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Accurate estimation of root-zone soil moisture (SM) is of great significance for accurate irrigation management. This study was purposed to identify planted-by-planted mapping of root-zone SM on three critical fruit growth periods based on UAV multispectral images using three machine learning (ML) algorithms in a kiwifruit orchard in Shaanxi, China. Several spectral variables were selected based on variable importance (VIP) rankings, including reflectance Ri at wavelengths 560, 668, 740, and 842 nm. Results indicated that the VIP method effectively reduced 42 vegetation indexes (VIs) to less than 7 with an evaluation accuracy of root-zone SM models. Compared with deep root-zone SM models (SM40 and SM60), shallow root-zone SM models (SM10, SM20, and SM30) have better performance (R2 from 0.65 to 0.82, RRMSE from 0.02 to 0.03, MAE from 0.20 to 0.54) in the three fruit growth stages. Among three ML algorithms, random forest models were recommended for simulating kiwi root-zone SM during the critical fruit growth period. Overall, the proposed planted-by-planted root-zone SM estimation approach can be considered a great tool to upgrade the toolbox of the growers in site-specific field management for the high spatiotemporal resolution of SM maps.

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Reference evapotranspiration (ET0) is an essential component in hydrological and ecological processes. The Penman–Monteith (PM) model of Food and Agriculture Organization of the United Nations (FAO) model requires a number of meteorological parameters; it is urgent to develop high-precision and computationally efficient ET0 models with fewer parameter inputs. This study proposed the genetic algorithm (GA) to optimize extreme learning machine (ELM), and evaluated the performances of ELM, GA-ELM, and empirical models for estimating daily ET0 in Southwest China. Daily meteorological data including maximum temperature (Tmax), minimum temperature (Tmin), wind speed (u2), relative humidity (RH), net radiation (Rn), and global solar radiation (Rs) during 1992–2016 from meteorological stations were used for model training and testing. The results from the FAO-56 Penman–Monteith formula were used as a control group. The results showed that GA-ELM models (with R2 ranging 0.71–0.99, RMSE ranging 0.036–0.77 mm·d−1) outperformed the standalone ELM models (with R2 ranging 0.716–0.99, RMSE ranging 0.08–0.77 mm·d−1) during training and testing, both of which were superior to empirical models (with R2 ranging 0.36–0.91, RMSE ranging 0.69–2.64 mm·d−1). ET0 prediction accuracy varies with different input combination models. The machine learning models using Tmax, Tmin, u2, RH, and Rn/Rs (GA-ELM5/GA-ELM4 and ELM5/ELM4) obtained the best ET0 estimates, with R2 ranging 0.98–0.99, RMSE ranging 0.03–0.21 mm·d−1, followed by models with Tmax, Tmin, and Rn/Rs (GA-ELM3/GA-ELM2 and ELM3/ELM2) as inputs. The machine learning models involved with Rn outperformed those with Rs when the quantity of input parameters was the same. Overall, GA-ELM5 (Tmax, Tmin, u2, RH and Rn as inputs) outperformed the other models during training and testing, and was thus recommended for daily ET0 estimation. With the estimation accuracy, computational costs, and availability of input parameters accounted, GA-ELM2 (Tmax, Tmin, and Rs as inputs) was determined to be the most effective model for estimating daily ET0 with limited meteorological data in Southwest China.

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