Environmental Modulation of Interpolation Strategies and Algorithm Adaptability in Remote Sensing Retrieval of Farmland Soil Moisture: A Theoretical Framework

Abstract

Accurate retrieval of farmland soil moisture from multi-source remote sensing data requires simultaneous consideration of time-series interpolation strategies and machine learning algorithm selection. However, the theoretical basis explaining how environmental conditions modulate their joint effects remains insufficiently articulated. This paper proposes a unified conceptual framework termed the "Interpolation-Algorithm-Environment" (IAE) triad, which explains why no universally optimal retrieval pipeline exists and why optimal choices are inherently environment-contingent. We further introduce the concept of Post-Treatment Spurious Correlation (PTSC) to characterize a failure mode in which interpolation processing inadvertently introduces artificial feature structure that retrieval models learn and amplify, degrading generalization. Theoretical analysis of algorithm-environment matching, interpolation-induced information distortion, and synergistic constraint mechanisms provides a transferable decision basis for designing robust soil moisture retrieval systems under diverse agro-climatic conditions.