These include: (a) data fusion (e.g., Landsat, MODIS time-series, secondary), and (b) new satellite sensor constellations such as Rapideye that is specifically targeted for agriculture with nominal 5 m data in 5 bands including a band in red-edge. [PERSON] _et al._ demonstrated the high value of time-series MODIS data to rapidly estimate global cropland extent. The approach is easily repeatable and rapid. The MODIS NDVI threshold approach in their study matches MODIS remote sensing derived areas with areas reported by countries (e.g., when they match a MODIS NDVI threshold derives areas to a specific country statistic or areas reported by Foreign Agricultural Service (FAS) of USDA). _However, such an approach matches areas, but does not always match the precise location of these areas- leading to significant uncertainties in both areas and their precise locations_. Also, area calculations using MODIS data will be more accurate when sub-pixel area fractions are determined precisely [1,2]. One of the strengths of MODIS data is in its ability to determine cropland intensities (e.g., double cropping, triple cropping). [PERSON] _et al._ determine global patterns of cropland and fallow land intensities. This paper makes an important contribution as almost all global cropland studies have ignored fallow dynamics. Indeed, [PERSON] _et al._ found that of the 1.6 billion hectares of croplands, nearly 28% was fallow during year 2000. Cropland follows are ever changing and are more prominent in certain places (e.g., Africa) relative to other places (e.g., USA). Specific methods of irrigated cropland mapping using satellite sensor data fusion involving various resolutions (e.g., 2.5 m to 30 m) and a suite of methods (e.g., a rule based on tassel cap) is discussed by [PERSON] _et al_. Remote sensing data and methods offer an excellent platform for Crop yield and productivity modeling as demonstrated by [PERSON] and [PERSON], [PERSON] _et al._, and [PERSON] _et al._ using multi-sensor vegetation indices, neural network techniques, multiple data fusion techniques, and modeling. Typically, broad-band optical remote sensing explains about 70% variability in crop yields. However, with innovative approaches involving multi-data fusion, it is possible to improve models to explain greater variability. [PERSON] _et al._, for example, found SAR/ASAR data better explained yield accumulation, grain filling, and senescence estimation during generative phases of crop growth. Further, ability to observe change over space and time in a consistent and continual manner is one of the biggest advantages offered by remote sensing. This is well demonstrated in a study [PERSON] and [PERSON]' They showed how large scale mining in agricultural lands are continually monitored using Landsat data and how agricultural land cover changes effect food security. These papers provide an excellent mix of data, approaches, and methods of global and local cropland area mapping, modeling, and monitoring. Opportunities to use the above discussed data, methods, and approaches in a monitoring framework is presented and discussed by [PERSON] _et al_. They present a comprehensive remote sensing based global agricultural monitoring system under a Global Agriculture Monitoring Project (GLAM)--a joint effort of NASA, USDA, UMD and SDSU. This system presents remote sensing data, derived products, and analysis tools of GLAM for agricultural crop-condition monitoring and production assessment at global level. The goal of the effort is to supplement and complement and improve the existing USDA's Foreign Agricultural Service (FAS) Decision Support System (DSS) for crop-condition monitoring and production assessment. An overwhelming proportion of human water use is consumed for food production through irrigated areas (blue water use) and rainfed areas (green water use)- see the paper by [PERSON] _et al_.. Crop water use is determined using surface energy balance modeling as discussed by [PERSON] _et al_., [PERSON] _et al_., and [PERSON] _et al_.