The crop water requirement is a key factor in the agricultural process. It is usually estimated throughout actual evapotranspiration (ET a ). This parameter is the key to develop irrigation strategies, to improve water use efficiency and to understand hydrological, climatic, and ecosystem processes. Currently, it is calculated with classical methods, which are difficult to extrapolate, or with land surface energy balance models (LSEB), such as METRIC and SEBAL, which are based on remote sensing data. This paper describes water, an open implementation of LSEB. The package provides several functions to estimate the parameters of the LSEB equation from satellite data and proposes a new object class to handle weather station data. One of the critical steps in METRIC is the selection of "cold" and "hot" pixels, which water solves with an automatic method. The water package can process a batch of satellite images and integrates most of the already published sub-models for METRIC. Although water implements METRIC, it will be expandable to SEBAL and others in the near future. Finally, two different procedures are demonstrated using data that is included in water package.

A study was performed to evaluate the clumped model in estimating olive orchard evapotranspiration (ET a ) using meteorological data and high-resolution thermal infrared (TIR) imagery obtained from a camera onboard an unmanned aerial vehicle (UAV). An experimental site was established within a superintensive drip-irrigated olive (cv. Arbequina) orchard located in the Pencahue Valley (35.49° S, 71.73°W, and 85 m above sea level), Maule Region, Chile. UAV-based TIR images were collected to obtain the land surface temperature at a very high resolution on 12 clear-sky days during the 2015-2016 growing season. Measurements of the latent heat flux (LE) obtained from an eddy covariance (EC) system were analyzed to assess the clumped model. In addition, submodels to calculate the net radiation (R n ) and soil heat flux (G) were evaluated using a four-way net radiometer and soil heat flux plates with soil thermocouples, respectively. Comparisons indicated that the root mean square error (RMSE) and mean absolute error (MAE) values for LE were 37 and 27 W m −2 , respectively, while those for ET a were 0.44 and 0.35 mm day −1 , respectively. Both UAV-based values for R n and G were estimated with RMSE < 31 W m −2 and MAE < 18 W m −2 . The relative RMSE (rRMSE) values were 26% for LE, 24% for ET a , 5% for R n , and 11% for G. The results suggest that the clumped model based on UAV-based TIR imagery and meteorological data could produce maps with a very high resolution to estimate the intraorchard spatial variability in olive orchard water requirements.

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The presence of micro and nanoplastics in the food chain constitutes an emergent multifactorial food safety and physiological stress problem, which must be approached with a strategic perspective since it affects public health when consuming products that have this pollutant, such as fish and crustaceans, fruits, and vegetables. In this review, the authors present the results by scientists from different disciplines who are dedicated to discovering their chemical constitution and origin, the contents of these microparticles in edible plants, the contamination of water-irrigated soils, the mechanisms that concentrate microplastics in these soils, methods to determine them, contamination of freshwater sources of cities, and the negative effect of nano and microplastics on various food products and their detrimental impact on the environment. Recent findings of plant uptake mechanisms complement this, but more research is needed.

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