The biomass of rice at maturity serves as a vital indicator for assessing overall productivity, and its accurate estimation holds significant importance for ensuring food security and promoting sustainable agriculture. To improve the precision of current biomass estimation methods for mature rice, this study employed support vector regression to integrate RGB vegetation indices from rice canopy images with growth parameters, thereby developing a biomass estimation model. The model was validated by applying it to the experimental area. The results indicated that screening RGB vegetation indices and combining them with growth parameters enhanced estimation accuracy. Specifically, the model integrating RGB vegetation indices (g, RGBVI) with rice plant height and moisture content demonstrated high estimation accuracy (R2 = 0.78, RMSE = 0.32 kg/m2). The absolute difference between the estimated and measured biomass values ranged from 0.15 to 0.39 kg/m2. Additionally, the estimated biomass showed a strong correlation with yield (R2 = 0.86), with a fitted equation of y = 0.04x + 0.59. These results suggest that the model is reliable for large-area estimation of mature rice biomass. However, the degree of rice maturity and the lodging phenomenon were identified as the primary factors influencing the precision of model application. Overall, integrating RGB vegetation indices of the rice canopy, obtained via UAV-based remote sensing technology, with growth parameters provides an effective method for estimating mature rice biomass and offers a valuable reference for efficient yield estimation.

The random volume over ground (RVoG) model associates vegetation vertical structure parameters with multiple complex interferometric coherence observables. In this paper, on the basis of the RVoG model, a truncated singular value decomposition (TSVD)-based method is proposed for forest height inversion from single-baseline polarimetric interferometric synthetic aperture radar (PolInSAR) data. In addition, in order to improve the applicability of TSVD for this issue, a new truncation method is proposed for TSVD. Differing from the traditional three-stage method, the TSVD-based inversion method estimates the pure volume coherence directly from the complex interferometric coherence, and estimates the forest height from the estimated pure volume coherence with a least-squares method. As a result, the TSVD-based method can adjust the contributions of the polarizations in the estimation of the model parameters and avoid the null ground-to-volume ratio assumption. The simulated experiments undertaken in this study confirmed that the TSVD-based method performs better than the three-stage method in forest height inversion. The TSVD-based method was also applied to E-SAR P-band data acquired over the Krycklan Catchment, Sweden, which is covered with mixed pine forest. The results showed that the TSVD-based method improves the root-mean-square error by 48.6% when compared to the three-stage method, which further validates the performance of the TSVD-based method.

Forests play a crucial role in maintaining the ecological balance of the Earth. While existing publicly available datasets typically offer high accuracy in identifying large-scale forest concentrations, discrepancies arise in forest extraction within smaller regions. This variation complicates the selection of appropriate forest cover datasets for specific areas. This study focuses on the southern and northern regions of China, represented by Hunan Province and Heilongjiang Province, respectively. It systematically evaluates the performance of eight forest cover datasets from 2020 in terms of forest area estimation, spatial consistency, and classification accuracy. Through confusion analysis of classification in low-consistency areas, the study identifies the confusion patterns between forests and other land cover types in different regions. Additionally, the study explores the causes of discrepancies between datasets by considering topographic factors and human activities. The results show that the CRLC 2020 outperforms others in terms of both area estimation and classification accuracy, achieving classification accuracies of 90.88% in Hunan Province and 91.69% in Heilongjiang Province. High-consistency areas (levels 6–8) in Hunan account for 69.4%, lower than Heilongjiang’s 77%. This comprehensive analysis provides valuable insights for forestry practitioners in selecting appropriate forest cover datasets in areas with complex land cover, offering reliable recommendations for forest cover mapping and the formulation of sound mapping strategies.

Reliable short-term prediction of thermospheric states is important for satellite drag applications but remains difficult because of nonlinear, multiscale variability. We developed a multivariable Adaptive Fourier Neural Operator (AFNO) surrogate using 24 years (2000–2023) of Thermosphere–Ionosphere Electrodynamics General Circulation Model (TIE-GCM) simulations. The model predicts neutral density, temperature, winds, and geopotential height over 24-h autoregressive forecasts. We compared direct state prediction with increment-based flow prediction and tested logarithmic density scaling and one-hour historical inputs. Both formulations preserved dominant large-scale density structures and the equatorial mass density anomaly, with anomaly correlation coefficients above 0.94 for all variables. The Direct formulation maintained lower errors and greater stability at longer lead times, whereas Flow performed better only at early steps. Logarithmic density scaling produced variable- and altitude-dependent trade-offs, and historical inputs yielded no overall benefit. During a representative geomagnetic storm, the baseline reproduced broad density morphology but increasingly underestimated enhancement magnitude with lead time. Because evaluation used the independent 2023 TIE-GCM test year with prescribed forecast-time forcing and no observational validation, the framework should be interpreted as a TIE-GCM-consistent surrogate rather than a validated predictor of the observed thermosphere.