Land Surface Types Research Articles

Assessment and synergy analysis of outdoor thermal comfort and thermal infrared remote sensing for urban heat island studies

The aim of this research is to explore the potentialities and limits of the integration of remote sensing biophysical data (land surface temperature) in the outdoor thermal comfort studies. Accordingly, by examining correlations between land surface temperature and air temperature, and using respectively remote sensing satellite data MODIS and different weather stations archives alongside questionnaire surveys. Currently, the parameters of thermal comfort indices are usually calculated using the data from one, or few permanent or portable ground-based weather stations. Due to the lack of adequate distribution of weather stations, those calculations generally do not accurately represent the alteration of thermal comfort, through time and space. Nevertheless, it has been essentially proved that despite strong tendencies between in-situ measured parameters and remotely sensed ones, various elements need to be studied (e.g., location, land surface type, vegetation, and elevation). Finally, preliminary results confirm that the proposed linear approaches are providing considerable and promising performance suitable for future specific situations and studies purposes.

Evaluation of Precipitable Water Vapor Product From MODIS and MERSI-II NIR Channels Using Ground- Based GPS Measurements Over Australia

We performed a thorough validation of the precipitable water vapor (PWV) products from near-infrared bands of very similar instruments, i.e., Moderate Resolution Imaging Spectroradiometer (MODIS) and advanced Medium Resolution Spectral Imager (MERSI-II). The PWV products validated are those derived from MODIS/Aqua, MODIS/Terra, and MERSI-II/FY-3D sensors. The PWV data from global positioning system (GPS) in 453 in situ sites situated in the interior and coastal areas of Australia from June 1, 2019 to May 31, 2020 were utilized as reference values. The accuracy of the satellite PWV products was studied under different weather conditions. The evaluation results show that all the three PWV products did not provide good quality water vapor observations in the presence of clouds, with a correlation below 0.2 and a root-mean-square error (RMSE) above 10 mm. Under confident clear sky conditions, MERSI-II/FY-3D instrument had the highest retrieval accuracy with an RMSE of 2.801 mm, but had the worst correlation with reference GPS PWV ( <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">R</i> <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> = 0.841). On the contrary, MODIS/Terra instrument had the lowest retrieval accuracy (RMSE = 4.903 mm), but had the best correlation with a correlation of 0.903. Both MODIS/Aqua and MODIS/Terra instruments tended to overestimate PWV value, whereas the MERSI-II/FY-3D instrument tended to underestimate PWV value. All PWV data records showed better retrieval accuracy with higher correlation and lower RMSE under the dry condition than under the wet condition. The performance analysis of all the three satellite PWV products was also studied per day, per station, per season, and per land-surface type in this article.

Modeling Thermal Environment Responses to Terrain and Urbanization of Metropolis

Thermal environment responses to terrain and urbanization of Beijing, a metropolis of China, were simulated by the Weather Research and Forecasting model (WRF) with the single-layer Urban Canopy model (UCM). Four numerical experiments were designed: (1) the control experiment (Control exp), (2) no-terrain sensitive experiment (No-Terrain exp), (3) no-urban surface sensitive experiment (No-Urban exp), and (4) real land surface sensitive experiment (LU exp). Results indicate that better near-surface air temperature simulations were obtained after using MODIS land surface type product data MCD12Q1. The foehn effect due to northwestern mountains in Beijing were simulated to intensify the high temperature intensity in the plain area. Urban heat island effect existed in this area day and night, and the heat island intensity in the nighttime was obviously greater than that in the daytime. As a result of urbanization, the nighttime temperature had risen by 1.5∼3.5 °C and the expansion range of nighttime high temperature areas was basically consistent with the urban expansion range.

ANALYSIS OF TEMPERATURE REGIME OF LAND SURFACE FOR BYSTRYTSIA RIVER BASIN AND THE INFLUENCE OF TERRAIN ATTRIBUTES USING LANDSAT 8 DATA

Terrain morphology is a powerful factor influencing climate characteristics, which manifests itself at various scale levels. At the detailed scale, this effect is mainly due to the redistribution of solar radiation on surfaces and slopes of different slope and aspect, which causes their unequal heating, and local redistribution of air masses. Spatial distribution of land surface temperatures can be effectively studied using remote sensing data in far-infrared range, which can be recalculated into temperature values. The values of the land surface temperature in the Bystrytsia river basin were calculated with far-infrared channels of Landsat spatial images for three time slices: October 5, 2013, February 13, 2015, August 10, 2016. The statistical analysis has been carried out on the impact of terrain morphometric parameters and land surface type and characteristics on its temperature. To determine the relative influence of each specific factor, the method of hierarchical partitioning has been implemented with the hier.part package of R software environment. Significant seasonal differentiation of the influence of individual factors on temperature was revealed. During all the seasons of the year, absolute height appeared as the most significant factor among those analyzed. The influence of absolute height on temperature distribution was the strongest in autumn, somewhat weaker in winter and the weakest in summer. Artificial surfaces, dry grass and soil were heated more strongly despite their lower albedo due to smaller heat consumption by evaporation, whereas vegetation surface and wet soils were less heated. On the other hand, the influence of relative surface insolation, being differentiated by terrain elements appeared to be relatively weaker, which can be explained by the calculation method used (insolation has been calculated for the moment the images were taken, whereas surface heating takes some time). The influence of the level of surface moisture also appeared to be significant. Key words: land surface temperature, Landsat, Bystrytsia, morphometric parameters, hierarchical partitioning.

Downscaling MODIS spectral bands using deep learning

ABSTRACT MODIS sensors are widely used in a broad range of environmental studies, many of which involve joint analysis of multiple MODIS spectral bands acquired at disparate spatial resolutions. To extract land surface information from multi-resolution MODIS spectral bands, existing studies often downscale lower resolution (LR) bands to match the higher resolution (HR) bands based on simple interpolation or more advanced statistical modeling. Statistical downscaling methods rely on the functional relationship between the LR spectral bands and HR spatial information, which may vary across different land surface types, making statistical downscaling methods less robust. In this paper, we propose an alternative approach based on deep learning to downscale 500 m and 1000 m spectral bands of MODIS to 250 m without additional spatial information. We employ a superresolution architecture based on an encoder decoder network. This deep learning-based method uses a custom loss function and a self-attention layer to preserve local and global spatial relationships of the predictions. We compare our approach with a statistical method specifically developed for downscaling MODIS spectral bands, an interpolation method widely used for downscaling multi-resolution spectral bands, and a deep learning superresolution architecture previously used for downscaling satellite imagery. Results show that our deep learning method outperforms on almost all spectral bands both quantitatively and qualitatively. In particular, our deep learning-based method performs very well on the thermal bands due to the larger scale difference between the input and target resolution. This study demonstrates that our proposed deep learning-based downscaling method can maintain the spatial and spectral fidelity of satellite images and contribute to the integration and enhancement of multi-resolution satellite imagery.

Investigation of temporal baseline effect on DEMs derived from COSMO Sky-Med data

Digital elevation models (DEM) are indispensable elements of sensitive earth science studies. It is important the production and usage of DEMs. The science of remote sensing offers scientists an important source of data on this subject. Radar data, which is an active remote sensing system, has an important capacity in this regard. DEM production using InSAR data has been widely used in the literature in the last decade. The temporal baseline parameter, which is an important factor in data generation from InSAR pairs, also affects the final products. In this study, it is aimed to examine the usability of these data by producing short (4days), medium (84 days) and long (440 days) baseline DEMs using InSAR pairs of COSMO Sky-Med satellite. At the same time, photogrammetric DEMs were produced with unmanned aerial vehicles (UAV) in selected pilot areas. The DEMs produced were evaluated in 4 land surface types, namely plain-bare, agricultural, urban and rugged area. In addition, by performing statistical analyzes such as RMSE, MAE, the accuracy of the produced DEMs compared to the DEMs produced with UAV was examined. The results showed that short and medium baseline data give more accurate results than long baseline InSAR pairs. Increasing the temporal baseline, increases the amount of error in the DEMs produced. Also, the effect of land surface types on the produced DEMs was revealed in the results of the study.

Satellite Retrieval of Microwave Land Surface Emissivity under Clear and Cloudy Skies in China Using Observations from AMSR-E and MODIS

Microwave land surface emissivity (MLSE) is an important geophysical parameter to determine the microwave radiative transfer over land and has broad applications in satellite remote sensing of atmospheric parameters (e.g., precipitation, cloud properties), land surface parameters (e.g., soil moisture, vegetation properties), and the parameters of interactions between atmosphere and terrestrial ecosystem (e.g., evapotranspiration rate, gross primary production rate). In this study, MLSE in China under both clear and cloudy sky conditions was retrieved using satellite passive microwave measurements from Aqua Advanced Microwave Scanning Radiometer-Earth Observing System (AMSR-E), combined with visible/infrared observations from Aqua Moderate Resolution Imaging Spectroradiometer (MODIS), and the European Centre for Medium-Range Weather Forecasts (ECMWF) atmosphere reanalysis dataset of ERA-20C. Attenuations from atmospheric oxygen and water vapor, as well as the emissions and scatterings from cloud particles are taken into account using a microwave radiation transfer model to do atmosphere corrections. All cloud parameters needed are derived from MODIS visible and infrared instantaneous measurements. Ancillary surface skin temperature as well as atmospheric temperature-humidity profiles are collected from ECMWF reanalysis data. Quality control and sensitivity analyses were conducted for the input variables of surface skin temperature, air temperature, and atmospheric humidity. The ground-based validations show acceptable biases of primary input parameters (skin temperature, 2 m air temperature, near surface relative humidity, rain flag) for retrieving using. The subsequent sensitivity tests suggest that 10 K bias of skin temperature or observed brightness temperature may result in a 4% (~0.04) or 7% (0.07) retrieving error in MLSE at 23.5 GHz. A nonlinear sensitivity in the same magnitude is found for air temperature perturbation, while the sensitivity is less than 1% for 300 g/m2 error in cloud water path. Results show that our algorithm can successfully retrieve MLSE over 90% of the satellite detected land surface area in a typical cloudy day (cloud fraction of 64%), which is considerably higher than that of the 29% area by the clear-sky only algorithms. The spatial distribution of MLSE in China is highly dependent on the land surface types and topography. The retrieved MLSE is assessed by compared with other existing clear-sky AMSR-E emissivity products and the vegetation optical depth (VOD) product. Overall, high consistencies are shown for the MLSE retrieved in this study with other AMSR-E emissivity products across China though noticeable discrepancies are observed in Tibetan Plateau and Qinling-Taihang Mountains due to different sources of input skin temperature. In addition, the retrieved MLSE exhibits strong positive correlations in spatial patterns with microwave vegetation optical depth reported in the literature.

A novel‐optimal monitoring model of rocky desertification based on feature space models with typical surface parameters derived from LANDSAT_8 OLI

AbstractPrevious studies monitoring the spatial distribution of rocky desertification have used single indices, a comprehensive index, or image classification. However, these approaches could not distinguish between the degrees of rocky desertification as they did not consider various influencing factors and their interactions. To avoid the above shortcomings, this study used the feature space model and seven typical land surface parameters to establish two categories of rocky desertification model: (1) a point‐to‐point model; and (2) a point‐to‐line model. A novel model for the optimal monitoring of rocky desertification was then proposed, which could take the comprehensive impacts of the human‐nature system on the process of rocky desertification. The results showed that: (1) the feature space models provided a novel approach to large‐scale monitoring of rocky desertification; (2) the point‐to‐line model incorporating the rock bare index (RBI)‐dryness index feature space showed the optimal applicability for monitoring of rocky desertification, with a precision of 93.4%; and (3) the RBI performed the best in indicating the process of rocky desertification, with an average precision of 88.5%. The results of this study can act as a reference within the investigation of the spatiotemporal evolution of rocky desertification for karst mountain areas.

Spatial Distribution of Atmospheric Mercury Species in the Southern Ocean

AbstractAntarctica and the surrounding Southern Ocean act as an important sink in the global mercury cycle; however, corresponding studies of atmospheric mercury species in these regions are still scarce. Here, we report large‐scale observations of atmospheric gaseous elemental mercury (GEM) and gaseous oxidized mercury (GOM) in the Antarctic marine boundary layer (MBL) taken during a summer cruise. There is large variability in the spatial distribution of GOM, which is likely attributable to the diverse land surface types, including coastal Antarctica, sea‐ice region, and oceanic region, along the cruise route. In coastal Antarctica, the highest GOM level (56.23 ± 47.74 pg·m−3) might be attributed to the significant in‐situ oxidation there. Significant in‐situ oxidation of GEM could also occur in the sea‐ice region, causing the significant increase of GOM (up to 87.01 pg·m−3), while the uptake of the high content of sea‐salt aerosols in sea‐ice regions might efficiently eliminate GOM in the air, resulting in generally lower content of GOM (13.10 ± 14.48 pg·m−3) in the sea‐ice region. In the oceanic region, the lowest level of GOM (3.95 ± 4.69 pg·m−3) is due to both the uptake of sea‐salt aerosols and the seasonal melting of first‐year sea‐ice. This study provides insight to understand the mechanisms of the atmospheric mercury cycle in various land surface types in the Antarctic MBL.

Hydrostreamer v1.0 – improved streamflow predictions for local applications from an ensemble of downscaled global runoff products

Abstract. An increasing number of different types of hydrological, land surface, and rainfall–runoff models exist to estimate streamflow in river networks. Results from various model runs from global to local scales are readily available online. However, the usability of these products is often limited, as they often come aggregated in spatial units which are not compatible with the desired analysis purpose. We present here an R package, a software library Hydrostreamer v1.0, which aims to improve the usability of existing runoff products by addressing the modifiable area unit problem and allows non-experts with little knowledge of hydrology-specific modelling issues and methods to use them for their analyses. Hydrostreamer workflow includes (1) interpolation from source zones to target zones, (2) river routing, and (3) data assimilation via model averaging, given multiple input runoff and observation data. The software implements advanced areal interpolation methods and area-to-line interpolation not available in other products and is the first R package to provide vector-based routing. Hydrostreamer is kept as simple as possible – intuitive with minimal data requirements – and minimises the need for calibration. We tested the performance of Hydrostreamer by downscaling freely available coarse-resolution global runoff products from the Inter-Sectoral Impact Model Intercomparison Project (ISIMIP) in an application in 3S Basin in Southeast Asia. Results are compared to observed discharges as well as two benchmark streamflow data products, finding comparable or improved performance. Hydrostreamer v1.0 is open source and is available from http://github.com/mkkallio/hydrostreamer/ (last access: 5 May 2021) under the MIT licence.

Preflight Radiometric Calibration of TIS Sensor Onboard SDG-1 Satellite and Estimation of Its LST Retrieval Ability

The thermal Infrared Spectrometer (TIS) is the thermal infrared (TIR) sensor on-board the first Sustainable Development Goals (SDG-1) satellite. The TIS data can potentially be used to support improved monitoring of ground conditions with high-spatial resolutions, so accurate radiometric calibration is required. A meticulous radiometric calibration was conducted on the prototype of TIS to test its ability to convert a raw digital number (DN) to at-aperture radiance. The initial maximum radiometric error was 2.19 K at 300 K for Band 1(B1) and the minimum radiometric error was 0.25 K at 300 K rooted in Band 3 (B3). The R-Squared (R2) was over 0.99 for each band. The methodology was refined to divide the channel detectable temperature range into three sub-ranges and then the maximum radiometric errors were reduced to less than 1 K at 300 K for three bands. Subsequently, the Generalized Split-Window (SW) algorithm was preformed to estimate the ability of TIS on land surface temperature (LST) retrieval. In order to take advantage of its high-spatial resolution and make full use of TIR data, three-channel SW algorithm was also performed for intercomparison. Results showed that the SW algorithm can obtain LST with root-mean-square error (RMSE) less than 1K. Compared with two-channel algorithm with RMSE = 0.94 K, three-channel algorithm achieves better results in retrieving LST with RMSE = 0.82 K. For different land surface types, water samples achieved the minimum RMSE, and for different atmospheric column water vapor (CWV), dry atmospheres obtained better results. The sensitivity analysis of SW algorithm was considered along with noise-equivalent differential temperature (NEΔT), uncertainty of land surface emissivity (LSE) and input land surface temperature (Ts). Generally, three-channel algorithm was more stable to LSE uncertainties, and the error changes were within 40%. But when NEΔT and Ts uncertainties were included, the error percentage of three-channel SW method increases more, which means three-channel SW method is more sensitive to those two factors. All in all, the methodology and results used for radiometric calibration and LST retrieval in this study provide valuable guidance for the flight model of TIS and post-launch applications.

SCATSAT-1 backscattering coefficient over distinct land surfaces and its dependence on soil moisture and vegetation dynamics

ABSTRACT The objective of this study is to examine the potential of SCATSAT-1 data for estimating the soil moisture and vegetation over the continental regions by evaluating the variabilities in backscattering coefficients measured over different land surface types. The SCATSAT-1 is Ku-band wind Scatterometer, which provides backscattering coefficient measurements over the globe in HH and VV polarization at fixed incidence angles. Variabilities in the backscatter coefficients over different land surfaces, mainly over four different geo-climatological regions of India, are studied based on 3 years of SCATSAT-1 dataset in conjunction with other collocated measurements. The SCATSAT-1 backscattering coefficient provides the quantitative measure of the seasonality over a region due to unique distribution of soil moisture and vegetation characteristics of that region. The variabilities in soil moisture and vegetation in these regions are compared and applicability of change detection method for estimating the soil moisture over these regions has been analysed. Based on regression analysis on SMOS measured soil moisture and SCATSAT-1 measured backscattering coefficient, a relationship has been established for the soil moisture estimation from the SCATSAT-1 backscattering coefficient.

Description and Demonstration of the Coupled Community Earth System Model v2 - Community Ice Sheet Model v2 (CESM2-CISM2).

Earth system/ice‐sheet coupling is an area of recent, major Earth System Model (ESM) development. This work occurs at the intersection of glaciology and climate science and is motivated by a need for robust projections of sea‐level rise. The Community Ice Sheet Model version 2 (CISM2) is the newest component model of the Community Earth System Model version 2 (CESM2). This study describes the coupling and novel capabilities of the model, including: (1) an advanced energy‐balance‐based surface mass balance calculation in the land component with downscaling via elevation classes; (2) a closed freshwater budget from ice sheet to the ocean from surface runoff, basal melting, and ice discharge; (3) dynamic land surface types; and (4) dynamic atmospheric topography. The Earth system/ice‐sheet coupling is demonstrated in a simulation with an evolving Greenland Ice Sheet (GrIS) under an idealized high CO2 scenario. The model simulates a large expansion of ablation areas (where surface ablation exceeds snow accumulation) and a large increase in surface runoff. This results in an elevated freshwater flux to the ocean, as well as thinning of the ice sheet and area retreat. These GrIS changes result in reduced Greenland surface albedo, changes in the sign and magnitude of sensible and latent heat fluxes, and modified surface roughness and overall ice sheet topography. Representation of these couplings between climate and ice sheets is key for the simulation of ice and climate interactions.

A Robust Atmospheric Correction Procedure for Determination of Spectral Reflectance of Terrestrial Surfaces from Satellite Spectral Measurements

In this work, we propose simple and robust technique for the retrieval of underlying surface spectral reflectance using spaceborne observations. It can be used to process both multispectral moderate resolution satellite data and also multi-zone high spatial resolution data. The technique can work automatically for different types of land surfaces without using huge databases accumulated in advance. The new cloud discrimination and retrieval of the water vapor content in atmosphere procedures are presented. The key point of the proposed atmospheric correction technique is the suggested single-wavelength method for determining the atmospheric aerosol optical thickness without reference to a specific type of underlying surface spectrum. The retrievals of spectral reflectance for various land surfaces with developed technique, performed using computer simulation and experimental data, have demonstrated a high retrieval accuracy.

Evaluation of the AVHRR surface reflectance long term data record between 1984 and 2011

The long-term data record (LTDR) from the Advanced Very High-Resolution Radiometer (AVHRR) provides daily surface reflectance with global coverage from the 1980s to present day, making it a unique source of information for the study of land surface properties and their long-term dynamics. Surface reflectance is a critical input for the generation of products such as vegetation indices, albedo, and land cover. Therefore, it is of utmost importance to quantify its uncertainties to better understand how they might propagate into downstream products. Due to the prolonged length of the surface reflectance LTDR and previous unavailability of a well calibrated reference, no comprehensive evaluation of the complete record has been reported so far. Recently, the United States Geological Survey (USGS) began production of surface reflectance datasets from the Landsat 4–8 satellites, which provide a suitable reference against which the LTDR can be compared to. In this study, we evaluate the LTDRV5 between 1984 and 2011 using surface reflectance data from the Landsat-5 Thematic Mapper (TM5) Collection-1 as a reference. Data from TM5 was obtained from over 740,000 globally distributed scenes which gave a representative set of land surface types and atmospheric conditions. Differences due to observation geometry were accounted for using the Vermote-Justice-Breon (VJB) Bidirectional Reflectance Distribution Function (BRDF) normalization method to adjust the AVHRR surface reflectance to TM5 observation conditions; the spectral response differences were minimized using spectral band adjustment factors (SBAFs) derived from the Earth Observing One (EO-1) Hyperion atmospherically corrected hyperspectral spectra. The performance of the AVHRR record is reported in terms of the accuracy, precision, and uncertainty (APU). Results show that the LTDR performance is close or within the combined uncertainty specification of 0.071ρ + 0.0071, where ρ is the estimated reflectance.

Synthesis of global actual evapotranspiration from 1982 to 2019

Abstract. As a linkage among water, energy, and carbon cycles, global actual evapotranspiration (ET) plays an essential role in agriculture, water resource management, and climate change. Although it is difficult to estimate ET over a large scale and for a long time, there are several global ET datasets available with uncertainty associated with various assumptions regarding their algorithms, parameters, and inputs. In this study, we propose a long-term synthesized ET product at a kilometer spatial resolution and monthly temporal resolution from 1982 to 2019. Through a site-pixel evaluation of 12 global ET products over different time periods, land surface types, and conditions, the high-performing products were selected for the synthesis of the new dataset using a high-quality flux eddy covariance (EC) covering the entire globe. According to the study results, Penman–Monteith–Leuning (PML), the operational Simplified Surface Energy Balance (SSEBop), the Moderate Resolution Imaging Spectroradiometer (MODIS, MOD16A2105), and the Numerical Terradynamic Simulation Group (NTSG) ET products were chosen to create the synthesized ET set. The proposed product agreed well with flux EC ET over most of the all comparison levels, with a maximum relative mean error (RME) of 13.94 mm (17.13 %) and a maximum relative root mean square error (RRMSE) of 38.61 mm (47.45 %). Furthermore, the product performed better than local ET products over China, the United States, and the African continent and presented an ET estimation across all land cover classes. While no product can perform best in all cases, the proposed ET can be used without looking at other datasets and performing further assessments. Data are available on the Harvard Dataverse public repository through the following Digital Object Identifier (DOI): https://doi.org/10.7910/DVN/ZGOUED (Elnashar et al., 2020), as well as on the Google Earth Engine (GEE) application through this link: https://elnashar.users.earthengine.app/view/synthesizedet (last access: 21 January 2021).

Averaging over spatiotemporal heterogeneity substantially biases evapotranspiration rates in a mechanistic large-scale land evaporation model

Abstract. Evapotranspiration (ET) influences land–climate interactions, regulates the hydrological cycle, and contributes to the Earth's energy balance. Due to its feedback to large-scale hydrological processes and its impact on atmospheric dynamics, ET is one of the drivers of droughts and heatwaves. Existing land surface models differ substantially, both in their estimates of current ET fluxes and in their projections of how ET will evolve in the future. Any bias in estimated ET fluxes will affect the partitioning between sensible and latent heat and thus alter model predictions of temperature and precipitation. One potential source of bias is the so-called “aggregation bias” that arises whenever nonlinear processes, such as those that regulate ET fluxes, are modeled using averages of heterogeneous inputs. Here we demonstrate a general mathematical approach to quantifying and correcting for this aggregation bias, using the GLEAM land evaporation model as a relatively simple example. We demonstrate that this aggregation bias can lead to substantial overestimates in ET fluxes in a typical large-scale land surface model when sub-grid heterogeneities in land surface properties are averaged out. Using Switzerland as a test case, we examine the scale dependence of this aggregation bias and show that it can lead to an average overestimation of daily ET fluxes by as much as 10 % across the whole country (calculated as the median of the daily bias over the growing season). We show how our approach can be used to identify the dominant drivers of aggregation bias and to estimate sub-grid closure relationships that can correct for aggregation biases in ET estimates, without explicitly representing sub-grid heterogeneities in large-scale land surface models.

Improving Satellite Waveform Altimetry Measurements With a Probabilistic Relaxation Algorithm

The Geoscience Laser Altimeter System onboard the NASA Ice, Cloud, and land Elevation Satellite (ICESat/GLAS) provided elevation measurements of Earth’s surface between 2003 and 2009. The centroid and maximum-amplitude-peak (MAP) retracking methods have been designed and applied to process the returned laser waveforms for elevation measurements. Although these two methods work well in general, they may generate erroneous measurements when the returned waveform was complicated by adverse atmospheric conditions (clouds, ice fogs, blowing snow, and dust storms). The centroid retracking method is often more severely affected when compared with the MAP retracking method. In this study, we present a new retracking method that exploits the spatial contextual information from neighboring footprints along the satellite ground track, in addition to the single return waveform shape information. Our method uses a probabilistic relaxation (PR) algorithm to integrate the spatial contextual information and the waveform shape information to identify the waveform peak that most likely represents the true surface elevation, rather than simply detecting the peak with the maximum magnitude. For different types of land surfaces, such as inland lakes, polar tundra, ice sheet, and sand deserts, we demonstrate that our new PR retracking method is able to produce more reliable, consistent, and accurate elevation measurements than the standard NASA ICESat/GLAS data products. The root mean squares error (RMSE) is reduced from 0.85 to 0.17 m for inland lake, from 0.81 to 0.23 m for polar tundra, from 1.25 to 0.33 m for ice sheet, and from 2.48 to 2.34 m for sand desert.

Improved evaluation method of the soil wind erosion intensity based on the cloud–AHP model under the stress of global climate change

Under the stress of global climate change, soil wind erosion has become a major environmental issue in the Three-River Source Region (TRSR) of China. However, few large-scale studies have been conducted on soil wind erosion owing to the lack of investigational data or complex parameters. Moreover, the uncertainty and randomness in the weight determination process cannot be avoided using the traditional method. Thus, a cloud–analytic hierarchy process (cloud–AHP) model was proposed to construct a wind erosion intensity index model for the TRSR based on seven typical land surface parameters. The following results were obtained. (1) The cloud–AHP model can better eliminate the randomness and uncertainty in the weight determination process. (2) The proposed evaluation method of wind erosion intensity has better applicability in the TRSR with overall accuracy of 93%. (3) The overall wind erosion intensity in this region is moderate. The wind erosion intensity was the largest in the Yangtze River (0.55, moderate erosion) and smallest in the source region of the Lancang River (0.50, mild erosion). (4) Significant differences are observed in the influences of various vegetation types on wind erosion intensity. Bare land exhibits the highest wind erosion intensity, whereas a coniferous forest exhibits the smallest. Moreover, grassland is a key control zone of soil and water conservation because it has the largest spatial heterogeneity of internal erosion intensity. These results can provide data and technical support for preventing and controlling soil erosion and protecting the environment in the region.

Effects of an oasis protective system on aeolian sediment deposition: a case study from Gelintan oasis, southeastern edge of the Tengger Desert, China

Desert-oasis ecotones are boundary areas between oases and desert ecosystems. Large efforts to control sediment and stabilize these boundaries depend on understanding sedimentary processes, especially aeolian transport and deposition. Previous studies on aeolian sediment deposition have focused primarily on a single land surface type or a single engineering approach. Few studies have considered deposition in a multi-layer oasis protective system. A complete oasis protective system consists of an outer bare sand area, a sand barrier zone, a shrub and herbaceous plant zone, and a farmland shelter zone. This study used sedimentary analysis to quantify grain-size characteristics in samples from the four land surfaces under different types of weather conditions in the Gelintan oasis of the Tengger Desert, the fourth largest desert in China. The results showed that aeolian sediment deposition decreased from the outer bare sand area through the oasis protective system and into the interior. The four land surface types showed significant differences in deposition volume (P < 0.05). Deposited sediment showed gradual decrease in dominant grain-size from sand to silt, but sediment deposited during dust weather contained a larger coarse-grained fraction. From the outer desert to the inner oasis, transport mechanisms shifted from saltation (sand) to suspension (silt and smaller) in non-dust weather. During dust weather, deposition primarily occurs from near-surface aeolian sand transport with saltation. Sediment sorting decreased from exterior to interior zones of the protective system while skewness and kurtosis showed no significant change (P < 0.05). These results can help inform strategies for stabilizing and protecting desert-oasis ecotones in this region and other localities.