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Changes in temperature extremes can be linked to mean temperature changes and variability. This study aims to quantify observed trends in mean and extreme temperature values and to analyse the relationship between mean and extreme temperatures in mainland China, based on daily data from 1960 to 2015. This is the first analysis undertaken of the relationship between mean and extreme temperatures in mainland China. Based on the 95th and 99th percentiles of daily T max and the 5th and 1st percentiles of daily T min , warm days (TX95p), hot days (TX99p), cold nights (TN05p), and very cold nights (TN01p) were defined. The results showed the following: (1) large increasing tendencies of TX95p and TX99p nearly all occurred in locations where mean temperature had substantially increased, and large decreasing tendencies of TN05p were more probably at locations of warming in mainland China; (2) the rise of mean temperature significantly increased the frequency of TX95p and TX99p, and decreased the frequency of TN05p, which indicates a simple shift of the entire distribution towards a warmer climate and greater potential risk of heat waves in the future. The likelihood of occurrence of TX95p and TX99p increased by about 3 and 1 day, respectively, and the occurrence of TN05p was reduced by about 4 days with a mean temperature increase of 1 ∘ C, but the occurrence of TN01p was hardly affected, indicating increased variability of T min temperatures; and (3) the mean and extreme temperatures increased with the urbanization rate in China, and advanced phenologies and unaffected frequency of very cold nights (TN01p) could pose more potential risk of frost and freeze injury to crops in China in the future.

The synthetic aperture radar (SAR) interferometric coherence can complement optical data for the estimation of crop growth parameters, but it has not been yet investigated for predicting crop yield. Many studies have used machine learning methods, such as neural networks, random forest, and Gaussian process regression, to estimate crop yield from remotely sensed data. However, their performance depends on the amount of available ground truth data. This study proposed Gaussian kernel regression for rice yield estimation from optical and SAR imagery using a limited amount of ground truth data. The main objective was to investigate the synergetic use of Sentinel-2 vegetation indices and Sentinel-1 interferometric coherence data through Gaussian kernel regression for estimating rice grain yield. The prediction accuracy was assessed using in situ measured yield data collected in 2019 and 2020 over Xinghua county in Jiangsu Province, China. In all cases, Gaussian kernel regression outperformed the probabilistic Gaussian regression and Bayesian linear inference. With the independently used optical and SAR data, a better prediction accuracy was achieved with the optical red edge difference vegetation index (RDVI1) (r 2 = 0.65, RMSE = 0.61 t/ha) than with the interferometric coherence (r 2 = 0.52 and RMSE = 0.79 t/ha).The highest prediction accuracy can be achieved by combining RDVI1 with interferometric coherence at the heading stage (r 2 = 0.81 and RMSE = 0.55 t/ha). The results suggest the value of synergy between satellite interferometric coherence and optical indices for crop yield mapping with Gaussian kernel regression.

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