Готові списки джерел за темами / Satellite Soil Moisture Retrievals

Добірка наукової літератури з теми "Satellite Soil Moisture Retrievals"

Автор: Grafiati
Опубліковано: 6 вересня 2023

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Статті в журналах з теми "Satellite Soil Moisture Retrievals"

1

Zhang, Ke, Long Zhao, Kun Yang, Lisheng Song, Xiang Ni, Xujun Han, Mingguo Ma, and Lei Fan. "Uncertainty Quantification of Satellite Soil Moisture Retrieved Precipitation in the Central Tibetan Plateau." Remote Sensing 15, no. 10 (May 16, 2023): 2600. http://dx.doi.org/10.3390/rs15102600.

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Анотація:
SM2RAIN is a well-established methodology for estimating precipitation from satellite or observed soil moisture and it has been applied as a complementary approach to conventional precipitation monitoring methods. However, satellite soil moisture retrievals are usually subject to various biases and limited number of retrievals (and therefore large intervals) in remote areas, such as the Tibetan Plateau (TP), and little is known about their potential impacts on precipitation estimation. This study seeks to quantify the uncertainties in Soil Moisture Active and Passive (SMAP) soil moisture estimated precipitation through the commonly used SM2RAIN by referring to in situ soil moisture observations from the central Tibetan Plateau soil moisture network. The estimated precipitation is evaluated against rain gauge observations. Additional attention is paid to different orbits of the SMAP retrievals. Results show that the original SM2RAIN algorithm tends to underestimate the precipitation amount in the central TP when using SMAP soil moisture retrievals as input. The retrieval accuracy and sampling interval of SMAP soil moisture from ascending (descending) orbits each count for 1.04 mm/5 d (−0.18 mm/5 d) and 1.67 mm/5 d (0.72 mm/5 d) of estimated precipitation uncertainties as represented by root mean square error. Besides, the descending product of SMAP with a relatively less sampling interval and higher retrieval accuracy outperforms the ascending one in estimating precipitation, and the combination of both two orbits does add value to the overall SM2RAIN estimation. This study is expected to provide guidance for future applications of SM2RAIN-derived precipitation. Meanwhile, more reliable SM2RAIN precipitation estimations are desired when using higher quality satellite soil moisture products with better retrieval accuracy and smaller intervals.
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2

Gao, Huilin, Eric F. Wood, Matthias Drusch, and Matthew F. McCabe. "Copula-Derived Observation Operators for Assimilating TMI and AMSR-E Retrieved Soil Moisture into Land Surface Models." Journal of Hydrometeorology 8, no. 3 (June 1, 2007): 413–29. http://dx.doi.org/10.1175/jhm570.1.

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Анотація:
Abstract Assimilating soil moisture from satellite remote sensing into land surface models (LSMs) has potential for improving model predictions by providing real-time information at large scales. However, the majority of the research demonstrating this potential has been limited to datasets based on either airborne data or synthetic observations. The limited availability of satellite-retrieved soil moisture and the observed qualitative difference between satellite-retrieved and modeled soil moisture has posed challenges in demonstrating the potential over large regions in actual applications. Comparing modeled and satellite-retrieved soil moisture fields shows systematic differences between their mean values and between their dynamic ranges, and these systematic differences vary with satellite sensors, retrieval algorithms, and LSMs. This investigation focuses on generating observation operators for assimilating soil moisture into LSMs using a number of satellite–model combinations. The remotely sensed soil moisture products come from the Tropical Rainfall Measuring Mission (TRMM) Microwave Imager (TMI) and the NASA/Earth Observing System (EOS) Advanced Microwave Scanning Radiometer (AMSR-E). The soil moisture model predictions are from the Variable Infiltration Capacity (VIC) hydrological model; the 40-yr European Centre for Medium-Range Weather Forecasts (ECMWF) Re-Analysis (ERA-40); and the NCEP North American Regional Reanalysis (NARR). For this analysis, the satellite and model data are over the southern Great Plains region from 1998 to 2003 (1998–2002 for ERA-40). Previous work on observation operators used the matching of cumulative distributions to transform satellite-retrieved soil moisture into modeled soil moisture, which implied perfect correlations between the ranked values. In this paper, a bivariate statistical approach, based on copula distributions, is employed for representing the joint distribution between retrieved and modeled soil moisture, allowing for a quantitative estimation of the uncertainty in modeled soil moisture when merged with a satellite retrieval. The conditional probability distribution of model-based soil moisture conditioned on a satellite retrieval forms the basis for the soil moisture observation operator. The variance of these conditional distributions for different retrieval algorithms, LSMs, and locations provides an indication of the information content of satellite retrievals in assimilation. Results show that the operators vary by season and by land surface model, with the satellite retrievals providing more information in summer [July–August (JJA)] and fall [September–November (SON)] than winter [December–February (DJF)] or spring [March–May (MAM)] seasons. Also, the results indicate that the value of satellite-retrieved soil moisture is most useful to VIC, followed by ERA-40 and then NARR.
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3

Liu, Y. Y., R. M. Parinussa, W. A. Dorigo, R. A. M. de Jeu, W. Wagner, A. I. J. M. van Dijk, M. F. McCabe, and J. P. Evans. "Developing an improved soil moisture dataset by blending passive and active microwave satellite-based retrievals." Hydrology and Earth System Sciences Discussions 7, no. 5 (September 2, 2010): 6699–724. http://dx.doi.org/10.5194/hessd-7-6699-2010.

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Анотація:
Abstract. Combining information derived from satellite-based passive and active microwave sensors has the potential to offer improved retrievals of surface soil moisture variations at global scales. Here we propose a technique to take advantage of retrieval characteristics of passive (AMSR-E) and active (ASCAT) microwave satellite estimates over sparse-to-moderately vegetated areas to obtain an improved soil moisture product. To do this, absolute soil moisture values from AMSR-E and relative soil moisture derived from ASCAT are rescaled against a reference land surface model date set using a cumulative distribution function (CDF) matching approach. While this technique imposes the bias of the reference to the rescaled satellite products, it adjusts both satellite products to the same range and almost preserves the correlation between satellite products and in situ measurements. Comparisons with in situ data demonstrated that over the regions where the correlation coefficient between rescaled AMSR-E and ASCAT is above 0.65 (hereafter referred to as transitional regions), merging the different satellite products together increases the number of observations while minimally changing the accuracy of soil moisture retrievals. These transitional regions also delineate the boundary between sparsely and moderately vegetated regions where rescaled AMSR-E and ASCAT are respectively used in the merged product. Thus the merged product carries the advantages of better spatial coverage overall and increased number of observations particularly for the transitional regions. The combination approach developed in this study has the potential to be applied to existing microwave satellites as well as to new microwave missions. Accordingly, a long-term global soil moisture dataset can be developed and extended, enhancing basic understanding of the role of soil moisture in the water, energy and carbon cycles.
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4

Zhan, W., M. Pan, N. Wanders, and E. F. Wood. "Correction of real-time satellite precipitation with satellite soil moisture observations." Hydrology and Earth System Sciences Discussions 12, no. 6 (June 16, 2015): 5749–87. http://dx.doi.org/10.5194/hessd-12-5749-2015.

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Анотація:
Abstract. Rainfall and soil moisture are two key elements in modeling the interactions between the land surface and the atmosphere. Accurate and high-resolution real-time precipitation is crucial for monitoring and predicting the on-set of floods, and allows for alert and warning before the impact becomes a disaster. Assimilation of remote sensing data into a flood-forecasting model has the potential to improve monitoring accuracy. Space-borne microwave observations are especially interesting because of their sensitivity to surface soil moisture and its change. In this study, we assimilate satellite soil moisture retrievals using the Variable Infiltration Capacity (VIC) land surface model, and a dynamic assimilation technique, a particle filter, to adjust the Tropical Rainfall Measuring Mission Multi-satellite Precipitation Analysis (TMPA) real-time precipitation estimates. We compare updated precipitation with real-time precipitation before and after adjustment and with NLDAS gauge-radar observations. Results show that satellite soil moisture retrievals provide additional information by correcting errors in rainfall bias. High accuracy soil moisture retrievals, when merged with precipitation, generally increase both rainfall frequency and intensity, and are most effective in the correction of rainfall under dry to normal surface condition while limited/negative improvement is seen over wet/saturated surfaces. Errors from soil moisture, mixed among the real signal, may generate a false rainfall signal approximately 2 mm day−1 and thus lower the precipitation accuracy after adjustment.
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5

Gevaert, Anouk I., Luigi J. Renzullo, Albert I. J. M. van Dijk, Hans J. van der Woerd, Albrecht H. Weerts, and Richard A. M. de Jeu. "Joint assimilation of soil moisture retrieved from multiple passive microwave frequencies increases robustness of soil moisture state estimation." Hydrology and Earth System Sciences 22, no. 9 (September 3, 2018): 4605–19. http://dx.doi.org/10.5194/hess-22-4605-2018.

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Анотація:
Abstract. Soil moisture affects the partitioning of water and energy and is recognized as an essential climate variable. Soil moisture estimates derived from passive microwave remote sensing can improve model estimates through data assimilation, but the relative effectiveness of microwave retrievals in different frequencies is unclear. Land Parameter Retrieval Model (LPRM) satellite soil moisture derived from L-, C-, and X-band frequency remote sensing were assimilated in the Australian Water Resources Assessment landscape hydrology model (AWRA-L) using an ensemble Kalman filter approach. Two sets of experiments were performed. First, each retrieval was assimilated individually for comparison. Second, each possible combination of two retrievals was assimilated jointly. Results were evaluated against field-measured top-layer and root-zone soil moisture at 24 sites across Australia. Assimilation generally improved the coefficient of correlation (r) between modeled and field-measured soil moisture. L- and X-band retrievals were more informative than C-band retrievals, improving r by an average of 0.11 and 0.08 compared to 0.04, respectively. Although L-band retrievals were more informative for top-layer soil moisture in most cases, there were exceptions, and L- and X-band were equally informative for root-zone soil moisture. The consistency between L- and X-band retrievals suggests that they can substitute for each other, for example when transitioning between sensors and missions. Furthermore, joint assimilation of retrievals resulted in a model performance that was similar to or better than assimilating either retrieval individually. Comparison of model estimates obtained with global precipitation data and with higher-quality, higher-resolution regional data, respectively, demonstrated that precipitation data quality does determine the overall benefit that can be expected from assimilation. Further work is needed to assess the potentially complementary spatial information that can be derived from retrievals from different frequencies.
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6

Liu, Qing, Rolf H. Reichle, Rajat Bindlish, Michael H. Cosh, Wade T. Crow, Richard de Jeu, Gabrielle J. M. De Lannoy, George J. Huffman, and Thomas J. Jackson. "The Contributions of Precipitation and Soil Moisture Observations to the Skill of Soil Moisture Estimates in a Land Data Assimilation System." Journal of Hydrometeorology 12, no. 5 (October 1, 2011): 750–65. http://dx.doi.org/10.1175/jhm-d-10-05000.1.

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Анотація:
Abstract The contributions of precipitation and soil moisture observations to soil moisture skill in a land data assimilation system are assessed. Relative to baseline estimates from the Modern Era Retrospective-analysis for Research and Applications (MERRA), the study investigates soil moisture skill derived from (i) model forcing corrections based on large-scale, gauge- and satellite-based precipitation observations and (ii) assimilation of surface soil moisture retrievals from the Advanced Microwave Scanning Radiometer for Earth Observing System (AMSR-E). Soil moisture skill (defined as the anomaly time series correlation coefficient R) is assessed using in situ observations in the continental United States at 37 single-profile sites within the Soil Climate Analysis Network (SCAN) for which skillful AMSR-E retrievals are available and at 4 USDA Agricultural Research Service (“CalVal”) watersheds with high-quality distributed sensor networks that measure soil moisture at the scale of land model and satellite estimates. The average skill of AMSR-E retrievals is R = 0.42 versus SCAN and R = 0.55 versus CalVal measurements. The skill of MERRA surface and root-zone soil moisture is R = 0.43 and R = 0.47, respectively, versus SCAN measurements. MERRA surface moisture skill is R = 0.56 versus CalVal measurements. Adding information from precipitation observations increases (surface and root zone) soil moisture skills by ΔR ~ 0.06. Assimilating AMSR-E retrievals increases soil moisture skills by ΔR ~ 0.08. Adding information from both sources increases soil moisture skills by ΔR ~ 0.13, which demonstrates that precipitation corrections and assimilation of satellite soil moisture retrievals contribute important and largely independent amounts of information.
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7

Crow, Wade T. "A Novel Method for Quantifying Value in Spaceborne Soil Moisture Retrievals." Journal of Hydrometeorology 8, no. 1 (February 1, 2007): 56–67. http://dx.doi.org/10.1175/jhm553.1.

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Анотація:
Abstract A novel methodology is introduced for quantifying the added value of remotely sensed soil moisture products for global land surface modeling applications. The approach is based on the assimilation of soil moisture retrievals into a simple surface water balance model driven by satellite-based precipitation products. Filter increments (i.e., discrete additions or subtractions of water suggested by the filter) are then compared to antecedent precipitation errors determined using higher-quality rain gauge observations. A synthetic twin experiment demonstrates that the correlation coefficient between antecedent precipitation errors and filter increments provides an effective proxy for the accuracy of the soil moisture retrievals themselves. Given the inherent difficulty of directly validating remotely sensed soil moisture products using ground-based observations, this assimilation-based proxy provides a valuable tool for efforts to improve soil moisture retrieval strategies and quantify the novel information content of remotely sensed soil moisture retrievals for land surface modeling applications. Using real spaceborne data, the approach is demonstrated for four different remotely sensed soil moisture datasets along two separate transects in the southern United States. Results suggest that the relative superiority of various retrieval strategies varies geographically.
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8

Miralles, Diego G., Wade T. Crow, and Michael H. Cosh. "Estimating Spatial Sampling Errors in Coarse-Scale Soil Moisture Estimates Derived from Point-Scale Observations." Journal of Hydrometeorology 11, no. 6 (December 1, 2010): 1423–29. http://dx.doi.org/10.1175/2010jhm1285.1.

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Анотація:
Abstract The validation of satellite surface soil moisture products requires comparisons between point-scale ground observations and footprint-scale (>100 km2) retrievals. In regions containing a limited number of measurement sites per footprint, some of the observed difference between the retrievals and ground observations is attributable to spatial sampling error and not the intrinsic error of the satellite retrievals themselves. Here, a triple collocation (TC) approach is applied to footprint-scale soil moisture products acquired from passive microwave remote sensing, land surface modeling, and a single ground-based station with the goal of the estimating (and correcting for) spatial sampling error in footprint-scale soil moisture estimates derived from the ground station. Using these three soil moisture products, the TC approach is shown to estimate point-to-footprint soil moisture sampling errors to within 0.0059 m3 m−3 and enhance the ability to validate satellite footprint-scale soil moisture products using existing low-density ground networks.
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9

Su, Z., J. Wen, L. Dente, R. van der Velde, L. Wang, Y. Ma, K. Yang, and Z. Hu. "A plateau scale soil moisture and soil temperature observatory for quantifying uncertainties in coarse resolution satellite products." Hydrology and Earth System Sciences Discussions 8, no. 1 (January 17, 2011): 243–76. http://dx.doi.org/10.5194/hessd-8-243-2011.

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Анотація:
Abstract. A plateau scale soil moisture and soil temperature observatory is established on the Tibetan Plateau for quantifying uncertainties in coarse resolution satellite products of soil moisture and soil temperature. The observatory consists of three regional networks across the Tibetan Plateau and provides reliable measurements of mean and variance in soil moisture and soil temperature of representative areas comparable in size to coarse satellite footprints. Using these in-situ measurements, a analysis is carried out to assess the reliability of several satellite products derived from AMSR-E and ASCAT data by three retrieval algorithms (henceforth the AMSR-E products, the ASCAT-L2 products and the ITC-model retrievals) for the first time. For the cold semiarid Naqu area, AMSR-E and ASCAT-L2 products overestimate significantly the regional soil moisture in the monsoon seasons. The ITC-model retrievals are closer to the in-situ measurements but the dynamics in the retrieved time series needs further investigation. The use of these datasets is therefore not recommended for cold semiarid conditions on the Tibetan Plateau. For the cold humid Maqu network area AMSR-E and ASCAT-L2 products have comparable accuracy as reported by previous studies in the humid monsoon period. AMSR-E products significantly overestimate and ASCAT-L2 products underestimate the soil moisture in the winter period. The ITC-model retrievals underestimate the soil moisture in general. It is concluded that global coarse resolution soil moisture products are useful but exhibit till now unreported uncertainties in cold and semiarid regions – use of them would be critically enhanced if uncertainties can be quantified and reduced using in-situ measurements.
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10

Ford, T. W., E. Harris, and S. M. Quiring. "Estimating root zone soil moisture using near-surface observations from SMOS." Hydrology and Earth System Sciences Discussions 10, no. 6 (June 28, 2013): 8325–64. http://dx.doi.org/10.5194/hessd-10-8325-2013.

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Анотація:
Abstract. Satellite-derived soil moisture provides more spatially and temporally extensive data than in situ observations. However, satellites can only measure water in the top few centimeters of the soil. Therefore estimates of root zone soil moisture must be inferred from near-surface soil moisture retrievals. The accuracy of this inference is contingent on the relationship between soil moisture in the near-surface and at greater depths. This study uses cross correlation analysis to quantify the association between near-surface and root zone soil moisture using in situ data from the United States Great Plains. Our analysis demonstrates that there is generally a strong relationship between near-surface (5 to 10 cm) and root zone (25 to 60 cm) soil moisture. An exponential decay filter is applied to estimate root zone soil moisture from near-surface observations. Reasonably skillful predictions of root zone soil moisture can be made using near-surface observations. The same method is then applied to evaluate whether soil moisture derived from the Soil Moisture and Ocean Salinity (SMOS) satellite can be used to accurately estimate root zone soil moisture. We conclude that the exponential filter method is a useful approach for accurately predicting root zone soil moisture from SMOS surface retrievals.
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Більше джерел

Дисертації з теми "Satellite Soil Moisture Retrievals"

1

Piles, Guillem Maria. "Multiscale soil moisture retrievals from microwave remote sensing observations." Doctoral thesis, Universitat Politècnica de Catalunya, 2010. http://hdl.handle.net/10803/77910.

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Анотація:
La humedad del suelo es la variable que regula los intercambios de agua, energía, y carbono entre la tierra y la atmósfera. Mediciones precisas de humedad son necesarias para una gestión sostenible de los recursos hídricos, para mejorar las predicciones meteorológicas y climáticas, y para la detección y monitorización de sequías e inundaciones. Esta tesis se centra en la medición de la humedad superficial de la Tierra desde el espacio, a escalas global y regional. Estudios teóricos y experimentales han demostrado que la teledetección pasiva de microondas en banda L es optima para la medición de humedad del suelo, debido a que la atmósfera es transparente a estas frecuencias, y a la relación directa de la emisividad del suelo con su contenido de agua. Sin embargo, el uso de la teledetección pasiva en banda L ha sido cuestionado en las últimas décadas, pues para conseguir la resolución temporal y espacial requeridas, un radiómetro convencional necesitaría una gran antena rotatoria, difícil de implementar en un satélite. Actualmente, hay tres principales propuestas para abordar este problema: (i) el uso de un radiómetro de apertura sintética, que es la solución implementada en la misión Soil Moisture and Ocean Salinity (SMOS) de la ESA, en órbita desde noviembre del 2009; (ii) el uso de un radiómetro ligero de grandes dimensiones y un rádar operando en banda L, que es la solución que ha adoptado la misión Soil Moisture Active Passive (SMAP) de la NASA, con lanzamiento previsto en 2014; (iii) el desarrollo de técnicas de desagregación de píxel que permitan mejorar la resolución espacial de las observaciones. La primera parte de la tesis se centra en el estudio del algoritmo de recuperación de humedad del suelo a partir de datos SMOS, que es esencial para obtener estimaciones de humedad con alta precisión. Se analizan diferentes configuraciones con datos simulados, considerando (i) la opción de añadir información a priori de los parámetros que dominan la emisión del suelo en banda L —humedad, rugosidad, temperatura del suelo, albedo y opacidad de la vegetación— con diferentes incertidumbres asociadas, y (ii) el uso de la polarización vertical y horizontal por separado, o del primer parámetro de Stokes. Se propone una configuración de recuperación de humedad óptima para SMOS. La resolución espacial de los radiómetros de SMOS y SMAP (40-50 km) es adecuada para aplicaciones globales, pero limita la aplicación de los datos en estudios regionales, donde se requiere una resolución de 1-10 km. La segunda parte de esta tesis contiene tres novedosas propuestas de mejora de resolución espacial de estos datos: • Se ha desarrollado un algoritmo basado en la deconvolución de los datos SMOS que permite mejorar la resolución espacial de las medidas. Los resultados de su aplicación a datos simulados y a datos obtenidos con un radiómetro aerotransportado muestran que es posible mejorar el producto de resolución espacial y resolución radiométrica de los datos. • Se presenta un algoritmo para mejorar la resolución espacial de las estimaciones de humedad de SMOS utilizando datos MODIS en el visible/infrarrojo. Los resultados de su aplicación a algunas de las primeras imágenes de SMOS indican que la variabilidad espacial de la humedad del suelo se puede capturar a 32, 16 y 8 km. • Un algoritmo basado en detección de cambios para combinar los datos del radiómetro y el rádar de SMAP en un producto de humedad a 10 km ha sido desarrollado y validado utilizando datos simulados y datos experimentales aerotransportados. Este trabajo se ha desarrollado en el marco de las actividades preparatorias de SMOS y SMAP, los dos primeros satélites dedicados a la monitorización de la variación temporal y espacial de la humedad de la Tierra. Los resultados presentados contribuyen a la obtención de estimaciones de humedad del suelo con la precisión y la resolución espacial necesarias para un mejor conocimiento del ciclo del agua y una mejor gestión de los recursos hídricos.
Soil moisture is a key state variable of the Earth's system; it is the main variable that links the Earth's water, energy and carbon cycles. Accurate observations of the Earth's changing soil moisture are needed to achieve sustainable land and water management, and to enhance weather and climate forecasting skill, flood prediction and drought monitoring. This Thesis focuses on measuring the Earth's surface soil moisture from space at global and regional scales. Theoretical and experimental studies have proven that L-band passive remote sensing is optimal for soil moisture sensing due to its all-weather capabilities and the direct relationship between soil emissivity and soil water content under most vegetation covers. However, achieving a temporal and spatial resolution that could satisfy land applications has been a challenge to passive microwave remote sensing in the last decades, since real aperture radiometers would need a large rotating antenna, which is difficult to implement on a spacecraft. Currently, there are three main approaches to solving this problem: (i) the use of an L-band synthetic aperture radiometer, which is the solution implemented in the ESA Soil Moisture and Ocean Salinity (SMOS) mission, launched in November 2009; (ii) the use of a large lightweight radiometer and a radar operating at L-band, which is the solution adopted by the NASA Soil Moisture Active Passive (SMAP) mission, scheduled for launch in 2014; (iii) the development of pixel disaggregation techniques that could enhance the spatial resolution of the radiometric observations. The first part of this work focuses on the analysis of the SMOS soil moisture inversion algorithm, which is crucial to retrieve accurate soil moisture estimations from SMOS measurements. Different retrieval configurations have been examined using simulated SMOS data, considering (i) the option of adding a priori information from parameters dominating the land emission at L-band —soil moisture, roughness, and temperature, vegetation albedo and opacity— with different associated uncertainties and (ii) the use of vertical and horizontal polarizations separately, or the first Stokes parameter. An optimal retrieval configuration for SMOS is suggested. The spatial resolution of SMOS and SMAP radiometers (~ 40-50 km) is adequate for global applications, but is a limiting factor to its application in regional studies, where a resolution of 1-10 km is needed. The second part of this Thesis contains three novel downscaling approaches for SMOS and SMAP: • A deconvolution scheme for the improvement of the spatial resolution of SMOS observations has been developed, and results of its application to simulated SMOS data and airborne field experimental data show that it is feasible to improve the product of the spatial resolution and the radiometric sensitivity of the observations by 49% over land pixels and by 30% over sea pixels. • A downscaling algorithm for improving the spatial resolution of SMOS-derived soil moisture estimates using higher resolution MODIS visible/infrared data is presented. Results of its application to some of the first SMOS images show the spatial variability of SMOS-derived soil moisture observations is effectively captured at the spatial resolutions of 32, 16, and 8 km. • A change detection approach for combining SMAP radar and radiometer observations into a 10 km soil moisture product has been developed and validated using SMAP-like observations and airborne field experimental data. This work has been developed within the preparatory activities of SMOS and SMAP, the two first-ever satellites dedicated to monitoring the temporal and spatial variation on the Earth's soil moisture. The results presented contribute to get the most out of these vital observations, that will further our understanding of the Earth's water cycle, and will lead to a better water resources management.
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2

Kolassa, Jana. "Soil moisture retrieval from multi-instrument satellite observations." Paris 6, 2013. http://www.theses.fr/2013PA066392.

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Анотація:
Dans cette thèse, un algorithme de restitution à base de réseaux de neurones a été développé afin d’estimer l’humidité du sol à partir d’une combinaison d’observations satellitaires en micro-ondes, infrarouge et visible. Une estimation globale des valeurs mensuelles d’humidité du sol a été obtenue pour la période 1993-2000 et est fournie sur une grille à pixel de surface constante avec une résolution équatoriale de 0,25 ◦. Cette estimation de l’humidité du sol a été évaluée avec des données modélisées, des données de télédétec- tion et des observations in situ et a montré une bonne performance à différentes échelles spatiales et temporelles. Une analyse de contenu en information a montré que chacune des différentes observations satellites contribue à une information différente sur l’humidité du sol, avec les données micro-ondes actives plus sensibles à l’évolution temporelle et les données infrarouges thermiques reproduisant mieux les structures spatiales. En outre, une analyse de synergie a révélé que la combinaison de toutes les observations permet une réduction de l’incertitude de restitution de plus de 18 % et que la méthode des réseaux de neurones exploite de manière optimale la synergie des observations par comparaison avec autres approches. Une analyse a démontré la cohérence de l’humidité du sol resti- tuée avec d’autres produits satellitaires réprésentatifs d’autres paramètres hydrologiques (inondations, précipitations) à l’échelle du globe. Cela souligne le potentiel de nôtre jeu de données d’humidité du sol pour les études du cycle de l’eau terrestre. Enfin, il a été démon- tré que la méthode de réseaux de neurones proposée, constitue également un outil efficace pour évaluer les modèles de surface continentale ainsi que la modélisation des processus
In this thesis, a neural network based retrieval algorithm has been developed to compute surface soil moisture from a combination of microwave, infrared and visible satellite obser- vations. A global estimate of monthly mean soil moisture values has been computed for the period 1993-2000 and is provided on an equal-area grid with an equatorial resolution of 0. 25◦. This soil moisture estimate has been evaluated against modelled, remotely sensed and in situ observations and was found to perform well on different spatial and temporal scales. An information content showed that each of the various satellite observations con- tributes information about a different soil moisture variation, with the active microwave data being more sensitive to the temporal evolution and the thermal infrared data better capturing the spatial patterns. Furthermore, a synergy analysis revealed that the combina- tion of all observations permits a reduction of the retrieval uncertainty by more than 18% and that the neural network methodology optimally exploits the synergy of observations compared to other approaches. A joint analysis of various remotely sensed datasets of ter- restrial water cycle components demonstrated the coherence of the retrieved soil moisture with other retrieval products and with global hydrological processes. This underlined its potential to be used for observation-based studies of the terrestrial water cycle. Finally, it has been shown that the proposed neural network methodology also provides an effective tool to evaluate Earth System Models on both a variable and a process basis
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Kong, Xin. "Near-surface soil moisture retrieval at field and regional scales in UK : coupling of field measurements, a dynamic model and satellite imagery." Thesis, University of East Anglia, 2006. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.429801.

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Soriano, Melissa. "Estimation of soil moisture in the southern united states in 2003 using multi-satellite remote sensing measurements." Fairfax, VA : George Mason University, 2008. http://hdl.handle.net/1920/3361.

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Анотація:
Thesis (M.S.)--George Mason University, 2008.
Vita: p. 65. Thesis director: John Qu. Submitted in partial fulfillment of the requirements for the degree of Master of Science in Earth System Science. Title from PDF t.p. (viewed Jan. 11, 2009). Includes bibliographical references (p. 59-64). Also issued in print.
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Baban, Serwan M. J. "The derivations of hydrological variables (including soil moisture) from satellite imagery." Thesis, University of East Anglia, 1991. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.292298.

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6

Haas, Jan. "Soil moisture modelling using TWI and satellite imagery in the Stockholm region." Thesis, KTH, Geoinformatik och Geodesi, 2010. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-49704.

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Анотація:
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.
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Srivastava, Prashant K. "Soil moisture estimation from SMOS satellite and mesoscale model for hydrological applications." Thesis, University of Bristol, 2013. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.617590.

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Soil moisture is an integral part of the Earth's hydrological cycle. Therefore, accurate estimation of the Earth's changing soil moisture is required to achieve sustainable land and water management to augment Numerical Weather Prediction (NWP) and forecasting skill, and to develop improved flood and drought monitoring capability. Unfortunately, most of the locations in the world do not have accurate soil moisture information on a relevant spatial and temporal scale. However, after latest advances in remote sensing and mesoscale models, it is now possible to estimate soil moisture using passive microwave satellite imaging such as Soil Moisture and Ocean Salinity (SMOS) and/or mesoscale model like Weather Research and Forecasting (WRF)-NOAH Land Surface Model (LSM). L-band passive remote sensing and WRF-NOAH LSM are potentially very useful for soil moisture sensing due to its all-weather capabilities and in-depth physics oriented relationship between soil emissivity and soil moisture, applicable for a diverse land use/land cover. In commensurate with new era in soil moisture remote sensing, this thesis explores the potential of SMOS satellite and WRFNOAH LSM for soil moisture retrieval over the temperate maritime climate. Also, soil moisture deficit (SM I) is found to be an integral component for irrigation scheduling, drought and flood prediction. Hence, the main focus of this thesis is the evaluation of soil moisture datasets as a method to effectively determine the SMD. All major areas of the improvement aided by the SMOS and WRF NOAH LSM arc addressed. Several novel approaches and investigations dealing with the SMOS soil moisture retrieval using Microwave Polarisation Difference Index (M PDI) and Radiative Transfer Equations arc examined. Input data (soil roughness, land surface temperature and vegetation opacity) sensitivity of different retrieval configurations are evaluated using the various algorithms. Thus, the thesis includes ( I) initial evaluation of SMOS satellite and ECMWF downscaled soil moisture using WRF-NOAH LSM with special reference to sensitivity of growing and non growing seasons; (2) assessment of land parameter retrieval model and tau-omega rationale; (3) a modified soil moisture retrieval algorithm from SMOS brightness temperature; and (4) sensitivity and uncertainty analysis of mesoscale model based product for SMD prediction. Further through this study, SMOS soil moisture downscaling schemes using artificial intelligence techniques with MODIS LST have also been proposed to improve the spatial resolution at a catchment scale and finally, data fusion techniques for improving soil l moisture deficit are presented with the SMOS and WRF-NOAII LSM. The overall finding indicates that the SMOS and WRF-NOAH LSM using ECMWF have been proven not only to improve data quality and soil moisture deficit estimation, but also have a great potential in fostering the soil moisture research and applications. The studies presented in this thesis will enhance our understanding of the Earth's water cycle, will help improve ECMWF forecast, SMOS algorithm, NWP and will lead to better water resource management practices.
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Timoncini, Marta. "Detection of soil moisture under changing vegetation cover using synthetic aperture radar satellite imagery." Thesis, Queen Mary, University of London, 2006. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.430133.

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9

Zhuo, Lu. "Advances in enhanced compatibility between satellite remote sensing of soil moisture and hydrological modelling." Thesis, University of Bristol, 2016. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.702218.

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Анотація:
Soil moisture is a key element in the hydrological cycle, regulating evapotranspiration, precipitation, infiltration and overland flow. However, its effective utilisation in hydrological modelling is still in a state of infancy. Generally, hydrological application of soil moisture data requires: 1) soil moisture data relevant to hydrology, and 2) appropriate hydrological model structure compatible with such data. This thesis focuses on tackling those two aspects by enhancing the compatibility between soil moisture observations and operational hydrological models' soil moisture state variable. In this study satellite soil moisture observations are mainly focused on, because of their increasing availability and large-scale global coverage
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Al-Shrafany, Deleen Mohammed Saleh. "Soil moisture estimation using satellite remote sensing and numerical weather prediction model for hydrological applications." Thesis, University of Bristol, 2012. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.574260.

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Анотація:
Soil moisture is an important variable in hydrological modelling used for real time flood forecasting and water resources management. However, it is a very challenging task to measure soil moisture over a hydrological catchment using conventional in-situ sensors. Remote sensing is gaining popularity due to its large coverage suitable for soil moisture measurement at a catchment scale albeit there are still many knowledge gaps to be filled in. This thesis focuses on investigating soil moisture estimation from remote sensing satellite and land surface model (LSM) coupled with a Numerical Weather Prediction (NWP) model. A hydrological-based approach has been conducted to assess/evaluate the estimated soil moisture using event-based water balance and Probability Distributed Model (PDM). An Advance Microwave Scanning Radiometer (AMSR) and a physically- based Land Parameters Retrieval Model (LPRM) have been used to retrieve surface soil moisture over the sturdy area. The LPRM vegetation and roughness parameters have been empirically calibrated by a new approach proposed in this thesis. The relevant parameters are calibrated on the hydrological model through achieving the best correlation between the observation-based catchment storage and the retrieved surface soil moisture. The development of the land surface model coupled with the NWP model is used to estimate soil moisture at different combinations of soil layers. The optimal combination of the top two layers is found to have the best performance when compared to the catchment water storage. Regression-based mathematical models have been derived to predict the catchment storage from the estimated soil moisture based on both satellite remote sensing and the LSM-NWP model. Three schemes are proposed to examine the behaviour of soil moisture products over different seasons in order to find the appropriate formulas in different scenarios. Finally, weighted coefficients and arithmetic average data fusion methods are explored to integrate two independent soil moisture products from the AMSR-E satellite and the LSM-NWP. It has been found that the merged output is a significant improvement over their individual estimates. The implementation of the fusion technique has provided a new opportunity for information integration from satellite and NWP model. Keywords: Soil moisture, Satellite remote sensing, satellite, land surface model, NWP model, rainfall-runoff model, water balance, PDM model
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Книги з теми "Satellite Soil Moisture Retrievals"

1

Wolfgang, Wagner. Soil moisture retrieval from ERS scatterometer data. Wien: Veröffentlichung des Instituts für Photogrammetrie und Fernerkundung, 1998.

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Dąbrowska-Zielińska, Katarzyna. Szacowanie ewapotranspiracji wilgotności gleb i masy zielonej łąk na podstawie zdjęć satelitarnych NOAA =: Assessment of evapotranspiration, soil moisture and green biomass of grassland using NOAA satellite images. Wrocław: Wydawn. Continuo, 1995.

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3

United States. National Aeronautics and Space Administration., ed. An investigation of satellite sounding products for the remote sensing of the surface energy balance and soil moisture. Madison, WI: Cooperative Institute for Meteorological Satellite Studies, Space Science and Engineering Center, University of Wisconsin-Madison, 1990.

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4

Földes, Péter. A design study for the use of a multiple aperture deployable antenna for soil moisture remote senisng satellite applications. Hampton, Va: National Aeronautics and Space Administration, Langley Research Center, 1986.

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Center, Langley Research, ed. A design study for the use of a multiple aperture deployable antenna for soil moisture remote sensing satellite applications. Hampton, Va: National Aeronautics and Space Administration, Langley Research Center, 1986.

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6

Carroll, Thomas R. Airborne gamma radiation snow water equivalent and soil moisture measurements and satellite areal extent of snow cover measurements: A user's guide : version 3.0. Minneapolis, Minn: Office of Hydrology, National Weather Service, National Oceanic and Atmospheric Administration, U.S. Dept. of Commerce, 1988.

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7

Center, Langley Research, ed. A conceptual thermal design study of an electronically scanned thinned array radiometer. Hampton, Va: National Aeronautics and Space Administration, Langley Research Center, 1995.

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8

Satellite Soil Moisture Retrieval. Elsevier, 2016. http://dx.doi.org/10.1016/c2014-0-03396-5.

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9

Srivastava, Prashant K., George Petropoulos, and Y. H. Kerr. Satellite Soil Moisture Retrieval: Techniques and Applications. Elsevier Science & Technology Books, 2016.

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10

Srivastava, Prashant K., Y. H. Kerr, and George P. Petropoulos. Satellite Soil Moisture Retrieval: Techniques and Applications. Elsevier, 2016.

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Частини книг з теми "Satellite Soil Moisture Retrievals"

1

K., Anush Kumar, Raj Setia, Dharmendra Kumar Pandey, Deepak Putrevu, Arundhati Misra, and Brijendra Pateriya. "Soil Moisture Retrieval Techniques Using Satellite Remote Sensing." In Geospatial Technologies for Crops and Soils, 357–85. Singapore: Springer Singapore, 2020. http://dx.doi.org/10.1007/978-981-15-6864-0_10.

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Ciabatta, Luca, Stefania Camici, Christian Massari, Paolo Filippucci, Sebastian Hahn, Wolfgang Wagner, and Luca Brocca. "Soil Moisture and Precipitation: The SM2RAIN Algorithm for Rainfall Retrieval from Satellite Soil Moisture." In Advances in Global Change Research, 1013–27. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-35798-6_27.

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Hirpa, Feyera A., Mekonnen Gebremichael, Thomas M. Hopson, Rafal Wojick, and Haksu Lee. "Assimilation of Satellite Soil Moisture Retrievals into a Hydrologic Model for Improving River Discharge." In Remote Sensing of the Terrestrial Water Cycle, 319–29. Hoboken, NJ: John Wiley & Sons, Inc, 2014. http://dx.doi.org/10.1002/9781118872086.ch19.

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Li, Jiangyang, Yongchao Zhu, Tingye Tao, and Juntao Wang. "Soil Moisture Retrieval Based on Satellite-Borne GNSS-R Technology." In Lecture Notes in Electrical Engineering, 54–59. Singapore: Springer Singapore, 2021. http://dx.doi.org/10.1007/978-981-16-3138-2_6.

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Jackson, Thomas J., Michael Cosh, and Wade Crow. "Some Issues in Validating Satellite-Based Soil Moisture Retrievals from SMAP with in Situ Observations." In Remote Sensing of the Terrestrial Water Cycle, 245–53. Hoboken, NJ: John Wiley & Sons, Inc, 2014. http://dx.doi.org/10.1002/9781118872086.ch15.

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Zaman, Rawfin, William W. Edmonson, and Manoj K. Jha. "Designing a Remote In Situ Soil Moisture Sensor Network for Small Satellite Data Retrieval." In Proceedings of the 2013 National Conference on Advances in Environmental Science and Technology, 35–46. Cham: Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-19923-8_4.

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Arya, K. V., and Suggula Jagadeesh. "Time Series Forecasting of Soil Moisture Using Satellite Images." In Communications in Computer and Information Science, 385–97. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-07005-1_33.

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Palagiri, Hussain, and Manali Pal. "Agricultural Drought Assessment Using Satellite-Based Surface Soil Moisture Estimate." In Disaster Resilience and Green Growth, 411–31. Singapore: Springer Nature Singapore, 2022. http://dx.doi.org/10.1007/978-981-19-7100-6_22.

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Kuenzer, Claudia, Ursula Gessner, and Wolfgang Wagner. "Soil Moisture from Thermal Infrared Satellite Data: Synergies with Microwave Data." In Thermal Infrared Remote Sensing, 315–30. Dordrecht: Springer Netherlands, 2013. http://dx.doi.org/10.1007/978-94-007-6639-6_16.

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Enenkel, Markus, Daniel Osgood, and Bristol Powell. "The Added Value of Satellite Soil Moisture for Agricultural Index Insurance." In Remote Sensing of Hydrometeorological Hazards, 69–83. Boca Raton, FL : Taylor & Francis, 2018.: CRC Press, 2017. http://dx.doi.org/10.1201/9781315154947-4.

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Тези доповідей конференцій з теми "Satellite Soil Moisture Retrievals"

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"Assimilating satellite soil moisture retrievals to improve operational water balance modelling." In 23rd International Congress on Modelling and Simulation (MODSIM2019). Modelling and Simulation Society of Australia and New Zealand, 2019. http://dx.doi.org/10.36334/modsim.2019.h6.tian.

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Owe, Manfred, Thomas R. H. Holmes, and Richard A. M. De Jeu. "Spatial distributions of global soil moisture retrievals from satellite microwave observations." In Remote Sensing, edited by Manfred Owe, Guido D'Urso, Ben T. Gouweleeuw, and Anne M. Jochum. SPIE, 2004. http://dx.doi.org/10.1117/12.565257.

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Chen, Huailiang, Xiangde Xu, Yujie Liu, Yusheng Li, and Shitao Wang. "Soil moisture prediction based on retrievals from satellite sensing and a regional climate model." In Optics & Photonics 2005, edited by Wei Gao and David R. Shaw. SPIE, 2005. http://dx.doi.org/10.1117/12.616557.

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Bolten, J., W. Crow, X. Zhan, and C. Reynolds. "Implementation of a global-scale operational data assimilation system for satellite-based soil moisture retrievals." In Optical Engineering + Applications, edited by Mitchell D. Goldberg, Hal J. Bloom, Philip E. Ardanuy, and Allen H. Huang. SPIE, 2008. http://dx.doi.org/10.1117/12.795272.

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Lu, Hui, Wei Wang, Fuqiang Tian, and Kun Yang. "Improving satellite rainfall estimates over Tibetan plateau using in situ soil moisture observation and SMAP retrievals." In 2017 IEEE International Geoscience and Remote Sensing Symposium (IGARSS). IEEE, 2017. http://dx.doi.org/10.1109/igarss.2017.8127375.

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Santi, E., S. Paloscia, P. Pampaloni, S. Pettinato, and M. Brogioni. "Retrieval of soil moisture with airborne and satellite microwave sensors." In 2009 IEEE International Geoscience and Remote Sensing Symposium. IEEE, 2009. http://dx.doi.org/10.1109/igarss.2009.5418252.

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Shi, J., E. Njoku, T. Jackson, and P. O'Neill. "Evaluation of Potential Error Sources for Soil Moisture Retrieval from Satellite Microwave Radiometer." In 2006 IEEE International Symposium on Geoscience and Remote Sensing. IEEE, 2006. http://dx.doi.org/10.1109/igarss.2006.118.

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Cros, S., A. Chanzy, T. Pellarin, J. C. Calvet, and J. P. Wigneron. "Using Optical Satellite based Data to Improve Soil Moisture Retrieval from SMOS Mission." In 2006 IEEE International Symposium on Geoscience and Remote Sensing. IEEE, 2006. http://dx.doi.org/10.1109/igarss.2006.523.

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Zhou, Guoqing, Yue Sun, Linjun Jiang, Na Liu, Chenyang Li, Mingyan Li, and Tao Yue. "Comparison and analysis of soil moisture retrieval model from CBERS-02B satellite imagery." In IGARSS 2015 - 2015 IEEE International Geoscience and Remote Sensing Symposium. IEEE, 2015. http://dx.doi.org/10.1109/igarss.2015.7325854.

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Tsagkatakis, Grigorios, Mahta Moghaddam, and Panagiotis Tsakalides. "Deep multi-modal satellite and in-situ observation fusion for Soil Moisture retrieval." In IGARSS 2021 - 2021 IEEE International Geoscience and Remote Sensing Symposium. IEEE, 2021. http://dx.doi.org/10.1109/igarss47720.2021.9553848.

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Звіти організацій з теми "Satellite Soil Moisture Retrievals"

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Pradhan, Nawa Raj. Estimating growing-season root zone soil moisture from vegetation index-based evapotranspiration fraction and soil properties in the Northwest Mountain region, USA. Engineer Research and Development Center (U.S.), September 2021. http://dx.doi.org/10.21079/11681/42128.

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Анотація:
A soil moisture retrieval method is proposed, in the absence of ground-based auxiliary measurements, by deriving the soil moisture content relationship from the satellite vegetation index-based evapotranspiration fraction and soil moisture physical properties of a soil type. A temperature–vegetation dryness index threshold value is also proposed to identify water bodies and underlying saturated areas. Verification of the retrieved growing season soil moisture was performed by comparative analysis of soil moisture obtained by observed conventional in situ point measurements at the 239-km2 Reynolds Creek Experimental Watershed, Idaho, USA (2006–2009), and at the US Climate Reference Network (USCRN) soil moisture measurement sites in Sundance, Wyoming (2012–2015), and Lewistown, Montana (2014–2015). The proposed method best represented the effective root zone soil moisture condition, at a depth between 50 and 100 cm, with an overall average R2 value of 0.72 and average root mean square error (RMSE) of 0.042.
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2

Thoma, David. Landscape phenology, vegetation condition, and relations with climate at Canyonlands National Park, 2000–2019. Edited by Alice Wondrak Biel. National Park Service, June 2023. http://dx.doi.org/10.36967/2299619.

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Анотація:
Quantitatively linking satellite observations of vegetation condition and climate data over time provides insight to climate influences on primary production, phenology (timing of growth), and sensitivity of vegetation to weather and longer-term patterns of weather referred to as climate. This in turn provides a basis for understanding potential climate impacts to vegetation—and the potential to anticipate cascading ecological effects—such as impacts to forage, habitat, fire potential, and erosion—as climate changes in the future. This report provides baseline information about vegetation production and condition over time at Canyonlands National Park (NP), as derived from satellite remote sensing. Its objective is to demonstrate methods of analysis, share findings, and document historic climate exposure and sensitivity of vegetation to weather and climate as a driver of vegetation change. This report represents a quantitative foundation of vegetation–climate relationships on an annual timestep. The methods can be modified to finer temporal resolution and other spatial scales if further analyses are needed to inform park planning and management. The knowledge provided in this report can inform vulnerability assessments for Climate Smart Conservation planning by park managers. Patterns of pivot points and responses can serve as a guide to anticipate what, where, when, and why vegetation change may occur. For this analysis, vegetation alliance groups were derived from vegetation-map polygons (Von Loh et al. 2007) by lumping vegetation types expected to respond similarly to climate. Relationships between vegetation production and phenology were evaluated for each alliance map unit larger than a satellite pixel (~300 × 300 m). We used a water-balance model to characterize the climate experienced by plants. Water balance translates temperature and precipitation into more biophysically relevant climate metrics, such as soil moisture and drought stress, that are often more strongly correlated with vegetation condition than temperature or precipitation are. By accounting for the interactions between temperature, precipitation, and site characteristics, water balance helps make regional climate assessments relevant to local scales. The results provide a foundation for interpreting weather and climate as a driver of changes in primary production over a 20-year period at the polygon and alliance-group scale. Additionally, they demonstrate how vegetation type and site characteristics, such as soil properties, slope, and aspect, interact with climate at local scales to determine trends in vegetation condition. This report quantitatively defines critical water needs of vegetation and identifies which alliance types, in which locations, may be most susceptible to climate-change impacts in the future. Finally, this report explains how findings can be used in the Climate Smart Conservation framework, with scenario planning, to help manage park resources through transitions imposed by climate change.
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Thoma, David. Landscape phenology, vegetation condition, and relations with climate at Capitol Reef National Park, 2000–2019. Edited by Alice Wondrak Biel. National Park Service, March 2023. http://dx.doi.org/10.36967/2297289.

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Анотація:
Quantitatively linking satellite observations of vegetation condition and climate data over time provides insight to climate influences on primary production, phenology (timing of growth), and sensitivity of vegetation to weather and longer-term patterns of weather referred to as climate. This in turn provides a basis for understanding potential climate impacts to vegetation—and the potential to anticipate cascading ecological effects, such as impacts to forage, habitat, fire potential, and erosion, as climate changes in the future. This report provides baseline information about vegetation production and condition over time at Capitol Reef National Park (NP), as derived from satellite remote sensing. Its objective is to demonstrate methods of analysis, share findings, and document historic climate exposure and sensitivity of vegetation to weather and climate as a driver of vegetation change. This report represents a quantitative foundation of vegetation–climate relationships on an annual timestep. The methods can be modified to finer temporal resolution and other spatial scales if further analyses are needed to inform park planning and management. The knowledge provided in this report can inform vulnerability assessments for Climate Smart Conservation planning by park managers. Patterns of pivot points and responses can serve as a guide to anticipate what, where, when, and why vegetation change may occur. For this analysis, vegetation alliance groups were derived from vegetation-map polygons (Von Loh et al. 2007) by lumping vegetation types expected to respond similarly to climate. Relationships between vegetation production and phenology were evaluated for each alliance map unit larger than a satellite pixel (~300 × 300 m). We used a water-balance model to characterize the climate experienced by plants. Water balance translates temperature and precipitation into more biophysically relevant climate metrics, such as soil moisture and drought stress, that are often more strongly correlated with vegetation condition than temperature or precipitation are. By accounting for the interactions between temperature, precipitation, and site characteristics, water balance helps make regional climate assessments relevant to local scales. The results provide a foundation for interpreting weather and climate as a driver of changes in primary production over a 20-year period at the polygon and alliance-group scale. Additionally, they demonstrate how vegetation type and site characteristics, such as soil properties, slope, and aspect, interact with climate at local scales to determine trends in vegetation condition. This report quantitatively defines critical water needs of vegetation and identifies which alliance types, in which locations, may be most susceptible to climate-change impacts in the future. Finally, this report explains how findings can be used in the Climate Smart Conservation framework, with scenario planning, to help manage park resources through transitions imposed by climate change.
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