Global Drought and Flood. Группа авторов

Global Drought and Flood - Группа авторов


Скачать книгу
drought monitoring and complementary information about root zone soil moisture needs to be provided using modeling and data assimilation (e.g., Mladenova et al. 2019). Surface soil moisture data are derived mainly from passive or active microwave satellites (De Jeu et al., 2008; Njoku et al., 2003; Takada et al., 2009; Wagner et al., 1999). Currently, the Soil Moisture Active Passive (SMAP; Figure 1.4; Entekhabi et al., 2010) and the Soil Moisture Ocean Salinity (SMOS; Kerr et al., 2010) missions are the main sources of the remote‐sensing‐based soil moisture estimates. These data sets have been used extensively for drought monitoring (e.g., Mishra et al., 2017; Sadri et al., 2018; Sánchez et al., 2016). Soil moisture also can be inferred from other microwave sensors (Entekhabi et al., 2010; Martínez‐Fernández et al., 2016; Moradkhani, 2008; Scaini et al., 2015) such as: the Scanning Multichannel Microwave Radiometer (SMMR), the SSM/I, the European Remote Sensing (ERS) scatterometer, the TRMM microwave imager, the Advanced Scatterometer (ASCAT), and Advanced Microwave Scanning Radiometer2 (AMSR2). Long‐term soil moisture data appropriate for monitoring drought can be obtained through certain databases such as the Water Cycle Multimission Observation Strategy (WACMOS), which is derived from multiple satellites (Ambaw, 2013). Similarly, the European Space Agency's Climate Change Initiative (ESA CCI) offers a soil‐moisture data set with a record of over 30 years that is particularly suitable for monitoring agricultural drought. The ESA CCI merges soil moisture retrievals of a number of different satellites and provides three types of product: active microwave, passive microwave, and combined active–passive microwave (Gruber et al., 2019). The ESA CCI soil‐moisture data set, however, has large gaps over densely vegetated areas. Martínez‐Fernández et al. (2016) show the reliability of the CCI soil‐moisture data set for purposes of modeling agricultural drought.

Schematic illustration of the soil moisture observation by NASA’s Soil Moisture Active Passive (SMAP) satellite.

      (Courtesy of NASA’s earth observatory: https://earthobservatory.nasa.gov/images).

      Monitoring agricultural drought requires high‐resolution data to reveal detailed variations of soil moisture. To improve the spatial resolution of soil moisture data, several downscaling methods have been used, such as machine learning frameworks (Im et al., 2016; Park et al., 2017), DISaggregation based on Physical And Theoretical scale CHange (DISPATCH) which uses shortwave and thermal data from Moderate‐Resolution Imaging Spectroradiometer (MODIS) to downscale SMOS data (Merlin et al., 2015), and Smoothing Filter‐based Intensity Modulation (SFIM) which integrates microwave data from SMAP, Sentinel‐1, and AMSR2 to downscale soil moisture data to an enhanced resolution of 0.1° × 0.1° (Santi et al. 2018).

      1.2.3. Relative Humidity

Schematic illustration of the standardized Relative Humidity Index (SRHI) for (a) August 2010, (b) probability of drought detection, and (c) missed drought ratio, which indicates that relative humidity can be used in conjunction with other drought indices for early detection of drought onset.

      (Farahmand et al., 2015).

      Measurements of relative humidity via remote sensing are often undertaken with IR‐based observing platforms (e.g., the AIRS20) (Fetzer et al., 2006; B. Tian et al., 2004). However, clouds tend to bias the IR observations, which is a major limiting factor since no observation of wet conditions will be available after a strict cloud screening (John et al., 2011). Another major issue is the variation of relative humidity due to changes in saturated vapor pressure, as it is significantly influenced by air temperature. Therefore, even with a fixed water vapor content, changes in air temperature will result in variations in relative humidity (Moradi et al., 2016). On the other hand, microwave sounder retrievals can produce large errors owing to modeling errors of Earth’s limb radiances (e.g., Microwave Limb Sounder) (Lambert et al., 2007). In general, too much uncertainty arises from observations of water vapor in diurnal and spatial distribution of the troposphere (Boyle & Klein, 2010), and having a course resolution of 2–3 km in both IR and microwave sounders, these instruments are unable to portray a detailed vertical structure of water vapor.

      The frequency of unusually dry and hot conditions has increased in various parts of the world (Griffin & Anchukaitis, 2014; Seager & Hoerling, 2014). Some studies reported that the ever‐increasing anthropogenic radiative forcing is responsible for the recent changes in Earth’s hydrological


Скачать книгу