In order to further improve the utility of unmanned aerial vehicle (UAV) remote-sensing for quickly and accurately monitoring the growth of winter wheat under film mulching, this study examined the treatments of ridge mulching, ridge–furrow full mulching, and flat cropping full mulching in winter wheat.  Based on the fuzzy comprehensive evaluation (FCE) method, four agronomic parameters (leaf area index, above-ground biomass, plant height, and leaf chlorophyll content) were used to calculate the comprehensive growth evaluation index (CGEI) of the winter wheat, and 14 visible and near-infrared spectral indices were calculated using spectral purification technology to process the remote-sensing image data of winter wheat obtained by multispectral UAV.   Four machine learning algorithms, partial least squares, support vector machines, random forests, and artificial neural network networks (ANN), were used to build the winter wheat growth monitoring model under film mulching, and accuracy evaluation and mapping of the spatial and temporal distribution of winter wheat growth status were carried out.  The results showed that the CGEI of winter wheat under film mulching constructed using the FCE method could objectively and comprehensively evaluate the crop growth status.  The accuracy of remote-sensing inversion of the CGEI based on the ANN model was higher than for the individual agronomic parameters, with a coefficient of determination of 0.75, a root mean square error of 8.40, and a mean absolute value error of 6.53.  Spectral purification could eliminate the interference of background effects caused by mulching and soil, effectively improving the accuracy of the remote-sensing inversion of winter wheat under film mulching, with the best inversion effect achieved on the ridge–furrow full mulching area after spectral purification.  The results of this study provide a theoretical reference for the use of UAV remote-sensing to monitor the growth status of winter wheat with film mulching.

Controlled-release urea (CRU) has better characteristics than conventional urea for synchronizing nitrogen (N) release with plant uptake.  Understanding the effects of CRU on crop yield and N use efficiency (NUE) has long been the key to evaluate the performance of CRU.  A long-term experiment over five consecutive years was conducted in Changsha, Hunan Province, China, to investigate the effects of polyethylene-coated urea with a 90-d release period on the yield and NUE of double rice (early and late crops are grown in the same year), the amount of residual soil mineral N and the soil–plant N balance, as well as on the economic benefits.  Four N fertilizer treatments including CK (no N fertilizer), U (conventional urea), CRU1 (polyethylene-coated urea with equal N application rate to U) and CRU2 (20% reduction in N application rate of CRU1) were established.  The results indicated that CRU1 application increased the yield and NUE of double rice by 11.0 and 13.5%, respectively, compared with U.  Higher yield and NUE of late rice were found than in early rice in CRU treatments.  Compared with conventional U, the yield and NUE of early rice in the CRU1 treatment were increased by 6.0 and 10.2%, respectively, and those of late rice were increased by 15.4 and 13.8%, respectively.  There was no significant difference between CRU1 and CRU2 in double rice yield.  Furthermore, CRU treatments (including CRU1 and CRU2) had higher apparent residual N_min rate (ARNR) and apparent N recovery rate (ANRR), but lower apparent N loss (N_S) than the conventional U treatment.  Concentrations of NH₄⁺-N and NO₃^–-N were greater in the surface soil (0–20 cm) and lower in the deeper soil layer (40–60 cm) with CRU treatments than in the U treatment after harvest.  Moreover, CRU application produced a greater economic benefit than conventional U application.  In general, CRU outperformed U fertilizer in terms of rice yield, NUE, soil–plant N balance, economic benefit, and CRU2 provided greater comprehensive benefits than CRU1.  It is suggested that CRU application is beneficial for solving N management challenges in the production of rice.