The ratio between nitrogen and phosphorus (N/P) in plant leaves has been widely used to assess the availability of nutrients. However, it is challenging to rapidly and accurately estimate the leaf N/P ratio, especially for mixed forest. In this study, we collected 301 samples from nine typical karst areas in Guangxi Province during the growing season of 2018 to 2020. We then utilized five models (partial least squares regression (PLSR), backpropagation neural network (BPNN), general regression neural network (GRNN), PLSR+BPNN, and PLSR+GRNN) to estimate the leaf N/P ratio of plants based on these samples. We also applied the fractional differentiation to extract additional information from the original spectra of each sample. The results showed that the average leaf N/P ratio of plants was 17.97. Plant growth was primarily limited by phosphorus in these karst areas. The sensitive spectra to estimate leaf N/P ratio had wavelengths ranging from 400–730 nm. The prediction capabilities of these five models can be ranked in descending order as PLSR+GRNN, PLSR+BPNN, PLSR, GRNN, and BPNN when considering both accuracy and robustness. The PLSR+GRNN model yielded high R² and performance to deviation (RPD), and low root mean squared error (RMSE) with values of 0.91, 3.15, and 1.98, respectively, for the training test and 0.81, 2.25, and 2.46, respectively, for validation test. Compared with the PLSR model, both PLSR+BPNN and PLSR+GRNN models had higher accuracy and were more stable. Moreover, both PLSR+BPNN and PLSR+GRNN models overcame the issue of overfitting, which occurs when a single model is used to predict leaf N/P ratio. Therefore, both PLSR+BPNN and PLSR+GRNN models can be used to predict the leaf N/P ratio of plants in karst areas. Fractional differentiation is a promising spectral preprocessing technique that can improve the accuracy of models. We conclude that the leaf N/P ratio of mixed forest can be effectively estimated using combined models based on field spectroradiometer data in karst areas.

Over the past half-century, environmental degradation and human disturbances have threatened the aboveground biomass carbon (AGC) in China’s karst ecosystems. However, recent ecological programs have led to environmental improvements, leaving it unclear whether China’s karst ecosystems act as an AGC sink or AGC source. In this study, we utilized L-band vegetation optical depth to quantify the dynamics of AGC across the karst regions of China from 2015 to 2021. We observed an increase in AGC density of 0.73 Mg C ha⁻¹ yr⁻¹, suggesting that karst ecosystems in China functioned as an AGC sink throughout the research period. The largest increase in AGC density, 1.29 Mg C ha⁻¹ yr⁻¹, was observed in Central China, indicating an AGC sink capacity stronger than that of other regions. Among the different land-use types, forests played a dominant role, exhibiting the largest net change in AGC density at 1.03 Mg C ha⁻¹ yr⁻¹. Furthermore, using the random forest model, temperature, soil clay content, and altitude were identified as the primary factors driving AGC changes. Our results enhance the understanding of the role of China’s karst terrestrial ecosystem in the global carbon cycle, emphasizing its contribution to the global carbon sink.

Detecting spatiotemporal changes in ecological environment quality (EEQ) is of great importance for maintaining regional ecological security and supporting sustainable economic and social development. However, research on EEQ detection from a remote sensing perspective is insufficient, especially at the basin scale. Based on two indices, namely, the Ecological Index (EI) and the Remote Sensing Ecological Index (RSEI), we established a dual model, combining the remote sensing ecological comprehensive index (RSECI) and its differential change model, to study the spatiotemporal evolutionary characteristics of EEQ in the Lijiang River Basin (LRB) from 2000 to 2020. The RSECI combines the following five indicators: greenness, wetness, heat, dryness, and aerosol optical depth. The results of this study show that the area of good and excellent EEQ in the LRB decreased from 3676.22 km² in 2000 to 2083.89 km² in 2020, while the area of poor and fair EEQ increased from 80.81 km² in 2000 to 1375.91 km² in 2020. From 2000 to 2020, the change curve of the EEQ difference in the LRB first rose, fell, and then rose again. The wetness and greenness indicators had positive effects on promoting EEQ, while the heat, aerosol optical depth, and dryness indicators had restraining effects. The results of stepwise regression analysis showed that, among the selected indicators, wetness and greenness were the key factors for improving the EEQ in the LRB during the study period. The RSECI approach and the difference change model proposed in this study can be used to quantitatively evaluate the EEQ and facilitate the analysis of the spatial and temporal dynamic changes and difference changes in EEQ.