하여 위성 영상의 제한된 시공간 해상도로 밝혀지지 않은 식물변화의 과정을 발견하길 기대한다. 식생의 공간분포은 정밀농업과 토지 피복 변화 모니터링을 위해 필수적이다. 고해상도 위성영상으로 지구 표면을 관측하는 것을 용이하게 해졌다. 특히 Planet Fusion은 초소형위성군 데이터를 최대한 활용해 데이터 결측이 없는 3m 공간 해상도의 지표 표면 반사도이다. 그러나 과거 위성 센서(Landsat의 경우 30~60m)의 공간 해상도는 식생의 공간적 변화를 상세 분석하는 것을 제한했다. 제3장에서는 Landsat 데이터의 공간 해상도를 향상하기 위해 Planet Fusion 및 Landsat 8 데이터를 사용하여 이중 적대적 생성 네트워크(the dual RSS-GAN)를 학습시켜, 고해상도 정규화 식생 지수(NDVI)와 식물 근적외선 반사(NIRv)도를 생성하는 한다. 타워기반 현장 식생지수(최대 8년)와 드론기반 초분광지도로 the dual RSS-GAN의 성능을 대한민국 내 두 대상지(농경지와 활엽수림)에서 평가했다. The dual RSS-GAN은 Landsat 8 영상의 공간해상도를 향상시켜 공간 표현을 보완하고 식생 지수의 계절적 변화를 포착했다(R2> 0.96). 그리고 the dual RSS-GAN은 Landsat 8 식생 지수가 현장에 비해 과소 평가되는 것을 완화했다. 현장 관측에 비해 이중 RSS-GAN과 Landsat 8의 상대 편향 값 각각 -0.8% 에서 -1.5%, -10.3% 에서 -4.6% 였다. 이러한 개선은 Planet Fusion의 공간정보를 이중 RSS-GAN로 학습하였기에 가능했다. 헤당 연구 결과는 Landsat 영상의 공간 해상도를 향상시켜 숨겨진 공간 정보를 제공하는 새로운 접근 방식이다. 고해상도에서 식물 광합성 지도는 토지피복이 복잡한 공간에서 탄소 순환 모니터링시 필수적이다. 그러나 Sentinel-2, Landsat 및 MODIS와 같이 태양 동조 궤도에 있는 위성은 공간 해상도가 높거나 시간 해상도 높은 위성영상만 제공할 수 있다. 최근 발사된 초소형위성군은 이러한 해상도 한계을 극복할 수 있다. 특히 Planet Fusion은 초소형위성 자료의 시공간 해상도로 지표면을 관측할 수 있다. 4장에서, Planet Fusion 지표반사도를 이용하여 식생에서 반사된 근적외선 복사(NIRvP)를 3m 해상도 지도를 일간격으로 생성했다. 그런 다음 미국 캘리포니아주 새크라멘토-샌 호아킨 델타의 플럭스 타워 네트워크 데이터와 비교하여 식물 광합성을 추정하기 위한 NIRvP 지도의 성능을 평가하였다. 전체적으로 NIRvP 지도는 습지의 잦은 수위 변화에도 불구하고 개별 대상지의 식물 광합성의 시간적 변화를 포착하였다. 그러나 대상지 전체에 대한 NIRvP 지도와 식물 광합성 사이의 관계는 NIRvP 지도를 플럭스 타워 관측범위와 일치시킬 때만 높은 상관관계를 보였다. 관측범위를 일치시킬 경우, NIRvP 지도는 식물 광합성을 추정하는 데 있어 현장 NIRvP보다 우수한 성능을 보였다. 이러한 성능 차이는 플럭스 타워 관측범위를 일치시킬 때, 연구 대상지 간의 NIRvP-식물 광합성 관계의 기울기가 일관성을 보였기 때문이다. 본 연구 결과는 위성 관측을 플럭스 타워 관측범위와 일치시키는 것의 중요성을 보여주고 높은 시공간 해상도로 식물 광합성을 원격으로 모니터링하는 초소형위성군 자료의 잠재력을 보여준다.Monitoring changes in terrestrial vegetation is essential to understanding interactions between atmosphere and biosphere, especially terrestrial ecosystem. To this end, satellite remote sensing offer maps for examining land surface in different scales. However, the detailed information was hindered under the clouds or limited by the spatial resolution of satellite imagery. Moreover, the impacts of spatial and temporal resolution in photosynthesis monitoring were not fully revealed. In this dissertation, I aimed to enhance the spatial and temporal resolution of satellite imagery towards daily gap-free vegetation maps with high spatial resolution. In order to expand vegetation change monitoring in time and space using high-resolution satellite images, I 1) improved temporal resolution of satellite dataset through image fusion using geostationary satellites, 2) improved spatial resolution of satellite dataset using generative adversarial networks, and 3) showed the use of high spatiotemporal resolution maps for monitoring plant photosynthesis especially over heterogeneous landscapes. With the advent of new techniques in satellite remote sensing, current and past datasets can be fully utilized for monitoring vegetation changes in the respect of spatial and temporal resolution. In Chapter 2, I developed the integrated system that implemented geostationary satellite products in the spatiotemporal image fusion method for monitoring canopy photosynthesis. The integrated system contains the series of process (i.e., cloud masking, nadir bidirectional reflectance function adjustment, spatial registration, spatiotemporal image fusion, spatial gap-filling, temporal-gap-filling). I conducted the evaluation of the integrated system over heterogeneous rice paddy landscape where the drastic land cover changes were caused by cultivation management and deciduous forest where consecutive changes occurred in time. The results showed that the integrated system well predict in situ measurements without data gaps (R2 = 0.71, relative bias = 5.64% at rice paddy site; R2 = 0.79, relative bias = -13.8% at deciduous forest site). The integrated system gradually improved the spatiotemporal resolution of vegetation maps, reducing the underestimation of in situ measurements, especially during peak growing season. Since the integrated system generates daily canopy photosynthesis maps for monitoring dynamics among regions of interest worldwide with high spatial resolution. I anticipate future efforts to reveal the hindered information by the limited spatial and temporal resolution of satellite imagery. Detailed spatial representations of terrestrial vegetation are essential for precision agricultural applications and the monitoring of land cover changes in heterogeneous landscapes. The advent of satellite-based remote sensing has facilitated daily observations of the Earths surface with high spatial resolution. In particular, a data fusion product such as Planet Fusion has realized the delivery of daily, gap-free surface reflectance data with 3-m pixel resolution through full utilization of relatively recent (i.e., 2018-) CubeSat constellation data. However, the spatial resolution of past satellite sensors (i.e., 30–60 m for Landsat) has restricted the detailed spatial analysis of past changes in vegetation. In Chapter 3, to overcome the spatial resolution constraint of Landsat data for long-term vegetation monitoring, we propose a dual remote-sensing super-resolution generative adversarial network (dual RSS-GAN) combining Planet Fusion and Landsat 8 data to simulate spatially enhanced long-term time-series of the normalized difference vegetation index (NDVI) and near-infrared reflectance from vegetation (NIRv). We evaluated the performance of the dual RSS-GAN against in situ tower-based continuous measurements (up to 8 years) and remotely piloted aerial system-based maps of cropland and deciduous forest in the Republic of Korea. The dual RSS-GAN enhanced spatial representations in Landsat 8 images and captured seasonal variation in vegetation indices (R2 > 0.95, for the dual RSS-GAN maps vs. in situ data from all sites). Overall, the dual RSS-GAN reduced Landsat 8 vegetation index underestimations compared with in situ measurements; relative bias values of NDVI ranged from −3.2% to 1.2% and −12.4% to −3.7% for the dual RSS-GAN and Landsat 8, respectively. This improvement was caused by spatial enhancement through the dual RSS-GAN, which captured fine-scale information from Planet Fusion. This study presents a new approach for the restoration of hidden sub-pixel spatial information in Landsat images. Mapping canopy photosynthesis in both high spatial and temporal resolution is essential for carbon cycle monitoring in heterogeneous areas. However, well established satellites in sun-synchronous orbits such as Sentinel-2, Landsat and MODIS can only provide either high spatial or high temporal resolution but not both. Recently established CubeSat satellite constellations have created an opportunity to overcome this resolution trade-off. In particular, Planet Fusion allows full utilization of the CubeSat data resolution and coverage while maintaining high radiometric quality. In Chapter 4, I used the Planet Fusion surface reflectance product to calculate daily, 3-m resolution, gap-free maps of the near-infrared radiation reflected from vegetation (NIRvP). I then evaluated the performance of these NIRvP maps for estimating canopy photosynthesis by comparing with data from a flux tower network in Sacramento-San Joaquin Delta, California, USA. Overall, NIRvP maps captured temporal variations in canopy photosynthesis of individual sites, despite changes in water extent in the wetlands and frequent mowing in the crop fields. When combining data from all sites, however, I found that robust agreement between NIRvP maps and canopy photosynthesis could only be achieved when matching NIRvP maps to the flux tower footprints. In this case of matched footprints, NIRvP maps showed considerably better performance than in situ NIRvP in estimating canopy photosynthesis both for daily sum and data around the time of satellite overpass (R2 = 0.78 vs. 0.60, for maps vs. in situ for the satellite overpass time case). This difference in performance was mostly due to the higher degree of consistency in slopes of NIRvP-canopy photosynthesis relationships across the study sites for flux tower footprint-matched maps. Our results show the importance of matching satellite observations to the flux tower footprint and demonstrate the potential of CubeSat constellation imagery to monitor canopy photosynthesis remotely at high spatio-temporal resolution.Chapter 1. Introduction 2 1. Background 2 1.1 Daily gap-free surface reflectance using geostationary satellite products 2 1.2 Monitoring past vegetation changes with high-spatial-resolution 3 1.3 High spatiotemporal resolution vegetation photosynthesis maps 4 2. Purpose of Research 4 Chapter 2. Generating daily gap-filled BRDF adjusted surface reflectance product at 10 m resolution using geostationary satellite product for monitoring daily canopy photosynthesis 6 1. Introduction 6 2. Methods 11 2.1 Study sites 11 2.2 In situ measurements 13 2.3 Satellite products 14 2.4 Integrated system 17 2.5 Canopy photosynthesis 21 2.6 Evaluation 23 3. Results and discussion 24 3.1 Comparison of STIF NDVI and NIRv with in situ NDVI and NIRv 24 3.2 Comparison of STIF NIRvP with in situ NIRvP 28 4. Conclusion 31 Chapter 3. Super-resolution of historic Landsat imagery using a dual Generative Adversarial Network (GAN) model with CubeSat constellation imagery for monitoring vegetation changes 32 1. Introduction 32 2. Methods 38 2.1 Real-ESRGAN model 38 2.2 Study sites 40 2.3 In situ measurements 42 2.4 Vegetation index 44 2.5 Satellite data 45 2.6 Planet Fusion 48 2.7 Dual RSS-GAN via fine-tuned Real-ESRGAN 49 2.8 Evaluation 54 3. Results 57 3.1 Comparison of NDVI and NIRv maps from Planet Fusion, Sentinel 2 NBAR, and Landsat 8 NBAR data with in situ NDVI and NIRv 57 3.2 Comparison of dual RSS-SRGAN model results with Landsat 8 NDVI and NIRv 60 3.3 Comparison of dual RSS-GAN model results with respect to in situ time-series NDVI and NIRv 63 3.4 Comparison of the dual RSS-GAN model with NDVI and NIRv maps derived from RPAS 66 4. Discussion 70 4.1 Monitoring changes in terrestrial vegetation using the dual RSS-GAN model 70 4.2 CubeSat data in the dual RSS-GAN model 72 4.3 Perspectives and limitations 73 5. Conclusion 78 Appendices 79 Supplementary material 82 Chapter 4. Matching high resolution satellite data and flux tower footprints improves their agreement in photosynthesis estimates 85 1. Introduction 85 2. Methods 89 2.1 Study sites 89 2.2 In situ measurements 92 2.3 Planet Fusion NIRvP 94 2.4 Flux footprint model 98 2.5 Evaluation 98 3. Results 105 3.1 Comparison of Planet Fusion NIRv and NIRvP with in situ NIRv and NIRvP 105 3.2 Comparison of instantaneous Planet Fusion NIRv and NIRvP with against tower GPP estimates 108 3.3 Daily GPP estimation from Planet Fusion -derived NIRvP 114 4. Discussion 118 4.1 Flux tower footprint matching and effects of spatial and temporal resolution on GPP estimation 118 4.2 Roles of radiation component in GPP mapping 123 4.3 Limitations and perspectives 126 5. Conclusion 133 Appendix 135 Supplementary Materials 144 Chapter 5. Conclusion 153 Bibliography 155 Abstract in Korea 199 Acknowledgements 202박

Satellite and in situ observations for advancing global Earth surface modelling: a review

In this paper, we review the use of satellite-based remote sensing in combination with in situ data to inform Earth surface modelling. This involves verification and optimization methods that can handle both random and systematic errors and result in effective model improvement for both surface monitoring and prediction applications. The reasons for diverse remote sensing data and products include (i) their complementary areal and temporal coverage, (ii) their diverse and covariant information content, and (iii) their ability to complement in situ observations, which are often sparse and only locally representative. To improve our understanding of the complex behavior of the Earth system at the surface and sub-surface, we need large volumes of data from high-resolution modelling and remote sensing, since the Earth surface exhibits a high degree of heterogeneity and discontinuities in space and time. The spatial and temporal variability of the biosphere, hydrosphere, cryosphere and anthroposphere calls for an increased use of Earth observation (EO) data attaining volumes previously considered prohibitive. We review data availability and discuss recent examples where satellite remote sensing is used to infer observable surface quantities directly or indirectly, with particular emphasis on key parameters necessary for weather and climate prediction. Coordinated high-resolution remote-sensing and modelling/assimilation capabilities for the Earth surface are required to support an international application-focused effort

An Integrative Information Aqueduct to Close the Gaps between Satellite Observation of Water Cycle and Local Sustainable Management of Water Resources

[EN] The past decades have seen rapid advancements in space-based monitoring of essential water cycle variables, providing products related to precipitation, evapotranspiration, and soil moisture, often at tens of kilometer scales. Whilst these data effectively characterize water cycle variability at regional to global scales, they are less suitable for sustainable management of local water resources, which needs detailed information to represent the spatial heterogeneity of soil and vegetation. The following questions are critical to effectively exploit information from remotely sensed and in situ Earth observations (EOs): How to downscale the global water cycle products to the local scale using multiple sources and scales of EO data? How to explore and apply the downscaled information at the management level for a better understanding of soil-water-vegetation-energy processes? How can such fine-scale information be used to improve the management of soil and water resources? An integrative information flow (i.e., iAqueduct theoretical framework) is developed to close the gaps between satellite water cycle products and local information necessary for sustainable management of water resources. The integrated iAqueduct framework aims to address the abovementioned scientific questions by combining medium-resolution (10 m-1 km) Copernicus satellite data with high-resolution (cm) unmanned aerial system (UAS) data, in situ observations, analytical- and physical-based models, as well as big-data analytics with machine learning algorithms. This paper provides a general overview of the iAqueduct theoretical framework and introduces some preliminary results.The authors would like to thank the European Commission and Netherlands Organisation for Scientific Research (NWO) for funding, in the frame of the collaborative international consortium (iAqueduct) financed under the 2018 Joint call of the Water Works 2017 ERA-NET Cofund. This ERA-NET is an integral part of the activities developed by the Water JPI (Project number: ENWWW.2018.5); the EC and the Swedish Research Council for Sustainable Development (FORMAS, under grant 2018-02787); Contributions of B. Szabo was supported by the Janos Bolyai Research Scholarship of the Hungarian Academy of Sciences (grant no. BO/00088/18/4).Su, Z.; Zeng, Y.; Romano, N.; Manfreda, S.; Francés, F.; Ben Dor, E.; Szabó, B.... (2020). An Integrative Information Aqueduct to Close the Gaps between Satellite Observation of Water Cycle and Local Sustainable Management of Water Resources. Water. 12(5):1-36. https://doi.org/10.3390/w12051495S13612

Feasibility of Using the Two-Source Energy Balance Model (TSEB) with Sentinel-2 and Sentinel-3 Images to Analyze the Spatio-Temporal Variability of Vine Water Status in a Vineyard

In viticulture, detailed spatial information about actual evapotranspiration (ETa) and vine water status within a vineyard may be of particular utility when applying site-specific, precision irrigation management. Over recent decades, extensive research has been carried out in the use of remote sensing energy balance models to estimate and monitor ETa at the field level. However, one of the major limitations remains the coarse spatial resolution in the thermal infrared (TIR) domain. In this context, the recent advent of the Sentinel missions of the European Space Agency (ESA) has greatly improved the possibility of monitoring crop parameters and estimating ETa at higher temporal and spatial resolutions. In order to bridge the gap between the coarse-resolution Sentinel-3 thermal and the fine-resolution Sentinel-2 shortwave data, sharpening techniques have been used to downscale the Sentinel-3 land surface temperature (LST) from 1 km to 20 m. However, the accurate estimates of high-resolution LST through sharpening techniques are still unclear, particularly when intended to be used for detecting crop water stress. The goal of this study was to assess the feasibility of the two-source energy balance model (TSEB) using sharpened LST images from Sentinel-2 and Sentinel-3 (TSEB-PTS2+3) to estimate the spatio-temporal variability of actual transpiration (T) and water stress in a vineyard. T and crop water stress index (CWSI) estimates were evaluated against a vine water consumption model and regressed with in situ stem water potential (Ψstem). Two different TSEB approaches, using very high-resolution airborne thermal imagery, were also included in the analysis as benchmarks for TSEB-PTS2+3. One of them uses aggregated TIR data at the vine+inter-row level (TSEB-PTairb), while the other is based on a contextual method that directly, although separately, retrieves soil and canopy temperatures (TSEB-2T). The results obtained demonstrated that when comparing airborne Trad and sharpened S2+3 LST, the latter tend to be underestimated. This complicates the use of TSEB-PTS2+3 to detect crop water stress. TSEB-2T appeared to outperform all the other methods. This was shown by a higher R2 and slightly lower RMSD when compared with modelled T. In addition, regressions between T and CWSI-2T with Ψstem also produced the highest R2.info:eu-repo/semantics/publishedVersio