LAND SURFACE ENERGY BUDGET OVER THE TIBETAN PLATEAU BASED ON SATELLITE REMOTE SENSING DATA

AbstractData assimilation provides a practical way to improve the accuracy of soil moisture simulation by integrating a land surface model and satellite data. This study establishes a multi-source remote sensing data assimilation framework by incorporating a simultaneous state and parameter estimation method to acquire an accurate estimation of the soil moisture over the Tibetan Plateau. The brightness temperature of the Advanced Microwave Scanning Radiometer 2 (AMSR2) is directly assimilated into the coupled system of the Common Land Model (CoLM) and a microwave radiative transfer model (RTM) to improve the soil moisture simulation. The Moderate Resolution Imaging Spectroradiometer (MODIS) land surface temperature product and the Beijing Normal University (BNU) leaf area index product are employed to not only improve the estimation of temperature and vegetation variables from the CoLM, but also provide more accurate background information for the RTM during the brightness temperature assimilation. In situ measurements from the Naqu network are used to evaluate the results. The model simulation showed an obvious underestimation of soil moisture and overestimation of soil temperature, which was alleviated by the assimilation experiments, particularly in the shallow soil layers. The estimated parameters also showed advantages in the soil moisture simulation when compared with the default parameters. The assimilation experiment presents promising results in the combination of model and multi-source remote sensing data for estimating soil moisture over the complex mountainous region in Tibet.

Download Full-text

A Soil Moisture Spatial and Temporal Resolution Improving Algorithm Based on Multi-Source Remote Sensing Data and GRNN Model

Remote Sensing ◽

10.3390/rs12030455 ◽

2020 ◽

Vol 12 (3) ◽

pp. 455 ◽

Cited By ~ 5

Author(s):

Yaokui Cui ◽

Xi Chen ◽

Wentao Xiong ◽

Lian He ◽

Feng Lv ◽

...

Keyword(s):

Remote Sensing ◽

Soil Moisture ◽

Temporal Resolution ◽

Land Surface ◽

Regional Scale ◽

Remote Sensing Data ◽

The Tibetan Plateau ◽

Sensing Data ◽

Spatio Temporal ◽

Input Variables

Surface soil moisture (SM) plays an essential role in the water and energy balance between the land surface and the atmosphere. Low spatio-temporal resolution, about 25–40 km and 2–3 days, of the commonly used global microwave SM products limits their application at regional scales. In this study, we developed an algorithm to improve the SM spatio-temporal resolution using multi-source remote sensing data and a machine-learning model named the General Regression Neural Network (GRNN). First, six high spatial resolution input variables, including Land Surface Temperature (LST), Normalized Difference Vegetation Index (NDVI), albedo, Digital Elevation Model (DEM), Longitude (Lon) and Latitude (Lat), were selected and gap-filled to obtain high spatio-temporal resolution inputs. Then, the GRNN was trained at a low spatio-temporal resolution to obtain the relationship between SM and input variables. Finally, the trained GRNN was driven by the high spatio-temporal resolution input variables to obtain high spatio-temporal resolution SM. We used the Fengyun-3B (FY-3B) SM over the Tibetan Plateau (TP) to test the algorithm. The results show that the algorithm could successfully improve the spatio-temporal resolution of FY-3B SM from 0.25° and 2–3 days to 0.05° and 1-day over the TP. The improved SM is consistent with the original product in terms of both spatial distribution and temporal variation. The high spatio-temporal resolution SM allows a better understanding of the diurnal and seasonal variations of SM at the regional scale, consequently enhancing ecological and hydrological applications, especially under climate change.

Download Full-text