Rogier van der Velde

Decadal variations of land surface temperature anomalies observed over the Tibetan Plateau by the Special Sensor Microwave Imager (SSM/I) from 1987 to 2008

In this paper, we analyze the standardized anomalies of land surface temperature (LST) retrieved from the Special Sensor Microwave Imager (SSM/I) vertically polarized 37 GHz (

SMAP soil moisture drying more rapid than observed in situ following rainfall events

T^v_{B,37~{\rm GHz}}

Impact of soil moisture dynamics on ASAR sigma degrees signatures and its spatial variability observed over the Tibetan plateau

) brightness temperature over the Tibetan Plateau for the period 1987 to 2008. A radiative transfer model is used to derive LST from SSM/I

Augmentations to the Noah Model Physics for Application to the Yellow River Source Area. Part I: Soil Water Flow

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Assessment of the SMAP Level-4 Surface and Root-Zone Soil Moisture Product Using In Situ Measurements

, which is calibrated and validated using time series of field measured soil surface temperatures. Additional Plateau-scale verification is performed with monthly LST products from the Moderate Resolution Imaging Spectroradiometer, the Noah land surface model and air temperature measured by Chinese Meteorological Administration. Trend analysis shows that the annual and monthly standardized anomalies are increasing at an averaged rate of 0.5 decade − 1. The highest positive trends are noted over the central part of the Plateau, which is on average 0.80 decade − 1 with a maximum of 1.44 decade − 1. Conversely, a negative trend in the anomalies is found for the Taklamakan desert and the Himalayan foothills with a rate of −0.27 decade − 1 and reaching a maximum of −1.4 decade − 1. In addition, we find that LST anomaly trends on the Plateau are seasonally dependent and increase with the elevation. These observed trends are in agreement with previous studies conducted with in-situ measurements, which demonstrates the use of long-term earth observation programmes for climate studies as has also been articulated in the 2007 IPCC report.

Soil Moisture Mapping Using Combined Active/Passive Microwave Observations Over the East of the Netherlands

We examine soil drying rates by comparing surface soil moisture observations from the NASA Soil Moisture Active Passive (SMAP) mission to those from networks of in situ probes upscaled to SMAPs sensing footprint. SMAP and upscaled in situ probes record different soil drying dynamics after rainfall. We modeled this process by fitting an exponential curve to 63 drydown events: the median SMAP drying timescale is 44% shorter and the magnitude of drying is 35% greater than in situ measurements. We also calculated drying rates between consecutive observations from 193 events. For 6 days after rainfall, soil moisture from SMAP dries at twice the rate of in situ measurements. Restricting in situ observations to times of SMAP observations does not change the drying timescale, magnitude, or rate. Therefore, observed differences are likely due to differences in sensing depths: SMAP measures shallower soil moisture than in situ probes, especially after rainfall.

Blending satellite observed, model simulated, and in situ measured soil moisture over Tibetan Plateau

This paper reports on the analysis of a 2.5 year-long time series of ASAR wide swath mode (WSM) observations for characterizing the soil moisture dynamics. The employed ASAR WSM data set consists of 152 VV-polarized scenes acquired in the period between April 2005 and September 2007 over the Naqu river basin located on the Tibetan Plateau. For four different spatial domains, with areas of 30×30 km2, 5×5 km2 and (two domains of) 1×1 km2, the mean backscatter (σo) and the standard deviation (stdev) have been computed for each ASAR acquisition. Comparison of the mean σo values with the stdev values results in a specific triangular distribution of data points for all spatial domains. Analysis of the mean σo and stdev with respect to in-situ soil moisture measurements demonstrates that this triangular shaped distribution can be explained by soil moisture dynamics during monsoon and winter periods. This shows that the relationship between the spatial mean soil moisture and variability is not uniquely defined and may change throughout seasons. Downscaling of coarse resolution soil moisture products should, therefore, be ideally based on additional near real time data sources. In this context, the presented results could form a basis for the development of SAR-based soil moisture downscaling methodologies.

Use of Radarsat-2 and Landsat TM images for spatial parameterization of Manning's Roughness Coefficient in hydraulic modeling

AbstractThis is the first part of a study focusing on evaluating the performance of the Noah land surface model (LSM) in simulating surface water and energy budgets for the high-elevation source region of the Yellow River (SRYR). A comprehensive dataset is utilized that includes in situ micrometeorological and profile soil moisture and temperature measurements as well as laboratory soil property measurements of samples collected across the SRYR. Here, the simulation of soil water flow is investigated, while Part II concentrates on the surface heat flux and soil temperature simulations. Three augmentations are proposed: 1) to include the effect of organic matter on soil hydraulic parameterization via the additivity hypothesis, 2) to implement the saturated hydraulic conductivity as an exponentially decaying function with soil depth, and 3) to modify the vertical root distribution to represent the Tibetan conditions characterized by an abundance of roots in the topsoil. The diffusivity form of Richards’ equ...

Impacts of Noah model physics on catchment‐scale runoff simulations

AbstractThe Soil Moisture Active Passive (SMAP) mission Level-4 Surface and Root-Zone Soil Moisture (L4_SM) data product is generated by assimilating SMAP L-band brightness temperature observations into the NASA Catchment land surface model. The L4_SM product is available from 31 March 2015 to present (within 3 days from real time) and provides 3-hourly, global, 9-km resolution estimates of surface (0–5 cm) and root-zone (0–100 cm) soil moisture and land surface conditions. This study presents an overview of the L4_SM algorithm, validation approach, and product assessment versus in situ measurements. Core validation sites provide spatially averaged surface (root zone) soil moisture measurements for 43 (17) “reference pixels” at 9- and 36-km gridcell scales located in 17 (7) distinct watersheds. Sparse networks provide point-scale measurements of surface (root zone) soil moisture at 406 (311) locations. Core validation site results indicate that the L4_SM product meets its soil moisture accuracy requiremen...

Under-canopy turbulence and root water uptake of a Tibetan meadow ecosystem modeled by Noah-MP

A coarse resolution soil moisture product is downscaled to 1, 5, and 10 km using synthetic aperture radar (SAR) observations acquired over the east of the Netherlands. The combination of phased array L-band SAR (PALSAR) backscatter and VUA-NASA C-band Advanced Microwave Scanning Radiometer-Earth Observing System (AMSR-E) soil moisture product is adopted to mimic the radar/radiometer setup as will be available from NASAs soil moisture active passive (SMAP) mission. The validation of retrievals is based on measurements collected by a sparse network of 20 stations distributed across 50 × 75 km study area selected as one of the key validation sites for the SMAP soil moisture products. Reasonable agreements between the measurements and soil moisture retrieved at 1-, 5-, and 10-km resolution are found that lead to coefficients of determination of 0.37, 0.36, and 0.36, respectively. The retrievals, however, severely overestimate the measured soil moisture, which is attributed to the well-known positive bias of the selected AMSR-E product. After bias-correction, root mean squared differences reach as low as 0.046 m3 m-3 for individual locations and are 0.067, 0.068, and 0.069 m3 m-3 on average for the soil moisture retrieved at 1-, 5-, and 10-km resolutions, respectively. These error levels do not satisfy SMAPs targeted accuracy of 0.04 m3 m-3, but the radar/radiometer setup as well as the characterization of the soil moisture conditions representative are not optimal. On the other hand, it is demonstrated that the sequence of soil moisture maps does capture valuable hydrological and hydrometeorological information.