An in-situ data based model to downscale radiometric satellite soil moisture products in the Upper Hunter Region of NSW, Australia

Senanayake, I. P.; Yeo, In‐Young; Tangdamrongsub, Natthachet; Willgoose, Garry; Hancock, Gregory R.; Wells, T.; Fang, Bin; Lakshmi, V.; Walker, Jeffrey P.

doi:10.1016/j.jhydrol.2019.03.014

Cited by 32 publications

(22 citation statements)

References 94 publications

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“…The 1 km and 3 km SMAP SM products are obtained by disaggregating the coarse-resolution SMAP TB or SM data using Sentinel-1 SAR data; however, a trade-off is observed between the finer spatial resolution and noise associated with SAR data [25]. Overall, reducing the uncertainty caused by complex surface scattering conditions on vegetation and surface roughness is critical for SM downscaling methods depending on active microwaves [25,27].Some downscaling methods based on fine-resolution optical/infrared data mainly use triangular/trapezoidal feature space (non-linear fitting methods with VIs and LST [15,[36][37][38][39], Peng's methods [40,41], the University of California, Los Angeles (UCLA) method [42]), the soil evaporation process (evaporative fraction (EF), soil evaporative efficiency (SEE), disaggregation based on physical and theoretical scale change (DisPATCh) [43-45])), and thermal inertia theory [46]. In the downscaling process, a SM proxy variable is usually used to construct the relationship model with SM such as Peng's methods [40,41], UCLA, EF, SEE, and DisPATCh.…”

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confidence: 99%

An Approach for Downscaling SMAP Soil Moisture by Combining Sentinel-1 SAR and MODIS Data

et al. 2019

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A method is proposed for the production of downscaled soil moisture active passive (SMAP) soil moisture (SM) data by combining optical/infrared data with synthetic aperture radar (SAR) data based on the random forest (RF) model. The method leverages the sensitivity of active microwaves to surface SM and the triangle/trapezium feature space among vegetation indexes (VIs), land surface temperature (LST), and SM. First, five RF architectures (RF1-RF5) were trained and tested at 9 km. Second, a comparison was performed for RF1-RF5, and were evaluated against in situ SM measurements. Third, two SMAP-Sentinel active-passive SM products were compared at 3 km and 1 km using in situ SM measurements. Fourth, the RF5 model simulations were compared with the SMAP L2_SM_SP product based on the optional algorithm at 3 km and 1 km resolutions. The results showed that the downscaled SM based on the synergistic use of optical/infrared data and the backscatter at vertical-vertical (VV) polarization was feasible in semi-arid areas with relatively low vegetation cover. The RF5 model with backscatter and more parameters from optical/infrared data performed best among the five RF models and was satisfactory at both 3 km and 1 km. Compared with L2_SM_SP, RF5 was more superior at 1 km. The input variables in decreasing order of importance were backscatter, LST, VIs, and topographic factors over the entire study area. The low vegetation cover conditions probably amplified the importance of the backscatter and LST. A sufficient number of VIs can enhance the adaptability of RF models to different vegetation conditions. 2 of 20 salinity (SMOS) satellites [10] based on L-band passive microwaves can provide high-accuracy global daily SM products [16]. However, coarse-resolution passive microwave SM data cannot reflect the detailed distribution of surface SM; therefore, many researchers have downscaled coarse-resolution passive microwave SM data based on fine-resolution auxiliary data [17][18][19][20][21][22][23].Some downscaling methods are based on active or passive microwave data including downscaling coarse-resolution microwave brightness temperature (TB) data or coarse-resolution SM data based on microwave backscatter data [22,[24][25][26][27][28][29][30], and downscaling low-frequency passive microwave data based on high-frequency passive microwave data [31][32][33]. Active microwave techniques offer higher spatial resolutions than passive microwave techniques. Downscaling methods based on active microwaves usually leverage the linear or near-linear relationship between microwave TB or SM data and microwave backscatter data in a time series. Vegetation and surface roughness are generally considered time-invariant during long-time series [26]. However, active microwaves are greatly affected by vegetation and surface roughness [34]; therefore, their applications in most areas are limited [35]. Another assumption is that invariance of vegetation and surface roughness occurs in adjacent observations (several days); thus, these techniques req...

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An Approach for Downscaling SMAP Soil Moisture by Combining Sentinel-1 SAR and MODIS Data

et al. 2019

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“…In other words, μSM shows an inverse relationship with ΔT. This relationship was used in the studies carried out by Fang et al (2013), Fang and Lakshmi (2014), Senanayake et al (2017Senanayake et al ( , 2018Senanayake et al ( and 2019 to build the regression models to disaggregate coarse resolution satellite soil moisture products. However, this relationship is modulated by a number of factors such as vegetation density, soil texture, topography, daily mean temperature, etc., affecting the linearity of the regression model.…”

Section: Research Hypothesismentioning

confidence: 99%

“…This area was instrumented for measuring soil moisture and soil temperature along with other auxiliary data from 2002 under the Scaling and Assimilation of Soil Moisture and Streamflow (SASMAS) project (Rüdiger et al 2003 and. Therefore, the area has been evaluated for soil moisture variability by a number of research studies (Martinez et al 2007;Chen et al 2014;Senanayake et al 2017Senanayake et al , 2018Senanayake et al , 2019. This includes a high resolution airborne campaign, National Airborne Field Experiment 2005 (NAFE'05) (Panciera et al 2008), which was conducted on 4 consecutive Mondays, (31 st October, 7 th , 14 th and 21 st November 2005) over a 40 × 40 km land area covering Krui and Merriwa River catchments using an L-band radiometer (Panciera et al 2008).…”

Section: Study Areamentioning

confidence: 99%

“…They have used vegetation density, as a modulating factor in their model considering its influence for the ΔT-μSM relationship. Senanayake et al (2017 and2019) developed a similar model to downscale SMAP and SMOS soil moisture retrievals over south-east Australian catchment using a long-term in-situ dataset. In addition, Senanayake et al (2018) have tested a model based on the soil thermal inertia using Global Land Data Assimilation System (GLDAS) dataset.…”

Section: Introductionmentioning

confidence: 99%

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Using an artificial neural network to enhance the spatial resolution of satellite soil moisture products based on soil thermal inertia

2019

El Sawah, S. (Ed.) MODSIM2019, 23rd International Congress on Modelling and Simulation.

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High resolution soil moisture information is vital for a number of environmental applications including hydrologic and climatic modelling. However, the available point-scale field measurements and coarse spatial resolution satellite soil moisture products (~10s of km) are unable to provide the spatial resolution requirements for many of these applications, especially at regional scales. Downscaling the L-band satellite soil moisture products appears to be a viable solution for this problem. Many research teams have tested methods and algorithms to downscale the satellite soil moisture retrievals, yet there is no universally applicable model. Among those methods, thermal data based downscaling models have shown promising results over arid and semi-arid regions. The downscaling approach, based on the soil thermal inertia relationship between the diurnal temperature difference (ΔT) and the daily mean soil moisture (μSM), is one of the thermal data-based downscaling methods tested in the United States and Australia. These studies have used this method by building regressions between ΔT and μSM modulated by the vegetation density. However, there are a number of possible factors affecting the linearity of this regression model. Therefore, this study employed a machine learning model to build a more complex algorithm between ΔT, μSM and vegetation density. The Global Land Data Assimilation System (GLDAS) derived 25 km resolution ΔT values (from 2000 to 2017) and aggregated Moderate Resolution Imaging Spectroradiometer (MODIS) derived Normalized Difference Vegetation Index (NDVI) values were used as inputs, whereas GLDAS derived μSM values were used as targets to train an artificial neural network (ANN). The Levenberg-Marquardt algorithm with 50 hidden neurons was used as the network architecture in building this model. Thereafter, 1 km resolution MODIS derived ΔT and NDVI values of November 2005 were input into the model to estimate soil moisture at high spatial resolution (1 km). The estimated soil moisture values were then used to downscale aggregated NAFE'05 airborne soil moisture retrievals. The downscaled soil moisture products were compared with the 1 km resolution soil moisture retrievals from the National Airborne Field Experiment 2005 (NAFE'05). This study has been conducted over two medium-scale catchments, Krui and Merriwa River, located in the Upper Hunter region of the southeastern Australia. The comparison between downscaled and airborne soil moisture showed root mean square errors (RMSE) of 0.088, 0.072 and 0.058 cm 3 /cm 3 on 7 th , 14 th and 21 st November 2005, respectively. The downscaled soil moisture products were able to capture the detailed spatial patterns of soil moisture over the study area, showing a good match with the airborne retrievals. However, the algorithm performed better under dry catchment conditions compared to wet catchment conditions.

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“…Given correlations between SM and geoformation data, topography is also generally used as ancillary information within the downscaling approaches (Peng et al, 2017). Long‐term dense in situ SM observations allow training regression models to generate finer resolution SM retrievals; however, operational application of these empirical polynomial fitting methods is hampered by requirements of extensive in situ SM observations (Abbaszadeh et al, 2019; Senanayake et al, 2019; Zhao et al, 2018). Optimizing land surface model (LSM) variables to provide fine‐scale SM estimations for the overlapping coarse resolution pixels is also proposed to downscale L‐band SM observations; yet differences in climatology between remote sensing and LSM SM estimates limit their applicability (Fang et al, 2018).…”

Section: Introductionmentioning

confidence: 99%

Near‐real‐time one‐kilometre Soil Moisture Active Passive soil moisture data product

Yin

Zhan

Liu³

et al. 2020

Hydrological Processes

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The coarse resolution soil moisture (SM) data from NASA SMAP mission have been steadily produced with the expected performance since April 2015. These coarse resolution observations could be downscaled to fine resolution using fine scale observations of SM sensitive quantities from existing satellite sensors. For operational users who need near‐real‐time (NRT) high resolution SM data, the downscaling approach should be feasible for operational implementation, requiring limited ancillary information and primarily depending on readily available satellite observations. Based on these principles, nine potential candidate downscaling schemes were selected for developing an optimal downscaling strategy. Using remotely sensed land surface temperature (LST) and enhanced vegetation index (EVI) observations, the optimal downscaling approach was tested for operational producing a NRT 1 km SM data product from SMAP. Comprehensive assessments on the 1 km SM product were conducted based on agreement statistics with in‐situ SM measurements. Statistical results show that the accuracy of the original coarse spatial resolution SMAP SM product can be significantly improved by 8% by the downscaled 1 km SM. With respect to the in‐situ measurements, the 1 km SM mapping capability developed here presents a clear advantage over the SMAP/Sentinel SM data product; and it also provides better data availability for users. This study suggests that a NRT 1 km SMAP SM data product could be routinely generated from SMAP at the centre for Satellite Applications and Research of NOAA NESDIS for operational users.

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An in-situ data based model to downscale radiometric satellite soil moisture products in the Upper Hunter Region of NSW, Australia

Cited by 32 publications

References 94 publications

An Approach for Downscaling SMAP Soil Moisture by Combining Sentinel-1 SAR and MODIS Data

An Approach for Downscaling SMAP Soil Moisture by Combining Sentinel-1 SAR and MODIS Data

Using an artificial neural network to enhance the spatial resolution of satellite soil moisture products based on soil thermal inertia

Near‐real‐time one‐kilometre Soil Moisture Active Passive soil moisture data product

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