The panchromatic and multispectral (PMS) sensor is an optical imaging sensor aboard the Gao Fen-1 (GF-1) satellite. This work describes an approach to validate the calibration coefficients of the PMS sensors based on the image data of the Landsat 8 Operational Land Imager (OLI). Two image pairs, one obtained over the Dunhuang test site and the other over the Golmud test site, were used in this paper. Two spectral band adjustment factors (SBAF), given as the radiance SBAF and reflectance SBAF, were applied to correct the differences in the respective images caused by the relative spectral responses of the PMS sensor and OLI. Uncertainties in the SBAF values arising from atmospheric parameters and the absence of a measured ground spectrum were analyzed in this paper. The results show that the average relative differences of top-of-atmosphere radiance and reflectance values for each band between the PMS sensor and OLI images are 2%–5% for most bands, after SBAF correction with a measured ground spectrum or fitted spectrum. It is demonstrated that the OLI image can be used to validate the calibration coefficient of the PMS sensor, even if the image pairs are not imaged on the same day.

On-orbit radiometric calibration of a space-borne sensor is of great importance for quantitative remote sensing applications. Cross-calibration is a common method with high calibration accuracy, and the core and emphasis of this method is to select the appropriate reference satellite sensor. As for the cross-calibration of high-spatial resolution and narrow-swath sensor, however, there are some scientific issues, such as large observation angles of reference image, and non-synchronization (or quasi-synchronization) between the imaging date of reference image and the date of sensor to be calibrated, which affects the accuracy of cross-calibration to a certain degree. Therefore, taking the GaoFen-1 (GF-1) Panchromatic and Multi-Spectral (PMS) sensor as an example in this research, an innovative radiometric cross-calibration method is proposed to overcome this bottleneck. Firstly, according a set of criteria, valid MODIS (Moderate Resolution Imagine Spectroradiometer) images of sunny day in one year over the Dunhuang radiometric calibration site in China are extracted, and a new and distinctive bidirectional reflectance distribution function (BRDF) model based on top-of-atmosphere (TOA) reflectance and imaging angles of the sunny day MODIS images is constructed. Subsequently, the cross-calibration of PMS sensor at Dunhuang and Golmud radiation calibration test sites is carried out by using the method presented in this paper, taking the MODIS image with large solar and observation angles and Landsat 8 Operational Land Imager (OLI) with different dates from PMS as reference. The validation results of the calibration coefficients indicate that our proposed method can acquire high calibration accuracy, and the total calibration uncertainties of PMS using MODIS as reference sensor are less than 6%.

Vegetation classification has always been the focus of remote sensing applications, especially for tropical regions with fragmented terrain, cloudy and rainy climates, and dense vegetation. How to effectively classify vegetation in tropical regions by using multi-spectral remote sensing with high resolution and red-edge spectrum needs to be further verified. Based on the experiment in Wenchang, Hainan, China, which is located in the tropical monsoon region, and combined with the ZY-1 02D 2.5 m fused images in January, March, July, and August, this paper discusses whether NDVI and four red-edge vegetation indices (VIs), CIre, NDVIre, MCARI, and TCARI, can promote vegetation classification and reduce the saturation. The results show that the schemes with the highest classification accuracies in all phases are those in which the red-edge VIs are involved, which suggests that the red-edge VIs can effectively contribute to the classification of vegetation. The maximum accuracy of the single phase is 86%, and the combined accuracy of the four phases can be improved to 92%. It has also been found that CIre and NDVIre do not reach saturation as easily as NDVI and MCARI in July and August, and their ability to enhance the separability between different vegetation types is superior to that of TCARI. In general, red-edge VIs can effectively promote vegetation classification in tropical monsoon regions, and red-edge VIs, such as CIre and NDVIre, have an anti-saturation performance, which can slow down the confusion between different vegetation types due to saturation.

Since its launch on September 6, 2008, HJ-1A has been in the orbit for 13 years. The CCD1 sensor on the HJ-1A has four reflected solar bands. Since the calibration frequency is limited to the annual site calibration, cross-calibration is an effective method to improve the calibration frequency. In this paper, we use 420 image pairs of HJ-1A CCD1 and Terra MODIS over the Dunhuang test site for gains calculation, where we take MODIS as the reference sensor. The spectral band adjustment factors (SBAFs) for cross-calibration are then calculated to compensate for the spectral mismatch. The cross-calibration results are also validated by the field calibration results. From 2008 to 2019, a total of six campaigns have been cross-calibrated on the same day. The gain difference between the site calibration and cross-calibration is less than 3%. The long-term cross-calibration results further indicate that due to the adjustment of HJ-1A CCD gain state in October 2009, an abrupt change occurred 405 days after launch. After 12 years of on-orbit operation, the attenuation rate has reached 23.51%, 21.89%, 8.11%, and 13.37%, respectively by the end of 2019 based on the cross-calibration results.

A novel satellite cross-calibration method is proposed using image pairs with large view zenith angle observations. Previous studies on cross calibration have focused primarily on image pairs with nadir or small view zenith angles, while this study introduces a technique for large view zenith angle observations using bidirectional reflectance distribution function (BRDF) correction. This technique was applied to images from the wide field of view (WFV) onboard the GAO FEN-1 (GF-1) satellite and Terra Moderate Resolution Imaging Spectroradiometer (MODIS) sensors as it transited over the Dunhuang site in China. A total of 62 image pairs, collected from 2013 to 2016, were used in the cross-calibration process. However, only 12 of 62 image pairs included a small view zenith angle difference (less than 10°), while the other image pairs featured a large view zenith angle difference. An experimental time series calibration was conducted using the proposed technique as well as conventional cross-calibration methods for comparison purposes. Time series calibration results also showed an attenuation trend for GF-1 WFV sensor and produced a calibration equation for day since launch (DSL). Then, the calibration equation by DSL was compared with four vicarious calibration results carried out over the Dunhuang test site from 2013 to 2016 to demonstrate its consistency. The calibration coefficients using the proposed method were also compared with the coefficients from other cross-calibration methods, taking the vicarious calibration coefficient as the reference. The comparison results showed that the calibration coefficients using the proposed method were with higher calibration accuracy and higher calibration frequency than other cross-calibration methods. The cross-calibration uncertainties caused by difference influence factors were analysed quantitatively. The results showed that the calibration uncertainties were decreased from 35.03–44.60% to 5.95–8.26% after BRDF correction, which demonstrated the proposed method to be superior for processing large view zenith angle images. These results suggest the proposed technique could be an effective new tool for future time series satellite calibration.

The validation of satellite remote sensing surface reflectance products is aimed at comparing the reflectance pixel values of products with ground measurement values at the pixel scale. Due to the existence of surface heterogeneity, we cannot obtain the satellite pixel scale truth value through ground sampling, and only the satellite relative pixel scale truth value that closely approximates it can be acquired. The process of converting the point-scale spectrum of ground sampling into a pixel-scale spectrum will produce certain errors, known as point-to-pixel-scale conversion uncertainty, which is closely related to the type of sample area and the ground sampling method. In this study, we conducted research on the uncertainty of point-to-pixel-scale conversion generated via different ground sampling methods in the upscaling process. We utilized unmanned aerial vehicle (UAV) hyperspectral images to invert the surface reflectance spectral curves of wheat, corn, bare soil, and soybeans at the pixel scale, and simulate the ground measurement spectra and satellite pixel scale ground truth of different sampling methods, so as to realize the quantitative calculation of the uncertainty of the ground truth at the satellite pixel scale. On this basis, we analyzed in depth the effects of the sampling method, measurement height, and number of spectra on the scale conversion uncertainty. The research results show that airborne hyperspectral images can accurately simulate the spectra of ground measurements, and can be used as an effective means of ground spectral sampling and uncertainty analysis. When using the systematic sampling method, the more the sampling points, the smaller the uncertainty. However, the uncertainty of scale conversion tends to stabilize when the number of sampling points is increased to a certain quantity. As the height of ground measurement increases, the number of spectra within the elementary sampling unit (ESU) increases, leading to smaller scale conversion uncertainties. The research results of this study will provide support for the subsequent optimization of ground sampling methods and the improvement of measurement efficiency and measurement accuracy.