Assessment of methods for land surface temperature retrieval from Landsat-5 TM images applicable to multiscale tree-grass ecosystem modeling

Land Surface Temperature (LST) is one of the key inputs for Soil-Vegetation-Atmosphere transfer modeling in terrestrial ecosystems. In the frame of BIOSPEC (Linking spectral information at different spatial scales with biophysical parameters of Mediterranean vegetation in the context of global change) and FLUXPEC (Monitoring changes in water and carbon fluxes from remote and proximal sensing in Mediterranean “dehesa” ecosystem) projects LST retrieved from Landsat data is required to integrate ground-based observations of energy, water, and carbon fluxes with multi-scale remotely-sensed data and assess water and carbon balance in ecologically fragile heterogeneous ecosystem of Mediterranean wooded grassland (dehesa). Thus, three methods based on the Radiative Transfer Equation were used to extract LST from a series of 2009–2011 Landsat-5 TM images to assess the applicability for temperature input generation to a Landsat-MODIS LST integration. When compared to surface temperatures simulated using MODerate resolution atmospheric TRANsmission 5 (MODTRAN 5) with atmospheric profiles inputs (LSTref), values from Single-Channel (SC) algorithm are the closest (root-mean-square deviation (RMSD) = 0.50 °C); procedure based on the online Radiative Transfer Equation Atmospheric Correction Parameters Calculator (RTE-ACPC) shows RMSD = 0.85 °C; Mono-Window algorithm (MW) presents the highest RMSD (2.34 °C) with systematical LST underestimation (bias = 1.81 °C). Differences between Landsat-retrieved LST and MODIS LST are in the range of 2 to 4 °C and can be explained mainly by differences in observation geometry, emissivity, and time mismatch between Landsat and MODIS overpasses. There is a seasonal bias in Landsat-MODIS LST differences due to greater variations in surface emissivity and thermal contrasts between landcover components

Earth Resources: A continuing bibliography with indexes, issue 19

This bibliography lists 337 reports, articles, and other documents introduced into the NASA Scientific and Technical Information System between July 1 and September 30, 1978. Emphasis is placed on the use of remote sensing and geophysical instrumentation in spacecraft and aircraft to survey and inventory natural resources and urban areas. Subject matter is grouped according to agriculture and forestry, environmental changes and cultural resources, geodesy and cartography, geology and mineral resources, hydrology and water management, data processing and distribution systems, instrumentation and sensors, and economic analysis

Earth Resources: A continuing bibliography with indexes

This bibliography lists 475 reports, articles and other documents introduced into the NASA scientific and technical information system between January 1 and March 31, 1984. Emphasis is placed on the use of remote sensing and geophysical instrumentation in spacecraft and aircraft to survey and inventory natural resources and urban areas. Subject matter is grouped according to agriculture and forestry, environmental changes and cultural resources, geodesy and cartography, geology and mineral resources, hydrology and water management, data processing and distribution systems, instrumentation and sensors, and economical analysis

Accounting for Almond Crop Water Use under Different Irrigation Regimes with a Two-Source Energy Balance Model and Copernicus-Based Inputs

Accounting for water use in agricultural fields is of vital importance for the future prospects for enhancing water use efficiency. Remote sensing techniques, based on modelling surface energy fluxes, such as the two-source energy balance (TSEB), were used to estimate actual evapotranspiration (ETa) on the basis of shortwave and thermal data. The lack of high temporal and spatial resolution of satellite thermal infrared (TIR) missions has led to new approaches to obtain higher spatial resolution images with a high revisit time. These new approaches take advantage of the high spatial resolution of Sentinel-2 (10–20 m), and the high revisit time of Sentinel-3 (daily). The use of the TSEB model with sharpened temperature (TSEBS2+S3) has recently been applied and validated in several study sites. However, none of these studies has applied it in heterogeneous row crops under different water status conditions within the same orchard. This study assessed the TSEBS2+S3 modelling approach to account for almond crop water use under four different irrigation regimes and over four consecutive growing seasons (2017–2020). The energy fluxes were validated with an eddy covariance system and also compared with a soil water balance model. The former reported errors of 90 W/m2 and 87 W/m2 for the sensible (H) and latent heat flux (LE), respectively. The comparison of ETa with the soil water balance model showed a root-mean-square deviation (RMSD) ranging from 0.6 to 2.5 mm/day. Differences in cumulative ETa between the irrigation treatments were estimated, with maximum differences obtained in 2019 of 20% to 13% less in the most water-limited treatment compared to the most well-watered one. Therefore, this study demonstrates the feasibility of using the TSEBS2+S3 for monitoring ETa in almond trees under different water regimes.info:eu-repo/semantics/publishedVersio

Multi-Timescale Dynamics of Land Surface Temperature

Spatial and temporal patterns of land surface temperature (LST) have been used in studies of surface energy balance, landscape thermal patterns and water management. An effective way to investigate the landscape thermal dynamics is to utilize the Landsat legacy and consistent records of the thermal state of earth’s surface since 1982. However, only a small proportion of studies emphasize the importance of historical Landsat TIR data for investigating the relationship between the urbanization process and surface thermal properties. This occurred due to the lack of standardized LST product from Landsat and the unevenly distributed remote sensing datasets caused by poor atmospheric effects and/or clouds. Despite the characterization of annual temperature cycles using remote sensing data in previous studies, yet the statistical evidence to confirm the existence of the annual temperature cycle is still lacking. The objectives of the research are to provide statistical evidence for the existence of the annual temperature cycle and to develop decomposition technique to explore the impact of urbanization on surface thermal property changes. The study area is located in Los Angeles County, the corresponding remotely sensed TIR data from Landsat TM over a decadal year (2000-2010) was selected, and eventually a series of 82 cloud-free images were acquired for the computation of LST. The hypothesis technique, Lomb-Scargle periodogram analysis was proposed to confirm whether decadal years’s LSTs showed the annual temperature cycle. Furthermore, the simulated LSTs comprised of seasonality, trend, and noise components are generated to test the robustness of the decomposition scheme. The periodogram analysis revealed that the annual temperature cycle was confirmed statistically with p-value less than 0.01 and the identified periodic time at 362 days. The sensitivity analysis based on the simulated LSTs suggested that the decomposition technique was very robustness and able to retrieve the seasonality and trend components with errors up to 0.6 K. The application of the decomposition technique into the real 82 remote sensing data decomposed the original LSTs into seasonality, trend, and noise components. Estimated seasonality component by land cover showed an agreement with previous studies in Weng & Fu (2014). The derived trend component revealed that the impact of urbanization on land surface temperature ranged from 0.2 K to 0.8 K based on the comparison between urban and non-urban land covers. Further applications of the proposed Lomb-Scargle technique and the developed decomposition technique can also be directed to data from other satellite sensors

Object-Based Greenhouse Mapping Using Very High Resolution Satellite Data and Landsat 8 Time Series

Greenhouse mapping through remote sensing has received extensive attention over the last decades. In this article, the innovative goal relies on mapping greenhouses through the combined use of very high resolution satellite data (WorldView-2) and Landsat 8 Operational Land Imager (OLI) time series within a context of an object-based image analysis (OBIA) and decision tree classification. Thus, WorldView-2 was mainly used to segment the study area focusing on individual greenhouses. Basic spectral information, spectral and vegetation indices, textural features, seasonal statistics and a spectral metric (Moment Distance Index, MDI) derived from Landsat 8 time series and/or WorldView-2 imagery were computed on previously segmented image objects. In order to test its temporal stability, the same approach was applied for two different years, 2014 and 2015. In both years, MDI was pointed out as the most important feature to detect greenhouses. Moreover, the threshold value of this spectral metric turned to be extremely stable for both Landsat 8 and WorldView-2 imagery. A simple decision tree always using the same threshold values for features from Landsat 8 time series and WorldView-2 was finally proposed. Overall accuracies of 93.0% and 93.3% and kappa coefficients of 0.856 and 0.861 were attained for 2014 and 2015 datasets, respectively

Quarterly literature review of the remote sensing of natural resources

The Technology Application Center reviewed abstracted literature sources, and selected document data and data gathering techniques which were performed or obtained remotely from space, aircraft or groundbased stations. All of the documentation was related to remote sensing sensors or the remote sensing of the natural resources. Sensors were primarily those operating within the 10 to the minus 8 power to 1 meter wavelength band. Included are NASA Tech Briefs, ARAC Industrial Applications Reports, U.S. Navy Technical Reports, U.S. Patent reports, and other technical articles and reports

Application of advanced technology to space automation

Automated operations in space provide the key to optimized mission design and data acquisition at minimum cost for the future. The results of this study strongly accentuate this statement and should provide further incentive for immediate development of specific automtion technology as defined herein. Essential automation technology requirements were identified for future programs. The study was undertaken to address the future role of automation in the space program, the potential benefits to be derived, and the technology efforts that should be directed toward obtaining these benefits

Land Surface Temperature (LST) estimated from Landsat images: applications in burnt areas and tree-grass woodlands (dehesas)

A lo largo de los últimos 40 años, las diferentes misiones del proyecto Landsat han proporcionado una gran cantidad de información espectral sobre la superficie terrestre. Las imágenes obtenidas por estos satélites se caracterizan por una resolución espacial de tipo medio, bandas espectrales situadas en diferentes regiones del espectro electromagnético (ópticas y térmicas) y una amplia cobertura terrestre. Si bien las bandas del óptico han sido utilizadas con éxito en numerosas aplicaciones, el uso del térmico ha sido mucho más limitado, a pesar de la gran importancia que representa el parámetro de la temperatura de superficie para numerosas aplicaciones ambientales, especialmente para aquellas relacionadas con la modelización de los flujos de energía en el sistema suelo-vegetación-atmósfera y con el cambio global. En este contexto, el objetivo principal de la presente investigación es explorar el potencial de la temperatura de superficie terrestre (siglas en inglés - LST), derivada de imágenes Landsat, en el estudio de ecosistemas heterogéneos, concretamente (i) áreas afectadas por los incendios forestales y (ii) ecosistemas de dehesa,formaciones constituidas por los árboles dispersos y pastizal/cultivos. En primer lugar, en el marco del proyecto BIOSPEC “Linking spectral information at different spatial scales with biophysical parameters of Mediterranean vegetation in the context of Global Change” (http://www.lineas.cchs.csic.es/biospec) se comparan las diferentes metodologías disponibles para la estimación de la LST a partir de la banda térmica de Landsat. Los mejores resultados, en condiciones atmosféricas caracterizadas por niveles medios de contenido de vapor, se obtuvieron usando el método mono-banda (en inglés - SingleChannel) (Jiménez-Muñoz et al., 2009), con un error de estimación <1º K. En el siguiente paso de la investigación la información sobre la distribución de LST derivada del sensor Thematic Mapper se utilizó en el análisis de la severidad del fuego en una zona forestal de Las Hurdes(Extremadura, España), y en el estudio de los efectos ocasionados por los diferentes tratamientos post-incendio en una zona quemada, esta vez localizada en los Montes de Zuera (Zaragoza, España). En relación con la severidad del fuego analizada en diferentes fechas post-incendio, se han detectado diferencias estadísticamente significativas entre los valores de LST correspondientes a las categorías de severidad establecidas a partir del índice espectral ΔNBR (Key y Benson, 2006).Los niveles de LST más elevados se observaron en las zonas donde la severidad del fuego fue mayor, debido a la menor emisividad de los productos de combustión y los cambios en el balance de energía relacionados con la ausencia de vegetación. En cuanto a las consecuencias de los tratamientos de madera quemada en la regeneración vegetal, se han observado diferencias estadísticamente significativas entre las áreas intervenidas y no intervenidas. En este sentido, en las áreas no intervenidas se registraron valores de LST ~1 K más bajos y niveles de recubrimiento vegetal ~10% más altos que en las intervenidas. En otro ámbito de aplicación, los datos de LST obtenidos mediante imágenes de Landsat-5 TM (período 2009-2011), se utilizaron en el análisis de los patrones espacio-temporales de la LST y su relación con el grado de ocupación de la fracción arbórea en ecosistemas de dehesa. Se ha detectado una relación negativa entre la LST y la cobertura arbórea, con diferencias a nivel estacional debido al dinamismo del ciclo fenológico del pastizal

Earth resources: A continuing bibliography (issue 26)

This bibliography lists 480 reports, articles, and other documents introduced into the NASA scientific and technical information system between April 1, 1980 and June 30, 1980. Emphasis is placed on the use of remote sensing and geophysical instrumentation in spacecraft and aircraft to survey and inventory natural resources and urban areas. Subject matter is grouped according to agriculture and forestry, environmental changes and cultural resources, geodesy and cartography, geology and mineral resources, hydrology and water management, data processing and distribution systems, instrumentation and sensors, and economic analysis