Accurately predicting the crown photosynthesis of trees is necessary for better understanding the C circle in terrestrial ecosystem. However, modeling crown for individual tree is still challenging with the complex crown structure and changeable environmental conditions. This study was conducted to explore model in modeling the photosynthesis light response curve of the tree crown of young Larix principis-rupprechtii Mayr. Plantation. The rectangular hyperbolic model (RHM), non-rectangular hyperbolic model (NRHM), exponential model (EM) and modified rectangular hyperbolic model (MRHM) were used to model the photosynthetic light response curves. The fitting accuracy of these models was tested by comparing determinants coefficients (R²), mean square errors (MSE) and Akaike information criterion (AIC). The results showed that the mean value of R² of MRHM (R² = 0.9687) was the highest, whereas MSE value (MSE = 0.0748) and AIC value (AIC = -39.21) were the lowest. The order of fitting accuracy of the four models for P_n-PAR response curve was as follows: MRHM > EM > NRHM > RHM. In addition, the light saturation point (LSP) obtained by MRHM was slightly lower than the observed values, whereas the maximum net photosynthetic rates (P_max) modeled by the four models were close to the measured values. Therefore, MRHM was superior to other three models in describing the photosynthetic response curve, the accurate values were that the quantum efficiency (α), maximum net photosynthetic rate (P_max), light saturation point (LSP), light compensation point (LCP) and respiration rate (R_d) were 0.06, 6.06 μmol·m^-2s^-1, 802.68 μmol·m^-2s^-1, 10.76 μmol·m^-2s^-1 and 0.60 μmol·m^-2s^-1. Moreover, the photosynthetic response parameters values among different layers were also significant. Our findings have critical implications for parameter calibration of photosynthetic models and thus robust prediction of photosynthetic response in forests.

Maintaining a supply–demand balance of ecosystem services (ES) is essential for enhancing the effectiveness of ecosystem restoration. However, inappropriate land use and reforestation practices can negatively impact this balance. In this study, the ES balance of the Saihanba region in China was quantified by integrating land use/cover change (LUCC) data, landscape metrics, and ES indicators. The relationship between ES balance and its driving factors was analyzed using spatial panel models. The spatiotemporal changes of landscape patterns from 2002 to 2020 were also explored. The results indicated that the overall ES supply capacity of the study region, especially in the southwestern area, increased during the research period. The ES balance and its determining factors exhibited significant spatial heterogeneity and spillover effects. Large–scale afforestation increased the local ES supply and provided economic benefits, but it also led to ecological issues, including declines in wetland area and landscape fragmentation. Our study emphasized the importance of considering the supply–demand balance in the planning and decision–making of ES, providing insight into multifunctional management and the sustainable development in the Saihanba area.

The LAI is a key parameter used to describe the exchange of material and energy between soil, vegetation and the atmosphere. It has become an important driving datum in the study of carbon and water cycle mechanism models at many regional scales. In order to obtain high temporal resolution and high spatial resolution LAI products, this study proposed a method to combine the high temporal resolution of MODIS LAI products with the high spatial resolution of Sentinel-2 data. The method first used the LACC algorithm to smooth the LAI time-series data and extracted the normalized growth curve of the MODIS LAI of forest and used this curve to simulate the annual variation of the LAI. Secondly, it estimated the LAI at the period of full leaf spread based on the traditional remote sensing statistical model and Sentinel-2 remote sensing data as the maximum value of the forest LAI in the study area and used it to control the LAI growth curve. Finally, the time-series LAI data set was created by multiplying the maximum LAI by the normalized forest LAI growth curve. The results indicate that: (1) the remote sensing statistical estimation model of LAI was developed using the atmospherically resistant vegetation index ARVI ($R^2$ = 0.494); (2) the MODIS LAI normalized growth curve keeps a good level of agreement with the actual variation. This study provides a simple and efficient method for obtaining effective time-series forest LAI data for the scope of small- and medium-sized areas.