# Remote Sensing for Irrigation of Horticultural Crops
## 4 Approaches to Precision Irrigation
### Remote Sensing and Soil
#### 4.3.1 Vegetation Indices  
Different indexes have been set up to estimate different crop parameters (e.g., crop density, biomass, chlorophyll, nitrogen, water content, etc.); the Enhanced Vegetation Index (EVI), the Soil Adjusted Vegetation Index (SAVI), the Simple Ratio (SR), and the Normalized Difference Vegetation Index (NDVI), are measures of canopy greenness, which may be related to physiological processes such as transpiration and photosynthesis ([PERSON] et al., 2012; [PERSON] et al., 2013).  
[PERSON] et al. (2012) studied the relationship between stem water potential (SWP) and VIs extracted from high spatial resolution images. A circle mission was conducted to evaluate the effects of bidirectional reflectance distribution fraction (BRDF) and canopy NDVI mean. The authors demonstrated the importance of BRDF to infer SWP from NDVI mean. Circle missions indicated that view angle influenced the canopy NDVI. This could lead to a prediction error of SWP of up to 12 bars. Further analysis of images identified a non-normalized difference vegetation index (NNDVI) based on ground-derived measurements over three weeks that showed a more significant relationship with SWP than the canopy NDVI mean.  
[PERSON] et al. (2013), in a study on a remote sensing-based WUE evaluation of sub-surface irrigated grape vines, compared several irrigation methods. Differences in NDVI values occurred among different irrigation strategies (surface and subsurface irrigation methods) and when irrigation rates were compared. Multispectral satellite imagery, particularly with a red edge waveband, demonstrated the potential for quantifying soil-moisture-tension variability, and hence could be used for variable rate irrigation management. Red Edge Normalized Difference Vegetation Index (RENDVI) was especially sensitive to soil moisture tension and demonstrated that a single image could be representative of variability up to two weeks after acquisition. However, it will be necessary to confirm the repeatability of these results at more maize growth stages and with other crops. Finally, an economic study to evaluate the monetary and environmental implications of such management at a field scale would help transition these findings into adoption by growers (2014).  
[PERSON] et al. (2015) presented work on the use of remote sensing for measuring soil water availability for optimizing crop growth. Data were determined by satellite and integrated with atmosphere and crop parameters to calculate biophysical indicators and indices of water stress by NDVI, Kc, and Kcb. The results showed that remote sensing based on multispectral images may be used, with a high degree of accuracy and spatial representation, to calculate crop water and irrigation requirements. However, frequent mapping of soil water status and crop irrigation requirements would be required; this is the weakest point of the satellite imagery methodology used. Temporal representation, once each 16 days with the Landsat 5 satellite, would not be enough for irrigation management. The Sentinel 2 satellite is available and could be combined with data from other satellites to provide more frequent images of the land surface.