Estimating Surface Soil Moisture from AMSR2 Tb with Artificial Neural Network Method and SMAP Products

Yao, Panpan; Lu, Hui; Yue, Siyu; Yang, Fan; Lyu, Haobo; Yang, Kun; McColl, Kaighin A.; Gianotti, Daniel J. Short; Entekhabi, Dara

doi:10.1109/igarss.2019.8898152

Cited by 6 publications

(5 citation statements)

References 4 publications

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“…Waters in land pixels dramatically decrease the T b , thereby leading to overestimated soil moisture. Because different methods are used to detect and correct small areas of water -either open water, wetlands, or partly inundated wetlands and croplands (Entekhabi et al, 2010;Kerr et al, 2001;Mladenova et al, 2014;Njoku et al, 2003) -microwave soil moisture data calibration and weight assignment based on the water fraction within land pixels make sense (Ye et al, 2015). In addition, the water fraction is a direct indicator of surface soil moisture.…”

Section: Quality Impact Factors Of Soil Moisture Retrievalsmentioning

confidence: 99%

“…Currently, ASCAT sensors have produced the longest continuous record of global surface soil moisture of microwave remote sensing (Bartalis et al, 2007), with the temporal span from 2007 until present. Satellite-based soil moisture retrievals may also suffer from various disturbances, such as lower quality over dense vegetation cover, high open-water fractions, and complex topography (Draper et al, 2012;Fan et al, 2020;Ye et al, 2015). Differences in the algorithms dealing with the disturbances make different microwave soil moisture products hardly comparable with each other (Kim et al, 2015a;Mladenova et al, 2014).…”

Section: Introductionmentioning

confidence: 99%

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An improved global remote-sensing-based surface soil moisture (RSSSM) dataset covering 2003–2018

Chen

Feng

2021

Earth Syst. Sci. Data

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Abstract. Soil moisture is an important variable linking the atmosphere and terrestrial ecosystems. However, long-term satellite monitoring of surface soil moisture at the global scale needs improvement. In this study, we conducted data calibration and data fusion of 11 well-acknowledged microwave remote-sensing soil moisture products since 2003 through a neural network approach, with Soil Moisture Active Passive (SMAP) soil moisture data applied as the primary training target. The training efficiency was high (R2=0.95) due to the selection of nine quality impact factors of microwave soil moisture products and the complicated organizational structure of multiple neural networks (five rounds of iterative simulations, eight substeps, 67 independent neural networks, and more than 1 million localized subnetworks). Then, we developed the global remote-sensing-based surface soil moisture dataset (RSSSM) covering 2003–2018 at 0.1∘ resolution. The temporal resolution is approximately 10 d, meaning that three data records are obtained within a month, for days 1–10, 11–20, and from the 21st to the last day of that month. RSSSM is proven comparable to the in situ surface soil moisture measurements of the International Soil Moisture Network sites (overall R2 and RMSE values of 0.42 and 0.087 m3 m−3), while the overall R2 and RMSE values for the existing popular similar products are usually within the ranges of 0.31–0.41 and 0.095–0.142 m3 m−3), respectively. RSSSM generally presents advantages over other products in arid and relatively cold areas, which is probably because of the difficulty in simulating the impacts of thawing and transient precipitation on soil moisture, and during the growing seasons. Moreover, the persistent high quality during 2003–2018 as well as the complete spatial coverage ensure the applicability of RSSSM to studies on both the spatial and temporal patterns (e.g. long-term trend). RSSSM data suggest an increase in the global mean surface soil moisture. Moreover, without considering the deserts and rainforests, the surface soil moisture loss on consecutive rainless days is highest in summer over the low latitudes (30∘ S–30∘ N) but mostly in winter over the mid-latitudes (30–60∘ N, 30–60∘ S). Notably, the error propagation is well controlled with the extension of the simulation period to the past, indicating that the data fusion algorithm proposed here will be more meaningful in the future when more advanced microwave sensors become operational. RSSSM data can be accessed at https://doi.org/10.1594/PANGAEA.912597 (Chen, 2020).

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Section: Quality Impact Factors Of Soil Moisture Retrievalsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

An improved global remote-sensing-based surface soil moisture (RSSSM) dataset covering 2003–2018

Chen

Feng

2021

Earth Syst. Sci. Data

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“…The ML techniques may be used to illustrate the nonlinear connections between SM and surface variables. As a result of their excellent generalization capacity and resilience, RF and ANN have been frequently employed in prior research [48,[53][54][55].…”

Section: Introductionmentioning

confidence: 99%

Spatial Downscaling and Gap-Filling of SMAP Soil Moisture to High Resolution Using MODIS Surface Variables and Machine Learning Approaches over ShanDian River Basin, China

et al. 2023

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High-resolution soil moisture (SM) information is essential for regional to global hydrological and agricultural applications. The Soil Moisture Active Passive (SMAP) offers daily global composites of SM at coarse-resolution 9 and 36 km, with data gaps limiting its local application to depict SM distribution in detail. To overcome the aforementioned problem, a downscaling and gap-filling novel approach was adopted, using random forest (RF) and artificial neural network (ANN) algorithms to downscale SMAP SM data, using land-surface variables from moderate-resolution imaging spectroradiometer (MODIS) onboard Aqua and Terra satellites from the years 2018 to 2019. Firstly, four combinations (RF+Aqua, RF+Terra, ANN+Aqua, and ANN+Terra) were developed. Each combination downscaled SMAP SM at a high resolution (1 km). These combinations were evaluated by using error matrices and in situ SM at different scales in the ShanDian River (SDR) Basin. The combination RF+Terra showed a better performance, with a low averaged unbiased root mean square error (ubRMSE) of 0.034 m3/m3 and high averaged correlation (R) of 0.54 against the small-, medium-, and large-scale in situ SM. Secondly, the impact of various land covers was examined by using downscaled SMAP and in situ SM. Vegetation attenuation makes woodland more error-prone and less correlated than grassland and farmland. Finally, the RF+Terra and ANN+Terra combinations were selected for their higher accuracy in gap filling of downscaled SMAP SM. The gap-filled downscaled SMAP SM results were compared spatially with China Land Data Assimilation System (CLDAS) SM and in situ SM. The RF+Terra combination outcomes were more humid than ANN+Terra combination results in the SDR basin. Overall, the RF+Terra combination gap-filled data showed high R (0.40) and less ubRMSE (0.064 m3/m3) against in situ SM, which was close to CLDAS SM. This study showed that the proposed RF- and ANN-based downscaling methods have a potential to improve the spatial resolution and gap-filling of SMAP SM at a high resolution (1 km).

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“…Generally, this study indicates that the expected order of data applicability among various global long-term surface soil moisture products is SIM (applicable to all studies)> GLEAM (suitable for studies of temporal variation)> ERA5-Land (applicable to temporal pattern studies)> GLDAS Noah V2.1 (somewhat applicable to all studies)> ASCAT-SWI> CCI. The training R 2 of the previous neural networks designed for global surface soil moisture mapping is 0.45~0.55, while the temporal R and RMSE values against measurements are 0.52 and 0.084 (Yao et al, 2017), and the overall R and RMSE are 0.44 and 0.113 (Yao et al, 2019). In this study, by elaborating the neural network, the training R 2 is elevated to 0.95, with the temporal R and RMSE (0.69 and 0.08) as well as overall R and RMSE (0.65 and 0.087) values also improved.…”

Section: The Data Quality Comparison Between Sim and The Soil Moisturmentioning

confidence: 99%

A new dataset of satellite observation-based global surface soil moisture covering 2003–2018

Chen

Feng

2020

Preprint

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Abstract. Soil moisture is an important variable linking the atmosphere and the terrestrial ecosystems. However, long-term satellite monitoring of surface soil moisture is still lacking at global scale. In this study, we conducted data calibration and fusion of 11 well-acknowledged microwave remote sensing-based soil moisture products since 2003 through neural network approach, with SMAP soil moisture data applied as the fundamental training target. The training efficiency proves to be high (R2 = 0.95) due to the selection of 9 quality impact factors of microwave soil moisture products and the elaborate organization structure of multiple various neural networks(5 rounds of simulation; 8 substeps; 74 independent neural networks; and > 106 regional subnetworks). We achieved global satellite monitoring of surface soil moisture during 2003–2018 at 0.1° resolution. This new dataset, once validated against the International Soil Moisture Network (ISMN) records, is supposed to be superior to the existing products (ASCAT-SWI, GLDAS Noah, ERA5-Land, CCI/ECV and GLEAM), and is applicable to studying both the spatial and temporal patterns. It suggests an increase in global mean surface soil moisture, and reveals that the surface moisture decline on rainless days is highest in summers over the low-latitudes but highest in winters over most mid-latitude areas. Notably, the error propagation with the extension of the simulation period to the past is well controlled, indicating that the fusion algorithm will be more meaningful in future when more advanced sensors are in operation. The dataset can be accessed at https://doi.pangaea.de/10.1594/PANGAEA.912597 (Chen, 2020).

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Estimating Surface Soil Moisture from AMSR2 Tb with Artificial Neural Network Method and SMAP Products

Cited by 6 publications

References 4 publications

An improved global remote-sensing-based surface soil moisture (RSSSM) dataset covering 2003–2018

An improved global remote-sensing-based surface soil moisture (RSSSM) dataset covering 2003–2018

Spatial Downscaling and Gap-Filling of SMAP Soil Moisture to High Resolution Using MODIS Surface Variables and Machine Learning Approaches over ShanDian River Basin, China

A new dataset of satellite observation-based global surface soil moisture covering 2003–2018

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