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Long-term warming trends across the globe have shifted the distribution of temperature variability, such that what was once classified as extreme heat relative to local mean conditions has become more common. This is also true for agricultural regions, where exposure to extreme heat, particularly during key growth phases such as the reproductive period, can severely damage crop production in ways that are not captured by most crop models. Here, we analyze exposure of crops to physiologically critical temperatures in the reproductive stage (T crit ), across the global harvested areas of maize, rice, soybean and wheat. Trends for the 1980-2011 period show a relatively weak correspondence (r = 0.19) between mean growing season temperature and T crit exposure trends, emphasizing the importance of separate analyses for T crit . Increasing T crit exposure in the past few decades is apparent for wheat in Central and South Asia and South America, and for maize in many diverse locations across the globe. Maize had the highest percentage (15%) of global harvested area exposed to at least five reproductive days over T crit in the 2000s, although this value is somewhat sensitive to the exact temperature used for the threshold. While there was relatively little sustained exposure to reproductive days over T crit for the other crops in the past few decades, all show increases with future warming. Using projections from climate models we estimate that by the 2030s, 31, 16, and 11% respectively of maize, rice, and wheat global harvested area will be exposed to at least five reproductive days over T crit in a typical year, with soybean much less affected. Both maize and rice exhibit non-linear increases with time, with total area exposed for rice projected to grow from 8% in the 2000s to 27% by the 2050s, and maize from 15 to 44% over the same period. While faster development should lead to earlier flowering, which would reduce reproductive extreme heat exposure for wheat on a global basis, this would have little impact for the other crops. Therefore, regardless of the impact of other global change factors (such as increasing atmospheric CO 2 ), reproductive extreme heat exposure will pose risks for global crop production without adaptive measures such as changes in sowing dates, crop and variety switching, expansion of irrigation, and agricultural expansion into cooler areas.

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Canopy temperature (T can) is a key driver of plant function that emerges as a result of interacting biotic and abiotic processes and properties. However, understanding controls on T can and forecasting canopy responses to weather extremes and climate change is difficult due to sparse measurements of T can at appropriate spatial and temporal scales. Burgeoning observations of T can from thermal cameras enable evaluation of energy budget theory and better understanding of how environmental controls, leaf traits, and canopy structure influence temperature patterns. The canopy scale is relevant for connecting to remote sensing and testing biosphere model predictions. We anticipate that future breakthroughs in understanding of ecosystem responses to climate change will result from multi-scale observations of T can across a range of ecosystems.

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For remote sensing to be useful for analyzing crop yield gaps, methods should be accurate at the eld scale without need for local ground calibration. We used an extensive eld-level data set of on-farm yields from 134 irrigated and 94 rainfed maize (Zea mays L.) elds in Nebraska during a 4-yr period to evaluate three methods that do not require ground-based calibration. e rst method is based on summing estimates of absorbed photosynthetically active radiation from the Moderate Resolution Imaging Spectroradiometer (MODIS) sensor, the second on using either MODIS or Landsat data to calibrate a crop model (Hybrid-Maize), and the third using Hybrid-Maize simulations to train a simple regression, which is then applied to MODIS or Landsat data. For MODIS, all three methods performed similarly poorly at predicting maize yields, with an R 2 between observed and predicted yields of roughly 0.10 for rainfed and 0.20 for irrigated elds. Estimates from Landsat were considerably more accurate, with up to 20% of rainfed and 50% of irrigated yield variation captured by the predictions. Across all methods and sensors, irrigated yield variations were more successfully captured than rainfed yields because of relatively smaller rainfed eld sizes and the added di culty of modeling crop water stress in rainfed elds. Agreement between observed and predicted yields was highest for the third approach, which is attractive because it leverages a crop model's ability to synthesize knowledge on crop physiology and year-to-year di erences in weather throughout the season, yet produces a simple regression that can be rapidly applied to Landsat imagery. Since the advent of Earth-observing satellites several decades ago, researchers have made eff orts to derive useful information about agricultural fi elds from satellite data. Perhaps the most relevant and sought-aft er quantity is yield (i.e., crop grain production per unit of land area), for which remotely sensed estimates would greatly benefi t farmers as well as researchers and policymakers concerned with food production. Ideally, satellite data would be able to provide an accurate yield estimate for each individual fi eld each year and even for individual parcels within fi elds, regardless of the crop being grown. Providing information about yield at this level of detail remains a challenge, however, both because of limitations imposed by the spatial, spectral, and temporal resolutions of available satellites and because of complicated relationships between remotely measurable parameters and crop productivity.Yield estimates based on satellite data are useful for a variety of applications, each with their own requirements for achieving desired levels of accuracy and spatial detail. For example, the Famine Early Warning System utilizes satellite data to provide nearreal-time assessments of crop conditions over large areas (http:// fews.net). For this application, the objective is to provide indications of large areas that are well below average yields, so high accuracy and fi ne spa...