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Aboveground biomass is an important indicator used to characterize the growth status of crops, as well as an important physical and chemical parameter in agroecosystems. Aboveground biomass is an important basis for formulating management measures such as fertilization and irrigation. We selected four irrigated wheat fields in a region near Kaifeng, Henan Province, for this study. The terrain in that region was undulating and had spatial differences. We used a low-altitude unmanned aerial vehicle (UAV) remote sensing platform equipped with a multispectral camera, thermal infrared camera, and RGB camera to simultaneously obtain different remote sensing parameters during the key growth stages of wheat. Based on the extracted spectral reflectivity, thermal infrared temperature, and digital elevation information, we calculated the spatial variability of remote sensing parameters and growth indices under different terrain characteristics. We also analyzed the correlations between vegetation indices, temperature parameters, structural topographic parameters and aboveground biomass. Three machine learning methods were used, including the multiple linear regression method (MLR), partial least squares regression method (PLSR) and random forest regression method (RFR). We compared the aboveground biomass (AGB) estimation capability of single-modal data versus multimodal data fusion frameworks. The results showed that slope was an important factor affecting crop growth and aboveground biomass. We therefore analyzed several remote sensing parameters for three different slope scales. We found significant differences among them for soil water content, water content of plants, and aboveground biomass at four growth stages. Based on the strength of their correlation with aboveground biomass, seven vegetation indices (NDVI, GNDVI, NDRE, MSR, OSAVI, SAVI, and MCARI), four canopy structure parameters (CH, VF, CVM, SLOPE) and two temperature parameters (NRCT, CTD) were selected as the final input variables for the model. There was some variability in the accuracy of the models at different growth stages. The average accuracy of the models was anthesis stage > booting stage > filling stage > jointing stage. For the single-modal data framework, the model constructed with the vegetation indices was better than the aboveground biomass model constructed using the temperature or structure parameters, and the highest accuracy was obtained with an RFR model based on vegetation indices at the anthesis stage (R2 = 0.713). For the double modal data fusion approach, the highest accuracy resulted at the anthesis stage, using the structural parameters combined with the vegetation indices of the RFR model (R2 = 0.842). Even higher accuracies were obtained using the multimodal data fusion approach with an RFR model based on vegetation indices, temperature parameters and structure parameters at the anthesis stage (R2 = 0.897). By introducing terrain factors and combining them with the RFR algorithm to effectively integrate multimodal data, the complementary and synergistic effects between different remote sensing information sources could be fully exerted. The accuracy and stability of the aboveground biomass estimation models were effectively improved, and a high-throughput phenotype acquisition method was explored, which provides a reference and basis for real-time monitoring of crop growth and decoding the correlation between genotype and phenotype. [ABSTRACT FROM AUTHOR]

Hyperspectral remote sensing can quickly, nondestructively and accurately monitor crop water concentration and provide technical support for winter wheat growth monitoring, drought assessment, and variable irrigation. In this study, canopy spectral reflectance, leaf water concentration (LWC), leaf nitrogen concentration (LNC), leaf area index (LAI), and leaf dry matter (LDM) of four wheat cultivars were measured under different irrigation and nitrogen treatments, and the effects of nitrogen treatment and growth period on spectral reflectance and LWC were analyzed. Canopy spectral reflectance for different growth periods, irrigation, and nitrogen treatments showed significant changes, leading to the phenomena of "nitrogen treatment differentiation" and "growth period differentiation" for the normalized difference spectral index [NDSI (762, 1458, 2301)] and normalized difference infrared index (NDII) monitoring models. To reduce the influence of nitrogen treatment and growth period on the LWC estimation model, a modified normalized difference water index (mNDWI) was constructed by introducing the nitrogen factor (ratio of left and right peak area, RIDA) into the optimized combination of water-sensitive bands [ND (815, 1080), ND (1585, 1740), and ND (2030, 2260)]. Compared with NDSI (762, 1458, 2301), the R2 of mNDWI was improved by 36.2%–41.1% under different nitrogen levels and 18.6%–22.4% in different growth periods; this effectively reduced the impact of nitrogen status on LWC monitoring and realized the unified modeling and accurate inversion of LWC for the entire growth period. The new index mNDWI, especially mNDWI (815, 1080) and mNDWI (2030, 2260), can effectively monitor the LWC status of wheat under different cultivation conditions, which is important for the real-time diagnosis of plant moisture to guide precision field irrigation applications. [ABSTRACT FROM AUTHOR]