Disaggregating satellite soil moisture products based on soil thermal inertia: A comparison of a downscaling model built at two spatial scales

Senanayake, I. P.; Yeo, In‐Young; Willgoose, Garry; Hancock, Greg

doi:10.1016/j.jhydrol.2020.125894

Cited by 24 publications

(16 citation statements)

References 69 publications

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“…It is used as a downscaling method for long time series MODIS SSM estimation in the study. Similar with STFM, the scale-invariant assumption also exists in traditional microwave SSM downscaling, but it refers to scale-invariance of the relation between microwave SSM and other remotely sensed parameters for traditional methods in most cases [22,24]. It has been shown that the downscaling capability of STFM is better than that of the traditional downscaling method [45], although the scale-invariant assumption of temporal change is fitted by a linear equation.…”

Section: Discussionmentioning

confidence: 99%

“…At present, passive microwave SSM downscaling mainly relies on high spatial resolution auxiliary data including optical/thermal infrared (OTI), radar, terrain, and other data [19][20][21][22][23][24]. The empirical and physical models between passive microwave SSM and auxiliary data are built for downscaling and for spatial resolution improvement.…”

Section: Introductionmentioning

confidence: 99%

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A Novel Method for Long Time Series Passive Microwave Soil Moisture Downscaling over Central Tibet Plateau

Jiang

Chen

et al. 2022

Remote Sensing

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The coarse scale of passive microwave surface soil moisture (SSM) is not suitable for regional agricultural and hydrological applications such as drought monitoring and irrigation management. The optical/thermal infrared (OTI) data-based passive microwave SSM downscaling method can effectively improve its spatial resolution to fine scale for regional applications. However, the estimation capability of SSM with long time series is limited by OTI data, which are heavily polluted by clouds. To reduce the dependence of the method on OTI data, an SSM retrieval and spatio-temporal fusion model (SMRFM) is proposed in the study. Specifically, a model coupling in situ data, MODerate-resolution Imaging Spectro-radiometer (MODIS) OTI data, and topographic information is developed to retrieve MODIS SSM (1 km) using the least squares method. Then the retrieved MODIS SSM and the spatio-temporal fusion model are employed to downscale the passive microwave SSM from coarse scale to 1 km. The proposed SMRFM is implemented in a grassland dominated area over Naqu, central Tibet Plateau, for Advanced Microwave Scanning Radiometer—Earth Observing System sensor (AMSR-E) SSM downscaling in unfrozen period. The in situ SSM and Noah land surface model 0.01° SSM are used to validate the estimated MODIS SSM with long time series. The evaluations show that the estimated MODIS SSM has the same temporal resolution with AMSR-E and obtains significantly improved detailed spatial information. Moreover, the temporal accuracy of estimated MODIS SSM against in situ data (r = 0.673, μbRMSE = 0.070 m3/m3) is better than the AMSR-E (r = 0.661, μbRMSE = 0.111 m3/m3). In addition, the temporal r of estimated MODIS SSM is obviously higher than that of Noah data. Therefore, this suggests that the SMRFM can be used to estimate MODIS SSM with long time series by AMSR-E SSM downscaling in the study. Overall, the study can provide help for the development and application of microwave SSM-related scientific research at the regional scale.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A Novel Method for Long Time Series Passive Microwave Soil Moisture Downscaling over Central Tibet Plateau

Jiang

Chen

et al. 2022

Remote Sensing

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“…Therefore, it is necessary to establish the relationship between SSM and precipitation for different land cover types or different terrain types. The establishment of a more reliable fitting relationship based on precipitation data with different land cover properties or topographic factors would be helpful to enhance the accuracy of the downscaling results (Senanayake et al, 2021;Zhao et al, 2021;Chen et al, 2020). Furthermore, although the relationship between SSM and precipitation has been well demonstrated in many previous studies, the sensitivity of SSM to precipitation will decrease when soil water storage becomes saturated after repeated precipitation.…”

Section: Validation At the Monthly Scalementioning

confidence: 99%

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Zhao

2022

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“…However, the influence of vegetation cover on soil reflectance and cloud pollution limits the use of optical technology [ 13 , 14 ]. Thermal infrared (TIR) RS can quantify soil thermal inertia, evapotranspiration (ET), surface temperature and soil evaporation efficiency, which are affected by SM [ 15 , 16 ]. TIR technology is usually based on a clear physical interpretation, making TIR technology easy to use [ 17 ].…”

Section: Introductionmentioning

confidence: 99%

The ATI-ET Triangle Model: A Novel Approach to Estimate Soil Moisture Applied to MODIS Data

Luo

Wen

et al. 2022

Sensors

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A simple soil moisture (SM) estimation method is proposed using apparent thermal inertia (ATI) and evapotranspiration (ET) data. Among the methods of estimating SM by using thermal infrared (TIR) remote sensing, the ATI method is widely used in bare soil and low vegetation areas. However, large surface ET will cause ATI error, resulting in lower accuracy of SM estimation. To overcome this problem, the potential of ATI-ET space for estimating the SM of bare and vegetated farmland in the dry season (no irrigation) is studied. ATI and ET data were used to construct triangle feature space, and six distance parameters are extracted from the positions of random pixels in the triangle. Some correlation estimates were made to derive those parameters that were useful for SM estimation, which were three in total. The SM estimation model consisting of these three parameters was built. Compared with the ATI model, the ATI-ET triangle model can not only be applied to areas with high ET, but also has higher accuracy in estimating SM. The ATI-ET triangle model is more suitable for application in bare soil and low vegetation areas. As the Normalized Difference Vegetation Index increases, the accuracy of the model estimates decreases. To show the high portability of the proposed model for SM estimation, we chose another set of in situ SM data acquired in Tibetan Plateau. The results proved the effectiveness of the model in other similar study regions.

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Disaggregating satellite soil moisture products based on soil thermal inertia: A comparison of a downscaling model built at two spatial scales

Cited by 24 publications

References 69 publications

A Novel Method for Long Time Series Passive Microwave Soil Moisture Downscaling over Central Tibet Plateau

A Novel Method for Long Time Series Passive Microwave Soil Moisture Downscaling over Central Tibet Plateau

Reply on RC2

The ATI-ET Triangle Model: A Novel Approach to Estimate Soil Moisture Applied to MODIS Data

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