Land Surface Conditions Research Articles

A Robust Algorithm for Estimating Surface Fractional Vegetation Cover from Landsat Data

Fractional vegetation cover (FVC) is an essential land surface parameter for Earth surface process simulations and global change studies. The currently existing FVC products are mostly obtained from low or medium resolution remotely sensed data, while many applications require the fine spatial resolution FVC product. The availability of well-calibrated coverage of Landsat imagery over large areas offers an opportunity for the production of FVC at fine spatial resolution. Therefore, the objective of this study is to develop a general and reliable land surface FVC estimation algorithm for Landsat surface reflectance data under various land surface conditions. Two machine learning methods multivariate adaptive regression splines (MARS) model and back-propagation neural networks (BPNNs) were trained using samples from PROSPECT leaf optical properties model and the scattering by arbitrarily inclined leaves (SAIL) model simulations, which included Landsat reflectance and corresponding FVC values, and evaluated to choose the method which had better performance. Thereafter, the MARS model, which had better performance in the independent validation, was evaluated using ground FVC measurements from two case study areas. The direct validation of the FVC estimated using the proposed algorithm (Heihe: R2 = 0.8825, RMSE = 0.097; Chengde using Landsat 7 ETM+: R2 = 0.8571, RMSE = 0.078, Chengde using Landsat 8 OLI: R2 = 0.8598, RMSE = 0.078) showed the proposed method had good performance. Spatial-temporal assessment of the estimated FVC from Landsat 7 ETM+ and Landsat 8 OLI data confirmed the robustness and consistency of the proposed method. All these results indicated that the proposed algorithm could obtain satisfactory accuracy and had the potential for the production of high-quality FVC estimates from Landsat surface reflectance data.

A comparison of gap-filling approaches for Landsat-7 satellite data

ABSTRACTThe purpose of this study is to assess the relative performance of four different gap-filling approaches across a range of land-surface conditions, including both homogeneous and heterogeneous areas as well as in scenes with abrupt changes in landscape elements. The techniques considered in this study include: (1) Kriging and co-Kriging; (2) geostatistical neighbourhood similar pixel interpolator (GNSPI); (3) a weighted linear regression (WLR) algorithm; and (4) the direct sampling (DS) method. To examine the impact of image availability and the influence of temporal distance on the selection of input training data (i.e. time separating the training data from the gap-filled target image), input images acquired within the same season (temporally close) as well as in different seasons (temporally far) to the target image were examined, as was the case of using information only within the target image itself. Root mean square error (RMSE), mean spectral angle (MSA), and coefficient of determination (R2) were used as the evaluation metrics to assess the prediction results. In addition, the overall accuracy (OA) and kappa coefficient (kappa) were used to assess a land-cover classification based on the gap-filled images. Results show that all of the gap-filling approaches provide satisfactory results for the homogeneous case, with R2 > 0.93 for bands 1 and 2 in all cases and R2 > 0.80 for bands 3 and 4 in most cases. For the heterogeneous example, GNSPI performs the best, with R2 > 0.85 for all tested cases. WLR and GNSPI exhibit equivalent accuracy when a temporally close input image is used (i.e. WLR and GNSPI both have an R2 equal to 0.89 for band 1). For the case of abrupt changes in scene elements or in the absence of ancillary data, the DS approach outperforms the other tested methods.

Future summer mega-heatwave and record-breaking temperatures in a warmer France climate

This study focuses on future very hot summers associated with severe heatwaves and record-breaking temperatures in France. Daily temperature observations and a pair of historical and scenario (greenhouse gas radiative concentration pathway 8.5) simulations with the high-resolution (∼12.5 km) ALADIN regional climate model provide a robust framework to examine the spatial distribution of these extreme events and their 21st century evolution.Five regions are identified with an extreme event spatial clustering algorithm applied to observed temperatures. They are used to diagnose the 21st century heatwave spatial patterns. In the 2070s, we find a simulated mega-heatwave as severe as the 2003 observed heatwave relative to its contemporaneous climate. A 20-member initial condition ensemble is used to assess the sensitivity of this future heatwave to the internal variability in the regional climate model and to pre-existing land surface conditions. Even in a much warmer and drier climate in France, late spring dry land conditions may lead to a significant amplification of summer extreme temperatures and heatwave intensity through limitations in evapotranspiration. By 2100, the increase in summer temperature maxima exhibits a range from 6 °C to almost 13 °C in the five regions in France, relative to historical maxima. These projections are comparable with the estimates given by a large number of global climate models.

An Improved Aerosol Optical Depth Retrieval Algorithm for Moderate to High Spatial Resolution Optical Remotely Sensed Imagery

To extract quantitative land information accurately and monitor the air pollution at city scale from moderate to high spatial resolution (MHSR) with a resolution no coarser than 30 m, optical remotely sensed imagery and aerosol parameters, especially aerosol optical depth (AOD), are a necessary step. In this paper, we introduce a new algorithm that can effectively estimate the spatial distribution of atmospheric aerosols and retrieve surface reflectance from moderate to high spatial resolution imagery under general atmosphere and land surface conditions. This algorithm has been improved in the following three aspects: (i) it has been developed for most of the moderate to high spatial resolution remotely sensed imagery; (ii) it can be applied to all kinds of land surface types including bright surface; and (iii) it is completely automatic. This algorithm is therefore suitable for operational applications. The derived AOD in Beijing from Landsat Thematic Mapper (TM), Landsat Enhanced Thematic Mapper Plus (ETM+), and Huan Jing 1 (HJ-1/CCD) data is validated with AErosol Robotic NETwork (AERONET) ground measurements at Beijng and Xianghe stations.

Feedback between surface air temperature and atmospheric circulation in high‐temperature weather in East China: a diurnal perspective

AbstractThis study proposes the generality of surface air temperature (SAT)–atmospheric circulation feedback during high‐temperature weather in late July 2003 over East China by using the Advanced Research Weather Research and Forecasting model (WRF; Version 3) simulations with a succession of 24‐h integrations, i.e. on a daily scale, the SAT increase leads to a weakened ridge of the western Pacific subtropical high in the lower troposphere (i.e. negative feedback), whereas it leads to a strengthened ridge in the upper troposphere (i.e. positive feedback) and vice versa. Additionally, using the balance equation of temperature, the feedbacks are clarified from the diurnal‐variation perspective. This shows many complex details, e.g. the changes in geopotential heights are more complex than those in air temperatures, and the overall daily feedback appears to be dominated by the feedback during the phase with intense daytime surface heating. All of the WRF‐modified land surface conditions can lead to large changes in the maximum, minimum, and average SATs over the mean diurnal scale, with generally larger differences induced by land surface schemes than those induced by initial soil moisture, suggesting that the SAT–circulation feedback can be greatly reduced (or amplified) by different land conditions over the diurnal weather scale and that diurnal variations could substantially contribute to longer timescale climate.

Assessment of MERRA-2 Land Surface Hydrology Estimates

The MERRA-2 atmospheric reanalysis product provides global, 1-hourly estimates of land surface conditions for 1980–present at ~50-km resolution. MERRA-2 uses observations-based precipitation to force the land (unlike its predecessor, MERRA). This paper evaluates MERRA-2 and MERRA land hydrology estimates, along with those of the land-only MERRA-Land and ERA-Interim/Land products, which also use observations-based precipitation. Overall, MERRA-2 land hydrology estimates are better than those of MERRA-Land and MERRA. A comparison against GRACE satellite observations of terrestrial water storage demonstrates clear improvements in MERRA-2 over MERRA in South America and Africa but also reflects known errors in the observations used to correct the MERRA-2 precipitation. Validation against in situ measurements from 220–320 stations in North America, Europe, and Australia shows that MERRA-2 and MERRA-Land have the highest surface and root zone soil moisture skill, slightly higher than that of ERA-Interim/Land and higher than that of MERRA (significantly for surface soil moisture). Snow amounts from MERRA-2 have lower bias and correlate better against reference data from the Canadian Meteorological Centre than do those of MERRA-Land and MERRA, with MERRA-2 skill roughly matching that of ERA-Interim/Land. Validation with MODIS satellite observations shows that MERRA-2 has a lower snow cover probability of detection and probability of false detection than MERRA, owing partly to MERRA-2’s lower midwinter, midlatitude snow amounts and partly to MERRA-2’s revised snow depletion curve parameter compared to MERRA. Finally, seasonal anomaly R values against naturalized streamflow measurements in the United States are, on balance, highest for MERRA-2 and ERA-Interim/Land, somewhat lower for MERRA-Land, and lower still for MERRA (significantly in four basins).

Land surface‐precipitation feedback analysis for a landfalling monsoon depression in the Indian region

AbstractA series of numerical experiments are carried out to investigate the sensitivity of a landfalling monsoon depression to land surface conditions using the Weather Research and Forecasting (WRF) model. Results suggest that precipitation is largely modulated by moisture influx and precipitation efficiency. Three cloud microphysical schemes (WSM6, WDM6, and Morrison) are examined, and Morrison is chosen for assessing the land surface‐precipitation feedback analysis, owing to better precipitation forecast skills. It is found that increased soil moisture facilitates Moisture Flux Convergence (MFC) with reduced moisture influx, whereas a reduced soil moisture condition facilitates moisture influx but not MFC. A higher Moist Static Energy (MSE) is noted due to increased evapotranspiration in an elevated moisture scenario which enhances moist convection. As opposed to moist surface, sensible heat dominates in a reduced moisture scenario, ensued by an overall reduction in MSE throughout the Planetary Boundary Layer (PBL). Stability analysis shows that Convective Available Potential Energy (CAPE) is comparable in magnitude for both increased and decreased moisture scenarios, whereas Convective Inhibition (CIN) shows increased values for the reduced moisture scenario as a consequence of drier atmosphere leading to suppression of convection. Simulations carried out with various fixed soil moisture levels indicate that the overall precipitation features of the storm are characterized by initial soil moisture condition, but precipitation intensity at any instant is modulated by soil moisture availability. Overall results based on this case study suggest that antecedent soil moisture plays a crucial role in modulating precipitation distribution and intensity of a monsoon depression.

Intercomparison of Alternate Soil Moisture Downscaling Algorithms Using Active–Passive Microwave Observations

Three active-passive soil moisture downscaling algorithms are tested to demonstrate the feasibility of each for application to NASA's Soil Moisture Active Passive (SMAP) mission launched in January 2015. These algorithms include the official baseline and optional downscaling algorithms, and a change detection method. These synergistically use 1-km synthetic aperture radar backscatter to downscale 36-km brightness temperature to 9 km, which is then converted into soil moisture at 9 km, or downscale soil moisture directly to 9-km resolution. While these algorithms have been tested previously, this was mostly using synthetic data sets. Moreover, there has never before been a direct comparison of the alternate methods using the same data sets. Thus, it is imperative that they be tested against each other for a comprehensive range of land surface conditions prior to global application. Consequently, this letter evaluates these three algorithms using data collected from the soil moisture active passive experiments (SMAPExs) in Australia, designed to closely simulate the SMAP data stream for a single SMAP radiometer pixel over a three-week interval. Results suggested that the average root-mean-square error (RMSE) in downscaled soil moisture at 9-km resolution was 0.019, 0.021, and 0.026 cm 3 /cm 3 for the baseline, optional, and change detection method, respectively. While there was a little difference in the RMSE, the optional method showed the best correlation between the downscaled soil moisture and the reference soil moisture map. Therefore, the optional algorithm is recommended for global implantation by SMAP.

Improved prediction of severe thunderstorms over the Indian Monsoon region using high-resolution soil moisture and temperature initialization

The hypothesis that realistic land conditions such as soil moisture/soil temperature (SM/ST) can significantly improve the modeling of mesoscale deep convection is tested over the Indian monsoon region (IMR). A high resolution (3 km foot print) SM/ST dataset prepared from a land data assimilation system, as part of a national monsoon mission project, showed close agreement with observations. Experiments are conducted with (LDAS) and without (CNTL) initialization of SM/ST dataset. Results highlight the significance of realistic land surface conditions on numerical prediction of initiation, movement and timing of severe thunderstorms as compared to that currently being initialized by climatological fields in CNTL run. Realistic land conditions improved mass flux, convective updrafts and diabatic heating in the boundary layer that contributed to low level positive potential vorticity. The LDAS run reproduced reflectivity echoes and associated rainfall bands more efficiently. Improper representation of surface conditions in CNTL run limit the evolution boundary layer processes and thereby failed to simulate convection at right time and place. These findings thus provide strong support to the role land conditions play in impacting the deep convection over the IMR. These findings also have direct implications for improving heavy rain forecasting over the IMR, by developing realistic land conditions.

流域水热耦合过程及其对气候与下垫面变化响应的文献计量分析

PDF HTML阅读 XML下载 导出引用 引用提醒 流域水热耦合过程及其对气候与下垫面变化响应的文献计量分析 DOI: 10.5846/stxb201708071410 作者: 作者单位: 作者简介: 通讯作者: 中图分类号: 基金项目: 国家重点研发计划课题（2016YFC0501602）；国家自然科学基金项目（41571036） A bibliometric analysis of the literature on coupled water-energy balance at catchment scale and its responses to changes in climate and land surface conditions Author: Affiliation: Fund Project: 摘要 | 图/表 | 访问统计 | 参考文献 | 相似文献 | 引证文献 | 资源附件 | 文章评论 摘要:生态水文学在水文过程与生态过程的交叉地带开展研究，现已成为水文学与生态学最为活跃的前沿领域之一，而流域水热耦合过程及其对气候与下垫面变化的响应则是其中的一个重要热点研究方向。以SCIE数据库为数据源，借助Thomson Data Analyzer （TDA）、Ucinet和Netdraw等工具，对1992-2015年间关于流域水热耦合过程及其对气候与下垫面变化的响应的研究论文进行数据挖掘，分析表明：（1）近24年来，该领域的论文数量总体呈显著增长态势，依据文章数量、研究主题、机构合作特点等划分为3个阶段：低产出探索期、拓展推进期和高产出活跃期。（2）气候变化、径流、水量平衡、水文模拟、土地利用与覆盖变化等是研究的核心主题，遥感与GIS技术的应用在不断得到加强，人类活动的水文效应及其时程变化等的研究逐渐受到更多的关注。（3）澳大利亚和美国在该研究方向表现突出。中国发文量高，论文质量不断提升。（4）国际合作范围不断扩大。近十年来，中国科学院、清华大学等国内多家科教机构逐渐加强了与国外同行的合作关系，研究工作取得长足进展。 Abstract:Ecohydrology, studying the interactions between hydrological and ecological processes, has become one of most active frontiers in both fields of hydrology and ecology. Coupled water-energy balance (CWEB) at the catchment scale and its responses to changes in climate and land surface conditions are important research area in the interdisciplinary field of eco-hydrology. In this study a bibliometric analysis was conduct to assess current status and future developments in the research area of CWEB. All research papers on the CWEB at the catchment scale published over the period of 1992-2015 were retrieved from the Science Citation Index Expanded (SCIE) database and analyzed by means of TDA (Thomson Data Analyzer), Ucinet and Netdraw. Results of the bibliometric analysis shown that:(1) the number of publications has increased greatly in the past 24 years. Research on the CWEB can be classified into three stages in terms of the number of papers published every year, research subjects and cooperation features, i.e. groping & low yield stage, expansion stage and active & high yield stage. (2) Core themes of the research are climate change, runoff, water balance, hydrological modelling, land use and land cover change, etc. The application of remote sensing and GIS technology has been continuously strengthened. More and more attentions have been paid to the study of hydrological effects of human activities and their temporal variability. (3) High impact publications were largely from Australia and American. There was a significant increase in publications from China in term of both quantity and quality. (4) The extent of international cooperation was expanding. In the past 10 years, Chinese Academy of Sciences, Tsinghua University and many other institutions in China have gradually strengthened the cooperation with a number of international scientific and educational institutions and have made great progresses in the research area of CWEB. 参考文献 相似文献 引证文献

Characteristics of Dust Outbreaks and their Relation to Strong Wind and Land Surface Conditions in the Gobi Desert and Northern China, 1999-2013

Characteristics of Dust Outbreaks and their Relation to Strong Wind and Land Surface Conditions in the Gobi Desert and Northern China, 1999-2013

Potential predictability sources of the 2012 U.S. drought in observations and a regional model ensemble

AbstractThe 2012 drought was the most severe and extensive summertime U.S. drought in half a century with substantial economic loss and impacts on food security and commodity prices. A unique aspect of the 2012 drought was its rapid onset and intensification over the Southern Rockies, extending to the Great Plains during late spring and early summer, and the absence of known precursor large‐scale patterns. Drought prediction therefore remains a major challenge. This study evaluates relationships among snow, soil moisture, and precipitation to identify sources of potential predictability of the 2012 summer drought using observations and a Weather Research and Forecasting model multiphysics ensemble experiment. Although underestimated in intensity, the drought signal is robust to the way atmospheric physical processes are represented in the model. For the Southern Rockies, soil moisture exhibits stronger persistence than precipitation in observations and the ensemble experiment. Correlations between winter/spring snowmelt and concurrent and following season soil moisture, and between soil moisture and concurrent and following season precipitation, in both observations and the model ensemble, suggest potential predictability beyond 1 and 2 month lead‐time reside in the land surface conditions for apparent flash droughts such as the 2012 drought.

Land’s complex role in climate change

To mitigate climate change at local, regional, and global scales, we must begin to think beyond greenhouse gases.

Effectiveness of Vegetation Index Transformation for Land Use Identifying and Mapping in the Area of Oil palm Plantation based on SPOT-6 Imagery (Case Study: PT.Tunggal Perkasa Plantations, Air Molek, Indragiri Hulu)

The reflection of land surface, atmosphere and vegetation conditions affect the reflectance value of the object is recorded on remote sensing image so that it can affect the outcome of information extraction from remote sensing imagery one multispectral classification. This study aims to assess the ability of the transformation of generic vegetation index (Wide Dynamic Range Vegetation Index), the vegetation index transformation that is capable reducing the influence of the atmosphere (Atmospherically Resistant Vegetation Index), and the transformation of vegetation index that is capable of reducing the influence of the background soil (Second Modified Soil Adjusted Vegetation Index) for the identification and mapping of land use in the oil palm plantation area based on SPOT-6 archived on June 13, 2013 from LAPAN. The study area selected oil palm plantations PT. Tunggal Perkasa Plantations, Air Molek, Indragiri Hulu, Riau Province. The method is using the transformation of the vegetation index ARVI, MSAVI2, and WDRVI. Sample selection method used was stratified random sampling. The test method used mapping accuracy of the confusion matrix. The results showed that the best transformation of the vegetation index for the identification and mapping of land use in the plantation area is ARVI transformation with a total of accuracy is 96%. Accuracy of mapping land use settlements 100%, replanting 82.35%, 81.25% young oil palm, old oil palm 99.46%, 100% bush, body of water 100%, and 100% bare-soil.

Contributions of soil moisture interactions to future precipitation changes in the GLACE-CMIP5 experiment

Changes in soil moisture are likely to contribute to future changes in latent heat flux and various characteristics of daily precipitation. Such contributions during the second half of the twenty-first century are assessed using the simulations from the GLACE-CMIP5 experiment, applying a linear regression analysis to determine the magnitude of these contributions. As characteristics of daily precipitation, mean daily precipitation, the frequency of wet days and the intensity of precipitation on wet days are considered. Also, the frequency and length of extended wet and dry spells are studied. Particular focus is on the regional (for nine selected regions) as well as seasonal variations in the magnitude of the contributions of the projected differences in soil moisture to the future changes in latent heat flux and in the characteristics of daily precipitation. The results reveal the overall tendency that the projected differences in soil moisture contribute to the future changes in response to the anthropogenic climate forcing for all the meteorological variables considered here. These contributions are stronger and more robust (i.e., there are smaller deviations between individual climate models) for the latent heat flux than for the characteristics of daily precipitation. It is also found that the contributions of the differences in soil moisture to the future changes are generally stronger and more robust for the frequency of wet days than for the intensity of daily precipitation. Consistent with the contributions of the projected differences in soil moisture to the future changes in the frequency of wet days, soil moisture generally contributes to the future changes in the characteristics of wet and dry spells. The magnitude of these contributions does not differ systematically between the frequency and the length of such extended spells, but the contributions are generally slightly stronger for dry spells than for wet spells. Distinguishing between the nine selected regions and between the different seasons, it is found that the strength of the contributions of the differences in soil moisture to the future changes in the various meteorological variables varies by region and, in particular, by season. Similarly, the robustness of these contributions varies between the regions and in the course of the year. The importance of soil moisture changes for the future changes in various aspects of daily precipitation and other aspects of the hydrological cycle illustrates the need for a comprehensive and realistic representation of land surface processes and of land surface conditions in climate models.

Seasonal transition of precipitation characteristics associated with land surface conditions in and around Bangladesh

AbstractThis study examined the seasonal transition of precipitation characteristics and its association with land surface conditions in and around Bangladesh, where land surface conditions are predominantly wet. Hourly rain rate data from the Global Satellite Mapping of Precipitation Microwave‐Infrared Combined Product and 10 day soil moisture data from the Advanced Microwave Scanning Radiometer Earth Observing System were used over the 7 years from 2003 to 2009. Area mean values of soil moisture, and precipitation amount, frequency, and intensity were calculated for each 10 day period. Results showed that higher precipitation amount and frequency were observed over the wet soil conditions, which indicates that soil moisture was influenced by previous precipitation events. However, the soil moisture could also control the precipitation characteristics. The seasonal and interannual variations in all regions suggested that precipitation amount and frequency increased in moist soil conditions, which is associated with an increase of water vapor supplied from the moist land surface. Over a flat plain (87°E–91°E, 23°N–25°N), a higher afternoon precipitation intensity was observed over drier land surfaces. This relationship was observed on seasonal and interannual variations. This suggests that the land surface conditions in this region can affect the afternoon precipitation intensity to some extent, although changes of atmospheric conditions can be a major factor particularly for the seasonal changes. However, this relationship was not observed in mountainous regions. This can be explained by other factors, such as thermally induced local circulations by the surrounding topography, being stronger than the impact of land surface conditions.

Disaggregation of Low-Resolution L-Band Radiometry Using C-Band Radar Data

For Earth observation data to be useful for a wide range of land surface applications, a kilometer or finer resolution is required. Unfortunately, passive microwave observations at low microwave frequencies (1–10 GHz), already providing important information on soil moisture and vegetation dynamics, are generally only available at a resolution of tens of kilometers. This letter presents a new downscaling method relating L-band radiometer and C-band radar observations for downscaling purposes. The data were obtained from two extensive airborne field experiments across a 80 000-km2 catchment in south-eastern Australia and coinciding Envisat Advanced Synthetic Aperture Radar acquisitions, performed during the Austral summer and spring of 2010. The novel approach of this study is in the downscaling of coarse-scale emissivities as observed by the radiometer with a new interpretation of the change detection methodology for the radar signal to relate the spatiotemporal changes of those two types of observations at 1 km. It is shown that, for most land surface conditions, a good spatial representation at high resolution is achieved, without considering land surface specific parameterizations, which is promising for using very high resolution radar data from the Sentinel-1 platform for downscaling of passive microwave data from current missions, such as National Aeronautics and Space Administration's Soil Moisture Active Passive and European Space Agency's SMOS.

Combining Field and Imaging Spectroscopy to Map Soil Organic Carbon in a Semiarid Environment

Semiarid regions are especially vulnerable to climate change and human-induced land-use changes and are of major importance in the context of necessary carbon sequestration and ongoing land degradation. Topsoil properties, such as soil carbon content, provide valuable indicators to these processes, and can be mapped using imaging spectroscopy (IS). In semiarid regions, this poses difficulties because models are needed that can cope with varying land surface and soil conditions, consider a partial vegetation coverage, and deal with usually low soil organic carbon (SOC) contents. We present an approach that aims at addressing these difficulties by using a combination of field and IS to map SOC in an extensively used semiarid ecosystem. In hyperspectral imagery of the HyMap sensor, the influence of nonsoil materials, i.e., vegetation, on the spectral signature of soil dominated image pixels was reduced and a residual soil signature was calculated. The proposed approach allowed this procedure up to a vegetation coverage of 40% clearly extending the mapping capability. SOC quantities are predicted by applying a spectral feature-based SOC prediction model to image data of residual soil spectra. With this approach, we could significantly increase the spatial extent for which SOC could be predicted with a minimal influence of a vegetation signal compared to previous approaches where the considered area was limited to a maximum of, e.g., 10% vegetation coverage. As a regional example, the approach was applied to a <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">${320\;\text{km}}^{2}$</tex-math> </inline-formula> area in the Albany Thicket Biome, South Africa, where land cover and land-use changes have occurred due to decades of unsustainable land management. In the generated maps, spatial SOC patterns were interpreted and linked to geomorphic features and land surface processes, i.e., areas of soil erosion. It was found that the chosen approach supported the extraction of soil-related spectral image information in the semiarid region with highly varying land cover. However, the quantitative prediction of SOC contents revealed a lack in absolute accuracy.

A Preliminary Evaluation of the SMAP Radiometer Soil Moisture Product Over United States and Europe Using Ground-Based Measurements

The Soil Moisture Active Passive (SMAP) mission, which is the newest L-band satellite that is specifically designed for soil moisture monitoring, was launched on January 31, 2015. A beta quality version of the SMAP radiometer soil moisture product was recently released to the public. It is crucial to evaluate the reliability of this product before it can be routinely used in hydrometeorological studies at a global scale. In this paper, we carried out a preliminary evaluation of the SMAP radiometer soil moisture product against in situ measurements collected from three networks that cover different climatic and land surface conditions, including two dense networks established in the U.S. and Finland, and one sparse network set up in Romania. Results show that the SMAP soil moisture product is in good agreement with the in situ measurements, although it exhibits dry or wet bias at different network regions. It well reproduces the temporal evolution and anomalies of the observed soil moisture with a favorable correlation greater than 0.7. The overall ubRMSE (unbiased root mean square error) of SMAP product is 0.036 m3 $\cdot$ m−3, well within the mission requirement of 0.04 m3 $\cdot$ m−3. The error sources of SMAP soil moisture product may be associated with the parameterization of vegetation and surface roughness but still needs to be tested and confirmed in more extent. Considering that the algorithms are still under refinement, it can be reasonably expected that hydrometeorological applications will benefit from the SMAP radiometer soil moisture product.

Implementation of satellite based fractional water cover indices in the pan-Arctic region using AMSR-E and MODIS

Abstract A new method to assess land surface fractional open water (fw) inundation dynamics was developed using high frequency (89 GHz) brightness temperatures (Tb) from the Advanced Microwave Scanning Radiometer for EOS (AMSR-E), with other ancillary inputs from AMSR-E and MODIS (Moderate Resolution Imaging Spectroradiometer). The resulting 89 GHz fw (fwWBand) retrievals provide 10-day temporal fidelity and enhanced (5 km) resolution relative to other passive microwave sensor based land observations. A look-up table was first established to provide reference microwave emissivities for water and land endmembers under a range of atmosphere and land surface conditions. The fwWBand retrievals were then obtained on a per pixel basis using difference ratio and double difference methods for respective barren and vegetated soil conditions. The resulting fwWBand summer (JJA) seasonal composites correspond favorably (R ≥ 0.86, p