Radiation estimation and crop growth trajectory reconstruction by novel algorithms improve MOD16 evapotranspiration predictability for global multi-site paddy rice ecosystems

• MOD16 products indicated significant underestimations in all paddy rice ET observations. • R n estimation in overcast conditions and LAI reconstruction were two key causes. • Daily R n estimations under all-sky conditions by a global cloudy index algorithm were improved by 40.6%. • Daily LAI dynamics estimated by the LTDG_PhenoS algorithm were improved by 818.7%. • Daily ET estimations were improved by 68.7%. Reliable estimations in evapotranspiration (ET) of paddy rice ecosystems by satellite products are critical because of their important roles in regional hydrological processes and climate change. However, the NASA MODIS ET products (MOD16A2) and its derivatives do not have good correlations with all global paddy rice ET observations. In this research, MOD16 model sensitivity analyses and parameter optimization strategies were conducted in order to solve the problem. Results suggested that underestimation of daily net radiation (R n) in overcast conditions and less satisfactory reconstruction of field-scale leaf area index (LAI) growth trajectory from the start date of field flooding and transplanting (FFTD) to the end of growing seasons by MODIS coarse vegetation index were identified as two major causes. A Light and Temperature-Driven Growth model and a Phenology-based LAI temporal Smoothing method fusion algorithm (LTDG_PhenoS) and an improved R n estimation method were introducted and evaluated in paddy rice fields in South Korea, Japan, China, Philippines, India, Spain, Italy, and the USA from 2002 to 2019. The LTDG_PhenoS algorithm considers Landsat and MODIS EVI observations and meteorological data as input variables and 30-m LAI daily time series as outcomes. Introducing the global cloudy index algorithm resulted in improved estimations of daily R n under all-sky conditions, with a significant decrease of root mean square error (RMSE) from 1.87 to 1.11 MJ m−2 day−1. The LTDG_PhenoS algorithm well reconstructed crop LAI growth dynamics from the FFTD to the end of rice growing seasons, with a substantial decline of RMSE from 1.49 to 0.27 m2/m−2. The FFTD estimations by the LTDG_PhenoS algorithm had an R2 of 0.97 and a small RMSE of less than 12-days. Daily ET rates estimated by novel algorithms had a substantial decline in RMSE from 2.88 to 0.90 mm day−1. [ FROM AUTHOR] Copyright of Journal of Hydrology is the property of Elsevier B.V. and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full . (Copyright applies to all s.)

Optimized empirical model based on whale optimization algorithm for simulate daily reference crop evapotranspiration in different climatic regions of China

• The accuracy of the temperature, radiation and hybrid models improved by 12.05 %, 11.06% and 10.46% after being optimized by WOA. • The estimation accuracy of the temperature, radiation and hybrid models optimized by the whale algorithm were higher than the prediction result of the ELM model. • The empirical model with more input parameters has higher estimation accuracy than the empirical model with fewer parameters. The accurate estimation of reference crop evapotranspiration (ET 0) is of great significance to improve agricultural water use efficiency and optimize regional water resources management. At present, the applicability evaluation system of ET 0 models is still lacking in several climate regions in China, leading to the confusion in application of the ET 0 model in some specific regions. In this study, the daily meteorological data of 84 representative stations in four climate regions of China during the past 30 years (1991–2019) were selected to evaluate the ET 0 simulation results of twelve empirical models (four temperature models, five radiation models, and three hybrid models) on the daily scale, and the optimal models suitable for each climate region were screened. Whale optimization algorithm (WOA) was used to optimize the optimal model to improve the simulation accuracy, and the ET 0 results were compared with those predicted by extreme learning machine (ELM). The results showed that the estimation accuracy of the hybrid model was the best throughout China, followed by the radiation model, and the temperature model was relatively poor, with R2 ranges of 0.77–0.88, 0.60–0.86, and 0.58–0.82, respectively. Among the temperature-based models, Hargreaves-Samani and Improve Baier-Robertson model had the highest accuracy, with R2 of 0.80 and 0.79. Among the radiation-based models, Priestley-Taylor and Jensen-Haise models had the best accuracy, with R2 of 0.82 and 0.79. Among the hybrid models, Penman model had the highest accuracy, with R2 of 0.84. The accuracy of Hargreaves-Samani and Improve Baier-Robertson model in SMZ climate region was higher than TCZ, TMZ, and MPZ, and the accuracy of Jensen-Haise model in TCZ was the highest. The estimation accuracy of Priestley-Taylor and Penman models was similar in SMZ, TCZ, TMZ and MPZ. Using WOA to optimize the optimal temperature, radiation, and hybrid models, the prediction accuracy was improved by 12.05 %, 11.06 %, and 10.46 %, which were higher than the result of ELM model, with R2 of 0.90, 0.91, 0.95 and 0.90, respectively. Therefore, it is recommended to adopt WOA to optimize the empirical model to estimate the ET 0 all over China. [ FROM AUTHOR] Copyright of Journal of Hydrology is the property of Elsevier B.V. and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full . (Copyright applies to all s.)

Managing spatial irrigation using remote-sensing-based evapotranspiration and soil water adaptive control model

Irrigation has traditionally been managed as uniform applications where an entire field receives the same depth of water. Motivation to improve current irrigation practices has led to different approaches utilizing remotely-sensed images to inform variable rate irrigation management. This study conducted in 2019 and 2020 implemented the Spatial EvapoTranspiration Modeling Interface (SETMI), a remote-sensing-based evapotranspiration (ET) and water balance model, for managing variable rate irrigation of a maize and soybean field. This model tracked soil water content through the estimation of daily ET and tracking of various water fluxes entering and leaving a field. SETMI was used in two different irrigation treatments informed using Planet satellite (SETMI-SAT) and unmanned aerial system (UAS, SETMI-UAS) remotely-sensed images. A uniform irrigation approach managed by a professional crop consultant and a non-irrigated approach were used as the baseline in comparing irrigation management approaches. The irrigation treatments were evaluated on dry grain yield, gross irrigation, actual ET, deep percolation, change in soil water content, and water productivity. The uniform irrigation approach managed by the crop consultant applied the highest irrigation in 2019 and 2020 for maize (2019: 155 mm, 2020: 213 mm) and soybean (2019: 124 mm;2020: 183 mm) while the SETMI irrigation treatments applied less irrigation for maize (2019: 131, 132 mm;2020: 154, 140 mm) and soybean (2019: 116, 94 mm;2020: 154, 175 mm). Maize yield was highest for the uniform irrigation approach in 2019 (14.9 Mg ha−1) and 2020 (13.3 Mg ha−1). The highest soybean yield was produced by the SETMI-SAT irrigation management approach in 2019 (5.0 Mg ha−1) and 2020 (4.8 Mg ha−1). Significant differences (p-value < 0.05) in applied irrigation between the uniform and SETMI irrigation management approaches were observed while there were no significant differences in dry grain yield for both maize and soybean in 2019 and 2020. At least one of the SETMI irrigation treatments produced higher crop, irrigation, and ET water productivity values in comparison to those produced by the uniform irrigation treatment for all crop-years. A post-season analysis was completed using the SETMI-UAS and SETMI-SAT treatments to evaluate the accuracy of estimated rootzone soil water depletion provided by SETMI. Rootzone depletion calculated from neutron probe volumetric soil water content measurements were compared to the modeled depletion from the SETMI-UAS and SETMI-SAT treatments. The 2020 modeled and measured depletion comparison produced better agreement resulting in a root mean squared error (RMSE) < 17 mm compared to 2019 (RMSE < 27 mm). The VRI center pivot malfunctioned during the 2019 season which caused unresolved discrepancies between actually applied irrigation and what the system was programmed to apply. The VRI system was fixed before the 2020 season. • Remotely-sensing-based evapotranspiration model can improve irrigation management. • Variable rate irrigation can be effective informed through remote sensing. • Variable rate irrigation can decrease applied irrigation while maintaining crop yields. [ FROM AUTHOR] Copyright of Agricultural Water Management is the property of Elsevier B.V. and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full . (Copyright applies to all s.)

Spatiotemporal evolution and suitability of apple production in China from climate change and land use transfer perspectives

Apple production in China, the world's largest apple producer and consumer, is challenged by a huge and growing population coupled with rapid industrialisation and urbanisation. China's apple output has increased continuously over the past 42 years with distinctive spatial differences. Herein, changes in the spatial patterns of apple production increases, and their potential impact factors in China are described at the provincial level. Between 1978 and 2019, the centre‐of‐gravity of apple production shifted southwest towards the upper reaches of the Yellow River, the main water source for agricultural irrigation in North China. Analysis of absolute and relative growth of apple output reveals that the Loess Plateau, characterised by fragile habitat and low land productivity, has gradually become a major contributor to apple production. Despite annual increases in apple output, apple production system has become more fragile and unstable overtime, especially in the Shaanxi‐Gansu region where apple cultivation is prevalent. With continuous changes in policy, the amount of forest transfer (i.e. the area of other land use types converted to forest) has significantly affected the impact of standardised precipitation evapotranspiration index on apple production increases in China. Thus, to prevent the degradation of new forests, a differentiated management and protection system should be implemented for apple planting sub‐regions. This should include altering subsidy policies on apple production, enhancing soil erosion control in the Loess Plateau and strengthening ecological management of forests and grassland.

Effects of Agrivoltaic Systems on the Surrounding Rooftop Microclimate

Agrivoltaic systems have the potential to maximize the usefulness of spaces in building rooftops. Urban farming systems improve the microclimatic conditions, which are beneficial to solar photovoltaic (PV) systems, as they lower the operating temperatures, resulting in a higher operating efficiency. Microclimate simulations by means of ENVI-met simulation showed that between 0800 h and 1800 h, PV temperatures in the plot that has crops below the PV system were on average lower by 2.83 °C and 0.71 °C as compared without crops on a typical sunny and cloudy day, respectively. Hence, we may see PV efficiency performance improvement of 1.13–1.42% and 0.28–0.35% on a sunny day and cloudy day, respectively. Data collected from a physical prototype of an agrivoltaic system suggested that evaporative cooling was responsible for the reduction in ambient temperatures. The presence of crops growing underneath the PV canopy resulted in the agrivoltaic prototype generating between 3.05 and 3.2% more energy over the day as compared to a control system with no crops underneath.

Irrigated agriculture potential of Australia's northern territory inferred from spatial assessment of groundwater availability and crop evapotranspiration

Agricultural expansion has been a hot topic in the Northern Territory (NT) of Australia in recent years. However, insufficient information on available water resources and crop evapotranspiration is a bottleneck to this expansion. Towards closing this gap, this study employs the newest Global Land Data Assimilation System (GLDAS;version 2.2) catchment products assimilated from the Gravity Recovery and Climate Experiment (GRACE;hereafter called GLDAS-DA) and the Food and Agriculture Organization (FAO) Penman-Monteith equation to spatially evaluate the Balance between water availability (i.e., groundwater and effective rainfall) and melons, maize and citrus crop evapotranspiration (water demand) of three representative (short-, medium-season and perennial) crop types over the NT for the 2010–2019 period. Specifically, this Balance is the estimated ratio of water availability and crop evapotranspiration, representing the crop area that can be planted in each GLDAS-DA grid cell. The larger the Balance , the greater the irrigated agriculture potential. Under the average 2010–2019 conditions, our results show that the northern part of the NT has the highest irrigated agriculture potentials with the average Balance of 9430 ha (15.7%), 5490 ha (9.1%) and 3520 ha (5.8%) for melons, maize and citrus, respectively, excluding non-agriculture areas. Irrigated agriculture in the central part of the NT shows less potential compared to the northern part of the NT, with the average Balance of 2780 ha (4.6%), 2000 ha (3.3%) and 970 ha (1.6%) for melons, maize and citrus, respectively (excluding non-agriculture areas). The southern part of the NT shows an average Balance below 1% of grid cell for all three crops, suggesting that only small-scale irrigated agriculture could be possible. In addition, the Balance across most of the northern and central parts of the NT decreased by 50% or more during 2019 dry period. Drought risk management should therefore be a serious consideration when exploring further expansion of irrigated agriculture in the NT. • Irrigated-agriculture potential in NT is assessed through groundwater and ETc. • The northern NT has highest potential for irrigated agriculture. • The shoreline of central NT shows potential for intensive irrigated-farming. • The southern NT has limited potential for irrigated agriculture. • Agriculture potential reduces 50% or more in the NT during the dry climate of 2019. [ FROM AUTHOR];Copyright of Agricultural Water Management is the property of Elsevier B.V. and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full . (Copyright applies to all s.)

Optimization of extreme learning machine model with biological heuristic algorithms to estimate daily reference crop evapotranspiration in different climatic regions of China

• Hybrid ELM models (PSO-ELM, GA-ELM and ABC-ELM) were proposed for estimating ET 0 in different climate zones of China. • PSO-ELM model had the highest accuracy, followed by GA-ELM and ABC-ELM. • Hybrid ELM models outperformed standalone ELM and empirical models in different climate zones. • PSO-ELM model with T max , T min and RH obtained accurate ET 0 estimates in TCZ, SMZ and TMZ. • PSO-ELM model with only T max and T min was better performance on ET 0 estimates in MPZ. Accurate prediction of reference crop evapotranspiration (ET 0) is important for regional water resources management and optimal design of agricultural irrigation system. In this study, three hybrid models (PSO-ELM, GA-ELM and ABC-ELM) integrating the extreme learning machine model (ELM) with three biological heuristic algorithms, i.e., PSO, GA and ABC, were proposed for predicting daily ET 0 based on daily meteorological data from 2000 to 2019 at twelve representative stations in different climatic zones of China. The performances of the three hybrid ELM models were further compared with the standalone ELM model and three empirical models (Hargreaves, Priestley-Talor and Makkink models). The results showed that the hybrid ELM models (R 2 = 0.973–0.999) all performed better than the standalone ELM model (R 2 = 0.955–0.989) in four climatic regions in China. The estimation accuracy of the empirical models was relatively lower, with R2 of 0.822–0.887 and RMSE of 0.381–1.951 mm/d. The R 2 values of PSO-ELM, GA-ELM and ABC-ELM models were 0.993, 0.986 and 0.981 and the RMSE values were 0.266 mm/d, 0.306 mm/d and 0.404 mm/d, respectively, indicating that the PSO-ELM model had the best performance. When setting T max , T min and RH as the model inputs, the PSO-ELM model presented better performance in the temperate continental zone (TCZ), subtropical monsoon region (SMZ) and temperate monsoon zone (TMZ) climate zones, with R 2 of 0.892, 0866 and 0.870 and RMSE of 0.773 mm/d, 0.597 mm/d and 0.832 mm/d, respectively. The PSO-ELM model also performed in the mountain plateau region (MPZ) when only T max and T min data were available, with R2 of 0.808 and RMSE of 0.651 mm/d. All the three biological heuristic algorithms effectively improved the performance of the ELM model. Particularly, the PSO-ELM was recommended as a promising model realizing the high-precision estimation of daily ET 0 with fewer meteorological parameters in different climatic zones of China. [ FROM AUTHOR] Copyright of Journal of Hydrology is the property of Elsevier B.V. and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full . (Copyright applies to all s.)