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Accurate subsurface soil moisture (SM) estimation is critical for vegetation growth, drought monitoring, and climate change mitigation, yet remains a significant challenge. Previous data assimilation (DA) approaches are limited to only surface SM assimilation. In this study, we utilized the proxy subsurface SM estimated via the exponential filter method (ExpF) as another assimilation variable in our 2‐dimensional DA. Meanwhile, the dual updating DA scheme was implemented to simultaneously update model parameters and states. The two DA pathways were incorporated into the proposed framework (DA_1E_D), which enhanced the subsurface SM accuracy, with the effects of 2‐dimensional assimilation being more significant. Under 2‐dimensional DA, the information transfer between layers was more accurately characterized, leading to overall improvements with unbiased root‐mean‐square error (ubRMSE) reductions of 0.015 and 0.005 m3 · m−3, and Kling–Gupta efficiency (KGE) increases of 0.248 and 0.067 for surface and subsurface SM, respectively, across five SM networks. The soil thickness (d2) and hydraulic conductivity exponent (expt2) are the most influential parameters affecting subsurface SM dynamics through model propagation. DA_1E_D also outperformed ExpF in subsurface SM accuracy, particularly in SM networks with weak surface‐subsurface correlation, achieving an average ubRMSE reduction of 0.003 m3 · m−3 and an average KGE increase of 0.202. It was also applied to Soil Moisture Active Passive data at regional scale, demonstrating significant improvements. The model surface‐subsurface SM coupling was adjusted toward the actual coupling after subsurface assimilation and dual updating. This study may provide new insights into the diagnosis and refinements of the model representation of surface‐subsurface processes. Plain Language Summary: Soil moisture (SM) is a key variable in the fields of hydrology, ecology, and agriculture, and in characterizing Earth's climate. Particularly, accurate subsurface SM estimation remains challenging for the Earth science community. Here, we used the empirical subsurface SM estimates derived from an exponential filter method (ExpF) as another assimilation variable, in combination with the data assimilation (DA) technique which updates model states and parameters simultaneously, to constrain the model estimates. We found these two pathways lead to significant improvements in subsurface SM estimation. The accuracy of subsurface SM was improved with biases (ubRMSE) significantly reduced by 0.005 m3 · m−3 on average over the modeling grid cells, due to the assimilation of empirical subsurface SM. The effects of parameter updating were relatively smaller but also positive. The proposed DA framework demonstrated superior accuracy to ExpF, with an average ubRMSE reduction of 0.003 m3 · m−3 and an average KGE increase of 0.202. This framework was also applicable to regional scale using satellite products. Moreover, both pathways proved effective in adjusting the physical surface‐subsurface correlation in the land surface model toward actual coupling. Our analysis has important implications for future studies on estimating subsurface SM using land surface models at larger spatiotemporal scales. Key Points: A 2‐dimensional data assimilation (DA) framework which accounted for a dual state‐parameter updating scheme was proposedThe accuracy of subsurface soil moisture (SM) was improved by assimilating empirical subsurface estimates and dual updatingThe proposed DA framework can also be applied to Soil Moisture Active Passive data to improve subsurface SM estimation at regional scale [ABSTRACT FROM AUTHOR]

【Objective】 Melon is one of the most important cash crops in China, and increasing its yield without compromising fruit quality is desirable to melon farmers. The purpose of this paper is to investigate how to achieve this goal by optimizing irrigation and calcium fertilization. 【Method】 We used the Tinglin variety as the model plant and compared three irrigations: sufficient irrigation using 220.45 mm of water (W1), and deficit irrigation using 2/3 (W2) and 1/2 (W3) of the water used in W1 respectively; and two calcium fertilizations: 0.68 L/per plant (Ca1) and 0 (Ca0). In each treatment, we measured sugar accumulation, yield and quality of the fruit, as well as the change in its physiological traits. 【Result】 ①In all treatments, fruit diameter increased asymptotically as time elapsed. ②Sufficient irrigation increased melon size and fruit yield but reduced irrigation water productivity. Imposing a moderate deficit irrigation was optimal for improving both irrigation water use efficiency and fruit quality, but if the deficiency was too severe, the resulting water stress would hinder melon growth and reduce fruit yield as a result. ③Compared with the control without calcium fertilization, applying Ca fertilizer not only increased fruit yield, weight of individual melons and irrigation water productivity, it also increased fruit quality by increasing fruit hardness and reducing titratable acid content in the fruits. 【Conclusion】 Irrigation and calcium fertilization both affected melon growth, sugar accumulation, and the ultimate fruit yield and quality. A moderate deficit irrigation combined with calcium fertilization was optimal for improving market value of the muskmelon without compromising its yield. These results have important implications for muskmelon production in northwestern China and in other regions which have similar climate and soil conditions.