Estimation of hourly actual evapotranspiration over the Tibetan Plateau from multi-source data

Xian, Wang; Zhong, Lei; Ma, Yaoming; Fu, Yunfei; Han, Cunbo; Li, Peizhen; Wang, Zixin; Qi, Yuting

doi:10.1016/j.atmosres.2022.106475

Cited by 11 publications

(2 citation statements)

References 60 publications

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“…To ensure its reliability, RTSIF has undergone comparative analysis with tower-based SIF observations and other satellite-derived SIF datasets (GOME-2 SIF and OCO-2 SIF), demonstrating its high accuracy 52 . Additionally, we selected the MCD15A3H product for Fraction of Photosynthetically Active Radiation (Fpar) and Leaf Area Index (Lai), both of which have a spatial resolution of 500 m. The Fpar data is essential for estimating the amount of solar radiation absorbed by the photosynthetic canopy 53 , while Lai data offers valuable insights into leaf biomass density, crucial for understanding plant growth, canopy structure, and overall ecosystem productivity 54 . We also included Gpp and PsnNet data from the MOD17A2H product, which provides data at an 8-day frequency, consistent with the spatial and temporal scope of our study.…”

Section: Methodsmentioning

confidence: 99%

GOA-optimized deep learning for soybean yield estimation using multi-source remote sensing data

Lu,

Fu,

Tang

et al. 2024

Sci Rep

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Accurately estimating large-area crop yields, especially for soybeans, is essential for addressing global food security challenges. This study introduces a deep learning framework that focuses on precise county-level soybean yield estimation in the United States. It utilizes a wide range of multi-variable remote sensing data. The model used in this study is a state-of-the-art CNN-BiGRU model, which is enhanced by the GOA and a novel attention mechanism (GCBA). This model excels in handling intricate time series and diverse remote sensing datasets. Compared to five leading machine learning and deep learning models, our GCBA model demonstrates superior performance, particularly in the 2019 and 2020 evaluations, achieving remarkable R2, RMSE, MAE and MAPE values. This sets a new benchmark in yield estimation accuracy. Importantly, the study highlights the significance of integrating multi-source remote sensing data. It reveals that synthesizing information from various sensors and incorporating photosynthesis-related parameters significantly enhances yield estimation precision. These advancements not only provide transformative insights for precision agricultural management but also establish a solid scientific foundation for informed decision-making in global agricultural production and food security.

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Section: Methodsmentioning

confidence: 99%

GOA-optimized deep learning for soybean yield estimation using multi-source remote sensing data

Lu,

Fu,

Tang

et al. 2024

Sci Rep

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“…Therefore, their application effect is inevitably affected by the accuracy and scale of the input data [19]. Many scholars have used EC data as the target set to train ML models [20,21]. However, due to the sparse distribution of flux towers and the limited observation times, it is difficult to meet the ET simulation requirements at any spatiotemporal scale.…”

Section: Introductionmentioning

confidence: 99%

A Hybrid Framework for Simulating Actual Evapotranspiration in Data-Deficient Areas: A Case Study of the Inner Mongolia Section of the Yellow River Basin

Jiang

Wang

et al. 2023

Remote Sensing

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Evapotranspiration (ET) plays an import role in transferring water and converting energy in the land‒atmosphere system. Accurately estimating ET is crucial for understanding global climate change, ecological environmental problems, the water cycle, and hydrological processes. Machine learning (ML) algorithms have been considered as a promising method for estimating ET in recent years. However, due to the limitations associated with the spatial–temporal resolution of the flux tower data commonly used as the target set in ML algorithms, the ability of ML to discover the inherent laws within the data is reduced. In this study, a hybrid framework was established to simulate ET in data-deficient areas. ET simulation results of a coupled model comprising the Budyko function and complementary principle (BC2021) were used as the target set of the random forest model, instead of using the flux station observation data. By combining meteorological and hydrological data, the monthly ET of the Inner Mongolia section of the Yellow River Basin (IMSYRB) was simulated from 1982 to 2020, and good results were obtained (R2 = 0.94, MAE = 3.82 mm/mon, RMSE = 5.07 mm/mon). Furthermore, the temporal and spatial variations in ET and the influencing factors were analysed. In the past 40 years, annual ET in the IMSYRB ranged between 241.38 mm and 326.37 mm, showing a fluctuating growth trend (slope = 0.80 mm/yr), and the summer ET accounted for the highest proportion in the year. Spatially, ET in the IMSYRB showed a regular distribution of high ET in the eastern region and low ET in the western area. The high ET value areas gradually expanded from east to west over time, and the area increased continuously, with the largest increase observed in the 1980s. Temperature, precipitation, and normalized difference vegetation index (NDVI) were found to be the most important factors affecting ET in the region and play a positive role in promoting ET changes. These results provide an excellent example of long-term and large-scale accurate ET simulations in an area with sparse flux stations.

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Moisture conditions are limiting evapotranspiration changes of Alpine mountains of Qilian Mountains

Wang,

Li,

Xue

et al. 2024

Hydrological Processes

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Variations in evapotranspiration and their sensitivity to controlling variables are pivotal for comprehending water balance dynamics and climate change, particularly in high‐altitude regions such as the Qilian mountains. Environmental shifts are bound to disrupt local water cycles and balance, with significant implications for these alpine areas. To enhance our understanding of evapotranspiration variability across different altitudes within the Qilian Mountains' high‐elevation region and to assess the model's adaptability and responsiveness to environmental factors, our study involved measuring actual evapotranspiration at three distinct elevations. This was achieved using meteorological stations and continuous data from a weighing‐type microlysimeter at the Shaliu River basin's gradients of 3797, 4250 and 4303 m, spanning the growing seasons from June 2020 to October 2022. We utilized 10 models to calculate the value of reference evapotranspiration, which were then matched against actual evapotranspiration data to identify the most appropriate model. Our research found that across the three elevation gradients, the daily average evapotranspiration were 3.663, 3.845 and 4.317 mm day−1, respectively. Across the three elevations, with consistent intra‐annual fluctuations. Notably, August experienced the highest monthly evapotranspiration at 4.750 mm day−1, and reach peak at 10:00 and 15:00 on the three elevation gradients. The results from the simulation of the 10 models indicate that the Dalton model is more suitable for our study area compared with the other models, showing the best R2, root mean square error and percentage error values. Partial least squares regression analysis, coupled with an enhanced regression tree model, identified precipitation as the most critical factor, with a variable importance in projection score of 2.079, contributing 52.6% to evapotranspiration. Collectively, precipitation were identified as key factors influencing evapotranspiration variability within our research area. Our study's insights are valuable for anticipating the impacts of future climate change. This conclusion is instrumental for refining water budget projections in Alpine regions under climate change scenarios.

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Estimation of hourly actual evapotranspiration over the Tibetan Plateau from multi-source data

Cited by 11 publications

References 60 publications

GOA-optimized deep learning for soybean yield estimation using multi-source remote sensing data

GOA-optimized deep learning for soybean yield estimation using multi-source remote sensing data

A Hybrid Framework for Simulating Actual Evapotranspiration in Data-Deficient Areas: A Case Study of the Inner Mongolia Section of the Yellow River Basin

Moisture conditions are limiting evapotranspiration changes of Alpine mountains of Qilian Mountains

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