Soil Moisture Retrieval Using Microwave Remote Sensing Data and a Deep Belief Network in the Naqu Region of the Tibetan Plateau

Soil moisture plays an important role in the land surface model. In this paper, a method of using VV polarization Sentinel-1 SAR and Landsat optical data to retrieve soil moisture data was proposed by combining the water cloud model (WCM) and the deep belief network (DBN). Since the simple combination of training data in the neural network cannot effectively improve the accuracy of the soil moisture inversion results, a WCM physical model was used to eliminate the effect of vegetation cover on the ground backscatter, in order to obtain the bare soil backscatter coefficient. This improved the correlation of ground soil backscatter characteristics with soil moisture. A DBN soil moisture inversion model based on the bare soil backscatter coefficients as the foundation training data combined with radar incidence angle and terrain factors obtained good inversion results. Studies in the Naqu area of the Tibetan Plateau showed that vegetation cover had a significant effect on the soil moisture, and the goodness of fit (R2) between the backscatter coefficient and soil moisture before and after the elimination of vegetation cover was 0.38 and 0.50, respectively. The correlation between the backscatter coefficient and the soil moisture was improved after eliminating the vegetation cover. The inversion results of the DBN soil moisture model were further improved through iterative parameters. The model prediction reached its highest level of accuracy when the restricted Boltzmann machine (RBM) was set to seven layers, the bias and R were 0.007 and 0.88, respectively. Ten-fold cross-validation showed that the DBN soil moisture model performed stably with different data. The prediction was further improved when the bare soil backscatter coefficient was used as the training data. The mean values of the root mean square error (RMSE), the inequality coefficient (TIC), and the mean absolute percent error (MAPE) were 0.023, 0.09, and 11.13, respectively.

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Developing the soil texture effects on the surface resistance to bare soil based on MOD16 algorithm to estimate evapotranspiration over the Tibetan Plateau

10.5194/egusphere-egu2020-3833 ◽

2020 ◽

Author(s):

Ling Yuan ◽

Yaoming Ma ◽

Xuelong Chen

Keyword(s):

Tibetan Plateau ◽

Soil Texture ◽

Land Surface ◽

Bare Soil ◽

The Other ◽

Accurate Estimation ◽

Surface Model ◽

The Tibetan Plateau ◽

The Mean ◽

Modified Algorithm

Evapotranspiration (ET), composed of evaporation (ETs) and transpiration (ETc) and intercept water (ETw), plays an indispensable role in the water cycle and energy balance of land surface processes. A more accurate estimation of ET variations is essential for natural hazard monitoring and water resource management. For the cold, arid, and semi-arid regions of the Tibetan Plateau (TP), previous studies often overlooked the decisive role of soil properties in ETs rates. In this paper, an improved algorithm for ETs in bare soil and an optimized parameter for ETc over meadow based on MOD16 model are proposed for the TP. The nonlinear relationship between surface evaporation resistance (rss) and soil surface hydration state in different soil texture is redefined by ground-based measurements over the TP. Wind speed and vegetation height were integrated to estimate aerodynamic resistance by Yang et al. (2008). The validated value of the mean potential stomatal conductance per unit leaf area (CL) is 0.0038m s-1. And the algorithm was then compared with the original MOD16 algorithm and a soil water index&#8211;based Priestley-Taylor algorithm (SWI&#8211;PT). After examining the performance of the three models at 5 grass flux tower sites in different soil texture over the TP, East Asia, and America, the validation results showed that the half-hour estimates from the improved-MOD16 were closer to observations than those of the other models under the all-weather in each site. The average correlation coefficient(R2) of the improved-MOD16 model was 0.83, compared with 0.75 in the original MOD16 model and 0.78 in SWI-PT model. The average values of the root mean square error (RMSE) are 35.77W m-2, 79.46 W m-2, and 73.88W m-2 respectively. The average values of the mean bias (MB) are -4.08W m-2, -52.36W m-2, and -11.74 W m-2 overall sites, respectively. The performance of these algorithms are better achieved on daily (R2=0.81, RMSE=17.22W m-2, MB=-4.12W m-2; R2=0.64, RMSE=56.55W m-2, MB=-48.74W m-2; R2=0.78, RMSE=22.3W m-2, MB=-9.82W m-2) and monthly (R2=0.93, RMSE=23.35W m-2, MB=-2.8W m-2; R2=0.86, RMSE=69.11W m-2, MB=-39.5W m-2; R2=0.79, RMSE=62.8W m-2, MB=-9.7W m-2) scales. Overall, the results showed that the newly developed MOD16 model captured ET more accurately than the other two models. The comparisons between the modified algorithm and two mainstream methods suggested that the modified algorithm could produce high accuracy ET over the meadow sites and has great potential for land surface model improvements and remote sensing ET promotion for the ET region.

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Some practical notes on the land surface modeling in the Tibetan Plateau

Hydrology and Earth System Sciences Discussions ◽

10.5194/hessd-6-1291-2009 ◽

2009 ◽

Vol 6 (1) ◽

pp. 1291-1320 ◽

Cited By ~ 4

Author(s):

K. Yang ◽

Y.-Y. Chen ◽

J. Qin

Keyword(s):

Soil Moisture ◽

Tibetan Plateau ◽

Land Surface ◽

Soil Surface ◽

Bare Soil ◽

Heat Fluxes ◽

The Tibetan Plateau ◽

Central Plateau ◽

Alpine Meadows ◽

Common Land Model

Abstract. The Tibetan Plateau is a key region of land-atmosphere interactions, as it provides an elevated heat source to the middle-troposphere. The Plateau surfaces are typically characterized by alpine meadows and grasslands in the central and eastern part while by alpine deserts in the western part. This study evaluates performance of three state-of-the-art land surface models (LSMs) for the Plateau typical land surfaces. The LSMs of interest are SiB2 (the Simple Biosphere), CoLM (Common Land Model), and Noah. They are run with default parameters at typical alpine meadow sites in the central Plateau and typical alpine desert sites in the western Plateau. The recognized key processes and modeling issues are as follows. First, soil stratification is a typical phenomenon beneath the alpine meadows, with dense roots and soil organic matters within the topsoil, and it controls the profile of soil moisture in the central and eastern Plateau; all models significantly under-estimate the soil moisture within the topsoil. Second, a soil surface resistance controls the surface evaporation from the alpine deserts but it has not been reasonably modeled in LSMs; a new scheme is proposed to determine this resistance from soil water content. Third, an excess resistance controls sensible heat fluxes from dry bare-soil or sparsely vegetated surfaces, and all LSMs significantly under-predict the ground-air temperature difference in the daytime. A parameterization scheme for this resistance has been shown effective to remove this bias.

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Improvement of summer precipitation simulation in China by assimilating spring soil moisture over the Tibetan Plateau

Theoretical and Applied Climatology ◽

10.1007/s00704-021-03840-5 ◽

2021 ◽

Author(s):

Jiali Shen ◽

Kechen Li ◽

Zhiqiang Cui ◽

Feimin zhang ◽

Kai Yang ◽

...

Keyword(s):

Soil Moisture ◽

Tibetan Plateau ◽

Summer Precipitation ◽

The Tibetan Plateau ◽

Precipitation Simulation

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Impact of Soil Moisture Dynamics on ASAR σo Signatures and Its Spatial Variability Observed over the Tibetan Plateau

Sensors ◽

10.3390/s8095479 ◽

2008 ◽

Vol 8 (9) ◽

pp. 5479-5491 ◽

Cited By ~ 18

Author(s):

Rogier Van der Velde ◽

Zhongbo Su ◽

Yaoming Ma

Keyword(s):

Soil Moisture ◽

Tibetan Plateau ◽

Spatial Variability ◽

The Tibetan Plateau ◽

Soil Moisture Dynamics ◽

Moisture Dynamics

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An evapotranspiration model self-calibrated from remotely sensed surface soil moisture, land surface temperature and vegetation cover fraction: application to disaggregated SMOS and MODIS data

10.5194/hess-2019-105 ◽

2019 ◽

Cited By ~ 3

Author(s):

Bouchra Ait Hssaine ◽

Olivier Merlin ◽

Jamal Ezzahar ◽

Nitu Ojha ◽

Salah Er-raki ◽

...

Keyword(s):

Soil Moisture ◽

Energy Balance ◽

Surface Temperature ◽

Land Surface Temperature ◽

Vegetation Cover ◽

Land Surface ◽

Surface Soil ◽

Surface Soil Moisture ◽

Wide Range ◽

The Mean

Abstract. Thermal-based two-source energy balance modeling is very useful for estimating the land evapotranspiration (ET) at a wide range of spatial and temporal scales. However, the land surface temperature (LST) is not sufficient for constraining simultaneously both soil and vegetation flux components in such a way that assumptions (on either the soil or the vegetation fluxes) are commonly required. To avoid such assumptions, a new energy balance model (TSEB-SM) was recently developed in Ait Hssaine et al. (2018a) to integrate the microwave-derived near-surface soil moisture (SM), in addition to the thermal-derived LST and vegetation cover fraction (fc). Whereas, TSEB-SM has been recently tested using in-situ measurements, the objective of this paper is to evaluate the performance of TSEB-SM in real-life using 1 km resolution MODIS (Moderate resolution imaging spectroradiometer) LST and fc data and the 1 km resolution SM data disaggregated from SMOS (Soil Moisture and Ocean Salinity) observations by using DisPATCh. The approach is applied during a four-year period (2014–2018) over a rainfed wheat field in the Tensift basin, central Morocco, during a four-year period (2014–2018). The field was seeded for the 2014–2015 (S1), 2016–2017 (S2) and 2017–2018 (S3) agricultural season, while it was not ploughed (remained as bare soil) during the 2015–2016 (B1) agricultural season. The mean retrieved values of (arss, brss) calculated for the entire study period using satellite data are (7.32, 4.58). The daily calibrated αPT ranges between 0 and 1.38 for both S1 and S2. Its temporal variability is mainly attributed to the rainfall distribution along the agricultural season. For S3, the daily retrieved αPT remains at a mostly constant value (∼ 0.7) throughout the study period, because of the lack of clear sky disaggregated SM and LST observations during this season. Compared to eddy covariance measurements, TSEB driven only by LST and fc data significantly overestimates latent heat fluxes for the four seasons. The overall mean bias values are 119, 94, 128 and 181 W/m2 for S1, S2, S3 and B1 respectively. In contrast, these errors are much reduced when using TSEB-SM (SM and LST combined data) with the mean bias values estimated as 39, 4, 7 and 62 W/m2 for S1, S2, S3 and B1 respectively.

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The South Asia Monsoon Break Promotes Grass Growth on the Tibetan Plateau

10.5194/egusphere-egu21-5776 ◽

2021 ◽

Author(s):

Yanghang Ren ◽

Kun Yang ◽

Han Wang

Keyword(s):

Soil Moisture ◽

Tibetan Plateau ◽

Solar Radiation ◽

Climate Variability ◽

South Asia ◽

The Tibetan Plateau ◽

The South ◽

Central Eastern ◽

Grass Growth ◽

The Impact

As region that is highly sensitive to global climate change, the Tibetan Plateau (TP) experiences an intra-seasonal soil water deficient due to the reduced precipitation during the South Asia monsoon (SAM) break. Few studies have investigated the impact of the SAM break on TP ecological processes, although a number of studies have explored the effects of inter-annual and decadal climate variability. In this study, the response of vegetation activity to the SAM break was investigated. The data used are: (1) soil moisture from in situ, satellite remote sensing and data assimilation; and (2) the Normalized Difference Vegetation Index (NDVI) and Solar-Induced chlorophyll Fluorescence&#160;(SIF). We found that in the region impacted by SAM break, which is distributed in the central-eastern part of TP, photosynthesis become more active during the SAM break. And temporal variability in the photosynthesis of this region is controlled mainly by solar radiation variability and has little sensitivity to soil moisture. We adopted a diagnostic process-based modeling approach to examine the causes of enhanced plant activity during the SAM break on the central-eastern TP. Our analysis indicates that active photosynthetic behavior in the reduced precipitation is stimulated by increases in solar radiation absorbed and temperature. This study highlights the importance of sub-seasonal climate variability for characterizing the relationship between vegetation and climate.

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