Retrieval of land surface temperature in the humid tropics from MODIS data by modeling the atmospheric transmission and thermal emission

International Geoscience and Remote Sensing Symposium (IGARSS)74572-4575IGRS

Metodología de validación de productos MODIS para la estimación de temperatura de la superficie en zonas heterogéneas y homogéneas de Colombia

La acelerada producción científica en geomática, ha permitido conocer fenómenos sobre la faz de la Tierra. Sus herramientas como el modelamiento espacial, los geodatos de sensores remotos, la administración de la información geoespacial y los estudios sobre la dinámica del planeta, han suministrado claves de acceso al mejoramiento del entendimiento de los fenómenos naturales y antropogénicos. El avance tecnológico provee el uso de mayor información para estudiar variables ambientales como la temperatura de la superficie (del aire a 2 metros). Sin embargo, obtener estos datos de manera tradicional involucra un costo económico y un cubrimiento espacial insuficiente, ya que esta información es requerida para adelantar estudios agronómicos expansivos, modelos matemáticos de interacción Tierra-Atmósfera y cambio climático. Frente a esta necesidad, el uso de información proveniente de datos satelitales crece a medida que los objetivos y metas en la investigación también aumentan. En Colombia, la baja cobertura espacial de las estaciones meteorológicas deja vacíos de información que pueden ser estimados a través de las herramientas geomáticas. En esta investigación, la explotación de los datos satelitales del sensor Modis en conjunto con los termodatos de las estaciones en terreno, permitieron establecer los parámetros que explican espacialmente el fenómeno de temperatura en el país y cómo ésta se comporta de acuerdo a las diferencias geográficas propias del territorio. A través del modelamiento geoestadístico, el conocimiento empírico y los ajustes teóricos, se estableció un Modelo de Regresión Lineal Múltiple, que estima las temperaturas de la superficie con alta fiabilidad. / Abstract. The growing scientific studies in geomatics allow understanding phenomena related to Earth system. Spatial modelling,remote sensing data, spatial information management and studies on planetary dynamism as tools are keys for ameliorating the knowledge on natural and human-derived phenomena. The advance on technology sets huge load information handle for studying the environmental variables as land surface temperature. Nevertheless, to obtain those data in the oldway involves economics costs and, certainly, an insufficient spatial cover. Because this information is required for large agronomic studies, Earth- Atmosphere mathematical models and climate change. Facing this necessity, the use for remote sensing data grows as much as objectives and goals in research. In Colombia, the low spatial cover by meteorology stations allow no-information places might be estimated using geomatics tools. In this research, Modis Land Surface Temperature –LST product with terrestrial stations data, allow establishing parameters that can explain land surface temperature phenomenon spatially in the country and how it behaves according to spatial geographic variances. Using geostatical modelling, statical processes and theorical basis, a Regression Model was stablished with high fidelity for estimating land surface temperatureMaestrí

Comparison of AVHRR, MODIS and VEGETATION for land cover mapping and drought monitoring at 1 km spatial resolution

Low spatial resolution remote sensors are one of the best data sources for large area land cover mapping and drought monitoring. This study was concerned with identifying which of the three most operational such sensors (AVHRR, MODIS, and VEGETATION), were likely to help produce the best results within the mentioned applications. A rigorous review of the sensors’ characteristics led to the hypothesis that in land cover mapping and drought monitoring applications MODIS is most likely to achieve the best results followed by VEGETATION and lastly by AVHRR. This hypothesis was tested against experimental results generated within this study. A methodology was developed allowing for unbiased relative comparison of the capacity of the sensors’ Solar Reflective Bands (SRBs) to map land cover, and was applied to data collected over the UK and Greece, for which maps were produced using data collected by each sensor over the same dates and sites, and accuracy estimated using reference data. In the majority of cases the most accurate maps were produced by MODIS data; however, there were cases when maps produced by AVHRR and particularly VEGETATION data were more accurate. Drought monitoring methodologies for low resolution data require historical Normalised Difference Vegetation Index (NDVI) records extending longer than MODIS and VEGETATION operational times. Towards solving this limitation, the relationships between the sensors’ NDVI measurements over the same targets were investigated. It was found that NDVI data for one sensor could be predicted from NDVI data collected by another sensor with considerable accuracy. Consequently, MODIS and VEGETATION historical NDVI records could be extended based on past AVHRR data, and applications could be benefited by interchanging sensors for provision of NDVI data in the event of a sensor failure. These extended datasets were used to assess drought conditions over Ethiopia with the aim of using the Vegetation Productivity Indicator (VPI) methodology. The sensors’ NDVI data responsiveness to rainfall was assessed, finding MODIS NDVI data to best reflect rainfall conditions, and likely to produce more accurate VPI results. Overall the experimental results generated in this study supported the initial hypothesis.EThOS - Electronic Theses Online ServiceGBUnited Kingdo