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Achieving food availability, advancing the long-term viability of farming, and propelling economic progress. However, current forecasting frameworks often overlook the specific characteristics of rice yield distributions, which vary across regions, exhibit skewness, and are constrained by fixed lower and upper thresholds, conforming to a four-parameter beta distribution. The consequence is outputs that are less reliable and can potentially misguide decision-making. Furthermore, a majority of techniques fall short in modeling the intricate variable interactions that arise when satellite observations are fused with field survey measurements, an integration widely viewed as essential for boosting forecast fidelity and streamlining cultivation methods. To remedy these deficiencies, the present research proposes a four-parameter beta Generalized Linear Mixed Model (GLMM) and advances it into a four-parameter beta Generalized Mixed Effect Tree (GMET). This four-parameter beta GMET extends the four-parameter beta GLMM architecture by embedding a regression tree mechanism, thereby providing superior flexibility for representing non-linear and interaction-laden dependencies. Deploying this analytical framework on empirical data from Central Kalimantan and Karawang yields significant gains over earlier approaches, as evidenced by markedly lower AIC and RRMSE metrics. Significantly, the investigation finds that time-lagged reflectance values from bands 4 and 8 and the NDVI index captured by Sentinel-2A satellites are the dominant determinants of rice productivity, overshadowing the explanatory contribution of farmer questionnaire responses. This finding underscores the promise of remotely sensed data as a cornerstone for rice yield estimation, offering a more streamlined, less resource-intensive alternative to reliance on farmer-reported data. By further refining satellite-based technologies, upcoming initiatives in yield forecasting can realize superior efficiency and precision, thereby underpinning evidence-based choices in agricultural governance.