The mutual constraints between temporal and spatial resolution are common challenges in the quantitative remote sensing monitoring of resources and the environment. High-temporal and high-spatial resolution NDVI time series data play a vital role in supporting decision-making across a range of domains, including surface vegetation monitoring, agricultural management, phenology change analysis, disaster early warning, and land use change mapping. In the field of agricultural engineering in particular, the spatiotemporal continuity of NDVI data is critical for improving the accuracy of crop yield prediction and enhancing the efficiency of farmland resource management. However, in mountainous regions, optical satellite imagery is often affected by cloud and fog, leading to incomplete data coverage and results in spatiotemporal discontinuities that challenge accurate vegetation monitoring. Traditional spatiotemporal fusion algorithms for remote sensing face limitations, including reduced accuracy in areas with complex terrain and heterogeneous landscapes, as well as difficulties in scaling to regional levels. To address these challenges, this study proposes a high spatiotemporal resolution NDVI reconstruction method (object-level gap filling and savitzky-golay filtering method, OLF-SG) based on an object-level gap filling strategy. This method fuses MODIS and Sentinel-2 data to reconstruct high-quality Sentinel-2 NDVI time series. A weighted Savitzky-Golay filter is applied to smooth the reconstructed NDVI time series, eliminating noise errors and generating high-resolution NDVI products with an 8-day temporal resolution and 10-meter spatial resolution. By using MODIS NDVI as a reference and introducing an object-level gap filling strategy to replace the traditional similar pixel filling method, it not only effectively reduces the computational complexity, but also avoids the memory limit problem caused by high computational load in the GEE environment. Two heterogeneous underlying surfaces, referred to as Area A (typical piedmont fragmented landform, rich vegetation types and obvious differentiation) and Area B (agricultural land-dominated area, complex planting structure), were selected within the Erhai Lake Basin to evaluate the reconstruction performance under different surface characteristics. Compared with the traditional Gap Filling and Savitzky-Golay filtering method (GF-SG) and the Object-Level Spatial and Temporal Adaptive Reflectance Fusion Model (OL-STARFM), the proposed OLF-SG method provides the reconstructed NDVI images closest to the reference images, reconstruction accuracy and efficiency. It also avoids the problem of base image pair selection and makes full use of the cloud-free observation data of pixels, thus improving the utilization rate of data. The average deviation (AD) of the reconstructed Sentinel-2 NDVI image and the reference image was as low as 0.039 and 0.006, respectively, the boundary clarity was optimal (Edge was -0.130 and -0.094, respectively), indicating the optimal stability of the model. The experiments were qualitatively and quantitatively verified the effectiveness of the model. In Area A, the OLF-SG achieved a root mean square error (RMSE) of 0.060 and a coefficient of determination (R²) of 0.984. In Area B, the RMSE was 0.068 and the R² reached 0.952. The high spatiotemporal resolution NDVI time series generated by the OLF-SG effectively captured the phenology patterns across complex landscapes. The strong potential can also be offered for a wide range of applications, including crop growth monitoring, vegetation phenology analysis, soil erosion assessment, and agricultural management.

Land Use and Land Cover (LULC) is the direct and concentrated reflection of the interaction between human activities and natural geographical environment. High-precision LULC information can also depend mainly on the global climate change, material cycle and dynamic balance of water and heat. Current environmental challenges were remained with the rapid increase in remote sensing observation platforms, the free disclosure of high-resolution satellite remote sensing data and the advancement of LULC mapping technology. Freely available medium and high-resolution land cover products are emerging for the open source. The medium and high resolution LULC datasets have also been constructed worldwide. However, there are different degrees of uncertainty in the multi-source data. It is a high demand for the suitable land cover products at the regional scale in various fields. Therefore, it is very important to evaluate the accuracy of the current commonly-used land cover data at the regional scale. Taking the Erhai Lake Basin as the study area, the consistency analysis was carried out to evaluate accuracy of commonly-used non-homologous LULC products. 2 947 validation samples were collected using Third National Land Survey data, the kilometer grid sampling, field surveys, and high-resolution image interpretation. Seven commonly-used heterogeneous LULC data products were evaluated, in terms of area, spatial consistency, confusion levels, and accuracy. The influence of LULC product mapping accuracy was quantitatively analyzed from four aspects: shrub forest proportion, landscape pattern index, elevation standard deviation and average patch area. The applicability of each dataset was also evaluated. The results reveal that the high, moderate, and low consistency areas were represented by 64.13%, 34.00%, and 1.87% of the total area, respectively, among the eight datasets of land cover. Notable confusion and misclassification occurred in shrub land and grassland, indicating the significant differences in the various products to represent different regions and land cover types. The overall accuracy of the LULC products was ranged from 69.5% to 81.1%. Notably, ESA_WC was offered the best data quality and spatial detail, especially for the cultivated land in fragmented landscapes. Additionally, the Shannon Diversity Index (SHDI) was found to share the most considerable impact on spatial consistency of land cover in the Erhai Basin, followed by the proportion of shrub land. In contrast, there was the less effect of some factors, such as elevation standard deviation, patch size, and cloud cover frequency. In all features of land cover, 10 m resolution data should prioritize the highest overall accuracy provided by ESA_WC. Among them, CRLC data was better performed, if the shrubland and grassland were not subdivided. While for 30 m resolution data, CLCD demonstrated the relatively high accuracy. The impermeable surface area was significantly underestimated, compared with the rest products unsuitable for urban expansion. This finding can serve as a valuable reference to assess the classification accuracy, strengths and weaknesses of the seven LULC products. Targeted data selection was facilitated for the specific applications. The accuracy of LULC products was directly evaluated for the applicability and adaptation of the data. The finding can provide the scientific basis for ecological environment protection, rational utilization of resources and sustainable development in plateau mountainous areas.