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Accurate estimation of evapotranspiration (ET) can be affected by cloud change, while cloud‐induced bias errors in ET change remain poorly understood. Here we evaluated seven ET data sets from satellite microwave and optical models, atmospheric reanalysis and land surface models over East Asia. Results showed that although the data sets estimated ET changes overall well under mild cloud increase, all of them had a systematic overestimation (by 73.8%–159.8%) and deteriorated capability of capturing ET change under heavy cloud increase, especially over dense vegetation with large cloud cover. Overestimated incident solar radiation under clouds contributed to the deteriorated performances of ET. Cloud‐induced change in radiation and its errors played larger role in summer ET change over densely vegetated lands, while those in air temperature and relative humidity were dominant jointly over grass and barren lands. These results can serve as a reference for improving ET models and their forcing inputs under clouds. Plain Language Summary: The change of cloud cover affects terrestrial evapotranspiration (ET), which is a fundamental process in the interaction between land and the atmosphere. ET can be estimated from satellite observations, reanalysis data sets and land surface models. Knowing the cloud‐induced errors in the estimation of ET and its drivers can be very helpful for us to improve the ET modeling under the changing cloud conditions. In this study, we evaluated seven ET data sets and their forcing data from clear sky to cloudy sky conditions over East Asia. We quantified the importance of incident radiation, air temperature and relative humidity inputs in ET data sets under cloudy sky using a machine learning algorithm. Our findings revealed that all ET data sets were overestimated systematically under heavy cloud increase. Cloud‐induced change in radiation was more important over densely vegetated lands, while air temperature and relative humidity played larger roles over grass and barren lands. The result highlights the importance of cloud effects on ET estimations. Key Points: Seven evapotranspiration (ET) data sets from satellite microwave and optical models, reanalysis and land surface models were evaluated under cloud changeAll data sets overestimated the absolute ET values and cloud‐induced changes in ET under heavy cloud increase conditionsSummer ET change under clouds was dominated by radiation in the southern dense vegetation, but by temperature and humidity in grass lands [ABSTRACT FROM AUTHOR]

Understanding the cloud impact on forest evapotranspiration (ET) is crucial for studying the interaction of vegetation‐cloud‐atmosphere. Combining long‐term (2003–2010) satellite passive microwave observations and in‐situ measurements, a non‐linear response of canopy‐scale ET to cloud increase was found at a temperate forest in Northeast China. As cloud increased, an initial enhancement (4%–10%) in ET occurred under less cloudy sky, while a significant reduction (>20%) in ET occurred under more cloudy sky. The phenomenon existed under both high and low vegetation water content (VWC) indicated by satellite microwave emissivity difference vegetation index (EDVI). Analysis showed that this was the combined effect from the enhancement (5%–30%) in evaporative fraction (EF) and the reduction (5%–50%) in net radiation under cloud increase. Decoupling analysis based on coefficients (ρ) of path analysis model showed that enhanced EF (ρ > 0.61) rather than radiation (ρ < 0.47) dominated the ET enhancement under less cloudy sky, while the control of reduced radiation became stronger (ρ > 0.63) and could not be compensated by increased EF (ρ < 0.48) under more cloudy sky. EF enhancement under clouds was strongly correlated with the decline in canopy resistance (Rs) which was dominated by vapor pressure deficit (VPD). Higher VWC increased ET via reducing Rs and enlarging EF. This positive effect of VWC was more noticeable under less cloudy sky. Associated mechanisms could be related to the dynamic controls of plant physiology and environmental conditions induced by VWC and clouds. This study highlighted the dynamic effect of clouds and VWC on forest ET and improved our knowledge of vegetation‐cloud interactions. Key Points: The non‐linear response of ET to cloud increase results from increase of evaporative fraction and reduction in net radiationVegetation water content (VWC) significantly affects ET status under various cloud conditions, particularly under clear skyLower VWC can restrain ET under clouds by increasing canopy resistance and lowering evaporative fraction [ABSTRACT FROM AUTHOR]

• A small bias in EDVI-ET is found over eight river basins using water balance method. • EDVI-ET performs a larger magnitude and higher accuracy than MOD16 over crop lands. • Cloud-induced changes in ET vary from 10% to 40% compared to clear sky conditions. • Cloud impacts are more significant for forests, savannas and crops than barrens. • EDVI-ET is most sensitive to radiation for forests, but to humidity for arid barrens. Evapotranspiration (ET) is an important component of the hydrological cycle and energy balance in a land-atmosphere system. Satellite remote sensing has been widely used to estimate regional and global ET, but most previous methods depend on optical measurements that are limited to cloud-free conditions. This makes ET estimation challenging under cloudy sky. Currently, evaluations of satellite ET estimation under various cloud conditions remain lacking at the regional scale. Owing to the ability to penetrate clouds, satellite passive microwave measurements are powerful tools for retrieving ET under clouds. This study evaluated a satellite microwave-based daily ET method under all sky conditions over the part of China between 18°N and 50°N from 2003 to 2010, using microwave emissivity difference vegetation index (EDVI) as the proxy of vegetation water content (VWC). Validations using the surface water balance method found that the estimated ET (EDVI-ET) had an overall small bias (6.18%) in eight river basins. EDVI-ET displayed consistent spatiotemporal patterns with global MOD16 ET, with high spatial correlation (R >0.71) and monthly temporal correlation (R >0.82) throughout four seasons. Their differences were also small (<0.56mmday−1) in forests, savannas, grass/shrubs, and croplands. Furthermore, cloud impacts on the regional ET were found to be significant and spatiotemporally heterogeneous. Both EDVI-ET and in-situ observations at seven flux towers indicated that cloud-induced reduction in daily ET could exceed 30% when the cloud cover increased by 60% (R2 of 0.42, fitting line slope of 0.80, p <0.001). Under increased cloud conditions in summer, the changes in net radiation dominated the ET over dense vegetation in southern China, while the roles of air temperature and humidity increased over water-stressed barrens and short vegetation in northwest China. VWC affected EDVI-ET under clouds in temperate transitional zones from flatlands to highlands. This study highlighted the importance of cloud impacts in satellite ET estimation. [ABSTRACT FROM AUTHOR]