A Parameter Optimized Method for InVEST Model in Sub-Pixel Scale Integrating Machine Learning Algorithm and Vegetation–Impervious Surface–Soil Model

The InVEST model, with its ability to perform spatial visualization and quantification, is an important tool for mapping ecosystem services. However, the spatial accuracy and simulating performance of the model are deeply influenced by the land use parameter, which often relies on the accuracy of land use/cover data. To address this issue, we propose a novel method for optimizing the land use parameter of the InVEST model based on the vegetation–impervious surface–soil (V–I–S) model and a machine learning algorithm. The optimized model is called Sub-InVEST, and it improves the performance of assessing ecosystem services on a sub-pixel scale. The conceptual steps are (i) extracting the V–I–S fraction of remote sensing images based on the spectral unmixing method; (ii) determining the mapping relationship of the V–I–S fraction between land use/cover type using a machine learning algorithm and field observation data; (iii) inputting the V–I–S fraction into the original model instead of the land use/cover parameter of the InVEST model. To evaluate the performance and spatial accuracy of the Sub-InVEST model, we employed the habitat quality module of InVEST and multi-source remote sensing data, which were applied to acquire Sub-InVEST and estimate the habitat quality of central Guangzhou city from 2000 to 2020 with the help of the LSMA and ISODATA methods. The experimental results showed that the Sub-InVEST model is robust in assessing ecosystem services in sets of complex ground scenes. The spatial distribution of the habitat quality of both models revealed a consistent increasing trend from the southwest to the northeast. Meanwhile, linear regression analyses observed a robust correlation and consistent linear trends, with R 2 values of 0.41, 0.35, 0.42, 0.39, and 0.47 for the years 2000, 2005, 2010, 2015, and 2020, respectively. Compared with the original model, Sub-InVEST had a more favorable performance in estimating habitat quality in central Guangzhou. The spatial depictions and numerical distribution of the results of the Sub-InVSET model manifest greater detail and better concordance with remote sensing imagery and show a more seamless density curve and a substantially enhanced probability distribution across interval ranges.