Downscaling of Satellite Remote Sensing Soil Moisture Products Over the Tibetan Plateau Based on the Random Forest Algorithm: Preliminary Results

Chen, Qingqing; Miao, Fang; Wang, Hao; Xu, Zi‐Xin; Tang, Zhiya; Yang, Ling; Qi, Shanshan

doi:10.1029/2020ea001265

Cited by 27 publications

(15 citation statements)

References 46 publications

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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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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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“…According to the results of Sadeghi et al [30], the greatest R 2 was 0.85 with an MAE of 2.4%. Finally, Chen et al in [31] obtained two correlations characterized by R 2 of 0.45 to 0.49 and MAEs of 3% to 8%. Those are the only found related work in which the R 2 and MAEs are provided for the developed indexes.…”

Section: Comparison Of Our Regression Models With Existing Moisture I...mentioning

confidence: 92%

The Combined Use of Remote Sensing and Wireless Sensor Network to Estimate Soil Moisture in Golf Course

Mauri¹,

Parra

Mostaza-Colado³

et al. 2021

Applied Sciences

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In gardening, particularly in golf courses, soil moisture management is critical for maximizing water efficiency. Remote sensing has been used to estimate soil moisture in recent years with relatively low accuracies. In this paper, we aim to use remote sensing and wireless sensor networks to generate soil moisture indexes for a golf course. In the golf course, we identified three types of soil, and data was gathered for three months. Mathematical models were obtained using data from Sentinel-2, bands with a resolution of 10 and 20 m, and sensed soil moisture. Models with acceptable accuracy were obtained only for one out of three soil types, the natural soil in which natural vegetation is grown. Two multiple regression models are presented with an R2 of 0.46 for bands at 10 m and 0.70 for bands at 20 m. Their mean absolute error was lower than 3% in both cases. For the modified soils, the greens, and the golf course fairway, it was not feasible to obtain regression models due to the temporal uniformity of the grass and the range of variation of soil moisture. The developed moisture indexes were compared with existing options. The attained accuracies improve the current models. The verification indicates that the model generated with band 4 and band 12 is the one with better accuracy.

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“…Uncertainty also helps to having the choice which variables of the input attribute are observed at each node in each decision tree. Once all the trees are robust for random division of the training records, by means of an arbitrary set of attribute variables for each node, the assembly of all the trees is used to provide the final forecast [92].…”

Section: Change Detectionmentioning

confidence: 99%

Detection of Urban and Environmental Changes via Remote Sensing

2021

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Rapid climate and environmental change at limited, regional, and general scales have been a major concern for researchers in a number of fields, such as topography, economy, environment, and sustainable development. Changes in land cover and land use have taken into account due to potential impacts on soil depletion, amplified runoff , water balance, and climate change. A detailed understanding of the characteristics of land exploitation and land structure is indispensable for the study of their influences on life and nature. In addition, urban extension is a major form of land extraction and land transformation, as it relates to the rise in population and the availability of financial services. Remote sensing records have been shown to be important for reporting and perceiving urban development and transition, and for providing critical information for future growth. Transformation and shift identification are the tools used to recognise distinctions in a land cover by tracking them at various times. In addition, various change identification and detection approaches are routinely tested with the goal of providing the greatest change detection deductions for a particular appliance. This review would aim to establish a practical plan that combines remote sensing techniques, on the one hand, and modelling approaches, on the other, to track land use, to cover changes, and to predict future trends.

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Downscaling of Satellite Remote Sensing Soil Moisture Products Over the Tibetan Plateau Based on the Random Forest Algorithm: Preliminary Results

Cited by 27 publications

References 46 publications

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

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

The Combined Use of Remote Sensing and Wireless Sensor Network to Estimate Soil Moisture in Golf Course

Detection of Urban and Environmental Changes via Remote Sensing

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