On some aspects of precipitation over the tropical Indian Ocean using satellite data

The annual and inter‐annual variability of precipitation over the tropical Indian Ocean is studied for the period 1979–1997, using satellite data from a variety of sensors. The Climate Prediction Center Merged Analysis Precipitation (CMAP), Microwave Sounding Unit (MSU) estimates of rainfall had better correlation with the island rainfall data than the National Centers for Environmental Prediction/National Center for Atmospheric Research Reanalysis (NRA) estimates. A comparison of the mean annual rainfall by different estimates (CMAP, MSU, NRA and GPCP (Global Precipitation Climatology Programme)) showed significant differences with the CMAP, GPCP and MSU estimates depicting maximum off the Indonesian Islands whilst the NRA exhibited maximum in the southern part of the Bay of Bengal and equatorial Indian Ocean. A study of the inter‐annual variability of the monsoon rainfall using the monthly CMAP data over the tropical Indian Ocean for different study areas, namely, Arabian Sea (AS), Bay of Bengal (BB), south Indian Ocean (SIO) and Indian Ocean (IO) showed significant differences during deficit years (1979, 1982, 1986 and 1987), excess monsoon years (1983 and 1988) and also during El Nino Southern Oscillation (ENSO) years (1982, 1987, 1992 and 1997). An analysis of the rainfall anomalies showed positive and negative anomalies in the north‐eastern Bay of Bengal during the summer season of deficit (1986) and excess (1988) monsoon years respectively, whilst the eastern equatorial Indian Ocean showed large positive and negative rainfall anomalies during the autumn season of El Niño years, 1987 (deficit monsoon) and 1997 (normal monsoon) respectively.     

Interannual variations in vegetation activities and climate variability caused by ENSO in tropical rainforests

Interannual variations in terrestrial carbon cycle over tropical rainforests affect the global carbon cycle. Terrestrial ecosystem models show the interannual relationship between climate changes due to El Niño‐Southern Oscillation (ENSO) and net primary production over tropical rainforests. However, we need an independent analysis using satellite‐based vegetation index and climate parameters. In the present study, we extracted the ENSO‐related interannual variations from time‐series in Normalized Difference Vegetation Index (NDVI) and climate data from 1981 to 2000, and analysed their relevance. We detected relationships among NDVI, ENSO, and climate parameters from long‐term data with negative NDVI–ENSO, NDVI–temperature, and positive NDVI–precipitation relations. These correlations suggest that interannual variability in vegetation activities over tropical rainforests could be extracted from NDVI time‐series despite noise components in NDVI data, and that interannual changes in precipitation and temperature caused by ENSO play a more important role in vegetation activities over tropical rainforests than in incoming surface solar radiation.     

Topographic controls on spatio-temporal snow cover distribution in Northwest Himalaya

The present study deals with spatio-temporal snow cover distribution in Northwest Himalaya (NWH) in a discourse on regional topography and prevalent climatology. Snow cover variation during 2001–2012 in NWH and eight major river basins was examined using MODIS data on board the Terra satellite. Slope match topographic correction was applied to eliminate the differential illumination effect on satellite imagery. The impact of cloud cover was removed by generating a 10-day maximum snow cover product. Annual and seasonal analysis shows a decreasing trend in snow cover area (SCA) over the entire NWH. Maximal SCA was observed in the windward river basins of the Lower and the Middle Himalayan zones and in the highly glaciated Shyok river basin of the Upper Himalaya. Monthly snow cover duration (SCD) maps revealed the effect of longitudinal variation as well as the strong influence of regional climatology and topography. The relationship of SCA with altitude and aspect was studied in all the river basins of NWH. The study shows a linear increment of SCA/D with increasing respect to elevation in all river basins. The maximum rate of SCA/D change with elevation was observed in the Jhelum river basin. In the Middle Himalayan Zone, an effect of basin orientation in regard to elevation was observed. Mean annual SCA at altitudes of up to 4500 m shows a decreasing trend. Seasonal analysis of aspect-wise snow cover shows that southern slopes have lower SCA during winter months. The difference in SCA between northern and southern slopes is even higher in summer and the monsoon period.     

Accuracy assessment of MODIS,NOAA and IRS data in snow cover mapping under Himalayan conditions

Snow cover information is an essential parameter for a wide variety of scientific studies and management applications, especially in snowmelt runoff modelling. Until now NOAA and IRS data were widely and effectively used for snow‐covered area (SCA) estimation in several Himalayan basins. The suit of snow cover products produced from MODIS data had not previously been used in SCA estimation and snowmelt runoff modelling in any Himalayan basin. The present study was conducted with the aim of assessing the accuracy of MODIS, NOAA and IRS data in snow cover mapping under Himalayan conditions. The total SCA was estimated using these three datasets for 15 dates spread over 4 years. The results were compared with ground‐based estimation of snow cover. A good agreement was observed between satellite‐based estimation and ground‐based estimation. The influence of aspect in SCA estimation was analysed for the three satellite datasets and it was observed that MODIS produced better results. Snow mapping accuracy with respect to elevation was tested and it was observed that at higher elevation MODIS sensed more snow and proved better at mapping snow under mountain shadow conditions. At lower elevation, IRS proved better in mapping patchy snow cover due to higher spatial resolution. The temporal resolution of MODIS and NOAA data is better than IRS data, which means that the chances of getting cloud‐free scenes is higher. In addition, MODIS has an automated snow‐mapping algorithm, which reduces the time and errors incorporated during processing satellite data manually. Considering all these factors, it was concluded that MODIS data could be effectively used for SCA estimation under Himalayan conditions, which is a vital parameter for snowmelt runoff estimation.     

Interpretation of scatterometer ocean surface wind vector EOFs over the Northwestern Pacific

Satellite scatterometer winds over the northwestern Pacific were analyzed with the vector empirical orthogonal function (VEOF) method. The Hilbert-Huang transform (HHT), a newly developed non-linear and non-stationary time series data processing method, was also employed in the analysis. A combination of European Remote Sensing Satellite (ERS) −1/2 scatterometer, NASA Scatterometer (NSCAT) and NASA's Quick Scatterometer (QuikSCAT) winds covering the period from January 1992 to April 2000 and the area of 0-50°N, 100-148°E constitutes the baseline for this study. The results indicate that annual cycles dominate the two leading VEOF modes. The first VEOF shows the East Asian monsoon features and the second represents a spring-autumn oscillation. We removed the annual signal from the data set and calculated the interannual VEOFs. The first interannual VEOF represents the interannual variability existing in the spring-autumn oscillation. The temporal mode is correlated with the Southern Oscillation Index (SOI), but has a half-year lag with respect to the SOI. The spatial mode of the first interannual VEOF reflects the response of the tropical and extratropical winds to ENSO events. The second interannual VEOF is another ENSO related mode, and the temporal VEOF mode is correlated with the SOI with a correlation coefficient of 0.78, revealing the wind variability over mid-latitudes, which is associated with ENSO events. Further analysis indicated that the wind variability over the coast of East Asia represents anomalies of a Hadley cell. The quasi-biennial oscillation (QBO) was found in the temporal mode, indicating and verifying that the QBO in the wind fields is related to ENSO events. The third VEOF shows the interannaul variability in the winter-summer mode and displays the interannual variability of the East Asian monsoon. The three leading interannual VEOFs are statistically meaningful as confirmed by a significance test.     

A Review of: “Bibliography of Geologic Studies Using Imaging Radar.” Compiled by M. LEONARD BRYAN. (JPL Publ. 79-53, 1979.) [Pp. 121.] Available from NASA/JPL,California Institute of Technology,Pasadena, California,U.S.A.

In high mountainous areas, such as the Himalayan Range where snow melt run-off contributes substantially to streamflow, information on snow depth and snow areal extent is vital for the estimation of streamflow and for water resources management. Microwave radiationpenetrates through clouds and snowpacks and is thus considered an important tool for providing water equivalent information on snow fields. The scanning multichannel microwave radiometer (SMMR) on board the NIMBUS-7 satellite acquired passive microwave data for 9 years (1978-1987). These SMMR data are used to test a snow algorithm that is applicable for high elevation areas (>3500m) in the Himalayan Range. The study demonstrates promising results, suggesting the application of SMMR data to derive snow-depth and snow-extent information for the Himalayan region. The generated snow maps can be used for various hydrological applications. The limited availability of field data and its comparison with the SMMR data (which are areal in nature), are major limitations in achieving close correlations between two observations. This is the first application of SMMR data for the determination of snow parameters in the Indian Himalayas. Thus, such an application sets a pace for further research and application of passive microwave data in the most rugged terrains of the world.     

The impact of simulated soil temperatures on the estimation of snow depth over Siberia from SSM/I compared to a multi-model climatology

The snowpack is a key variable of the hydrological cycle. In recent years, numerous studies have demonstrated the importance of long-term monitoring of the Siberian snowpack on large spatial scales owing to evidence of increased river discharge, changes in snow fall amount and alterations with respect to the timing of ablation. This can currently only be accomplished using remote sensing methods. The main objective of this study is to take advantage of a new land surface forcing and simulation database in order to both improve and evaluate the snow depths retrieved using a dynamic snow depth retrieval algorithm. The dynamic algorithm attempts to account for the spatial and temporal internal properties of the snow cover. The passive microwave radiances used to derive snow depth were measured by the Special Sensor Microwave/ Imager (SSM/I) data between July 1987 and July 1995.The evaluation of remotely sensed algorithms is especially difficult over regions such as Siberia which are characterized by relatively sparse surface measurement networks. In addition, existing gridded climatological snow depth databases do not necessarily correspond to the same time period as the available satellite data. In order to evaluate the retrieval algorithm over Siberia for a recent multi-year period at a relatively large spatial scale, a land surface scheme reanalysis product from the Global Soil Wetness Project-Phase 2 (GSWP-2) is used in the current study. First, the high quality GSWP-2 input forcing data were used to drive a land surface scheme (LSS) in order to derive a climatological near-surface soil temperature. Four different snow depth retrieval methods are compared, two of which use the new soil temperature climatology as input. Second, a GSWP-2 snow water equivalent (SWE) climatology is computed from 12 state-of-the-art LSS over the same time period covered by the SSM/I data. This climatology was compared to the corresponding fields from the retrievals. This study reaffirmed the results of recent studies which showed that the inclusion of ancillary data into a satellite data-based snow retrieval algorithm, such as soil temperatures, can significantly improve the results. The current study also goes a step further and reveals the importance of including the monthly soil temperature variation into the retrieval, which improves results in terms of the spatial distribution of the snowpack. Finally, it is shown that further improved predictions of SWE are obtained when spatial and temporal variations in snow density are accounted for.     

Interannual variability of convective activity over the tropical Indian Ocean during the El Niño/La Niña events

Convection over the tropical Indian Ocean is important to the global and regional climate. This study presents the monthly climatology of convection, inferred from the outgoing longwave radiation (OLR), over the tropical Indian Ocean. We also examine the impact of El Niño/La Niña events on the convection pattern and how variations in convection over the domain influence the spatial rainfall distribution over India. We used 35 recent years (1974–2008) of satellite-derived OLR over the area, the occurrence of El Niño/La Niña events and high resolution grid point rainfall data over India. The most prominent feature of the annual cycle of OLR over the domain is the movements of convection from south-east to north and north-west during the winter to the summer monsoon season. This feature represents the movement of the inter-tropical convergence zone (ITCZ). The climatology of OLR during the winter months (December–February) over the domain is characterized by high subsidence over central India with a decrease of OLR values towards the north and south. Moderate convection is also seen over the Himalayan Range and the south-east Indian Ocean. In contrast, during the summer (June–September) the OLR pattern indicates deep convection along the monsoon trough and over central India, with subsidence over the extreme north-west desert region. The annual march of convection over the Arabian Sea and Bay of Bengal sector shows that the Arabian Sea has a limited role, compared to the Bay of Bengal, in the annual cycle of the convection over the tropical Indian Ocean. The composite OLR anomalies for the El Niño cases during the summer monsoon season show suppressed convection over all of India and moderate convection over the central equatorial Indian Ocean and over the northern part of the Bay of Bengal. Meanwhile in La Niña events the OLR pattern is nearly opposite to the El Niño case, with deep convection over entire Indian region and adjoining seas and subsidence over the northern Bay of Bengal and extreme north-west region. The spatial variability of the 1°?×?1° summer monsoon rainfall data over India is also examined during El Niño/La Niña events. The results show that rainfall of the summer monsoon season over the southern peninsular of India and some parts of central India are badly affected during El Niño cases, while the region lying along the monsoon trough and the west coast of India have received good amounts of rainfall. This spatial seasonal summer monsoon rainfall distribution pattern seems to average out the influence of El Niño events on total summer monsoon rainfall over India. It seems that, in El Niño events, the convection pattern over the Bay of Bengal remains unaffected during summer monsoon months and thus this region plays an important role in giving good summer monsoon rainfall over the northern part of India, which dilutes the influence of El Niño on seasonal scale summer monsoon rainfall over India. These results are also confirmed by using a monthly bias-corrected OLR dataset.     

A satellite snow depth multi-year average derived from SSM/I for the high latitude regions

The hydrological cycle for high latitude regions is inherently linked with the seasonal snowpack. Thus, accurately monitoring the snow depth and the associated aerial coverage are critical issues for monitoring the global climate system. Passive microwave satellite measurements provide an optimal means to monitor the snowpack over the arctic region. While the temporal evolution of snow extent can be observed globally from microwave radiometers, the determination of the corresponding snow depth is more difficult. A dynamic algorithm that accounts for the dependence of the microwave scattering on the snow grain size has been developed to estimate snow depth from Special Sensor Microwave/Imager (SSM/I) brightness temperatures and was validated over the U.S. Great Plains and Western Siberia.

The purpose of this study is to assess the dynamic algorithm performance over the entire high latitude (land) region by computing a snow depth multi-year field for the time period 1987–1995. This multi-year average is compared to the Global Soil Wetness Project-Phase2 (GSWP2) snow depth computed from several state-of-the-art land surface schemes and averaged over the same time period. The multi-year average obtained by the dynamic algorithm is in good agreement with the GSWP2 snow depth field (the correlation coefficient for January is 0.55). The static algorithm, which assumes a constant snow grain size in space and time does not correlate with the GSWP2 snow depth field (the correlation coefficient with GSWP2 data for January is − 0.03), but exhibits a very high anti-correlation with the NCEP average January air temperature field (correlation coefficient − 0.77), the deepest satellite snow pack being located in the coldest regions, where the snow grain size may be significantly larger than the average value used in the static algorithm. The dynamic algorithm performs better over Eurasia (with a correlation coefficient with GSWP2 snow depth equal to 0.65) than over North America (where the correlation coefficient decreases to 0.29).     

Snow depth estimation over north‐western Indian Himalaya using AMSR‐E

This paper presents the estimation of snow depth over north‐western Indian Himalaya using the 18.7H and 36.5H GHz channels of Advanced Microwave Scanning Radiometer‐EOS (AMSR‐E). The Microwave Emission Model of Layered Snowpacks (MEMLS) was used along with AMSR‐E to understand the difference in the snow pack emitted and sensor received signals due to the prevailing topography. The study shows that the brightness temperature of AMSR‐E and MEMLS are comparable at 18.7 GHz with some differences in their values at 36.5 GHz showing the sensitivity of this channel to the prevailing topography.

Three years of AMSR‐E data were used to modify the 1.59 algorithm to suit the terrain and snow conditions of the north‐western Indian Himalayas. The retrieved snow depth is then compared with ground observations. Data from December to February 2003–2006 were used for the study of snow depths less than 1 m. The modified algorithm estimates the snow depth better than the old algorithm over the mountainous terrains of the north‐western Himalayas.     

The impact of seasonal rainfall variability on NDVI in the Tunga and Bhadra river basins,Karnataka, India

Rainfall is the major climatic factor that affects the growth and distribution of natural vegetation at a regional scale. The high space–time variability of rainfall in the Tunga and Bhadra river basins caused by the high-elevation Western Ghats mountains forces changes in the seasonal distribution of local vegetation. Understanding the relationship between vegetation greenness and rainfall is a key feature in managing the vegetation of the river basin. For this, we have analysed a 7-year (2005–2011) time series of the Moderate Resolution Imaging Spectroradiometer normalized difference vegetation index (NDVI) and Tropical Rainfall Measuring Mission 3B42 rainfall data. The results show that rainfall exerts seasonal control on vegetation greenness. A significant negative correlation was observed for the monsoon season and a favourable positive association for the rest of the seasons. We found a maximum amount of vegetation greenness in the post-monsoon season (October–December). The availability of enough soil moisture from the southwest monsoon season along with suitable climatic conditions triggers an increased greenness amount during the post-monsoon months. We also investigated seasonal and monthly correlations of monsoon rainfall with the NDVI of its subsequent months. The results suggest that monsoon rainfall is a key factor that sustains the long-term greenness in the river basins.     

Review of snow water equivalent retrieval methods using spaceborne passive microwave radiometry

ABSTRACT

The physical properties of a snowpack strongly influence the emissions from the substratum, making snow property retrievals feasible by means of the surface brightness temperature observed by passive microwave sensors. Depending on the spatial resolution observed, time series records of daily snow coverage and critical snowpack properties such as snow depth (SD) and snow water equivalent (SWE) could be helpful in applications ranging from modelling snow variations for water resources management in a catchment to global climatologic studies. However, the challenge of including spaceborne SWE products in operational hydrological and hydroclimate modelling applications is very demanding with limited uptake by these systems, mostly attributed to insufficient SWE estimation accuracy. The root causes of this challenge include the coarse spatial resolution of passive microwave (PM) observations that observe highly aggregated snowpack properties at the spaceborne scale, and inadequacies during the retrieval process caused by uncertainties with the forward emission modelling of snow and challenges to find robust parameterizations of the models. While the spatial resolution problem is largely in the realm of engineering design and constrained by physical restrictions, a better understanding of developed and adopted retrieval methodologies can provide the clarity needed to enhance our knowledge in this field. In this paper, we review snow depth and SWE retrieval methods using PM observations, taking only dry snow retrieval processes into consideration. Snow properties using PM observations can be modelled by purely empirical relations based on underlying physical processes, and SD and SWE can be estimated by regression-based approaches. Snow property retrievals have been refined gradually throughout four decades use of PM observations in tandem with better understanding of physical processes, inclusion of better snowpack parameterizations, improved uncertainty analysis frameworks, and applying better inversion algorithms. Studying available methods, we conclude that snowpack parameterization is key to accurate retrieval. By improving retrieval algorithm architectures to better capture dynamic snowpack evolution processes, SWE estimates are likely to improve. We conclude that this challenge can be addressed by coupling emission models and land surface models or integrating weather-driven snowpack evolution into emission models and performing inversion in a Bayesian framework.     

Interannual variability of vegetation over the Indian sub-continent and its relation to the different meteorological parameters

The dynamic nature of climate over Indian sub-continent is well known which influences Indian monsoon. Such dynamic variability of climate factors can also have significant implications for the vegetation and agricultural productivity of this region. Using empirical orthogonal function (EOF) and wavelet decomposition techniques, normalized difference vegetation index (NDVI) monthly data over Indian sub-continent for 18 years from 1982 to 2000 have been used to study the variability of vegetation. The present study shows that the monsoon precipitation and land surface temperature over the Indian sub-continent landmass have significant impact on the distribution of vegetation. Tropospheric aerosols exert a strong influence too, albeit secondary to monsoon precipitation and prove to be a powerful governing factor. Local climate anomaly is seen to be more effective in determining the vegetation change than any global teleconnection effects. The study documents the dominating influence of monsoon precipitation and highlights the importance of aerosols on the vegetation and necessitates the need for remedial measures. The present study and an earlier one point towards a possible global teleconnection pattern of ENSO as it is seen to affect a particular mode of vegetation worldwide.     

A comparison of SNOTEL and AMSR-E snow water equivalent data sets in western US watersheds

It is a consensus among earth scientists that climate change will result in an increased frequency of extreme events (e.g. floods, droughts). Streamflow forecasts and flood/drought analyses, given this high variability in the climatic driver (snowpack), are vital in the western USA. However, the ability to produce accurate forecasts and analyses is dependent upon the quality of these predictors. Run-off and stream volume analysis in the region is currently based upon in situ telemetry snow data products. Recent satellite deployments offer an alternative data source of regional snowpack. The proposed research investigates and compares remotely sensed snow water equivalent (SWE) data sets in western US watersheds in which snowpack is the primary driver of streamflow. Watersheds investigated include the North Platte, Upper Green and Upper Colorado. SWE data sets incorporated are in situ snowpack telemetry (SNOTEL) sites and the advanced microwave scanning radiometer-earth observing system (AMSR-E) aboard NASA's Aqua satellite. The time period analysed is 2003-2008, coincident with the deployment of the NASA Aqua satellite. Bivariate techniques between data sets are performed to provide valuable information on the time series of the snow products. Multivariate techniques including principal component analysis (PCA) and singular value decomposition (SVD) are also applied to determine similarities and differences between the data sets and investigate regional snowpack behaviours. Given the challenges (including costs, operation and maintenance) of deploying SNOTEL stations, the objective of the research is to determine whether remotely sensed SWE data provide a comparable option to in situ data sets. Correlation analysis resulted in only 11 of the 84 SNOTEL sites investigated being significant at 90% or greater with a corresponding AMSR-E cell. Agreement between SWE products was found to increase in lower elevation areas and later in the snowpack season. Two distinct snow regions were found to behave similarly between both data sets using a rotated PCA approach. Additionally, SVD linked both data products with streamflow in the region and found similar behaviour among data sets. However, when comparing SNOTEL data with the corresponding satellite cell, there was a consistent bias in the absolute magnitude (SWE) of the data sets. The streamflow forecasting results conclude regions that have few (or zero) land-based weather stations can incorporate the AMSR-E SWE product into a streamflow forecast model and obtain accurate values.     

Estimating northern hemisphere snow water equivalent for climate research through assimilation of space-borne radiometer data and ground-based measurements

The key variable describing global seasonal snow cover is snow water equivalent (SWE). However, reliable information on the hemispheric scale variability of SWE is lacking because traditional methods such as interpolation of ground-based measurements and stand-alone algorithms applied to space-borne observations are highly uncertain with respect to the spatial distribution of snow mass and its evolution. In this paper, an algorithm assimilating synoptic weather station data on snow depth with satellite passive microwave radiometer data is applied to produce a 30-year-long time-series of seasonal SWE for the northern hemisphere. This data set is validated using independent SWE reference data from Russia, the former Soviet Union, Finland and Canada. The validation of SWE time-series indicates overall strong retrieval performance with root mean square errors below 40 mm for cases when SWE < 150 mm. Retrieval uncertainty increases when SWE is above this threshold. The SWE estimates are also compared with results obtained by a typical stand-alone satellite passive microwave algorithm. This comparison demonstrates the benefits of the newly developed assimilation approach. Additionally, the trends and inter-annual variability of northern hemisphere snow mass during the era of satellite passive microwave measurements are shown.     

Error modelling for modified-INSAT multi-spectral rainfall algorithm

Considering the importance of the error estimates for satellite rainfall products in various applications, the present article deals with the development of an Error Model for Modified-INSAT Multi-Spectral Rainfall Algorithm (M-IMSRA) Estimates (EMME), a recently developed climate region scale rainfall algorithm across India. A non-parametric framework has been adopted to model all the four error components: Correct No-Rain Detection, Miss Rain, False Rain, and Hit Rain in M-IMSRA estimates at climate region scale. The developed error model generated convincing realization of reference rainfall for the estimated rainfall from M-IMSRA algorithm across all the climate regions of India. Exceptions are the high intensity hit rain events across arid Thar Desert and arid Himalayan regions and miss rain events across arid Himalayan region. Overall, the developed error model showed promising results in modelling hit, miss, and false error components of daily M-IMSRA estimates and thus can be associated with the M-IMSRA estimates.     

Monitoring snowpack evolution using interferometric synthetic aperture radar on the North Slope of Alaska,USA

This research investigates the use of Interferometric Synthetic Aperture Radar (InSAR) to generate a time-series of snow water equivalent (SWE) for dry snow within the Kuparuk watershed, North Slope, Alaska, during the winter of 1993/1994. Maps depicting relative change in phase and the theoretical relative change in SWE between satellite acquisitions are created for 3-day periods at the end of March 1994 using both ascending and descending ERS-1 overpasses. The theoretical coefficient relating relative change in phase and relative change in SWE for C-band is found to be at least twice as large as what is expected when using a simple single-layer snow model for this study area and time period. Without any direct measurements of SWE on the ground, station measurements of snow depth and hourly wind are linked to each 3-day relative change in phase map. Along with a qualitative assessment, quantitative measures of the rate and magnitude of phase change around these stations are directly compared to the hourly wind data for a given 3-day period. InSAR-derived maps acquired around a measured precipitation event show a considerable relationship to the predominant direction of strong winds over each 3-day period while maps acquired around no measureable precipitation depict much less correlation between phase change and predominant direction of strong winds. Despite limited ground measurements to infer snowpack conditions, these results show continued promise for the InSAR technique to measure changes in snowpack conditions (e.g. SWE) at much higher resolutions than manual sampling methods or passive microwave remote sensing. The extension of this technique to current L-band InSAR satellite platforms is also discussed.     

Cross-scalar satellite phenology from ground, Landsat, and MODIS data

Phenological records constructed from global mapping satellite platforms (e.g. AVHRR and MODIS) hold the potential to be valuable tools for monitoring vegetation response to global climate change. However, most satellite phenology products are not validated, and field checking coarse scale (≥ 500 m) data with confidence is a difficult endeavor. In this research, we compare phenology from Landsat (field scale, 30 m) to MODIS (500 m), and compare datasets derived from each instrument. Landsat and MODIS yield similar estimates of the start of greenness (r² = 0.60), although we find that a high degree of spatial phenological variability within coarser-scale MODIS pixels may be the cause of the remaining uncertainty. In addition, spatial variability is smoothed in MODIS, a potential source of error when comparing in situ or climate data to satellite phenology. We show that our method for deriving phenology from satellite data generates spatially coherent interannual phenology departures in MODIS data. We test these estimates from 2000 to 2005 against long-term records from Harvard Forest (Massachusetts) and Hubbard Brook (New Hampshire) Experimental Forests. MODIS successfully predicts 86% of the variance at Harvard forest and 70% of the variance at Hubbard Brook; the more extreme topography of the later is inferred to be a significant source of error. In both analyses, the satellite estimate is significantly dampened from the ground-based observations, suggesting systematic error (slopes of 0.56 and 0.63, respectively). The satellite data effectively estimates interannual phenology at two relatively simple deciduous forest sites and is internally consistent, even with changing spatial scale. We propose that continued analyses of interannual phenology will be an effective tool for monitoring native forest responses to global-scale climate variability.     

积雪属性非均匀性对被动微波遥感积雪的影响

积雪属性的非均匀性在水平方向上表现为像元内积雪未完全覆盖和雪深分布的不均匀,在垂直方向上表现为积雪剖面上粒径和密度的不一致导致的积雪分层现象。这些积雪属性的非均匀性对被动微波遥感反演雪深或雪水当量带来很大的不确定性,并且给反演结果的验证带来不确定性。通过野外积雪的微波辐射特性观测、遥感积雪产品对比分析、积雪辐射传输模型模拟对这些问题进行阐述和探讨,为今后积雪微波遥感反演算法发展和结果验证提供参考。