Soil texture mapping over low relief areas using land surface feedback dynamic patterns extracted from MODIS

Geoderma ◽  
2012 ◽  
Vol 171-172 ◽  
pp. 44-52 ◽  
Author(s):  
Feng Liu ◽  
Xiaoyuan Geng ◽  
A-Xing Zhu ◽  
Walter Fraser ◽  
Arnie Waddell
Keyword(s):  
Soil Texture ◽  
Land Surface ◽  
Texture Mapping ◽  
Dynamic Patterns

Accounting for soil structure in pedo-transfer functions: swelling vs non swelling clays

2021 ◽  
Author(s):  
Salma Tafasca ◽  
Agnès Ducharne ◽  
Christian Valentin
Keyword(s):  
Soil Texture ◽  
Land Surface ◽  
Transfer Functions ◽  
Land Surface Model ◽  
Texture Mapping ◽  
Tropical Soils ◽  
Global Scale ◽  
Pedotransfer Functions ◽  
Surface Model ◽  
Swelling Clays

<p>Tropical clay Oxisols have a strong granular structure, as opposed to other clay-textured soils, such as Vertisols. Their highly aggregated structure favors infiltration and drainage, while Vertisols favor runoff, due to their high content of swelling clays. Most global scale pedotransfer functions (PTFs) are derived from soils of temperate regions where Oxisols are absent, which does not allow to represent this type of tropical soils.</p><p>In ORCHIDEE, which is a state-of-the-art land surface model (LSM), soil hydraulic properties are derived from a soil texture map, using PTFs of temperate regions (Carsel and Parrish, 1988), which does not allow to represent tropical clay Oxisols. This has been shown to induce large negative evapotranspiration biases in areas effectively covered by clay Oxisols, and mapped as clay (Tafasca et al., 2020). In order to distinguish the two types of clays of different behavior, we introduce a new soil class to represent clay Oxisols. This is equivalent to splitting the clay soil texture in two sub-classes: clay Oxisols and swelling clay. First, we modify the Reynolds soil texture map (Reynolds et al., 2000) such as to represent clay Oxisols based on the FAO Soil Order Map of the World. Then we obtain the corresponding Van Genuchten parameters based on previous studies from the litterature. We evaluate the new PTFs and the modified soil texture map using the ORCHIDEE LSM.</p><p>Using the new soil texture mapping increases evapotranspiration by 11%, allowing to correct important negative bias in tropical areas. In these zones, the mean annual river discharge decreases, consistently with the observations.</p><p> </p>

scholarly journals A framework for quantifying hydrologic effects of soil structure across scales

2021 ◽  
Vol 2 (1) ◽  
Author(s):  
Sara Bonetti ◽  
Zhongwang Wei ◽  
Dani Or
Keyword(s):  
Soil Texture ◽  
Land Surface ◽  
Soil Structure ◽  
Pedotransfer Functions ◽  
Small Scale ◽  
Spatial Correlations ◽  
Surface Parameterization ◽  
Exchange Processes ◽  
Local Soil ◽  
Spatially Distributed

AbstractEarth system models use soil information to parameterize hard-to-measure soil hydraulic properties based on pedotransfer functions. However, current parameterizations rely on sample-scale information which often does not account for biologically-promoted soil structure and heterogeneities in natural landscapes, which may significantly alter infiltration-runoff and other exchange processes at larger scales. Here we propose a systematic framework to incorporate soil structure corrections into pedotransfer functions, informed by remote-sensing vegetation metrics and local soil texture, and use numerical simulations to investigate their effects on spatially distributed and areal averaged infiltration-runoff partitioning. We demonstrate that small scale soil structure features prominently alter the hydrologic response emerging at larger scales and that upscaled parameterizations must consider spatial correlations between vegetation and soil texture. The proposed framework allows the incorporation of hydrological effects of soil structure with appropriate scale considerations into contemporary pedotransfer functions used for land surface parameterization.

scholarly journals Tiling soil textures for terrestrial ecosystem modelling via clustering analysis: a case study with CLASS-CTEM (version 2.1)

2017 ◽  
Author(s):  
Joe R. Melton ◽  
Reinel Sospedra-Alfonso ◽  
Kelly E. McCusker
Keyword(s):  
Soil Texture ◽  
Land Surface ◽  
Clustering Analysis ◽  
Clustering Algorithms ◽  
Terrestrial Ecosystem ◽  
Grid Cell ◽  
Moisture Stress ◽  
Ecosystem Model ◽  
Global Scale ◽  
Terrestrial Ecosystem Model

Abstract. We investigate the application of clustering algorithms to represent sub-grid scale variability in soil texture for use in a global-scale terrestrial ecosystem model. Our model, the coupled Canadian Land Surface Scheme – Canadian Terrestrial Ecosystem Model (CLASS-CTEM), is typically implemented at a coarse spatial resolution (ca. 2.8° × 2.8°) due to its use as the land surface component of the Canadian Earth System Model (CanESM). CLASS-CTEM can, however, be run with tiling of the land surface as a means to represent sub-grid heterogeneity. We first determined that the model was sensitive to tiling of the soil textures via an idealized test case before attempting to cluster soil textures globally. To cluster a high-resolution soil texture dataset onto our coarse model grid, we use two linked algorithms (OPTICS (Ankerst et al., 1999; Daszykowski et al., 2002) and Sander et al. (2003)) to provide tiles of representative soil textures for use as CLASS-CTEM inputs. The clustering process results in, on average, about three tiles per CLASS-CTEM grid cell with most cells having four or less tiles. Results from CLASS-CTEM simulations conducted with the tiled inputs (Cluster) versus those using a simple grid-mean soil texture (Gridmean) show CLASS-CTEM, at least on a global scale, is relatively insensitive to the tiled soil textures, however differences can be large in arid or peatland regions. The Cluster simulation has generally lower soil moisture and lower overall vegetation productivity than the Gridmean simulation except in arid regions where plant productivity increases. In these dry regions, the influence of the tiling is stronger due to the general state of vegetation moisture stress which allows a single tile, whose soil texture retains more plant available water, to yield much higher productivity. Although the use of clustering analysis appears promising as a means to represent sub-grid heterogeneity, soil textures appear to be reasonably represented for global scale simulations using a simple grid-mean value.

scholarly journals Assessing the Impact of Changes in Land Surface Conditions on WRF Predictions in Arid Regions

2020 ◽  
Vol 21 (12) ◽  
pp. 2829-2853 ◽  
Author(s):  
Marouane Temimi ◽  
Ricardo Fonseca ◽  
Narendra Nelli ◽  
Michael Weston ◽  
Mohan Thota ◽  
...  
Keyword(s):  
Eddy Covariance ◽  
Soil Texture ◽  
Land Surface ◽  
Surface Wind ◽  
Surface Fluxes ◽  
Abu Dhabi ◽  
Horizontal Wind ◽  
Cold Bias ◽  
Near Surface ◽  
The Impact

AbstractA thorough evaluation of the Weather Research and Forecasting (WRF) Model is conducted over the United Arab Emirates, for the period September 2017–August 2018. Two simulations are performed: one with the default model settings (control run), and another one (experiment) with an improved representation of soil texture and land use land cover (LULC). The model predictions are evaluated against observations at 35 weather stations, radiosonde profiles at the coastal Abu Dhabi International Airport, and surface fluxes from eddy-covariance measurements at the inland city of Al Ain. It is found that WRF’s cold temperature bias, also present in the forcing data and seen almost exclusively at night, is reduced when the surface and soil properties are updated, by as much as 3.5 K. This arises from the expansion of the urban areas, and the replacement of loamy regions with sand, which has a higher thermal inertia. However, the model continues to overestimate the strength of the near-surface wind at all stations and seasons, typically by 0.5–1.5 m s−1. It is concluded that the albedo of barren/sparsely vegetated regions in WRF (0.380) is higher than that inferred from eddy-covariance observations (0.340), which can also explain the referred cold bias. At the Abu Dhabi site, even though soil texture and LULC are not changed, there is a small but positive effect on the predicted vertical profiles of temperature, humidity, and horizontal wind speed, mostly between 950 and 750 hPa, possibly because of differences in vertical mixing.

scholarly journals Utilization of Multi-Temporal Microwave Remote Sensing Data within a Geostatistical Regionalization Approach for the Derivation of Soil Texture

2020 ◽  
Vol 12 (16) ◽  
pp. 2660
Author(s):  
Philip Marzahn ◽  
Swen Meyer
Keyword(s):  
Remote Sensing ◽  
Soil Texture ◽  
Land Surface ◽  
Temporal Frequency ◽  
Management Strategies ◽  
Remote Sensing Data ◽  
Microwave Remote Sensing ◽  
Test Site ◽  
Soil Parameters ◽  
Sensing Data

Land Surface Models (LSM) have become indispensable tools to quantify water and nutrient fluxes in support of land management strategies or the prediction of climate change impacts. However, the utilization of LSM requires soil and vegetation parameters, which are seldom available in high spatial distribution or in an appropriate temporal frequency. As shown in recent studies, the quality of these model input parameters, especially the spatial heterogeneity and temporal variability of soil parameters, has a strong effect on LSM simulations. This paper assesses the potential of microwave remote sensing data for retrieving soil physical properties such as soil texture. Microwave remote sensing is able to penetrate in an imaged media (soil, vegetation), thus being capable of retrieving information beneath such a surface. In this study, airborne remote sensing data acquired at 1.3 GHz and in different polarization is utilized in conjunction with geostatistics to retrieve information about soil texture. The developed approach is validated with in-situ data from different field campaigns carried out over the TERENO test-site “North-Eastern German Lowland Observatorium”. With the proposed approach a high accuracy of the retrieved soil texture with a mean RMSE of 2.42 (Mass-%) could be achieved outperforming classical deterministic and geostatistical approaches.

scholarly journals Comparative Analysis of High-Resolution Soil Moisture Simulations from the Soil, Vegetation, and Snow (SVS) Land Surface Model Using SAR Imagery Over Bare Soil

Water ◽  
2019 ◽  
Vol 11 (3) ◽  
pp. 542 ◽  
Author(s):  
Mohammed Dabboor ◽  
Leqiang Sun ◽  
Marco Carrera ◽  
Matthew Friesen ◽  
Amine Merzouki ◽  
...  
Keyword(s):  
Soil Moisture ◽  
High Resolution ◽  
Soil Texture ◽  
Land Surface ◽  
Goodness Of Fit ◽  
Retrieval Algorithm ◽  
Surface Model ◽  
Land Surface Scheme ◽  
Sar Imagery ◽  
Surface Scheme

Soil moisture is a key variable in Earth systems, controlling the exchange of water andenergy between land and atmosphere. Thus, understanding its spatiotemporal distribution andvariability is important. Environment and Climate Change Canada (ECCC) has developed a newland surface parameterization, named the Soil, Vegetation, and Snow (SVS) scheme. The SVS landsurface scheme features sophisticated parameterizations of hydrological processes, including watertransport through the soil. It has been shown to provide more accurate simulations of the temporaland spatial distribution of soil moisture compared to the current operational land surface scheme.Simulation of high resolution soil moisture at the field scale remains a challenge. In this study, wesimulate soil moisture maps at a spatial resolution of 100 m using the SVS land surface scheme overan experimental site located in Manitoba, Canada. Hourly high resolution soil moisture maps wereproduced between May and November 2015. Simulated soil moisture values were compared withestimated soil moisture values using a hybrid retrieval algorithm developed at Agriculture andAgri-Food Canada (AAFC) for soil moisture estimation using RADARSAT-2 Synthetic ApertureRadar (SAR) imagery. Statistical analysis of the results showed an overall promising performanceof the SVS land surface scheme in simulating soil moisture values at high resolution scale.Investigation of the SVS output was conducted both independently of the soil texture, and as afunction of the soil texture. The SVS model tends to perform slightly better over coarser texturedsoils (sandy loam, fine sand) than finer textured soils (clays). Correlation values of the simulatedSVS soil moisture and the retrieved SAR soil moisture lie between 0.753–0.860 over sand and 0.676-0.865 over clay, with goodness of fit values between 0.567–0.739 and 0.457–0.748, respectively. TheRoot Mean Square Difference (RMSD) values range between 0.058–0.062 over sand and 0.055–0.113over clay, with a maximum absolute bias of 0.049 and 0.094 over sand and clay, respectively. Theunbiased RMSD values lie between 0.038–0.057 over sand and 0.039–0.064 over clay. Furthermore,results show an Index of Agreement (IA) between the simulated and the derived soil moisturealways higher than 0.90.

Developing the soil texture effects on the surface resistance to bare soil based on MOD16 algorithm to estimate evapotranspiration over the Tibetan Plateau

2020 ◽  
Author(s):  
Ling Yuan ◽  
Yaoming Ma ◽  
Xuelong Chen
Keyword(s):  
Tibetan Plateau ◽  
Soil Texture ◽  
Land Surface ◽  
Bare Soil ◽  
The Other ◽  
Accurate Estimation ◽  
Surface Model ◽  
The Tibetan Plateau ◽  
The Mean ◽  
Modified Algorithm

<p>Evapotranspiration (ET), composed of evaporation (ETs) and transpiration (ETc) and intercept water (ETw), plays an indispensable role in the water cycle and energy balance of land surface processes. A more accurate estimation of ET variations is essential for natural hazard monitoring and water resource management. For the cold, arid, and semi-arid regions of the Tibetan Plateau (TP), previous studies often overlooked the decisive role of soil properties in ETs rates. In this paper, an improved algorithm for ETs in bare soil and an optimized parameter for ETc over meadow based on MOD16 model are proposed for the TP. The nonlinear relationship between surface evaporation resistance (r<sub>s</sub><sup>s</sup>) and soil surface hydration state in different soil texture is redefined by ground-based measurements over the TP. Wind speed and vegetation height were integrated to estimate aerodynamic resistance by Yang et al. (2008). The validated value of the mean potential stomatal conductance per unit leaf area (C<sub>L</sub>) is 0.0038m s<sup>-1</sup>. And the algorithm was then compared with the original MOD16 algorithm and a soil water index–based Priestley-Taylor algorithm (SWI–PT). After examining the performance of the three models at 5 grass flux tower sites in different soil texture over the TP, East Asia, and America, the validation results showed that the half-hour estimates from the improved-MOD16 were closer to observations than those of the other models under the all-weather in each site. The average correlation coefficient(R<sup>2</sup>) of the improved-MOD16 model was 0.83, compared with 0.75 in the original MOD16 model and 0.78 in SWI-PT model. The average values of the root mean square error (RMSE) are 35.77W m<sup>-2</sup>, 79.46 W m<sup>-2</sup>, and 73.88W m<sup>-2</sup> respectively. The average values of the mean bias (MB) are -4.08W m<sup>-2</sup>, -52.36W m<sup>-2</sup>, and -11.74 W m<sup>-2</sup> overall sites, respectively. The performance of these algorithms are better achieved on daily (R<sup>2</sup>=0.81, RMSE=17.22W m<sup>-2</sup>, MB=-4.12W m<sup>-2</sup>; R<sup>2</sup>=0.64, RMSE=56.55W m<sup>-2</sup>, MB=-48.74W m<sup>-2</sup>; R2=0.78, RMSE=22.3W m<sup>-2</sup>, MB=-9.82W m<sup>-2</sup>) and monthly (R2=0.93, RMSE=23.35W m<sup>-2</sup>, MB=-2.8W m<sup>-2</sup>; R2=0.86, RMSE=69.11W m<sup>-2</sup>, MB=-39.5W m<sup>-2</sup>; R2=0.79, RMSE=62.8W m<sup>-2</sup>, MB=-9.7W m<sup>-2</sup>) scales. Overall, the results showed that the newly developed MOD16 model captured ET more accurately than the other two models. The comparisons between the modified algorithm and two mainstream methods suggested that the modified algorithm could produce high accuracy ET over the meadow sites and has great potential for land surface model improvements and remote sensing ET promotion for the ET region.</p>

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