The challenges in long-term altimetry calibration for addressing the problem of global sea level change

Long-term change of the global sea level resulting from climate change has become an issue of great societal interest. The advent of the technology of satellite altimetry has modernized the study of sea level on both global and regional scales. In combination with in situ observations of the ocean density and space observations of Earth’s gravity variations, satellite altimetry has become an essential component of a global observing system for monitoring and understanding sea level change. The challenge of making sea level measurements with sufficient accuracy to discern long-term trends and allow the patterns of natural variability to be distinguished from those linked to anthropogenic forcing rests largely on the long-term efforts of altimeter calibration and validation. The issues of long-term calibration for the various components of the altimeter measurement system are reviewed in the paper. The topics include radar altimetry, the effects of tropospheric water vapor, orbit determination, gravity field, tide gauges, and the terrestrial reference frame. The necessity for maintaining a complete calibration effort and the challenges of sustaining it into the future are discussed.     

Effect of surface roughness on microwave brightness temperature from lunar surface: Numerical analysis with a hybrid method

A hybrid method, combining the radiative transfer theory and the method of moments (MoM), is proposed to study the potential effect of the lunar surface roughness on the microwave brightness temperature. The total upward emission reaching the lunar surface from below media is calculated by the radiative transfer theory, and then the brightness temperature is obtained by weighting the bidirectional transmission coefficients which is computed using the MoM. The method is validated by both flat and rough surface models with analytic solutions. With the hybrid method, brightness temperatures from simulated lunar model are calculated and compared to those from a flat layered model. The comparisons show that the effect of rough surface on brightness temperature cannot be ignored and also depends on many other factors, such as observation angle and polarizations. For vertical polarization, an optimal observation angle may exist to reduce the effect of surface roughness. These results indicate that the knowledge of lunar surface roughness is important in microwave remote sensing to the Moon and may probably provide a guide to lunar projects in future.     

The Big Pine Creek watershed and climate change: A trend analysis of Landsat surface reflectance and PRISM datasets over the last 3 decades

Recent variations in normal meteorological conditions indicate the earth’s climate is changing in ways that may impact delicate ecological balances in sensitive regions. Identifying how those changes are affecting the biosphere is essential if we are going to be able to adapt to those changes and to potentially mitigate their harmful consequences. This paper presents a time series study of an alpine ecosystem in the Big Pine Creek watershed in California’s Eastern Sierra Nevada Mountain’s. Raw Landsat data covering the years 1984 through 2011 is converted to observed surface reflectance and analyzed for trends that would indicate a change in the ecosystem. We found that over the time period of the study, observed surface reflectance shows a general decline across the spectrum while our analysis of environmental data demonstrates statistically significant increases in temperatures. While declining reflectance in the visible and short wave bands are indicators of increased surface cover, the fact that the IR band also shows declines is consistent with a decline in tree density. This study provides a useful insight into the ecological response of the Big Pine Creek watershed to recent climate change. These findings suggest that alpine ecosystems are particularly sensitive to increasing temperatures. If these results are replicated in other alpine watersheds it will demonstrate that the biosphere is already showing the effects of a warmer environment.     

A comparison of different regression models for downscaling Landsat and MODIS land surface temperature images over heterogeneous landscape

Remotely sensed high spatial resolution thermal images are required for various applications in natural resource management. At present, availability of high spatial resolution (<200 m) thermal images are limited. The temporal resolution of such images is also low. Whereas, coarser spatial resolution (∼1000 m) thermal images with high revisiting capability (∼1 day) are freely available. To bridge this gap, present study attempts to downscale coarser spatial resolution thermal image to finer spatial resolution using relationships between land surface temperature (LST) and vegetation indices over a heterogeneous landscape of India. Five regression based models namely (i) Disaggregation of Radiometric Temperature (DisTrad), (ii) Temperature Sharpening (TsHARP), (iii) TsHARP with local variant, (iv) Least median square regression downscaling (LMS_DS) and (v) Pace regression downscaling (PR_DS) are applied to downscale LST of Landsat Thematic Mapper (TM) and Terra MODIS (Moderate Resolution Imaging Spectroradiometer) images. All the five models are first evaluated on Landsat image aggregated to 960 m resolution and downscaled to 480 m and 240 m resolution. The downscale accuracy is achieved using LMS_DS and PR_DS models at 240 m resolution at 0.61 °C and 0.75 °C respectively. MODIS data downscaled from 1000 m to 250 m spatial resolution results root mean square error (RMSE) of 1.43 °C and 1.62 °C for LMS_DS and PR_DS models, respectively. The LMS_DS model is less sensitive to outliers in heterogeneous landscape and provides higher accuracy when compared to other models. Downscaling model is found to be suitable for agricultural and vegetated landscapes up to a spatial resolution of 250 m but not applicable to water bodies, dry river bed sand sandy open areas.     

Water surface temperature estimation from Landsat 7 ETM+ thermal infrared data using the generalized single-channel method: Case study of Embalse del Río Tercero (Córdoba,Argentina)

Monitoring of warm distribution in water is fundamental to understand the performance and functioning of reservoirs and lakes. Surface water temperature is a key parameter in the physics of aquatic systems processes since it is closely related to the energy fluxes through the water–atmosphere interface. Remote sensing applied to water quality studies in inland waterbodies is a powerful tool that can provide additional information difficult to achieve by other means. The combination of good real-time coverage, spatial resolution and free availability of data makes Landsat system a proper alternative. Many papers have developed algorithms to retrieve surface temperature (principally, land surface temperature) from at-sensor and surface emissivity data. The aim of this study is to apply the single-channel generalized method (SCGM) developed by Jiménez-Muñoz and Sobrino (2003) for the estimation of water surface temperature from Landsat 7 ETM+ thermal bands. We consider a constant water emissivity value (0.9885) and we compare the results with radiative transfer classic method (RTM).