Wavelength of interest (WOI) hyperspectral imaging technology offers significant advantages in optimizing mission-specific goals and improving cost-effectiveness in hyperspectral Earth observation. However, conducting per-band measurements for periodic on-orbit radiometric calibration across thousands of wavebands over the entire spectral range presents a considerable challenge, especially given the limited resources available on satellite platforms and at ground stations. To overcome this, we propose the Curve-based Hyperspectral Imaging Radiometric Calibration (CHIRON) method. This method generates radiometric calibration coefficient (RCC) curves across the imaging spectrum by leveraging the wavelength-dependent optical properties of the system. Integrated within the vicarious calibration workflow of the Radiometric Calibration Network (RadCalNet), CHIRON is applied to the Compact Continuous Tunable LVF-based Hyperspectral Imager (CCTF-HI) onboard the QMX-1 microsatellite. The CCTF-HI is a hyperspectral imager based on a linear variable filter (LVF), operating across the 400–1000 nm spectral range with a 0.287 nm interval, enabling WOI hyperspectral imaging. We employ cubic polynomial regression models to reconstruct the RCC distribution, achieving optimal fits with all models showing R² ≥ 0.92. We validated the radiometric performance of the CHIRON method using three different WOI imaging modes, each capturing a distinct subset of selectable wavebands from QMX–1/CCTF–HI. Reflectance measurements from these modes were compared with RadCalNet products, ground-based spectroscopy, and near-coincident imagery from an independent reference sensor. All absolute errors remained within 0.05 reflectance units, demonstrating that the RCC curve preserves radiometric fidelity even without band–specific calibration. By eliminating exhaustive per–band radiometric characterization, CHIRON reduces calibration workload and computational demand, enabling precise yet resource–efficient WOI hyperspectral imaging – particularly valuable for microsatellite with constrained onboard processing.

Accurate assessment of the physicochemical parameters of rice is critical for increasing agricultural production and ensuring food security. The leaf area index (LAI), leaf chlorophyll content (expressed as the soil plant analysis development (SPAD)), and canopy chlorophyll content (CCC) of the rice at single and multiple periods were estimated using abundance correction indicators by integrating the normalized difference red edge index (NDRE) and refined abundance information, with the goal of developing a high-precision and unified estimation model (AC-NDRE) of the physicochemical parameters of rice that can be adapted to various temporal and spatial scales. The results showed that while not significant, the accuracy of predicting the LAI, SPAD, and CCC of rice using the NDRE_green with the soil background removed was better than that of the NDRE model. The NDRE_green yielded the highest coefficients of determination (R²) of 0.69, 0.71, and 0.70, root mean square errors (RMSEs) of 2.35, 2.19, and 97.61, relative RMSEs (RRMSEs) of 31.87%, 5.61%, and 33.06%, respectively. Furthermore, severe instability was observed in the accuracy of the NDRE_green model on both the spatial and temporal scales. The AC-NDRE-Ⅰ, which is based on strong light, and the AC-NDRE-Ⅱ, which is based on moderate light, exhibited evident advantages in estimating the LAI/CCC and SPAD, respectively. The optimal LAI, SPAD, and CCC estimation accuracies based on the AC-NDRE were R² values of 0.83, 0.74, and 0.82, RMSEs of 1.73, 2.06, and 76.41, RRMSEs of 23.49%, 5.29%, and 25.88%, respectively. The AC-NDRE approach achieved a stable performance under complicated circumstances. The conclusions of this study indicated that the AC-NDRE-based method for estimating the LAI, SPAD, and CCC of rice could effectively address the issues of a limited model estimation accuracy caused by soil background, NDRE saturation during the middle to late growth stages of rice, and shaded leaves.