# A Review of Irrigation Information Retrievals from Space and Their Utility for Users
## 4 Irrigation Modeling and Data Assimilation  
Irrigation modeling (i.e., providing models with tools to simulate irrigation) is a way to simulate the additional water applied on soils from internal or external sources (such as canals, groundwater, or pipelines). However, the reliability of the irrigation schemes used in hydrological, land surface, or crop models depends on the validity of a number of simplifying assumptions and input parameters. For example, irrigation water is often applied to the land surface only when soil moisture drops below arbitrarily chosen soil moisture thresholds in order to restore optimal growing conditions. These thresholds typically depend on soil and vegetation parameters, which in turn depend on large-scale soil texture maps. For example, [PERSON] et al. [108] and [PERSON] et al. [109] have set a critical value for the root-zone soil moisture (RZSM) within irrigated tiles based exclusively on the soil field capacity. Given the simplicity of these methods, both the timing and amount of such irrigation estimates can be very inaccurate as they assume that water resources are unlimited, and that the farmer's irrigation decision is based on soil water availability only. In reality, several other factors are considered, including specific practices such as soil leaching, water regulations, climate, and resource availability. In the recent years, the irrigation parameterization in large-scale Land Surface Models (LSMs) has seen considerable improvements owing to the development of more complex irrigation schemes [110]. For instance, [PERSON] et al. [111] included three trigger criteria to dynamically simulate daily irrigation in the Noah LSM [112], which are: (i) whether a tile is potentially irrigated, (ii) whether it is the growing season (defined by a threshold of 40% of annual range of greenness fraction), and (iii) how much water is available (or more importantly, lacking) in the root zone. In this scheme, irrigation is applied as additional precipitation when the RZSM falls below a triggering user-defined threshold until the field capacity is reached. Additional improvements in irrigation parameterization have been made by implementing various types of irrigation systems. The Noah LSM, for example, can be coupled with a drip as well as with a flood method (both built based on the work by [PERSON] and [PERSON] [113]). These two additional schemes, characterized by differences in frequency, timing, and amount of irrigation, are described by [PERSON] et al. [114]. Some studies have also attempted to incorporate irrigation schemes into other global LSMs, including the Community Land Model (CLM, [115]) and the Organizing Carbon and Hydrology in Dynamic Ecosystems (ORCHIDEE) model [116], demonstrating that intensive irrigation at the regional scale strongly affects river discharge and groundwater and has a regional impact on the partition of energy between sensible and latent fluxes. Despite these developments, however, irrigation modeling is still in its infancy and still prone to uncertainties. A potential remedy to model uncertainties due to missing or inaccurately modeled irrigation might be data assimilation. For example, [PERSON] et al. [117] investigated the extent to which GRACE TWS data assimilation in the Catchment Land Surface Model (CLSM) could correct for errors due to missing model processes such as groundwater extraction for irrigation. However, even though the satellite data assimilation largely improved the estimation of some processes, it also deteriorated the estimates of others due to inappropriate treatment of the irrigation water source. To reconcile the information contained in GRACE with LSM simulations, [PERSON] et al. [118] modified the Noah-MP LSM in a study over the High Plains Aquifer (HPA) to include a dedicated groundwater-based irrigation scheme. Additionally, to account for seasonal and interannual variability in irrigated areas, the authors applied a monthly time-varying greenness vegetation fraction (GVF) dataset within the model.