A synergistic integration of physics-based and data-driven approaches has emerged as promising research field for terrestrial evapotranspiration (ET) estimation, enabling robust modeling of land-atmosphere interactions. This study proposes a hybrid model by integrating machine learning (ML)-based canopy surface resistance (rs,c) estimation into the Shuttleworth-Wallace (S-W) dual-source scheme under the ETMonitor framework, replacing traditional physics-based rs,c parameterization. Three ML algorithms, Random Forest (RF), Gradient Boosting Regression Tree (GBRT) and Deep Neural Network (DNN) were tested in the hybrid model. A reference dataset of rs,c was derived by inverting S-W dual-source model with in-situ flux measurements. The model was trained on 179 global flux tower sites and independently validated on 45 sites. Three full ML-based models based on DNN, GBRT and RF, were also developed to estimate ET directly for comparison. The DNN-integrated hybrid model outperformed the original physics-based model, with Kling-Gupta Efficiency (KGE) increasing from 0.7 to 0.84 and coefficient of determination (R-2) increasing from 0.66 to 0.72. The three full ML models showed comparable performance to the hybrid models. Notably, the physics-ML hybrid framework balances physical interpretability with data-driven efficiency, minimizing reliance on prior knowledge and avoiding over-parameterization.

Water vapour flux, expressed as evapotranspiration (ET), is critical for understanding the earth climate system and the complex heat–water exchange mechanisms between the land surface and the atmosphere in the high-altitude Tibetan Plateau (TP) region. However, the performance of ET products over the TP has not been adequately assessed, and there is still considerable uncertainty in the magnitude and spatial variability in the water vapour released from the TP into the atmosphere. In this study, we evaluated 22 ET products in the TP against in situ observations and basin-scale water balance estimations. This study also evaluated the spatiotemporal variability of the total vapour flux and of its components to clarify the vapour flux magnitude and variability in the TP. The results showed that the remote sensing high-resolution global ET data from ETMonitor and PMLV2 had a high accuracy, with overall better accuracy than other global and regional ET data with fine spatial resolution (∼ 1 km), when comparing with in situ observations. When compared with water balance estimates of ET at the basin scale, ETMonitor and PMLV2 at finer spatial resolution and GLEAM and TerraClimate at coarse spatial resolution showed good agreement. Different products showed different patterns of spatiotemporal variability, with large differences in the central to western TP. The multi-year and multi-product mean ET in the TP was 333.1 mm yr−1, with a standard deviation of 38.3 mm yr−1. The ET components (i.e. plant transpiration, soil evaporation, canopy rainfall interception evaporation, open-water evaporation, and snow/ice sublimation) available from some products were also compared, and the contribution of these components to total ET varied considerably, even in cases where the total ET from different products was similar. Soil evaporation accounts for most of the total ET in the TP, followed by plant transpiration and canopy rainfall interception evaporation, while the contributions from open-water evaporation and snow/ice sublimation cannot be negligible.

Climate change, population growth, and economic development exacerbate water scarcity. This study investigates the impact of drought on water availability in the Belt and Road region using high-resolution remote sensing data from 2001 to 2020. The results revealed an average water availability (precipitation minus evapotranspiration) of 249 mm/year and a declining trend in the Belt and Road region. Approximately 13% of the Belt and Road region faces water deficits (evapotranspiration exceeds precipitation), primarily in arid and semi-arid regions with high drought frequency. The area in the water deficit is expanding, and the intensity of the water deficit is increasing. The annual trend of water availability is strongly related to the frequency of droughts, i.e. water availability decreases with increased drought frequency. Drought exacerbates seasonal water stress in approximately one-third of the Belt and Road region, mainly in Europe and northern Asia, where drought frequently occurs during seasons with low water availability. The more severe the drought, the larger the negative anomaly in water availability. The critical role of evapotranspiration in seasonal water availability variability is also highlighted. This research underscores the importance of understanding drought-induced changes in water availability, which is crucial for sustainable water resource management.

In the Tibetan Plateau (TP) region, the foreseeable increase in air temperature may have profound and complex effects on the local hydrological cycle, and is likely to increase water loss from the land surface to the atmosphere through evapotranspiration (ET). Quantifying ET and its regulatory mechanisms are major challenges for understanding the water cycle and land-atmosphere interactions in the TP region. We evaluated the performance of several Earth observation-based ET datasets in the TP region, and explored the spatiotemporal variation of ET in the same region. The accuracy of different global ET datasets was evaluated, and ETMonitor and PML-V2 provide the best accuracy with overall high correlation, low bias, and low root mean square error. ETMonitor ET is also the only product with both high spatial (similar to 1 km) and temporal (daily) resolution. ETMonitor ET may reflect the effect of mountain topography on ET better than other global products, i.e., ET values are higher in the humid valleys with denser vegetation cover and higher soil moisture, and ET values are lower on the mountain slopes at higher elevations with less vegetation cover and colder climate. Other ET products failed to capture the spatial patterns of ET in the mountainous regions, and this suggests that the spatial resolution is not the only dominant factor leading to the poorer performance of these ET products in the mountain regions of the TP. The results show that multi-year average ET is 339 mm/yr in the TP region during 2000-2021, which accounts for about 51% of the total precipitation in the TP region. From 2000 to 2021, ET over the Tibetan Plateau shows an overall increasing trend with large spatial variability.

Improving irrigation water management is a key concern for the agricultural sector, and it requires extensive and comprehensive tools that provide a complete knowledge of crop water use and requirements. This study presents a novel methodology to explicitly estimate daily gross and net crop water requirements, actual crop water use, and irrigation efficiency of center pivot irrigation systems, by mainly utilizing the Sentinel-2 MultiSpectral Instrument (MSI) imagery at the farm scale. ETMonitor model is adapted to estimate actual water use (as the sum of canopy transpiration and evaporation of water intercepted by canopy and evaporation from soil) at daily/10-m resolution, benefiting from the high-resolution Sentinel-2 data and thus to assess the irrigation efficiency at the farm scale. The gross irrigation water requirement is estimated from the net crop water requirement and the water loss, including the water droplet evaporation directly into the air during application before droplets fall on the canopy and canopy interception loss. The method was applied to a pilot farmland with two major crops (wheat and potato) in the Inner Mongolia Autonomous Region of China, where modern equipment and appropriate irrigation methods are deployed for efficient water use. The estimated actual crop water use showed good agreement with the ground observations, e.g. the determination coefficients range from 0.67 to 0.81 and root mean square errors range from 0.56 mm/day to 1.24 mm/day for wheat and potato when comparing the estimated evapotranspiration with the measurement by the eddy covariance system. It also showed that the losses of total irrigated volume were 25.4% for wheat and 23.7% for potato, respectively, and found that the water allocation was insufficient to meet the water requirement in this irrigated area. This suggests that the amount of water applied was insufficient to meet the crop water requirement and the inherent water losses in the center pivot irrigation system, which imply the necessity to improve the irrigation practice to use the water more efficiently.

中国科学院空天信息创新研究院牵头构建的大宗粮食作物农业气象灾害灾变过程监测预警技术体系重点关注灾害事件过程与农作物全生育期灾情状况,实现了灾前、灾中和灾后的跟踪监测预警,不仅能及时预测预估灾害的到来、发展过程和可能的严重状况,为防灾减灾救灾赢取珍贵的时间,还可以准确分析灾害的时空变化和产量影响,提升科技对粮食生产的贡献率.

Information on crop yield is important for food security, in particular under the conditions of climate change and growing population worldwide. We developed a new fully distributed, high spatial resolution, model of biomass accumulation and crop yield applicable to a highly heterogeneous desert-oasis agroecosystem. The bulk of required input data is obtained by retrieving pixel-wise biogeophysical variables from a suite of very diverse satellite data. Both temperature and water stress conditions at field-scale are given full consideration, while the model was designed to strike a balance between model applicability and satisfactory characterization of the heterogeneous desert-oasis system to estimate field-scale yield. The development of this model relies on three main innovations. First, the start and end of the growing season were estimated for each pixel by calibrating the high spatial and temporal resolution observations of Normalized Difference Vegetation Index (NDVI) by Sentinal-2 (S2) MSI (Multi-Spectral Instrument) against limited local phenological information. Second, to monitor crop water stress, account taken of irrigation, a process-based water and energy balance model was applied to estimate the actual evapotranspiration (ET). This requires knowledge of soil water availability, which is characterized by downscaling the ASCAT (Advanced SCATterrometer) soil moisture data product. To capture the dominant features of the eco-hydrological conditions in the desert and oasis agroecosystem, ET was further downscaled from the 1 km resolution. Third, likewise the water stress indicator, the air temperature stress indicator was mapped after characterizing the thermal contrast and heterogeneity of the desert-oasis system, by generating time series of air temperature at 1 km spatial resolution using the MODIS (Moderate Resolution Imaging Spectroradiometer) Land Surface Temperature (LST) data product. In the temporal dimension, gaps were mitigated by applying time series analysis techniques to reconstruct cloud-free time series of LST, NDVI, fAPAR and albedo. These innovations add up to a high resolution characterization of crop response to the geospatial variability of weather and climate forcing in the desert-oasis agroecosystem. The model was applied to the dominant crops, i.e., spring wheat, maize, sunflower, and melon, in the oases of the Shiyang River Basin (northwestern China) characterized by a rather fragmented land use. The high resolution of pixel-wise ecohydrological parameters, i.e., crop phenology, temperature stress and water stress factors successfully reflect differences of crops with different phenology and location in the oases. The relative errors for wheat and maize yields compared to the census data are less than 5% at district level. At the county level, the relative errors of wheat yields of Liangzhou, Minqin, Gulang, Jinchuan, and Yongchang equal to 0.87%, 24.2%, 9.7%, 12.5%, and 7.2%. For maize, the dominant crop, the error on estimated yields was less than 5%, except in Gulang. The relative error on estimated yield for sunflower was less than 10% compared to agricultural census data. The relative error on estimated melon yield was 16%. This performance highlights the applicability of the model to estimate field-scale yields in agroecosystems characterized by fragmented land use.

成熟期农作物的识别在农作物种植面积估算、农业生产及产量统计方面具有重要作用.为提供一种简便的成熟期农作物遥感识别方法,利用Sentinel-2A 数据,以安徽省滁州市凤阳县为研究区,通过归一化植被指数(Normalized difference vegetation index,NDVI)与归一化光谱分离指数(Normalized spectral separation index,NSSI)构成的空间,提取光合植被、非光合植被、裸土的纯端元,由像元三分模型,得到非光合植被覆盖度及成熟期农作物的空间分布.为进一步提取研究区内具有相同成熟期的冬小麦与油菜,利用油菜开花期Sentinel-2A 数据,由Hue saturation value(HSV)图像变换方法,分别提取出成熟期冬小麦与油菜.与地面观测数据和辅助数据相比,提取的成熟区冬小麦、油菜的总体精度为95.34％,Kappa 系数为0.904,高于支持向量机方法(总体精度91.66％,Kappa系数为0.813)与决策树方法(总体精度92.39％,Kappa 系数为0.838)的提取精度.结果表明,NDVI-NSSI 空间与HSV 变换相结合的方法,可以有效将非光合植被与土壤背景分离,识别成熟期冬小麦与油菜,具有对数据需求较少,易操作等优势,也为提取农作物成熟期内的裸地以及与裸地具有相似波谱的地物提供了思路与方法.

Evapotranspiration（ET） is one of the core variables for studying the surface water cycle and management of the field-scale water resources. Satellite remote sensing are widely adopted to obtain the variation of evapotranspiration at large spatial scale, but the resolution of existing ET products is mainly limited at low or medium resolution（1—25 km）, which cannot satisfy the field-scale water management application. The Chinese GF-1 Wide Field of View（WFV） camera has the characteristics of high spatial and temporal resolution, with a spatial resolution of 16m, and it can support to generate ET products with high spatial and temporal resolution, which has not yet been well presented. The objective of this study is to present the capacity of using the remote sensing data from the GF-1 satellite as the driving force to produce high resolution（16 m） ET. The ETMonitor model was adopted to estimate ET at 16 m resolution in this study.ETMonitor is a combined model with multi-process parameterizations, and it has been proven to be able to generate accurate regional and global ET estimation at relative coarse resolution（e.g., 1 km） mainly using the biophysical and hydrological parameters/variables retrieved from satellite observations. During the ET estimation procedure, the adopted GF-1 remote sensing datasets include the Leaf Area Index（LAI）, Fraction of Vegetation Cover（FVC）, Albedo, and NDVI datasets, which are retrieved from previous studies. Ground observation data from 16 sites in China was collected to validate the estimated ET, including 6 grassland sites, 4 cropland sites, 1 mixed forest site, 2shrubland sites, and 3 desert or Gobi sites. The validation results show that the overall Root Mean Square Error（RMSE） of estimated daily evapotranspiration based on GF-1 satellite remote sensing datasets is 0.85 mm d-1, the correlation coefficient（R） is 0.79, and the Bias is 0.16mm d-1, which can demonstrate the high accuracy of estimated ET. The GF-1 based ET at 16 m resolution also presented better performance in terms of spatial variation of ET comparing with the low-resolution（e. g., 1 km） ET, especially in the regions with high surface heterogeneity. These highlight the ability of Chinese GF-1 satellite remote sensing dataset could produce accurate ET at high spatial variation, and it has potential to meet the application of field-scale agricultural water resources management, irrigation management,ecological environment monitoring and government decision-making in China. However, due to the impact of the revisit cycle of the GF-1satellite and the impact of clouds, there are some gaps or missing values in GF-1 based LAI, FVC or Albedo, and these further cause gaps in the GF-1 ET data. In order to improve the availability of high-resolution ET products, it is necessary to produce spatially and temporally continuous high-resolution ET products, which will be the focus of follow-up research.

Evapotranspiration (ET) plays a crucial role in the global energy and water cycles. Clarifying the time-series characteristics of ET is essential for accurately estimating ET and comprehending the ET change. This study utilized the latest ETMonitor product to analyze ET time-series characteristics in China from 2001 to 2021. The standard deviation and coefficient of variation were used to measure the absolute and relative variability of ET. A monthly ET time series was decomposed using an additive decomposition method to analyze its components. The results showed that China's ET exhibited an increasing trend, especially in the middle regions of the Yellow River to northeastern China, at a rate exceeding 5 mm/year. Northwestern China showed larger variation coefficients due to lower ET. Seasonal and irregular components were found to dominate the monthly ET time series, with significant seasonal components observed in eastern China and irregular components dominating the western region. Moreover, the seasonal characteristic of ET was more evident in north and northeast China compared to southern and northwestern regions. Additionally, relative variation in ET during winter was more significant than in summer, primarily in northern river basins.

Evapotranspiration (ET) is an essential ecohydrological process linking the land surface energy, water and carbon cycles, and plays a critical role in the earth system. ET remains one of the most problematic components of the water cycle to be determined due to the heterogeneity of the landscape and the complexity of driving factors. The satellite-based earth observation is expected to provide ET information at large-scales. However, accurate global ET information, with spatially and temporally continuous coverage at moderate-to-high resolution, is still scarce. In this paper, a combined model, called ETMonitor, with multi-process parameterizations, was improved and applied to estimate the global ET, mainly using the biophysical and hydrological parameters/variables retrieved from satellite observations. The ETMonitor model was improved in several aspects in this study to generate the global ET datasets during 2000-2019 at daily/1-km resolution, including: 1) adopting high temporal resolution surface water cover and snow/ice cover as input, to simulate the impact of their seasonal change on ET variation; 2) parameterizing the impact of soil moisture on plant transpiration and soil evaporation using high resolution soil moisture, which was downscaled to 1-km resolution from the coarse resolution data retrieved from micro-wave remote sensing observation; 3) involving a better soil heat flux estimation to reduce its impact on the uncertainty of estimated ET; 4) being calibrated based on global ground flux observations to achieve better accuracy. The estimated daily ET was validated based on the global in situ observations at site scale across various ecosystems, with overall high correlation (0.75), low bias (0.08 mm d-1), and low root mean square error (0.93 mm d-1). It had good ability to partition total ET to plant transpiration and soil evaporation indicated by the good agreement with the ground isotope measurements in a growing season at one site in the northwest China. The estimated global ET was cross-validated by comparing with other existing ET products, and it showed the global ET estimated by ETMonitor could capture the expected global ET patterns both in space and in time. It also indicated the superiority of the ET product by ETMonitor in the following aspects: capability in capturing the seasonal dynamics of waterbody evaporation and sublimation; better performance in capturing the spatial variation of ET in the irrigated cropland regions and mountain regions with complex terrain than other global ET products, e.g., the GLEAM and MOD16 ET products; capability of ET component partitioning at high spatial (1 -km) and high temporal (daily) resolutions with good accuracy. The estimated plant transpiration, soil evapo-ration, canopy rainfall interception loss, and water body evaporation and snow sublimation accounted for 61.54 % (+/- 0.44 %), 19.08 % (+/- 0.54 %), 13.54 % (+/- 0.49 %), and 5.84 % (+/- 0.24 %), respectively, of the total ET on global average. The ETMonitor global ET dataset could provide important information on studies of global terrestrial water and energy cycles and climate change studies, and water resources management at global and regional scales.

The European Space Agency's Climate Change Initiative (ESA CCI) soil moisture could provide long-time microwave-retrieved soil moisture data but is limited to regional applications due to the low resolution (25 km). A new method of downscaling ESA CCI soil moisture to 1 km is presented in this study. First, the soil and vegetation component temperatures (SVCT) were estimated using MODIS land surface temperature and normalized difference vegetation index (NDVI) data. Following this, the relationship between ESA CCI soil moisture and 1-km SVCT was constructed based on the negative linear relationship between the temperature vegetation dryness index (TVDI) and soil moisture. The dry and wet lines used to estimate TVDI need not to be obtained in the method. The coefficients were obtained directly from 25-km ESA CCI soil moisture and 1-km SVCT by the upscaling algorithm of soil moisture. The method was applied to the Naqu area on the Tibetan Plateau. Downscaled soil moisture was validated with ground measurements collected at five sites within the soil moisture/temperature monitoring network on the central Tibetan Plateau from May to October 2014. The results show that the trend of the time series of the downscaled soil moisture is similar to the ground measurements during this period, and the root-mean-square error is 0.0568 m 3 /m 3 . The method is suitable for the condition with an NDVI higher than 0.4. The key points of the approach are to obtain SVCT and the relationship between soil moisture and SVCT.

The estimation of ET, NPP, and crop WUE in Shiyang River Basin, China.

Satellite-based models have been widely used to estimate gross primary production (GPP) of terrestrial ecosystems. Although they have many advantages for mapping spatiotemporal variations of regional or global GPP, the performance in agroecosystems is relatively poor. In this study, a light-use-efficiency model for cropland GPP estimation, named EF-LUE, driven by remote sensing data, was developed by integrating evaporative fraction (EF) as limiting factor accounting for soil water availability. Model parameters were optimized first using CO2 flux measurements by eddy covariance system from flux tower sites, and the optimized parameters were further spatially extrapolated according to climate zones for global cropland GPP estimation in 2001–2019. The major forcing datasets include the fraction of absorbed photosynthetically active radiation (FAPAR) data from the Copernicus Global Land Service System (CGLS) GEOV2 dataset, EF from the ETMonitor model, and meteorological forcing variables from ERA5 data. The EF-LUE model was first evaluated at flux tower site-level, and the results suggested that the proposed EF-LUE model and the LUE model without using water availability limiting factor, both driven by flux tower meteorology data, explained 82% and 74% of the temporal variations of GPP across crop sites, respectively. The overall KGE increased from 0.73 to 0.83, NSE increased from 0.73 to 0.81, and RMSE decreased from 2.87 to 2.39 g C m−2 d−1 in the estimated GPP after integrating EF in the LUE model. These improvements may be largely attributed to parameters optimized for different climatic zones and incorporating water availability limiting factor expressed by EF into the light-use-efficiency model. At global scale, the verification by GPP measurements from cropland flux tower sites showed that GPP estimated by the EF-LUE model driven by ERA5 reanalysis meteorological data and EF from ETMonitor had overall the highest R2, KGE, and NSE and the smallest RMSE over the four existing GPP datasets (MOD17 GPP, revised EC-LUE GPP, GOSIF GPP and PML-V2 GPP). The global GPP from the EF-LUE model could capture the significant negative GPP anomalies during drought or heat-wave events, indicating its ability to express the impacts of the water stress on cropland GPP.

Surface Soil Moisture (SSM) information is needed for agricultural water resource management, hydrology and climate analysis applications. Temporal and spatial sampling by the space-borne instruments designed to retrieve SSM is, however, limited by the orbit and sensors of the satellites. We produced a Global Daily-scale Soil Moisture Fusion Dataset (GDSMFD) with 25 km spatial resolution (2011~2018) by applying the Triple Collocation Analysis (TCA) and Linear Weight Fusion (LWF) methods. Using five metrics, the GDSMFD was evaluated against in-situ soil moisture measurements from ten ground observation networks and compared with the prefusion SSM products. Results indicated that the GDSMFD was consistent with in-situ soil moisture measurements, the minimum of root mean square error values of GDSMFD was only 0.036 cm3/cm3. Moreover, the GDSMFD had a good global coverage with mean Global Coverage Fraction (GCF) of 0.672 and the maximum GCF of 0.837. GDSMFD performed well in accuracy and global coverage fraction, making it valuable in applications to the global climate change monitoring, drought monitoring and hydrological monitoring.

Validation of remotely sensed evapotranspiration (RS_ET) products is important because their accuracy is critical for various scientific applications. In this study, an integrated validation framework was proposed for evaluating RS_ET products with coarse spatial resolution extending from homogenous to heterogeneous land surfaces. This framework was applied at the pixel and river basin scales, using direct and indirect validation methods with multisource validation datasets, which solved the spatial mismatch between ground measurements and remotely sensed products. The accuracy, rationality of spatiotemporal variations, and error sources of RS_ET products and uncertainties during the validation process were the focuses in the framework. The application of this framework is exemplified by validating five widely used RS_ET products (i.e., GLEAM, DTD, MOD16, ETMonitor, and GLASS) in the Heihe River Basin from 2012 to 2016. Combined with the results from direct (as the priority method) and indirect validation (as the auxiliary method), DTD showed the highest accuracy (1-MAPE) in the vegetation growing season (75%), followed by ETMonitor (71%), GLASS (68%), GLEAM (54%), and MOD16 (44%). Each product reasonably reflected the spatiotemporal variations in the validation dataset. ETMonitor exhibited the highest consistency with the ground truth ET at the basin scale (ETMap) (R = 0.69), followed by GLASS (0.65), DTD (0.63), MOD16 (0.62), and GLEAM (0.57). Error sources of these RS_ET products were mainly due to the limitations of the algorithms and the coarse spatial resolution of the input data, while the uncertainties in the validation process amounted to 15–28%. This work is proposed to effectively validate and improve the RS_ET products over heterogeneous land surfaces.

为进一步研究风云三号(FY-3B)土壤水分降尺度获取高分辨率土壤水分的方法,使其更适用于农业、水文、生态等区域尺度的应用要求,以MODIS为数据源,青藏高原那曲地区为研究区,利用表观热惯量模型(apparent thermal inertia,ATI)与温度植被指数(temperature vegetation index,TVI)模型在不同植被覆盖度下适用的特点,构建综合ATI与TVI的土壤水分反演模型;结合低分辨率FY-3B土壤水分产品,利用土壤水分降尺度方法,获取高分辨率下土壤水分反演模型系数,并得到高分辨率土壤水分.通过与地面观测数据对比,降尺度后土壤水分与实测数据的R2在0.4以上,RMSE在0.055～0.103 cm3/cm3之间,表明降尺度后的土壤水分能够较好地反映区域土壤水分的空间分布与变化.

A distributed hydrological energy-water-balance model (FEST-EWB) is calibrated over the Heihe Basin, a mainly desertic basin in China, employing remotely-sensed Land Surface Temperature (LST) (MODIS, 1-km resolution) as calibration variable. This approach overcomes the problem of model parameters characterization, which are usually difficult to define especially over large basins, allowing a pixel-by-pixel calibration, preserving the spatial heterogeneity. Hence, the spatial distribution of the modelled LST, but also of soil moisture (SM) and evapo-transpiration (ET) is improved. The accuracy of the calibration process is documented through common statistical indexes. The modelled ET is compared locally against two eddy covariance stations in the agricultural area, while distributively against the ET estimates of the ETMonitor model and some global re-analysis products (ERA-Interim, GLDAS2, GLEAM and MERRA-2). Calibration and validation performed in this study prove that a considerable model accuracy is attainable even in extremely arid environments. An average LST bias of 2.6 degrees C is obtained over the basin. A good adaptation of FEST-EWB is also obtained against eddy-covariance stations ET with a little bias around -1 mm/d. On the other hand, the reanalysis products display a much worse performance, with higher absolute biases (around -3.5 mm/d), although with high variability among the models.

Satellite-derived lake surface water temperature (LSWT) measurements can be used for monitoring purposes. However, analyses based on the LSWT of Lake Ontario and the surrounding land surface temperature (LST) are scarce in the current literature. First, we provide an evaluation of the commonly used Moderate Resolution Imaging Spectroradiometer (MODIS)-derived LSWT/LST (MOD11A1 and MYD11A1) using in situ measurements near the area of where Lake Ontario, the St. Lawrence River and the Rideau Canal meet. The MODIS datasets agreed well with ground sites measurements from 2015–2017, with an R2 consistently over 0.90. Among the different ground measurement sites, the best results were achieved for Hill Island, with a correlation of 0.99 and centered root mean square difference (RMSD) of 0.73 K for Aqua/MYD nighttime. The validated MODIS datasets were used to analyze the temperature trend over the study area from 2001 to 2018, through a linear regression method with a Mann–Kendall test. A slight warming trend was found, with 95% confidence over the ground sites from 2003 to 2012 for the MYD11A1-Night datasets. The warming trend for the whole region, including both the lake and the land, was about 0.17 K year−1 for the MYD11A1 datasets during 2003–2012, whereas it was about 0.06 K year−1 during 2003–2018. There was also a spatial pattern of warming, but the trend for the lake region was not obviously different from that of the land region. For the monthly trends, the warming trends for September and October from 2013 to 2018 are much more apparent than those of other months.