Enhancing regional estimates of evapotranspiration with earth observation data

Food security and food sustainability are high on the global policy agenda. Reliable information on crop water use and terrestrial water stress are important to ensure an optimal use of available water resources and for enhancing crop production. Remote sensing provides a feasible avenue to estimate regional evapotranspiration (ET), which can be employed to assess terrestrial water stress. However, the heterogeneity of land surfaces and the accumulated errors from various inputs often result in substantial biases in most global or regional ET models across different landscapes. Reducing uncertainties in available ET products or remote sensing (RS)-based models and obtaining regional ET estimates with improved accuracy is important for effectively using ET to support agricultural monitoring and water resources managements. This thesis first compared different Priestly-Taylor (PT)-based methods that use three Earth observation-based alternatives - apparent thermal inertia (ATI), microwave soil moisture (SM), and optical spectral indices based on shortwave infrared (SWIR) to assess soil evaporation over cropland and grassland regions. Using FLUXNET data as ET reference, the results illustrated that the incorporation of the SWIR-based soil moisture divergence index (SMDI) and microwave-based SM into monthly soil evaporation led to 6% and 5% increase in explained ET variances and reduced RMSE by 23.2% and 13.1% for cropland and grassland, respectively, as compared to PT-JPL using atmospheric reanalysis data only. The results suggested that a combination of optical SWIR and microwave SM has good potential to improve the PT-JPL model accuracy for agricultural landscapes. Based on the performance of different PT-based methods, ET estimates derived from the revised PT method were used to assess water budgets across 53 catchments in central-western Europe with a humid climate and were compared with three additional ET data sources (MOD16, GLEAM, and PT-JPL). Surprisingly, all RS-based ET estimates significantly diverged from water balance-based ET (ETWB) in 45 humid catchments, whereas most previous studies that focussed on arid catchments or on the global scale found significantly less divergence. Using ET retrievals from the Budyko framework and upscaled ET from FLUXCOM as references, the closure errors of water budgets were sensitive to errors arising from precipitation data in humid regions and the water balance approach was found to overestimate ET during heavy rainfall events. Instead, the Budyko framework that describes the partitioning of precipitation to ET as a functional balance between atmospheric water supply (precipitation, P) and demand (potential evapotranspiration, PET) had good correlation with ET ensemble from multiple data sources and upscaled ET from FLUXCOM product. 161 Summary The results indicated that errors from precipitation and terrestrial water storage anomalies introduce large uncertainties in ETWB, thereby complicating water balance validation in humid regions across multiple timesteps. To improve the application of ETWB for benchmarking ETEB in humid regions, high-quality input data should be used or – like the Budyko framework – energy constraints should be considered. The thesis then proceeds to explore causes for the notable deviations between observed and Budyko-predicted water balances in certain catchments. The results revealed that for humid catchments, topography and seasonal cumulative moisture surplus can explain the spatial distributions of Budyko scatter with r higher than 0.65, whereas soil properties and vegetation indices explained little of the variance (r≤0.30). Temporally, the interannual variability of Budyko scatter was negatively correlated with annual average vegetation indices, particularly for catchments with relatively low vegetation cover. This thesis provides valuable insights to the interpretation of the Budyko framework and offers possible solutions to improve its performance to predict the spatiotemporal variability of water balances. Lastly, to address the deviations from the predictive Budyko curve, additional controls of hydrological partitioning were introduced to correct Budyko scatter between catchments and between years. The results illustrated that the use of catchment climatic seasonality properties and topography attributes is effective in reproducing the Budyko parameter (w) with an r of 0.76 and RMSE of 0.49 for all 45 catchments in central-western Europe. After the correction of temporal Budyko scatter using interannual variability of vegetation information and the fraction of precipitation falling as snow, the performance of the modified Budyko-type equation improves with respect to the original Budyko framework, in comparison to ETWB at catchment scale (∆r of 0.26 and ∆RMSE of 19.19 mm/yr). When compared with the gridded ET ensemble using energy balance, the enhanced Budyko framework is generally effective to reproduce the spatial distribution of ET with good similarity, even in ungauged regions. Overall, the revised Budyko framework shows improved performance in predicting water balances and can be applied to assess crop water use and terrestrial water stress at regional scale, particularly in ungauged areas. Overall, this thesis contributes significantly to the enhancement of regional ET estimation using Earth observation. It proposes a novel blended parameterization for soil moisture constraints in the modified PT-JPL model, which is capable of capturing the soil evaporation more accurately within agroecosystems. Meanwhile, this thesis proposes a new water balance-based validation method that uses the Budyko framework integrated with environmental parameters. By developing improved RS-based models and water balance-based validation methods, this thesis provides valuable insights into the complexities of ET 162 Summary estimation at the regional scale. These findings are expected to advance the application of ET in decision-making regarding the management of agriculture and water resources

Provable Unrestricted Adversarial Training without Compromise with Generalizability

Adversarial training (AT) is widely considered as the most promising strategy to defend against adversarial attacks and has drawn increasing interest from researchers. However, the existing AT methods still suffer from two challenges. First, they are unable to handle unrestricted adversarial examples (UAEs), which are built from scratch, as opposed to restricted adversarial examples (RAEs), which are created by adding perturbations bound by an $l_p$ norm to observed examples. Second, the existing AT methods often achieve adversarial robustness at the expense of standard generalizability (i.e., the accuracy on natural examples) because they make a tradeoff between them. To overcome these challenges, we propose a unique viewpoint that understands UAEs as imperceptibly perturbed unobserved examples. Also, we find that the tradeoff results from the separation of the distributions of adversarial examples and natural examples. Based on these ideas, we propose a novel AT approach called Provable Unrestricted Adversarial Training (PUAT), which can provide a target classifier with comprehensive adversarial robustness against both UAE and RAE, and simultaneously improve its standard generalizability. Particularly, PUAT utilizes partially labeled data to achieve effective UAE generation by accurately capturing the natural data distribution through a novel augmented triple-GAN. At the same time, PUAT extends the traditional AT by introducing the supervised loss of the target classifier into the adversarial loss and achieves the alignment between the UAE distribution, the natural data distribution, and the distribution learned by the classifier, with the collaboration of the augmented triple-GAN. Finally, the solid theoretical analysis and extensive experiments conducted on widely-used benchmarks demonstrate the superiority of PUAT