MODIS) and found that higher-spatial and temporal-resolution images better
explained spatial variability of crop yield.
2.3.4 Crop Evapotranspiration (ET) and Water Use
Crop evapotranspiration (ET) includes soil evaporation (E) and crop transpiration
(T). The transpiration accounts for the loss of water as it vapors through the stomata
in leaves. The water is extracted by the root system in the root zone and represents a
loss of water in the soil, and thus ET is used interchangeably with crop water use.
Crop water stress can be detected using ET and Evaporative Stress Index (ESI)
(Anderson et al. 2016). For irrigated crops, ET measures the water used to grow
food. Temporal and spatial continuous ET data are needed for agricultural management and irrigation scheduling.
ET can be estimated using surface temperature retrieved from thermal infrared
imagery through an energy balance model. The Penman-Monteith (PM) equation is
found to be consistent over a wide range of climatic conditions. The MODIS ET
algorithm is based on the PM equation. MODIS ET data products are available since
2001 (Mu et al. 2007). Agricultural applications require ET information over a range
of temporal and spatial resolutions. MODIS 8-day ET product at 500-m spatial
resolution may be too coarse to assess water use at the field scale. USDA Hydrology
and Remote Sensing Laboratory (HRSL) has developed a multiscale flux modeling
system using TIR and LAI data from multiple satellite platforms (Anderson et al.
2007, 2012). This system is unique in that it merges low-spatial/high-temporal
resolution information available from geostationary satellites with higher-spatial/
lower-temporal information from polar orbiters such as MODIS, VIIRS, and
Landsat, generating self-consistent maps of water and energy fluxes from continental
to field scales. For more detailed spatial analyses, such as mapping variability in
water use across a watershed or between individual farm fields, an ALEXI flux
disaggregation approach (DisALEXI) can be applied using sharpened temperature
and LAI information from sensors like Landsat to map fluxes at 30-m resolution
(Anderson et al. 2012).
Many ET estimation methods have been developed in the past decades. Recently,
several trusted ET methods, including DisALEXI, METRIC, SEBAL, etc. are
ensembled in the OpenET platform. The platform uses multiple remote sensing
data sources and cloud computing techniques to estimate ET and provide easy
access at user-defined scales and dates (https://openetdata.org/).
2.3.5 Soil Moisture Retrieval
Soil moisture and its availability affect crop growth and yield. In the past decades,
microwave remote sensing has been used for soil moisture estimation. Passive and
20
F. Gao
explained spatial variability of crop yield.
2.3.4 Crop Evapotranspiration (ET) and Water Use
Crop evapotranspiration (ET) includes soil evaporation (E) and crop transpiration
(T). The transpiration accounts for the loss of water as it vapors through the stomata
in leaves. The water is extracted by the root system in the root zone and represents a
loss of water in the soil, and thus ET is used interchangeably with crop water use.
Crop water stress can be detected using ET and Evaporative Stress Index (ESI)
(Anderson et al. 2016). For irrigated crops, ET measures the water used to grow
food. Temporal and spatial continuous ET data are needed for agricultural management and irrigation scheduling.
ET can be estimated using surface temperature retrieved from thermal infrared
imagery through an energy balance model. The Penman-Monteith (PM) equation is
found to be consistent over a wide range of climatic conditions. The MODIS ET
algorithm is based on the PM equation. MODIS ET data products are available since
2001 (Mu et al. 2007). Agricultural applications require ET information over a range
of temporal and spatial resolutions. MODIS 8-day ET product at 500-m spatial
resolution may be too coarse to assess water use at the field scale. USDA Hydrology
and Remote Sensing Laboratory (HRSL) has developed a multiscale flux modeling
system using TIR and LAI data from multiple satellite platforms (Anderson et al.
2007, 2012). This system is unique in that it merges low-spatial/high-temporal
resolution information available from geostationary satellites with higher-spatial/
lower-temporal information from polar orbiters such as MODIS, VIIRS, and
Landsat, generating self-consistent maps of water and energy fluxes from continental
to field scales. For more detailed spatial analyses, such as mapping variability in
water use across a watershed or between individual farm fields, an ALEXI flux
disaggregation approach (DisALEXI) can be applied using sharpened temperature
and LAI information from sensors like Landsat to map fluxes at 30-m resolution
(Anderson et al. 2012).
Many ET estimation methods have been developed in the past decades. Recently,
several trusted ET methods, including DisALEXI, METRIC, SEBAL, etc. are
ensembled in the OpenET platform. The platform uses multiple remote sensing
data sources and cloud computing techniques to estimate ET and provide easy
access at user-defined scales and dates (https://openetdata.org/).
2.3.5 Soil Moisture Retrieval
Soil moisture and its availability affect crop growth and yield. In the past decades,
microwave remote sensing has been used for soil moisture estimation. Passive and
20
F. Gao