Remote sensing plays a pivotal role in forest inventory by enabling efficient large-scale monitoring while minimizing fieldwork costs. However, missing values pose a critical challenge in remote sensing applications, as ignoring or mishandling such data gaps can introduce systematic bias into the estimation of target variables for natural resource monitoring. This can lead to cascading errors that propagate through forest and ecosystem management decisions, ultimately hindering progress toward sustainable forest management, biodiversity conservation, and climate change mitigation strategies. This study aims to propose and demonstrate a procedure that employs hybrid estimators to address the limitations of missing remotely sensed data in forest inventory, using Landsat 7 ETM+ SLC-off data as an archived source for forest resource monitoring as a case in point. We compared forest inventory estimates from the hybrid estimator with those from a conventional model-based (CMB) estimator using Sentinel-2 data without missing values. Monte Carlo simulations revealed three key findings: (1) The hybrid estimator, leveraging missing-data remote sensing represented by Landsat 7 ETM+ SLC-off data, achieved a sampling precision of over 90%, meeting China's national standard for the National Forest Inventory (NFI); (2) The hybrid estimator demonstrated comparable efficiency to the CMB estimator; (3) The uncertainty associated with hybrid estimators was primarily dominated by model parameter estimation, which could be effectively mitigated by slightly increasing the training sample size or refining model specification. Overall, in forest inventory, the hybrid estimator can surmount the limitations posed by missing values in remotely sensed auxiliary data, effectively balancing cost-effectiveness and flexibility.

Remote sensing (RS) facilitates forest inventory across a wide range of variables required by the UNFCCC as well as by other agreements and processes. The Conventional model-based (CMB) estimator supports wall-to-wall RS data, while Hybrid estimators support surveys where RS data are available as a sample. However, the connection between these two types of monitoring procedures has been unclear, hindering the reconciliation of wall-to-wall and non-wall-to-wall use of RS data in practical applications and thus potentially impeding cost-efficient deployment of high-end sensing instruments for large area monitoring. Consequently, our objectives are to (1) shed further light on the connections between different types of Hybrid estimators, and between CMB and Hybrid estimators, through mathematical analyses and Monte Carlo simulations; and (2) compare the effects and explore the tradeoffs related to the RS sampling design, coverage rate, and cluster size on estimation precision. Primary findings are threefold: (1) the CMB estimator represents a special case of Hybrid estimators, signifying that wall-to-wall RS data is a particular instance of sample-based RS data; (2) the precision of estimators in forest inventory can be greater for stratified non-wall-to-wall RS data compared to wall-to-wall RS data; (3) otherwise cost-prohibitive sensing, such as LiDAR and UAV, can support large scale monitoring through collecting RS data as a sample. These conclusions may reconcile different perspectives regarding choice of RS instruments, data acquisition, and cost for continuous observations, particularly in the context of surveys aiming at providing data for mitigating climate change.