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In this paper, we propose equations for narrowband-to-broadband (NTB) albedo conversion for glacier ice and snow for four types of satellite sensors: thematic mapper (TM), advanced very high resolution radiometer (AVHRR), moderate resolution imaging spectroradiometer (MODIS), and multi-angle imaging spectroradiometer (MISR). We do this on the basis of spectral albedos and incident spectral irradiances generated with radiative-transfer models of the (sub-)surface (a two-stream model) and the atmosphere, respectively. First, we establish equations for reference values of atmospheric components and the surface elevation. These equations describe measurements with root-mean-square differences of ∼0.016. We then show that the “reference equations” also perform well when total ozone and aerosol optical depth are changed with respect to the reference. The negative effect of humidity and elevation variations on the performance of the equations can be eliminated by adding a correction term. We argue that narrowband albedos are much less sensitive to variations in the incident spectral irradiance than broadband albedos. Hence, our conclusions about the effects of variations in atmospheric composition and elevation are also valid for equations for NTB conversion proposed in other papers. [Copyright &y& Elsevier]

Precipitation products with high spatial resolution are important for basin-scale hydrological and meteorological applications. Downscaling techniques commonly used with satellite-derived rainfall data build statistical regression relationships between the precipitation and land surface characteristics to obtain rainfall estimates with improved spatial resolution. However, these relationships tend to be extended mistakenly from the regional scale to the hill slope scale. This paper introduces a quadratic parabolic profile (QPP) model for downscaling precipitation. The proposed technique uses a quadratic parabolic equation to express the rule for changes of precipitation with elevation. It is assumed that precipitation is the primary factor restricting vegetation growth during the growing season. Therefore, an ordinary least square regression method is used to fit an “elevation–normalized difference vegetation index (NDVI)” function to determine the parameters of the QPP model. This method was implemented in the Three-River Headwaters Region (TRHR) during the growing seasons of 2009–2013 for both monthly and total precipitation. The results indicated that the precipitation estimates downscaled using the QPP method had higher accuracies than those of commonly used exponential regression, multiple linear regression, and geographically weighted regression models. The average root mean square errors (RMSEs) and mean absolute percent errors (MAPEs) of total precipitation during the growing season of the commonly used models were 17%–69% and 17%–92% higher, respectively, than those of the QPP model. Meanwhile, the precipitation downscaled using the QPP technique also had lower MAPEs and RMSEs than the PERSIANN-CCS, PERSIANN-CDR, GSMaP-RNL, and GSMaP-RNLG products. Downscaled precipitation estimates from the QPP model exhibited patterns with elevation that were more detailed and more reliable than from the commonly used downscaling methods and another four satellite products. In addition, the QPP model is insensitive to errors in the NDVI or elevation. These findings suggest the proposed approach could be implemented successfully to downscale both monthly and total precipitation of the Tropical Rainfall Measuring Mission (TRMM) 3B43 product throughout the growing season in the TRHR. [ABSTRACT FROM AUTHOR]

Deriving high quality precipitation estimates at high spatial resolution is of prime importance for many hydrological, meteorological, and environmental investigations. Rain gauge observations and satellite-derived precipitation data are two main sources of precipitation estimates. Gauge observations are accurate and reliable, but are heavily point-based and sparse in areas of rugged or complex terrains. Satellite-derived precipitation products can cover large areas, but they are generally characterized by inherent bias. To optimize the use of both datasets, we propose in this paper, a downscaling-integration framework to generate high quality monthly precipitation datasets at 1 km spatial resolution by merging rain gauge observations and TRMM 3B43 products. Firstly, an area-to-point kriging (ATPK) approach is used to downscale the original TRMM product to 1 km, so as to ensure a fair comparison with rain gauge data. Then, the downscaled TRMM precipitation datasets are integrated with the gauge observations using geographically weighted regression kriging (GWRK). The geographical factors (i.e. longitude, latitude and elevation) are also used as auxiliary variables in the GWRK model. Applying this approach to an experiment conducted at the middle and lower reaches of the Yangtze River in China from 2001 to 2014 shows that: (1) the downscaled monthly TRMM precipitation data by ATPK are more accurate than the original TRMM estimates; (2) the GWRK model employing the downscaled TRMM precipitation data and geographical factors provides better monthly precipitation estimates than the conventional ordinary kriging (OK) interpolation and the commonly used merging methods (i.e. geographical difference analysis, GDA and kriging with external drift, KED); (3) the GWRK method reduces the influence of the inaccuracy (bias) of satellite-derived precipitation data on the precipitation estimates compared to GDA. The approach presented in this study has provided an efficient alternative for solving the scale mismatch problem between point-based gauge data and low resolution satellite data, and producing improved precipitation data at high spatial resolution. [ABSTRACT FROM AUTHOR]

Climate change projections foresee an increasing number of intense precipitation events with consequent flash and riverine floods. An accurate and rapid mapping of these phenomena is a key component of effective emergency management and disaster risk reduction plans. Earth Observation big data such as the ones acquired by the Copernicus programme, are providing unprecedented opportunities to detect changes and assess economic impacts in case of disasters. This paper presents an innovative flood mapping technique based on an index which is computed using multi-temporal statistics of Synthetic Aperture Radar images. The index compares a large amount of reference scenes to those acquired during the investigated flood and allows an easy categorization of “flooded” areas; either areas solely temporarily covered by water or areas with mixed water and vegetation. The method has been developed specifically to exploit Sentinel-1 data but can be applied to any other sensor. It has been tested for the 2010 flood of Veneto (Italy) and the floods of 2015 in Malawi and Uganda. Extensive qualitative analysis and cross-comparison with other state-of-the art methods, proved the proposed approach highly reliable and particularly effective, allowing a precise, simple and fast flood mapping. Compared to the maps produced for emergency management for the event analyzed, we obtained an overall agreement of 96.7% for Malawi and an average of 96.5% for Veneto for the 5 maps presented. [ABSTRACT FROM AUTHOR]

During the last decades, satellite observations have increasingly been used to study the global water cycle over land. Although their value is now appreciated by the hydrological community, they are still limited by their uncertainties and their inability to close the water budget. In a previous study, we optimally integrated several datasets for each component (precipitation, evapotranspiration, storage change and discharge) to close this budget at a basin scale. Furthermore, an independent and simple calibration of each satellite dataset was designed to reduce the budget residual. In this paper, we extend the calibration procedure to the global scale. Pixels are first classified into surface types characterized by their NDVI and net precipitation values. We show that the global calibration transforms the original datasets towards a consensus that is hydrologically more coherent, with a budget residual reduced by 26%. The calibrated datasets are compared to ground-based observations, showing an improvement for more than 65% of the sites tested. This opens new perspectives to generate long-term datasets at global scale based purely on all available satellites observations, which describe all the terrestrial water components useful for climate purposes. Beyond the simple calibration presented here, inconsistencies among the various satellite datasets can be used as a proxy for satellite observation uncertainties. The quality of our calibration procedure is constrained by the availability of discharge measurements, and could therefore be improved in the future, as discharge measurement networks become more extensive. [ABSTRACT FROM AUTHOR]

Dryland degradation is an issue of international significance as dryland regions play a substantial role in global food production. Remotely sensed data provide the only long term, large scale record of changes within dryland ecosystems. The Residual Trend, or RESTREND, method is applied to satellite observations to detect dryland degradation. Whilst effective in most cases, it has been shown that the RESTREND method can fail to identify degraded pixels if the relationship between vegetation and precipitation has broken-down as a result of severe or rapid degradation. This paper presents an extended version of the RESTREND methodology that incorporates the Breaks For Additive Seasonal and Trend method to identify step changes in the time series that are related to significant structural changes in the ecosystem, e.g. land use changes. When applied to Australia, this new methodology, termed Time Series Segmentation and Residual Trend analysis (TSS-RESTREND), was able to detect degradation in 5.25% of pixels compared to only 2.0% for RESTREND alone. This modified methodology was then assessed in two regions with known histories of degradation where it was found to accurately capture both the timing and directionality of ecosystem change. [ABSTRACT FROM AUTHOR]

This paper presents a new atmospheric precipitable water vapor (PWV) retrieval method based on three thermal infrared band observations from geostationary satellites. The proposed method is similar to the traditional physical split-window (PSW) retrieval technique, but a water vapor channel observation near 6.7 μm was included. Sensitivity analyses and simulation retrievals were carried out respectively according to the instrument characteristics of the Stretched Visible and Infrared Spin Scan Radiometer onboard FengYun-2G (SVISSR/FY-2G) and the Moderate Resolution Imaging Spectroradiometer aboard Terra (MODIS/Terra). The results indicate that the proposed 3-band algorithm can significantly reduce PWV retrieval errors caused by surface emissivity uncertainty and observation errors, especially in dry atmospheric conditions (i.e., PWV < 2 cm). The proposed algorithm was validated using SVISSR/FY-2G and MODIS/Terra observations, and was compared with radiosonde and GPS PWV. The determination coefficient (R 2 ), root mean square error (RMSE), and bias between the SVISSR retrieved PWV and the radiosonde PWV are 0.87, 0.43 cm and 0.14 cm, respectively. The R 2 , RMSE and bias of the MODIS retrieved PWV are 0.89, 0.10 cm and − 0.042 cm, respectively, which are slightly better than the MODIS L2 thermal infrared and near-infrared PWV products. [ABSTRACT FROM AUTHOR]

Atmospheric water vapor plays an important role in hydrologic cycle and climate change of the Earth. A number of studies have focused on retrieval of the total precipitable water (TPW) using microwave or optical remote sensing. In this paper, the global quarter-degree gridded TPW over land was retrieved using water vapor sensitivity parameter ∆ Tb 18.7 /∆ Tb 23.8 based on the combination of AMSR-E and MODIS observations. There are two major improvements in the retrieval algorithm, including optimization of the estimation model of surface emissivity ∆ ε 18.7 /∆ ε 23.8 and correction of the terrain influence to the retrieval of TPW using DEM. To obtain a high resolution TPW, we also developed an algorithm to downscale the retrieved quarter-degree gridded TPW to a fine scale of 0.05° × 0.05° using DEM and NDVI. In addition, the downscaled TPW was further calibrated using high precision TPW from MODIS in the clear-sky condition to improve its accuracy. Finally, both quarter-degree and 0.05° × 0.05° gridded TPW were validated against SuomiNet GPS retrieved TPW on a global scale. The RMSE for the retrieved quarter-degree gridded global TPW is 3.45 mm, with a correlation coefficient of 0.95. In addition, the RMSE for the downscaled 0.05° × 0.05° gridded global TPW is 4.18 mm, with a correlation coefficient of 0.95. An obvious advantage of our algorithm compared with MODIS TPW product is that it can retrieve TPW under cloudy sky condition over land. The algorithm developed in this study can be easily transferred to AMSR2 on board GCOM-W1 and provides the long-term global daily TPW over land since the launch of Aqua to present day to support hydrologic cycle and climate change studies. [ABSTRACT FROM AUTHOR]

Abstract: Seasonal water flow to semi-arid to arid lands worldwide is regularly controlled by precipitation and snowmelt processes in nearby mountain ranges, where few monitoring stations exist. In this paper we present a new approach to assess the seasonal and inter-annual development of precipitation and associated snow-cover area (SCA) and snow–water storage in the Qilian Mountains of NW China. The approach addresses the spatial enhancement and calibration of remote sensing (RS) data acquired from coarse-resolution TRMM (Tropical Rainfall Measuring Mission; ~26-km resolution) and MODIS images in the development of image-surfaces of in-mountain precipitation and surface air temperature at 250-m resolution for input to a new spatially-distributed monthly snow-accumulation and snowmelt model. Spatially-enhanced precipitation and air temperature surfaces were subsequently calibrated using either geographically-weighted regression or simple linear regression and point-data from climate stations. When point-extractions from the calibrated products were compared against a new set of independent climate-station data, their respective values were comparable, giving an overall r 2 of 0.87 for precipitation (RMSE=10mmmonth−1) and 0.96 for surface air temperature. With input from calibrated surfaces, monthly SCA and snow–water equivalence (SWE) in the Qilian Mountains were subsequently modeled over a 10-year period (2000–2009). Seasonal values of SCA and SWE were compared with MODIS-based (optical) and AMSR-E passive microwave-based estimates of SCA and SWE. In most cases, comparisons revealed suitable agreement across the various evaluations of SCA. Minor discrepancy between MODIS- and AMSR-E-based estimates of SCA resulted in a mean overlap of 73% when modeled SCA was compared to MODIS-based SCA and 84% overlap, when compared to AMSR-E-based SCA. Modeled and AMSR-E-based estimates of SWE at lower- to upper-mountain elevations (≤3900m above mean sea level; AMSL) compared fairly well. At elevations >3900m AMSL, discrepancies between estimates were largely attributed to an overestimation of local precipitation. [Copyright &y& Elsevier]

Abstract: The availability of precipitation data with high spatial resolution is of fundamental importance in several applications such as hydrology, meteorology and ecology. At present, there are mainly two sources of precipitation estimates: raingauge stations and remote sensing technology. However, a large number of studies demonstrated that traditional point measurements based on raingauge stations cannot reflect the spatial variation of precipitation effectively, especially in ungauged basins. The technology of remote sensing has greatly improved the quality of precipitation observations and produced reasonably high resolution gridded precipitation fields. These products, derived from satellites, have been widely used in various parts of the world. However, when applied to local basins and regions, the spatial resolution of these products is too coarse. In this paper, we present a statistical downscaling algorithm based on the relationships between precipitation and other environmental factors in the Qaidam Basin such as topography and vegetation, which was developed for downscaling the spatial precipitation fields of these remote sensing products. This algorithm is demonstrated with the Tropical Rainfall Measuring Mission (TRMM) 3B43 dataset, the Digital Elevation Model (DEM) from the Shuttle Radar Topography Mission (SRTM) and SPOT VEGETATION. The statistical relationship among precipitation, DEM and Normalized Difference Vegetation Index (NDVI), which is a proxy for vegetation, is variable at different scales; therefore, a multiple linear regression model was established under four different scales (0.25°, 0.50°, 0.75° and 1.00°, respectively). By applying a downscaling methodology, TRMM 3B43 0.25°×0.25° precipitation fields were downscaled to 1×1km pixel precipitation for each year from 1999 to 2009. On the basis of three criteria, these four downscaled results were compared with each other and the regression model established at the resolution of 0.50° was selected as the final downscaling algorithm in this study. The final downscaled results were validated by applying the observations for a duration of 11years obtained from six raingauge stations in the Qaidam Basin. These results indicated that the downscaled result effectively captured the trends in inter-annual variability and the magnitude of annual precipitation with the coefficient of determination r 2 ranging from 0.72 to 0.96 at six different raingauge stations. [Copyright &y& Elsevier]

Abstract: A multi-resolution analysis (MRA) based on the wavelet transform (WT) has been implemented to study NDVI time series. These series, which are non-stationary and present short-term, seasonal and long-term variations, can be decomposed using this MRA as a sum of series associated with different temporal scales. The main focus of the paper is to check the potential of this MRA to capture and describe both intra- and inter-annual changes in the data, i.e., to discuss the ability of the proposed procedure to monitor vegetation dynamics at regional scale. Our approach concentrates on what wavelet analysis can tell us about a NDVI time series. On the one hand, the intra-annual series, linked to the seasonality, has been used to estimate different key features related to the vegetation phenology, which depend on the vegetation cover type. On the other hand, the inter-annual series has been used to identify the trend, which is related to land-cover changes, and a Mann-Kendall test has been applied to confirm the significance of the observed trends. NDVI images from the MEDOKADS (Mediterranean Extended Daily One-km AVHRR Data Set) imagery series over Spain are processed according to a per-pixel strategy for this study. Results show that the wavelet analysis provides relevant information about vegetation dynamics at regional scale, such as the mean and minimum NDVI value, the amplitude of the phenological cycle, the timing of the maximum NDVI and the magnitude of the land-cover change. The latter, in combination with precipitation data, has been used to interpret the observed land-cover changes and identify those subtle changes associated to land degradation. [Copyright &y& Elsevier]

Observations in the visible and infrared spectral bands from the Imager instrument onboard Geostationary Operational Environmental Satellite (GOES) have been used to derive snow depth. The technique makes use of correlation between depth of the snow pack and satellite-derived subpixel fractional snow cover. Previous efforts to infer snow depth from satellite data with this technique were focused on grasslands and croplands, where the snow depth/snow fraction relationship is most pronounced. In this paper we improve the retrieval algorithm to extend snow depth estimates to forested areas. The enhanced algorithm accounts for the tree cover fraction and for the type of forest, deciduous or coniferous. The developed technique was used to derive maps of snow depth over mid-latitude areas of North America during winter seasons of 2003–2004 and 2004–2005. Satellite-based snow depth maps were produced daily at 4 km spatial resolution. To validate the retrievals we compared them with surface observations of snow depth and with the snow depth analysis prepared at the NOAA National Operational Hydrological Remote Sensing Center (NOHRSC). The estimated retrieval error was about 30% for snow depths below 30 cm and increased to 50% for snow depths ranging from 30 to 50 cm. Snow depth retrievals were limited to scenes with less than 80% deciduous forest cover fraction and less than 50% needle leaf forest cover. [Copyright &y& Elsevier]