Validation and scaling of soil moisture in a semi-arid environment: SMAP validation experiment 2015 (SMAPVEX15)

Colliander, Andreas; Cosh, Michael H.; Misra, Sidharth; Jackson, Thomas J.; Crow, Wade T.; Chan, S.; Bindlish, Rajat; Chae, Chang-Joon; Collins, Chandra Holifield; Yueh, Simon

doi:10.1016/j.rse.2017.04.022

Cited by 76 publications

(65 citation statements)

References 45 publications

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“…Field campaigns were performed during preliminary test flights of the NASA Passive–Active L‐ and S‐band (PALS) microwave sensor (Wilson et al, 2001) prior to both Soil Moisture Active–Passive Validation Experiments in 2015 (SMAPVEX15) (Colliander et al, 2017a) and 2016 (SMAPVEX16) (Colliander et al, 2019). In all, 14 campaigns occurred primarily during the spring of 2015 and 2016 (Fig.…”

Section: Resultsmentioning

confidence: 99%

The Texas Soil Observation Network:A Comprehensive Soil Moisture Dataset for Remote Sensing and Land Surface Model Validation

Caldwell

Bongiovanni

Cosh

et al. 2019

Vadose Zone Journal

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Core Ideas TxSON is a Core Cal/Val Site for the NASA SMAP, SMOS, and Sentinel programs. TxSON consists of 40 in situ locations nested within a 36‐km EASE2 grid cell. Locations monitor soil moisture, temperature, and precipitation at 5, 10, 20, and 50 cm. Hourly, quality‐controlled data from 2015 to 2018 are available. The spatiotemporal variability of soil water content (SWC) at the remote sensing scale requires dense monitoring for calibration and validation. Here, we present an overview of the Texas Soil Observation Network (TxSON), an intensively monitored area in the semiarid rangelands of the central Texas Hill Country. TxSON is a dense network consisting of 40 in situ locations nested at 36, 9, and 3 km within the Equal‐Area Scalable Earth Grid and serves as a Core Calibration and Validation Site for NASA's Soil Moisture Active Passive mission. The 4‐yr dataset consists of hourly SWC measured at 5, 10, 20, and 50 cm. The SWC data are upscaled using arithmetic, Voronoi, and inverse distance weighting for 36‐, (2) 9‐, and (5) 3‐km grid cells. We present the site selection, environmental characteristics, network design, quality assurance (QA), upscaling algorithms, and data structure. Ancillary data include bulk density, particle size, and carbon content for each site and depth. We also provide automated QA for each location and scripts to use our binary flagging in upscaling methods. To summarize, the 36‐km grid cell has a mean bulk density of 1.34 ± 0.18 g cm−3 and a loam textural class. The in situ SWC has a root mean square error of 0.029 m3 m−3 against gravimetric data from 14 field campaigns. TxSON continues to add new locations with additional dense networks planned in other ecotones of the Edwards Plateau. The time series data along with scripts to import, plot, and upscaled SWC are available at https://doi.org/10.18738/T8/JJ16CF.

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Section: Resultsmentioning

confidence: 99%

The Texas Soil Observation Network:A Comprehensive Soil Moisture Dataset for Remote Sensing and Land Surface Model Validation

Caldwell

Bongiovanni

Cosh

et al. 2019

Vadose Zone Journal

Self Cite

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“…The experiment was the second such postlaunch validation experiment, entitled the SMAP Validation Experiment in 2016 (SMAPVEX16), with the first taking place around Tombstone, Arizona. 10 These domains were established to coincide with the SMAP Core Validation Sites operated by the USDA Agricultural Research Service near Ames, Iowa (42.03 N, 93.62 W; South Fork), and Agriculture and Agri-food Canada (AAFC) near Winnipeg, Manitoba (49.90 N, 97.14 W). 11 The South Fork experimental watershed covers an area of ∼1300 km 2 and has a subhumid climate with an average annual precipitation of 800 mm.…”

Section: Study Sitesmentioning

confidence: 99%

Estimating vegetation water content during the Soil Moisture Active Passive Validation Experiment 2016

Cosh

White

Colliander

et al. 2019

J. Appl. Rem. Sens.

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“…To meet the science requirements, one of its post-launch objectives is to validate the accuracy of the science data products [14]. However, so far, only a few validations of the SMAP products have been conducted, and almost all of them have been carried out at the core validation sites (CVS) [12,[16][17][18][19][20][21][22]. Because observations from the CVS have been used to refine and validate retrieval algorithms, it is important to validate the SMAP products over other regions beyond the CVS [12,14,21,23].…”

Section: Introductionmentioning

confidence: 99%

Multi-Scale Evaluation of the SMAP Product Using Sparse In-Situ Network over a High Mountainous Watershed, Northwest China

Zhang

2017

Remote Sensing

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Abstract:As the latest L-band mission to date, evaluation of the Soil Moisture Active Passive (SMAP) products is one of its post-launch objectives. However, almost all previous studies have been conducted at the core validation sites (CVS) of the SMAP mission. This paper presents an evaluation of the SMAP soil moisture Level 3 (L3) and Level 4 (L4) products under different vegetation types at multiple tempo-spatial scales over the upper reach of the Heihe River Watershed, a topographically complex mountainous area in Northwest China. This was done through comparisons of the L3 and L4 products with ground-based observations from a sparse in situ network of permanent and temporary stations from 1 April 2015 to 22 June 2017. Results show that, compared with in situ observations at point scale, both the L3 and L4 products represent the temporal trends of the in situ observations in the study area well, with R values of 0.601 and 0.538 for the L3 ascending and descending products, respectively, and ranging from 0.353 to 0.410 for the L4 product at eight overpassing moments. However, because of the uncertainties of brightness temperature T Bp and effective temperature T eff as well as their propagations in the inversion algorithm, both products did not achieve the accuracy of 0.04 m 3 /m 3 in mountainous area. These uncertainties also result in the "dry bias" of the SMAP products in almost all the evaluations to date. Compared with areal average values at the watershed scale, the L3 product is far beyond the accuracy of 0.04 m 3 /m 3 and the L4 product basically achieves the accuracy. In vegetation-covered land, the suitability and the variability of the coefficient b p result in both products performing best in cropland, then coniferous forest, sparse grassland, dense grassland, and alpine meadow, and worst in shrub. In barren land, the errors in estimating surface roughness h caused by the complex topography lead to poor performance of the SMAP products. With the relative errors of the SMAP brightness temperature observations and the corresponding land model forecast in the assimilation; the L3 and L4 products show different performance at both temporal and spatial scales; and the L3 product provides more reliable soil moisture estimates in the study area. Based on the results of this study, we propose: quantifying the uncertainties in estimating brightness temperature T Bp and effective temperature T eff ; determine coefficient b p and surface roughness h factor under various conditions; improving Goddard Earth Observing Model System Version 5 (GEOS-5) model; and deriving the SMAP-only climatology to improve the SMAP soil moisture estimates in the future.

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Validation and scaling of soil moisture in a semi-arid environment: SMAP validation experiment 2015 (SMAPVEX15)

Cited by 76 publications

References 45 publications

The Texas Soil Observation Network:A Comprehensive Soil Moisture Dataset for Remote Sensing and Land Surface Model Validation

The Texas Soil Observation Network:A Comprehensive Soil Moisture Dataset for Remote Sensing and Land Surface Model Validation

Estimating vegetation water content during the Soil Moisture Active Passive Validation Experiment 2016

Multi-Scale Evaluation of the SMAP Product Using Sparse In-Situ Network over a High Mountainous Watershed, Northwest China

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