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Following the launch of the ZY1-02E satellite, the thermal infrared sensor aboard the satellite experienced alterations in the space environment, leading to varying degrees of attenuation in some components. The laboratory calibration accuracy could not satisfy the demands of quantitative production, and a certain degree of deviation was observed in on-orbit calibration. To accurately characterize the on-orbit radiation properties of thermal infrared remote sensing payloads, an absolute radiometric calibration campaign was carried out at the Ulansuhai Nur and Baotou calibration sites in Inner Mongolia in July 2022. This paper outlines the processes of onboard calibration and vicarious calibration for the ZY1-02E satellite, comparing the outcomes of onboard calibration with those of vicarious calibration. The onboard calibration method involved internal calibration, while the vicarious calibration method utilized an on-orbit absolute radiometric calibration technique based on various natural features that were not constrained by satellite–Earth spectrum matching requirements. Calibration coefficients were acquired, and the absolute radiometric calibration results of on-orbit vicarious and onboard calibration were compared, analyzed, and verified using the radiance computed from measured data and the reference sensor data. The accuracy of on-orbit absolute vicarious calibration was determined, and the causes for the decline in the radiation calibration accuracy on the orbiting satellite were examined. The findings revealed that the vicarious calibration results exhibited a lower percentage of radiance deviation compared with the onboard calibration results, meeting the quantitative requirements of remote sensing data. These results were significantly better than those obtained from onboard blackbody calibration, offering a data foundation for devising satellite calibration plans and enhancing calibration algorithms. In the future, the developmental trend of on-orbit radiometric calibration technology will encompass high-precision and slow-attenuation onboard calibration techniques, as well as high-frequency and simplified-step vicarious calibration methods. [ABSTRACT FROM AUTHOR]

In this study, an on-orbit radiometric calibration campaign of the GF5-02 AHSI was performed at the RadCalNet Baotou site, based on the automated observation of reflectance and atmospheric parameters of a 300 m × 300 m homogeneous desert area. The consistency of the radiometric calibration coefficients was validated both at the Dunhuang calibration site and the Baotou site. The average relative difference between the calibrated top-of-atmospheric (TOA) radiance and the predicted TOA radiance were less than 7%. The R2 of these two TOA radiances were all higher than 0.99. These results showed that the accuracy of calibration coefficients could meet the requirements of hyperspectral quantification applications. The uncertainty of GF5-02 AHSI radiometric calibration was 6.18%. This study also demonstrated that automated observation data of the Baotou site were reliable for high-frequency radiometric calibration and radiometric performance monitoring of GF5-02 AHSI. [ABSTRACT FROM AUTHOR]

The ZY1-02E satellite carrying a thermal infrared sensor was successfully launched from the Taiyuan Satellite Launch Center on 26 December 2021. The quantitative characteristics of this thermal infrared camera, for use in supporting applications, were acquired as part of an absolute radiometric calibration campaign performed at the Ulansuhai Nur and Baotou calibration site (Inner Mongolia, July 2022). In this paper, we propose a novel on-orbit absolute radiometric calibration technique, based on multiple ground observations, that considers the radiometric characteristics of the ZY1-02E thermal infrared sensor. A variety of natural surface objects were selected as references, including bodies of water, bare soil, a desert in Kubuqi, and sand and vegetation at the Baotou calibration site. During satellite overpass, the 102F Fourier transform thermal infrared spectrometer and the SI-111 infrared temperature sensor were used to measure temperature and ground-leaving radiance for these surface profiles. Atmospheric water vapor, aerosol optical depth, and ozone concentration were simultaneously obtained from the CIMEL CE318 Sun photometer and the MICROTOP II ozonometer. Atmospheric profile information was acquired from radiosonde instruments carried by sounding balloons. Synchronous measurements of atmospheric parameters and ECMWF ERA5 reanalysis data were then combined and input to an atmospheric radiative transfer model (MODTRAN6.0) used to calculate apparent radiance. Calibration coefficients were determined from the measured apparent radiance and satellite-observed digital number (DN), for use in calculating the on-orbit observed radiance of typical surface objects. These values were then compared with the apparent radiance of each object, using radiative transfer calculations to evaluate the accuracy of on-orbit absolute radiometric calibration. The results show that the accuracy of this absolute radiometric calibration is better than 0.6 K. This approach allows the thermal infrared channel to be unrestricted by the limitations of spectrum matching between a satellite and field measurements, with strong applicability to various types of calibration sites. [ABSTRACT FROM AUTHOR]

In this paper, an irradiance-based absolute radiometric calibration campaign at Baotou calibration site during June and July 2018 was described. This radiometric calibration campaign made use of six radiometric calibration tarps. The synchronous measurements of parameters such as surface reflectance, atmospheric parameters, and diffuse-to-global irradiance ratio were collected at the satellite overpass. The top-of-atmospheric radiance was predicted by radiative transfer model with these synchronous measurements. The linear relationship between DNs of satellite sensor and band-specific top-of-atmospheric spectral radiance was established, and a stable and reliable absolute calibration coefficient of ZY3-02 MUX was determined in this campaign. We compared the calibration results of the irradiance-based method with those of the reflectance-based method. The results suggested that the irradiance-based method is better than reflectance-based method. [ABSTRACT FROM AUTHOR]

Under the National Ambient Air Quality Standard released in 2012 (NAAQS-2012), Beijing began to publicize hourly Air Quality Index as well as real time concentrations of 6 pollutants in its web platform to provide detailed information for air quality assessment from 2013. In this study, hourly air quality monitoring data from May 2014 to April 2015 were collected for all 35 monitoring stations in Beijing to analyze the temporal and spatial variations of air pollutants and air quality. It is found that in spatial pattern, the air qualities in southern and northern Beijing are totally different. The association between heavy pollution concentrations and wind situations suggested that neighboring area's air quality has an important role in the air quality of Beijing combining with air quality attainment rates in all 35 monitoring stations and northern China. For temporal variations, late night and early morning are the most polluted time while afternoon is the least polluted time for all pollutants except O 3 with most polluted time in afternoon. Summer time in Beijing has the best air quality while winter time has the worst air quality coinciding with the heating season in the winter. [ABSTRACT FROM AUTHOR]

This study described the on-orbit vicarious radiometric calibration of Chinese civilian high-resolution stereo mapping satellite ZY3-02 multispectral imager (MUX). The calibration was based on gray-scale permanent artificial targets, and multiple radiometric calibration tarpaulins (tarps) using a reflectance-based approach between July and September 2016 at Baotou calibration site in China was described. The calibration results reveal a good linear relationship between DN and TOA radiances of ZY3-02 MUX. The uncertainty of this radiometric calibration was 4.33%, indicating that radiometric coefficients of ZY3-02 MUX are reliable. A detailed discussion on the validation analysis of the comparison results between the different radiometric calibration coefficients is presented in this paper. To further validate the reliability of the three coefficients, the calibrated ZY3-02 MUX was compared with Landsat-8 Operational Land Imager (OLI). The results also indicate that radiometric characteristics of ZY3-02 MUX imagery are reliable and highly accurate for quantitative applications. [ABSTRACT FROM AUTHOR]

China's first civilian, sub-meter, high-resolution stereo mapping satellite, GF-7, launched on 3 November 2019. Radiometric characterization of GF-7 multispectral imagery has been performed in this study. A relative radiometric accuracy evaluation of the GF-7 multispectral imagery was performed using several large uniform scenes, and the results showed that the accuracy is better than 2%. The absolute radiometric evaluation of the GF-7 satellite sensor was conducted at the Baotou and Dunhuang calibration sites, using the reflectance-based vicarious approach. The synchronous measurements of surface reflectance and atmospheric parameters were collected as the input for the radiative transfer model. The official radiometrically calibrated coefficient of the GF-7 multispectral imagery was evaluated with the predicted top-of-atmosphere (TOA) radiance from the radiative transfer model. The results indicated that the absolute radiometric accuracy of GF-7 multispectral imagery is better than 5%. In order to monitor the radiometric stability of the GF-7 satellite multispectral sensor, a relative and absolute radiometric accuracy assessment campaign should be performed several times a year. [ABSTRACT FROM AUTHOR]

A panchromatic and multispectral sensor (PMS) and a wide-field-of-view (WFV) sensor were fitted aboard the Gaofen6 (GF6) satellite, which was launched on 2 June 2018. This study used the Landsat9-Operational Land Imager 2 and Sentinel2-Multispectral Instrument as reference sensors to perform radiometric cross-calibration on GF6-PMS and WFV data at the Dunhuang calibration site. The four selected sensor images were all acquired on the same day. The results indicate that: the calibration results between different reference sensors can be controlled within 3%, with the maximum difference from the official coefficients being 8.78%. A significant difference was observed between the coefficients obtained by different reference sensors when spectral band adjustment factor (SBAF) correction was not performed; from the two sets of validation results, the maximum mean relative difference in the near-infrared band was 9.46%, with the WFV sensor showing better validation results. The validation of calibration coefficients based on synchronous ground observation data and the analysis of the impact of different SBAF methods on the calibration results indicated that Landsat9 is more suitable as a reference sensor for radiometric cross-calibration of GF6-PMS and WFV. [ABSTRACT FROM AUTHOR]

The role of land surface temperature (LST) is of the utmost importance in multiple academic disciplines, such as climatology, hydrology, ecology, and meteorology. To date, many methods have been proposed to estimate LST from satellite thermal infrared data. The single-channel (SC) algorithm can provide an accurate result in retrieving LST based on prior knowledge of known land surface emissivity (LSE). The SC algorithm is extensively employed for retrieving LST from Landsat series data due to its simplicity and its reliance on just one thermal infrared channel. The Thermal Infrared Sensor (IRS) on the Chinese ZY1-02E satellite is a pivotal instrument employed for gathering thermal infrared (TIR) data of land surfaces. The objective of this research is to evaluate the feasibility of a single-channel approach based on water vapor scaling (WVS) for deriving LST from ZY1-02E IRS data because of its wide spectrum range, i.e., 7~12 μm, which is affected strongly by both atmospheric water vapor and ozone. Three study areas, namely the Baotou, Heihe River Basin, and Yantai Sea sites, were selected as validation sites to evaluate the LST inversion accuracy. This evaluation was also conducted via cross-comparison between the retrieved LST and MODIS LST products. The results revealed that the WVS-based method exhibited an average bias of 0.63 K and an RMSE of 1.62 K compared to the in situ LSTs. The WVS-based method demonstrated reasonable accuracy through cross-validation with the MODIS LST product, with an average bias of 0.77 K and an RMSE of 2.0 K. These findings indicate that the WVS-based method is effective in estimating LST from ZY1-02E IRS data. [ABSTRACT FROM AUTHOR]

The acquisition of high-accuracy reflectance in mid-infrared channels is of great significance for the on-orbit cross-calibration of other bands using the mid-infrared band. However, due to the phenomenon that some sensors have a wide range of wavelengths covered by adjacent channels in the mid-infrared band, the traditional method of estimating the mid-infrared reflectivity assumes that the sea surface reflectivity in different mid-infrared bands is equal, which will lead to a large error during calculation. To solve this problem, this study proposes a nonlinear split-window algorithm involving ocean sun glint data to retrieve reflectivity of FY-3D/MERSI-2 channels 20. The results show that the variation range of sea surface reflectivity of channel 20 in the glint area is 10~25%, the mean value of the reflectivity difference obtained by the nonlinear split-window algorithm is 0.27%, and the RMSE is 0.0066. Among the main influencing factors, the atmospheric conditions have the greatest impact, and the effects of the uncertainties in the water vapor content and aerosol optical thickness on the calculation results are 1.16% and 0.34%, respectively. The initial value limits of the mid-infrared sea surface reflectivity also contribute approximately 0.84%, and their contribution to the uncertainty represents one of the main components. This work shows that the nonlinear split-window algorithm can calculate the infrared sea surface reflectivity with high accuracy and can be used as a reference for in-orbit cross-calibration between different bands. [ABSTRACT FROM AUTHOR]

• Remote sensing identified chemical indicator-induced water quality changes. • Combining FOD and spectral indices enhanced spectral sensitivity. • Optical water classification modeling effectively handled large-scale inversions. • A high-accurate remote sensing model for water quality status was developed. • Chinese lake water quality variations were computed from 2000 to 2022. The changing climate and economic development have exerted significant pressure on the water quality of global lakes and reservoirs (hereinafter referred to as lakes). Existing sporadic in-situ monitoring limits a comprehensive understanding of water quality changes at large spatiotemporal scales. Although remote sensing techniques offer efficient water quality observations, they have primarily focused on monitoring single optical active substances, making it difficult to assess water quality changes caused by chemical indicators such as total phosphorus and total nitrogen. However, changes in chemical indicators within a certain range can cause responses to physical indicators, which are comprehensively reflected in the water-leaving reflectance. Based on this physical mechanism, this study obtained the water quality status categories (from excellent to severe pollution) by calculating the water quality index (WQI) and developed a new remote sensing assessment model of water quality status using moderate resolution imaging spectroradiometer (MODIS) imagery. This model, combining fractional-order derivatives and various-dimensional spectral indices, significantly enhanced the sensitivity of spectral reflectance to water quality states, achieving a maximum correlation of 0.72. To address the applicability of the model at large spatiotemporal scales, this study proposed a classification modeling scheme based on the optimal optical water categories, resulting in a 20.69%–34.48% improvement in model accuracy after classification. Additionally, this study proposed an improved ensemble learning model combining the concepts of Stacking and Bagging, achieving an average accuracy of 82% and enhancing accuracy by 5%–15% compared to the original single model. Subsequently, the model was used for the first time to assess the water quality status of 180 large lakes in China from 2000 to 2022. The results revealed that 76.11% of the lakes exhibited excellent and good water quality, with a spatial distribution pattern showing a "better in the west, worse in the east" pattern. Over the 23-year period, 28.33% and 58.89% of the lakes showed improvement and stability trends in water quality status, with stability and improvement predominating in the western and eastern regions, respectively. The research results provide technical support for the rapid assessment of water quality status and sustainable resource management, highlighting the potential of remote sensing technology in water quality monitoring. [ABSTRACT FROM AUTHOR]

Abstract: ZY3‐02 is the first Chinese earth‐observation satellite equipped with a laser altimeter, designed to assist stereo‐image mapping elevations. Launch vibrations, the space environment and other factors, both expected and unexpected, cause bias in the designed ranging and pointing of the laser altimeter, influencing its height measurement accuracy. This paper proposes an in‐orbit geometric calibration model which considers this bias as unknown systematic errors. The unknown parameters were calibrated by the minimum ranging error principle using laser spot geolocation data captured by a ground‐based laser detector array. Based on precise orbit and attitude data, the precise geolocation information of the laser footprints could be obtained. Validation experiments show that the elevation precision of the laser altimeter can reach 1 m. [ABSTRACT FROM AUTHOR]

• High dynamic range images improve the vegetation extraction accuracy of deep learning and threshold methods. • Deep learning methods are better than threshold methods for vegetation extraction from high dynamic range images with shadows. • Application of HDR image in field scenes with complex illumination. Due to factors such as light angle and vegetation density, shadows can exist in ground vegetation images; this can lead to highly varying natural illumination in the images, which greatly limits the accuracy of fractional vegetation cover (FVC) estimation. While high dynamic range (HDR) images can reduce the extreme illumination contrast, the characteristic vegetation information in shadow areas is often lost. At present, most threshold methods do not fully utilize the spatial features of vegetation, and the results of vegetation classification under complex shadow conditions are associated with large errors. Thus, two deep learning methods (the HDR U-Net method and HDR U-Net++ method) are proposed in this study based on fully convolutional neural networks to extract vegetation information from HDR images under shadow conditions. These methods are compared with threshold methods. For full HDR images, the kappa coefficient and mean intersection over union (MIoU) of the HDR U-Net method and HDR U-Net++ method are 0.926, 0.931, and 0.874, 0.887, respectively. The FVC estimation accuracy and vegetation segmentation effect of these two deep learning methods are better than those of the three threshold methods considered. In addition, compared with normal exposure (NE) images, camera-based HDR images improve the extraction accuracy of deep learning methods and threshold methods for vegetation under shadow conditions. The results of this study suggest that deep learning methods are not affected by the threshold determination strategy selected and can more completely extract vegetation feature information from HDR images. The classification effect of vegetation under shadow conditions is effectively improved, and the vegetation segmentation and FVC estimation results are more precise. The combination of HDR images and deep learning methods can be applied to field scenes with complex lighting, which is beneficial to improve the ground data verification of remote sensing FVC products. [ABSTRACT FROM AUTHOR]

• Vegetation under shadow conditions was extracted accurately. • Polarization information was added to the light intensity information during vegetation segmentation. • A new double input network DIR_DeepLabv3plus with three different fusion strategies is proposed. Fractional vegetation cover (FVC) plays an important role in the study of vegetation growth state, and the key issue is accurately segmenting and extracting green vegetation from the background. However, the shadows generated by natural lights produce extreme illuminance differences in images, which greatly reduces the vegetation extraction accuracy. The polarization information for ground objects is independent of the physical state of the reflectivity of ground objects, and it can be used to eliminate the influence of strong reflections in images to a certain extent, reduce the illuminance differences under extreme sunlight conditions, and help improve the vegetation recognition effect under shadow conditions. To improve the accuracy of vegetation segmentation under shadow conditions, this study introduces polarized reflection information for vegetation and an improved semantic segmentation network, notably a double input residual network based on DeepLabv3plus (DIR_DeepLabv3plus), with fusion strategies based on concatenation and addition is proposed. The network extracts low-level features and high-level features at different spatial scales from both light intensity (red-greenblue (RGB)) images and degree of linear polarization (DoLP) images independently by a deep residual network and atrous spatial pyramid pooling (ASPP) structure, effectively improving the accuracy of vegetation segmentation in shadow situations. The results show that the mean intersection over union (mIoU) values of vegetation without shadows, with light shadows and with shadows are 94.01%, 92.508% and 90.969%, respectively. Compared with the color index method and green fractional vegetation cover extraction from digital images using a shadow-resistant algorithm (SHAR-LABFVC), the proposed method provides a greatly improved extraction accuracy, and it has 0.18%, 1.00% and 1.49% higher mIoU values for vegetation under different shadow conditions than the method without polarization information. This study provides a new approach for vegetation segmentation and improves the accuracy of FVC calculations under shadow conditions. [ABSTRACT FROM AUTHOR]

Estimation of soil organic matter content (SOMC) is essential for soil quality evaluation. Compared with traditional multispectral remote sensing for SOMC mapping, the distribution of SOMC in a certain area can be obtained quickly by using hyperspectral remote sensing data. The Advanced Hyper-Spectral Imager (AHSI) onboard the ZY1-02D satellite can simultaneously obtain spectral information in 166 bands from visible (400 nm) to shortwave infrared (2500 nm), providing an important data source for SOMC mapping. In this study, SOMC-related spectral indices (SIs) suitable for this satellite were analyzed and evaluated in Shuyang County, Jiangsu Province. A series of SIs were constructed for the bare soil and vegetation-covered (mainly rice crops and tree seedlings) areas by combining spectral transformations (such as reciprocal and square root) and dual-band index formulas (such as ratio and difference), respectively. The optimal SIs were determined based on Pearson's correlation coefficient (ρ) and satellite data quality, and applied to SOMC level mapping and estimation. The results show that: (1) The SI with the highest ρ in the bare soil area is the ratio index of original reflectance at 654 and 679 nm (OR-RI(654,679)), whereas the SI in the vegetation area is the square root of the difference between the reciprocal reflectance at 551 and 1998 nm (V-RR-DSI(551,1998)); (2) the spatial distribution trend of regional SOMC results obtained by linear regression models of OR-RI(654,679) and V-RR-DSI(551,1998) is consistent with the samples; (3) based on the optimal SIs, support vector machine and tree ensembles were used to predict the SOMC of bare soil and vegetation-covered areas of Shuyang County, respectively. The determination coefficient of the soil–vegetation combined prediction results is 0.775, the root mean square error is 3.72 g/kg, and the residual prediction deviation is 2.12. The results show that the proposed SIs for ZY1-02D satellite hyperspectral data are of great potential for SOMC mapping. [ABSTRACT FROM AUTHOR]

The Gao Fen 7 (GF‐7) satellite is China's first submeter, high‐resolution Earth observation and remote sensing satellite for natural resource monitoring, land surveying, and other industrial applications in China. The GF‐7 laser altimeter system is the successor of the ZiYuan3‐02 (ZY3‐02 satellite) laser altimeter. The key objective of the GF‐7 laser altimetry system is to provide high‐precision ground elevation control points that assist in optical imaging to achieve 1:10000 mapping in China. This paper introduces the GF‐7 laser altimeter system payload specifications, mission objectives, in‐orbit calibration plan, data products, and service policy. In the future, the GF‐7 laser altimeter system will also serve multidisciplinary and professional applications through its precise elevation measurement capabilities. In addition, it will play a vital role in Earth observation, climate change monitoring, and environmental protection. Plain Language Summary: Spaceborne laser altimeters are widely used for planet surface measurements due to their high range accuracy. This paper details the GF‐7 laser altimeter system, which is China's first formal spaceborne laser altimeter system for Earth observations. This laser altimeter system will be used in scientific experiments for the first time in China. GF‐7 laser altimeter system is the first to be equipped with laser footprint cameras and a laser optical axis surveillance camera. The GF‐7 satellite will be launched in October 2019, and the scientific experiments will begin in November. All the data it gets will be released globally. In the future, these data will help researchers from different disciplines around the world to conduct scientific experiments and monitor the Earth's resources. The GF‐7 laser altimeter system will play a vital role in global climate change monitoring, environmental protection, disaster warning, etc. The content presented in this article will help students, researchers, and engineers in the fields of geography, oceanography, and glaciology understand and use GF‐7 satellite laser data, which will be of great significance. Key Points: A new laser altimeter system design consisting of a laser altimeter, two laser footprint cameras, and a laser optical axis surveillance camera equipped with the Gao Fen 7 satellite is introducedThe application of the Gao Fen 7 laser altimeter system including ground control point measurement, polar ice sheet monitoring, and forest biomass estimation is presentedScientific experiments on the Gao Fen 7 laser altimeter system to be performed in November 2019 are given. Six levels of data products are listed and will be provided to users worldwide [ABSTRACT FROM AUTHOR]