Soil moisture is a key state variable in many hydrological processes. The Global Land Data Assimilation System (GLDAS) can produce global and continuous soil moisture data sets which have been used in many applications. In this study, simulated soil moisture from four land surface models (LSM) (Mosaic, Noah, Community Land Model, and Variable Infiltration Capacity) in GLDAS‐1 and the more recent GLDAS‐2 were evaluated against in situ soil moisture measurements collected from two soil moisture networks located on the Tibetan Plateau at different soil depths. The two networks provide a representation of different climates and land surface conditions on the Tibetan Plateau which can make the evaluation results more robust and reliable. The results show that all the LSMs can well capture the temporal variation of observed soil moisture with the correlation coefficients mostly being above 0.5. However, they all display biases with the surface soil moisture being systematically underestimated in both of two network regions, and the Mosaic model always shows the largest bias that even reaches 0.192 m3/m3. The causes of the biases were investigated in detail, and we found that the biases may mainly be caused by the soil stratification phenomenon over the Tibetan Plateau. Moreover, errors in model parameters, especially the soil properties data, deficiencies in model structures, and mismatch of the spatial scale and soil depth between LSM simulations and in situ measurements may contribute to the biases as well. Additionally, it was found that GLDAS‐2 nearly does not show superior performance than GLDAS‐1 over the Tibetan Plateau.

The term remote sensing became common after 1962 and generally refers to nonintrusive Earth observation using electromagnetic waves from a platform some distance away from the object of the study. After more than five decades of development, humankind can now use different types of optical and microwave sensors to obtain large datasets with high precision and high resolution for the atmosphere, ocean, and land. The frequency of data acquisition ranges from once per month to once per minute, the spatial resolution ranges from kilometer to centimeter scales, and the electromagnetic spectrum covers wavebands ranging from visible light to microwave wavelengths. Technological progress in remote sensing sensors enables us to obtain data on the global scale, remarkably expanding humanity's understanding of its own living environment from spatial and temporal perspectives, and provides an increasing number of data resources for Digital Earth. This chapter introduces the developments and trends in remote sensing satellites around the world. Keywords Remote sensing • Digital Earth • Satellite • Earth observation 3.1 Development of Remote Sensing Remote sensing is a core technology for Earth observation. It covers information collection, in-orbit processing, information storage and transmission, ground reception, processing for applications, calibration, verification, applied research, and basic research, providing fundamental data resources for Digital Earth (Guo 2012).

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