Clarifying the spatiotemporal evolution pattern of cultivated land use system resilience (CLUSR) in major grain-producing areas and identifying the key obstacles constraining its enhancement is of great significance for promoting the sustainable development of cultivated land use systems and ensuring regional food security. Taking the Songnen Plain, a typical major grain-producing area in China, as the study area, we constructed a CLUSR evaluation index system based on the “Resources–Utilization–Production–Ecology” (RUPE) framework and analyzed the spatiotemporal dynamics of CLUSR. Furthermore, we identified the primary factors impeding CLUSR enhancement. The results were as follows: (1) From 2005 to 2020, CLUSR values in the Songnen Plain ranged from 0.3353 to 0.4256, indicating a moderately low level overall but showing an upward trend. Across subsystems, the mean resilience scores followed the order ESR (0.121) > RER (0.114) > GPSR (0.090) > CLUR (0.055). (2) Spatially, CLUSR exhibited a distinct “high in the east and low in the west” pattern, with significant growth in the northwestern region. High–High clusters were primarily concentrated in the southeastern part of the study area, while Low–Low clusters exhibited a divergent spatial pattern. (3) From an indicator perspective, agricultural output value per unit of cultivated area, water coverage degree, agricultural labor input, agricultural mechanization level, cultivated land area, per capita yield of grain, and agricultural capital investment were identified as the dominant obstacles to CLUSR improvement. From a subsystem perspective, grain production stability and cultivated land use subsystems were the primary factors limiting CLUSR improvement in the Songnen Plain. (4) At the county level, obstacle factors were classified into three types: single, dual, and multiple obstacles. Nearly half of the counties were facing multiple constraints simultaneously. This study provides theoretical and practical implications for the formulation of cultivated land use policies in the Songnen Plain and other major grain-producing areas worldwide, thereby contributing to the sustainable utilization of cultivated land.

Leaf mass per area (LMA) represents the allocation of carbon resources per unit leaf area, which is closely related to the photosynthetic capacity of tree leaves. Clarifying the distribution features of LMA is very useful in understanding nutrient and energy transmission and photosynthetic capacity in the canopy. To this aim, the leaf samples of varied forest types were collected, and LMA and related spectral data were measured. The Partial Least Squares (PLS), Linear Mixed Models (LMM), Support Vector Machine Regression (SVM), and Random Forest (RF) methods were used to establish a new model of three-dimension LMA prediction by using vegetation index, DBH, and the vertical and horizontal position of leaves in this study. The results found that the LMA varies significantly with the change in the spatial position of the leaves and horizontal distance to the tree trunk. Statistically speaking, changes in LMA were not significantly related to the direction where the leaves were located. The best model of 3D LMA estimation was RF with a 10-fold R² value of 0.939. Compared to the RF model, the maximum and minimum of R² of 10-fold testing of other models increased by 23.75% and 55.87%. The results indicated that RF has a strong generalization ability and can predict the LMA distribution in 3D with a high accuracy. This study showed a reference for LMA 3D feature distribution and is helpful in clarifying the photosynthetic capacity of the canopy.