Estimating the aboveground biomass (AGB) of rice using remotely sensed data is critical for reflecting growth status, predicting grain yield, and indicating carbon stocks in agroecosystems. A combination of multisource remotely sensed data has great potential for providing complementary datasets, improving estimation accuracy, and strengthening precision agricultural insights. Here, we explored the potential to estimate rice AGB by using a combination of spectral vegetation indices and wavelet features (spectral parameters) derived from canopy spectral reflectance and texture features and texture indices (texture parameters) derived from unmanned aerial vehicle (UAV) RGB imagery. This study aimed to evaluate the performance of the combined spectral and texture parameters and improve rice AGB estimation. Correlation analysis was performed to select the potential variables to establish the linear and quadratic regression models. Multivariate analysis (multiple stepwise regression, MSR; partial least square, PLS) and machine learning (random forest, RF) were used to evaluate the estimation performance of spectral parameters, texture parameters, and their combination for rice AGB. The results showed that spectral parameters had better linear and quadratic relationships with AGB than texture parameters. For the multivariate analysis and machine learning algorithm, the MSR, PLS, and RF regression models fitted with spectral parameters (R2 values of 0.793, 0.795, and 0.808 for MSR, PLS, and RF, respectively) were more accurate than those fitted with texture parameters (R2 values of 0.540, 0.555, and 0.485 for MSR, PLS, and RF, respectively). The MSR, PLS, and RF regression models fitted with a combination of spectral and texture parameters (R2 values of 0.809, 0.810, and 0.805, respectively) slightly improved the estimation accuracy of AGB over the use of spectral parameters or texture parameters alone. Additionally, the bior1.3 of wavelet features at 947 nm and scale 2 was used to predict the grain yield and had good accuracy for the quadratic regression model. Therefore, the combined use of canopy spectral reflectance and texture information has great potential for improving the estimation accuracy of rice AGB, which is helpful for rice productivity prediction. Combining multisource remotely sensed data from the ground and UAV technology provides new solutions and ideas for rice biomass acquisition.

Low-temperature and anaerobic conditions are two major limiting factors in the germination of direct-seeded rice. Sixteen hybrid Indica rice varieties were screened and subjected to two different temperatures and oxygen levels. The results revealed that relative to anaerobic conditons, low-temperature is the key limiting factor for seed germination. For all varieties, relative to normal temperature (30°C) and aerobic conditions (89.0% germination), the germination percentages when seeds were germinated under anaerobic (normal temperature), low-temperature (15°C) or combined anaerobic and low-temperature conditions were 64.0, 46.0 and 23.2%, respectively. Under the three latter treatments, the soluble sugar (SS) and gibberellin (GA) was significantly decreased, the abscisic acid (ABA) had significantly increased by 14.6–251.8%, and the superoxide dismutase (SOD) initially increased by 30.2–49.7% and decreased thereafter by 34.7%. Seeds of ‘Shen 9 you 28’ and ‘Jingliangyou 534’ performed better than seeds of the other varieties, with high germination percentages, SS, SOD, GA and low ABA. Correlation analysis revealed that the germination percentages were significantly correlated with the SS, SOD, GA, ABA and GA/ABA. Elevated SS, GA and SOD, as well as reduced ABA content were thought to account for the high germination of rice varieties resistant to combined low-temperature and anaerobic stress.

Productive tiller percentage (PTP) is the only available comprehensive indicator of rice population quality. However, productive panicle number (PN) has a great effect on its characterization accuracy. Panicle exsertion is an important but difficult to describe morphological index; therefore, it cannot be easily determined. The aims of this study were to develop heading uniformity (HU), which describes the difference in the degree of rice panicle exsertion, as a new comprehensive indicator by designing a representative sampling and calculation method and exploring the relationship between HU and yield components. HU first decreased then increased after initial heading, exhibiting a single-valley curve. Adequate HU was obtained by panicle sampling on day two or three (panicle N fertilizer proportion ≤40 or >40%) after initial heading. The explanatory power of PTP for grain yield variance was markedly insufficient in low- and high-PN rice populations. Compared with the percent contribution of PTP to grain yield variance (12.32–41.26%), that of HU (49.02–61.93%) was greater and more stable across rice populations of different PNs. Moreover, HU showed fewer interannual variations, despite large interannual differences in weather and soil conditions. Hence, HU may have applications as a comprehensive indicator of rice population quality.