Intercropping advantage occurs only when each species has adequate time and space to maximize cooperation and minimize competition between them. A field experiment was conducted for two consecutive years between 2013 and 2014 to investigate the effects of maize and soybean relay strip intercropping systems on the uptake and utilization of nitrogen, phosphorus, and potassium. The treatments included “40:160” (T1, maize narrow and wide row spacing of 40 and 160 cm, where two rows of soybean with a 40 cm row were planted in the wide rows. The area occupation ratio of maize and soybean both were 50% of the every experimental block), “80:120” (T2, maize narrow and wide row spacing of 80 and 120 cm, the soybean planting was the same as T1 treatment. The area occupation ratio of maize and soybean were 60% and 40% of the every experimental block), “100:100” (T3, one row of maize and one row of soybean with a 100-cm row. The area occupation ratio of maize and soybean was the same as T1 treatment), sole cropping of maize (CK1, The area occupation ratio of maize was 100% of the every experimental block), and sole cropping of soybean (CK2, The area occupation ratio of soybean was 100% of the every experimental block). The results show that, compared with the sole cropping system (sole maize), the economic yields in T1, T2, and T3 treatments increased by 761, 536, and 458 kg·ha−1, respectively, and the biological yields increased by 2410, 2127, and 1588 kg·ha−1. The uptake and utilization of nitrogen, phosphorus, and potassium in T1, T2, and T3 treatments were significantly higher than those in sole crops, and the nutrient advantage is mainly due to nutrient uptake rather than nutrient use efficiency. The land equivalent ratio values in T1, T2, and T3 treatments were 1.43, 1.32, and 1.20, respectively. In particular, the economic and biological yield in T1 treatment exhibited potential as an intercropping pattern.

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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.

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