Plastic mulch has been widely used to increase the soil temperature with the constant moisture for the high crop yields in the arid regions in northwestern China. Particularly, there has also the application in maize, wheat, and cotton cultivation in Hexi Corridor since the last century. Nevertheless, the mulch residues have significantly damaged the soil structure, leading to environmental degradation. Therefore, it is very necessary for the spatial and temporal distribution of plastic mulch, in order to quantify the impact on the local environment. However, the wide region and the complex interlocking surface types have posed a great challenge to the spatial and temporal distribution of plastic mulched cropland (PMC) from the field work. Fortunately, large-scale ground objects can be extracted with the development of remote sensing. However it is still lacking in the acquisition and processing of remote sensing data. This study aims to extract the spatiotemporal PMC variations in Hexi Corridor from 2013 to 2022. The multi-source remote sensing data was first collected using Google Earth Engine, including Sentinel-2, Landsat 8, Landsat 7, and MODIS. A time window of PMC extraction was also proposed using the variance of the Normalized Difference Vegetation Index (NDVI) within one year. Then, the Timely Plastic-mulched cropland Extraction Method (TPEM) was applied to the complex and variable ground objects for the extraction of the PMC area. About 500 sample points were randomly selected and then labelled as the PMC and non-PMC for the accuracy assessment. The Overall Accuracy (OA), User Accuracy (UA), Quantity Disagreement (QD), and Allocation Disagreement (AD) were also used to evaluate the performance of TPEM. Finally, the spatiotemporal pattern of PMC was analyzed to explore the potential controls on the interannual variation in the PMC area using Water Yearbook Bulletin data. The results showed that: 1) The best time window was the first half of April for the PMC extraction, which also coincided with the sowing of major local crops. The half month before and after this period was also beneficial to extracting the PMC information. 2) The TPEM reached 80% overall accuracy with the QD less than 4%. The error was attributed to the disagreement between the sample points and the real PMC area. 3) The average PMC area was about 383000 hm², of which about 55.6% was in the Heihe Basin. The PMC was located near the inland river systems. Approximately 41.9% of the cropland used plastic mulch, while the PMC area decreased by 32.4% in the last decade. Meanwhile, there was a 46.0% increase in the water-saving irrigation area, whereas, there was a 14.9% decrease in the irrigation water from 2013 to 2019, indicating the significant correlation with the decrease in the PMC area. In conclusion, the application of plastic mulch was ever decreasing with the development of water-saving irrigation. This finding can be an essential reference for the survey of agricultural resources in the Northwest Arid Region of China.

This research aims to evaluate the canopy-resistance models in the forest evapotranspiration (ET) estimates across various stages of canopy development. A systematic analysis was also conducted to compare the performances of different models after simulation. The principal influencing factors of canopy resistance and the underlying mechanisms were determined to clarify the impact on the estimation of ET. Nine frequently used canopy-resistance models were selected after optimization. The data source was the eddy-covariance flux observation that collected from 2021 to 2023 at the Wudang Flux Observation Station (Wudang Station), which was represented a typical forest ecosystem in the Hanjiang River Basin. Initially, the canopy resistance was calculated using these observational data. Subsequently, the Penman-Monteith (P-M) model was incorporated to estimate the ET of the forest ecosystem. A comparison was also carried out on the canopy resistance and ET of the forest ecosystem during different stages of canopy development. Specifically, the canopy resistance was simulated by each of the nine canopy-resistance models. While the forest ET was estimated in each stage of canopy development. Additionally, the main influencing factors of canopy resistance were determined after comparison. When considering the entire growing season, the Stannard model (ST) demonstrated the highest score 0.97 in the Global Performance Indicator (GPI), indicating the overall superior performance among all the models. The performance of the nine models was ranked in the descending order of the Stannard (ST) > Katerji - Perrier (KP) > Jarvis (JA) > Leuning (RL) > Massman (MA) > García - Santos (GA) > Farias (FA) > Ball - Berry - Leuning (BBL) > Todorovic (TD). The Stannard model can be expected to provide more accurate simulations of canopy resistance, thereby leading to a more reliable estimation of forest ET. Furthermore, the JA model emerged as the top-performing model in the low-coverage stage. The reason was that the leaf area index and air temperature were considered the two most crucial influencing factors on the canopy resistance. Among them, the leaf area index was used to determine the surface area available for transpiration. Additionally, there was some influence of air temperature on the stomatal conductance, which in turn affected the canopy resistance. For example, the stomata tended to open wider, as the temperature increased, thus reducing the canopy resistance for the high transpiration rates. The JA model successfully captured these dynamics, resulting in a more accurate simulation of canopy resistance and ET during the low-coverage stage. The KP model was used to integrate the dominant influencing factor of canopy resistance and the vapor pressure deficit. Specifically, the driving force was also represented for the water vapor diffusion from the leaf surface to the atmosphere. The KP model was used to simulate the relationship between vapor pressure deficit and canopy resistance, which was closely approximated to the actual canopy resistance. As a result, the highest accuracy of ET estimate by the KP model was achieved in the high-coverage stage among all the models. The vapor pressure deficit also shared a more pronounced impact on the water-loss process. The KP model was highly suitable for the ET estimates under such conditions. In conclusion, it is highly recommended to utilize the KP model in the high-canopy-coverage scenarios. Conversely, it is advisable to prioritize the canopy resistance that is estimated by the JA and P–M models for the low-canopy-coverage situations. The more accurate estimation of forest latent heat can greatly contribute to the forest water-energy balance in resource management, climate change research, and hydrological modeling.