遥感
多光谱图像
高光谱成像
环境科学
均方误差
计算机科学
卫星
天蓬
图像分辨率
光谱带
随机森林
多光谱模式识别
规范化(社会学)
相关系数
辐射测量
光谱分辨率
植被(病理学)
概率逻辑
大气校正
像素
作者
Tinghan Wang,Liangsheng Shi,Chenye Su
标识
DOI:10.1109/tgrs.2026.3654155
摘要
Leaf nitrogen content (LNC) is a key regulator of photosynthesis and crop productivity, and its timely monitoring underpins both precision nitrogen management and large-scale ecosystem assessment. Remote sensing offers non-destructive, spatially explicit monitoring of LNC, but retrieval accuracy is degraded by canopy structural effects and the limited spectral resolution of multispectral satellite sensors. Here we develop an integrated framework that combines spectral super-resolution with canopy structure correction to improve satellite-based LNC estimation. Sentinel-2 multispectral reflectance is first reconstructed into full hyperspectral spectra using a probabilistic diffusion model (DDPM). Directional area scattering factor (DASF) is then computed from the reconstructed spectra to derive the canopy scattering coefficient (CSC), which normalizes structural effects and serves as input to a random forest regression (RFR) model for LNC estimation. On simulated data, the DDPM achieves high reconstruction fidelity (PSNR = 41.49 dB, SAM = 0.9989). Using CSC derived from DDPM outputs improves LNC estimation compared with multispectral bidirectional reflectance factor (BRF) alone (R² = 0.579, RMSE = 0.431 %, RRMSE = 23.58%; RMSE reduced by 14.8%, R² increased by 0.134). Even when only RGB bands are available, the super-resolution + CSC pipeline increases LNC retrieval accuracy (R² +0.14), and the addition of an NIR band further enhances both reconstruction quality and LNC estimation. This study shows that hyperspectral super-resolution coupled with structural normalization can unlock more accurate, scalable LNC monitoring from existing satellite archives, supporting operational nitrogen management and ecosystem monitoring in data-limited regions.
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