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Drought hardening could promote the development of plant roots, potentially improving the resistance of crops to other adversities. To investigate the response and resistance of physiological and growth characteristics induced by drought hardening to salt stress in the later stages, a greenhouse experiment was carried out from 2021 to 2022 with one blank control treatment and twelve treatments that comprised combinations of four irrigation regimes (W1 = 85%, W2 = 70%, W3 = 55%, and W4 = 40% of the field capacity) and three irrigation water salinity levels (S2, S4, and S6, referring to 2 g, 4 g, and 6 g of sodium chloride added to 1000 mL of tap water, respectively). The results show that saline water irrigation introduced a large amount of salt into the soil, resulting in the deterioration of tomato growth, physiology, yield, and water use efficiency (WUE), but had a positive, significant effect on fruit quality. When the irrigation water salinity was 2 g L−1, the W2 treatment could reduce soil salt accumulation, even at the end of the maturation stage; consequently, enhancing the increments in plant height and leaf area index during the whole growing stage. The physiological activity of tomato plants under the W2 and W3 treatments showed a promoting effect. Correspondingly, the maximum values of the fruit quality of tomato plants irrigated with the same saline water were all obtained with the W2 or W3 treatment. However, the yield and WUE of the W3 treatment were lower than that of the W2 treatment, which was the highest among the same saline water irrigation treatments, consistent with the reflection of the changing trend of the ratio of fresh weight to dry weight. Overall, drought hardening can be considered an economically viable approach to mitigate the hazards of saline water irrigation, and the W2S2 combination is recommended for tomato production due to the maximum values of yield and WUE with a higher fruit quality among the twelve saline water irrigation treatments. [ABSTRACT FROM AUTHOR]

Analysis of the spatial and temporal variation patterns of surface evapotranspiration is important for understanding global climate change, promoting scientific deployment of regional water resources, and improving crop yield and water productivity. Based on Landsat 8 OIL_TIRS data and remote sensing image data of the lower Yangtze River urban cluster for the same period of 2016–2021, combined with soil and meteorological data of the study area, this paper constructed a multiple linear regression (MLR) model and an extreme learning machine (ELM) inversion model with evapotranspiration as the target and, based on the model inversion, quantitatively and qualitatively analyzed the spatial and temporal variability in surface evapotranspiration in the study area in the past six years. The results show that both models based on feature factors and spectral indices obtained a good inversion accuracy, with the fusion of feature factors effectively improving the inversion ability of the model for ET. The best model for ET in 2016, 2017, and 2021 was MLR, with an R2 greater than 0.8; the best model for ET in 2018–2019 was ELM, with an R2 of 0.83 and 0.62, respectively. The inter-annual ET in the study area showed a "double-peak" dynamic variation, with peaks in 2018 and 2020; the intra-annual ET showed a single-peak cycle, with peaks in July–August. Seasonal differences were obvious, and spatially high-ET areas were mainly found in rural areas north of the Yangtze River and central and western China where agricultural land is concentrated. The net solar radiation, soil heat flux, soil temperature and humidity, and fractional vegetation cover all had significant positive effects on ET, with correlation coefficients ranging from 0.39 to 0.94. This study can provide methodological and scientific support for the quantitative and qualitative estimation of regional ET. [ABSTRACT FROM AUTHOR]