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Color distortion is a common issue in Jilin-1 KF01 series satellite imagery, a phenomenon caused by the instability of the sensor during the imaging process. In this paper, we propose a data-driven method to correct color distortion in Jilin-1 KF01 imagery. Our method involves three key aspects: color-distortion simulation, model design, and post-processing refinement. First, we investigate the causes of color distortion and propose algorithms to simulate this phenomenon. By superimposing simulated color-distortion patterns onto clean images, we construct color-distortion datasets comprising a large number of paired images (distorted–clean) for model training. Next, we analyze the principles behind a denoising model and explore its feasibility for color-distortion correction. Based on this analysis, we train the denoising model from scratch using the color-distortion datasets and successfully adapt it to the task of color-distortion correction in Jilin-1 KF01 imagery. Finally, we propose a novel post-processing algorithm to remove boundary artifacts caused by block-wise image processing, ensuring consistency and quality across the entire image. Experimental results show that the proposed method significantly eliminates color distortion and enhances the radiometric quality of Jilin-1 KF01 series satellite imagery, offering a solution for improving its usability in remote sensing applications. [ABSTRACT FROM AUTHOR]

This article presents an innovative geometric calibration mode for a linear array camera used in the Jilin-1 satellite. Changing the camera from observing the ground to observing space and treating stars as control points allow for the accurate determination of the geometric parameters of the camera. In this article, several important calibration procedures and strategies are presented to gradually eliminate the influence of astronomical and attitude errors. Self-verification of the calibration image results in a star-based calibration accuracy is better than 0.3 pixels. Further verification of the star image that leads to an interior orientation accuracy is better than 0.6 pixels. The accuracy of geometric positioning without control is verified by ground images of different regions to be 30 m. The interior orientation accuracy of ground images processed by star-based calibration parameters is better than 1.5 m. The ground image corrected using the star-based calibration parameters is well overlaid on the reference image. The accuracies obtained with star-based and ground-based calibrations are comparable. However, ground calibration fields require high maintenance costs and may suffer from harsh imaging conditions. Contrarily, star-based calibration is not affected by the weather and climate conditions on the ground, which can be photographed from any location along the satellite orbit. Therefore, star-based calibration can meet the requirements for high-frequency geometric calibration of subsequent satellite constellations construction.

On-orbit absolute radiometric calibration is not only a prerequisite for the quantitative application of optical remote sensing satellite data but also a key step in ensuring the accuracy and reliability of satellite observation data. Due to the diversity of imaging conditions for optical remote sensing satellite sensors, on-orbit absolute radiometric calibration usually requires a large number of imaging tasks and manual labor to calibrate each imaging condition. This seriously limits the timeliness of on-orbit absolute radiometric calibration and is also an urgent problem to be solved in the context of the explosive growth of satellite numbers. Based on this, we propose a general on-orbit absolute radiometric calibration method compatible with multiple imaging conditions. Firstly, we use a large amount of laboratory radiometric calibration data to explore the mathematical relationship between imaging conditions (row transfer time, integration level and gain), radiance, and DN, and successfully build an imaging condition compatibility model. Secondly, we combine the imaging condition compatibility model with cross calibration to achieve a general on-orbit absolute radiometric calibration method. We use cross calibration to obtain the reference radiance and corresponding DN of the target satellites, which calculates the general coefficient by using row transfer time, integration level, and gain, and use the general coefficient to calibrate all imaging conditions. Finally, we use multiple imaging tasks of the JL1GF03D11 satellites to verify the effectiveness of the proposed method. The experiments show that the average relative difference was reduced to 2.79% and the RMSE was reduced to 1.51, compared with the laboratory radiometric calibration method. In addition, we also verify the generality of the proposed method by using 10 satellites of the Jilin-1 GF03D series. The experiment shows that the goodness of fit of the general coefficient is all greater than 95%, and the average relative difference between the reference radiance and the calibrated radiance of the proposed method is 2.46%, with an RMSE of 1.67. To sum up, by using the proposed method, all imaging conditions of optical remote sensing satellite sensor can be calibrated in one imaging task, which greatly improves the timeliness and accuracy of on-orbit absolute radiometric calibration. [ABSTRACT FROM AUTHOR]

In recent years, the advancement of CubeSat technology has led to the emergence of high-resolution, flexible imaging satellites as a pivotal source of information for the efficient and precise monitoring of crops. However, the dynamic geometry inherent in flexible side-view imaging poses challenges in acquiring the high-precision reflectance data necessary to accurately retrieve crop parameters. This study aimed to develop an angular correction method designed to generate nadir reflectance from high-resolution satellite side-swing imaging data. The method utilized the Anisotropic Flat Index (AFX) in conjunction with a fixed set of Bidirectional Reflectance Distribution Function (BRDF) parameters to compute the nadir reflectance for the Jilin-1 GP01/02 multispectral imager (PMS). Crop parameter retrieval was executed using regression models based on vegetation indices, the leaf area index (LAI), fractional vegetation cover (FVC), and chlorophyll (T850 nm/T720 nm) values estimated based on angle corrected reflectance compared with field measurements taken in the Inner Mongolia Autonomous Region. The findings demonstrate that the proposed angular correction method significantly enhances the retrieval accuracy of the LAI, FVC, and chlorophyll from Jilin-1 GP01/02 PMS data. Notably, the retrieval accuracy for the LAI and FVC improved by over 25%. We expect that this approach will exhibit considerable potential to improve crop monitoring accuracy from high-resolution satellite side-view imaging data. [ABSTRACT FROM AUTHOR]

The necessity of combining multi-source remote sensing sensors for multi-temporal observation of the Earth, arises from the influence of cloud cover and the limited operational period of single satellite. However, significant differences exist among satellites and among sensors within the same satellite system. In this case, conducting a cross-comparison analysis is crucial for reliable change detection. The Jilin-1 satellite constellation has a total of 108 satellites in orbit, forming the largest commercial remote sensing satellites constellation in China. Firstly, the top of atmosphere (TOA) data from the Jilin-1GF03B series satellites (Jilin-1GF03B04, Jilin-1GF03B05, and Jilin-1GF03B06), following radiometric calibration processing, were selected to evaluate their performance in a single phase by comparison to Sentinel-2 satellites. Subsequently, we compared four spectrum bands and four commonly used biophysical indices, and the results indicated a higher radiation consistency between Jilin-1GF03B and Sentinel-2 satellites, suggested by correlation coefficient (over 0.953), and R2 (over 0.907). Moreover, we assessed the band sensitivity of the two satellites across different land use classes. It was found that Sentinel-2 exhibited greater sensitivity in bare land, vegetation, and forest, whereas the near-infrared band of Jilin-1GF03B proved more sensitive in water areas. Finally, to mitigate the risk of randomness in a single temporal phase, we constructed two long-term vegetation index datasets for phenological analysis. The close performance alignment observed for both single-phase and phenological variations indicated that Jilin-1GF03B satellites and Sentinel-2 can be effectively utilized together for long-term monitoring of Earth surface attributes.

Shrimp farming and exporting is the main income source for the southern coastal districts of the Mekong Delta. Monitoring these shrimp ponds is helpful in identifying losses incurred due to natural calamities like floods, sources of water pollution by chemicals used in shrimp farming, and changes in the area of cultivation with an increase in demand for shrimp production. Satellite imagery, which is consistent with good spatial resolution and helpful in providing frequent information with temporal imagery, is a better solution for monitoring these shrimp ponds remotely for a larger spatial extent. The shrimp ponds of Cai Doi Vam township, Ca Mau Province, Viet Nam, were mapped using DMC-3 (TripleSat) and Jilin-1 high-resolution satellite imagery for the years 2019 and 2022. The 3 m spatial resolution shrimp pond extent product showed an overall accuracy of 87.5%, with a producer’s accuracy of 90.91% (errors of omission = 11.09%) and a user’s accuracy of 90.91% (errors of commission = 11.09%) for the shrimp pond class. It was noted that 66 ha of shrimp ponds in 2019 were observed to be dry in 2022, and 39 ha of other ponds had been converted into shrimp ponds in 2022. The continuous monitoring of shrimp ponds helps achieve sustainable aquaculture and acts as crucial input for the decision makers for any interventions.

Due to the limitation of the number of sensor pixels, optical splicing is commonly used to improve the imaging width of remote sensing satellites, and this optical stitching can cause vignetting in the image data of adjacent sensors. The weak energy, low signal-to-noise ratio, and poor response stability of vignetting are key factors that restrict the relative radiometric correction of optical splicing remote satellites. This paper proposes a stability analysis method and a relative radiometric correction method for vignetting. First, we analyzed the stability of the response and the noise impact of vignetting. Massive data from the Jilin-1 GF03D satellites was used to analyze the stability of the response using the vignetting stability analysis method. Secondly, the data on the deep sea during nighttime (DDSN) of Jilin-1 GF03D satellites was used to obtain the characteristics of the sensors' noise. Thirdly, by building a noise drift model, we calculated the coefficient of the noise drift according to its characteristics. Using the coefficient to eliminate the noise drift of each pixel in vignetting can improve the response stability of vignetting. The average response stability increased by 37.64% by this method. Finally, the automatic relative radiometric correction method was completed through histogram matching. Furthermore, we proposed color aberration metrics (CAMs) to evaluate the multi-spectral images after relative radiometric correction, and massive data from the 16 satellites of Jilin-1 GF03D was used to verify the effectiveness and generality. The experimental results show that the average CAM of the images increased by 15.97% using the proposed method compared to the traditional method. [ABSTRACT FROM AUTHOR]

Sentinel-2 serves as a crucial data source for monitoring forest cover change. In this study, a sub-pixel mapping of forest cover is performed on Sentinel-2 images, downscaling the spatial resolution of the positioned results to 2.5 m, enabling sub-pixel-level forest cover monitoring. A novel sub-pixel mapping with edge-matching correction is proposed on the basis of the Sentinel-2 images, combining edge-matching technology to extract the forest boundary of Jilin-1 images at sub-meter level as spatial constraint information for sub-pixel mapping. This approach enables accurate mapping of forest cover, surpassing traditional pixel-level monitoring in terms of accuracy and robustness. The corrected mapping method allows more spatial detail to be restored at forest boundaries, monitoring forest changes at a smaller scale, which is highly similar to actual forest boundaries on the surface. The overall accuracy of the modified sub-pixel mapping method reaches 93.15%, an improvement of 1.96% over the conventional Sub-pixel-pixel Spatial Attraction Model (SPSAM). Additionally, the kappa coefficient improved by 0.15 to reach 0.892 during the correction. In summary, this study introduces a new method of forest cover monitoring, enhancing the accuracy and efficiency of acquiring forest resource information. This approach provides a fresh perspective in the field of forest cover monitoring, especially for monitoring small deforestation and forest degradation activities. [ABSTRACT FROM AUTHOR]

Shrimp farming and exporting is the main income source for the southern coastal districts of the Mekong Delta. Monitoring these shrimp ponds is helpful in identifying losses incurred due to natural calamities like floods, sources of water pollution by chemicals used in shrimp farming, and changes in the area of cultivation with an increase in demand for shrimp production. Satellite imagery, which is consistent with good spatial resolution and helpful in providing frequent information with temporal imagery, is a better solution for monitoring these shrimp ponds remotely for a larger spatial extent. The shrimp ponds of Cai Doi Vam township, Ca Mau Province, Viet Nam, were mapped using DMC-3 (TripleSat) and Jilin-1 high-resolution satellite imagery for the years 2019 and 2022. The 3 m spatial resolution shrimp pond extent product showed an overall accuracy of 87.5%, with a producer's accuracy of 90.91% (errors of omission = 11.09%) and a user's accuracy of 90.91% (errors of commission = 11.09%) for the shrimp pond class. It was noted that 66 ha of shrimp ponds in 2019 were observed to be dry in 2022, and 39 ha of other ponds had been converted into shrimp ponds in 2022. The continuous monitoring of shrimp ponds helps achieve sustainable aquaculture and acts as crucial input for the decision makers for any interventions. [ABSTRACT FROM AUTHOR]

In recent decades, there has been an increase in artificial lighting in the world due to urbanization and the revolution of LED lighting. Artificial lighting is an indicator of human activity, but can adversely affect natural ecosystems and people due to negative impacts of light pollution. Space-borne and airborne imagery as well as ground-based measurements enable to measure the intensity and spectra of artificial lights. One of the challenges in remote sensing of night-time lights is how to ground truth night-time imagery acquired by satellites, and how much do space-borne measurements represent the brightness as perceived by organisms. Most of the studies on night-time lights to-date were done using panchromatic sensors at large spatial extents, which did not allow to examine intra-urban variation in night light intensity and spectra. The aim of this study was to test the capability of the new Chinese satellite Jilin-1, which is the first commercial satellite to offer multispectral night-light imagery at a spatial resolution below 1 m, to characterize the night-time properties of urban areas. We examined the correspondence between light intensities as measured from different sensors at different spatial resolutions: two Jilin-1 images of the Jerusalem metropolitan area (0.89 m), VIIRS/DNB (500 m), Loujia-1 (130 m), unmanned aerial vehicle (UAV) color image (0.05 m) and hemispherical color photographs taken by a calibrated ground DSLR (digital single-lens reflex camera). In all the comparisons between different remote sensing tools, as the spatial resolution coarsened, the Pearson correlation coefficient increased, reaching r > 0.5 (after resampling to 100 m). Stronger correlations were found for the red band, and weaker correlations were found for the blue band, probably due to atmospheric scattering. By identifying specific objects such as buildings and lightings, we found good correspondence ( R 2 = 0.51) between Jilin-1 and the ground-based measurements of night-time brightness. We further examined the variability of night lights within different land use types and within different ethnic/religion composition of statistical areas. We found that residential areas of Orthodox Jews were characterized with the highest brightness at night compared with residential areas of Arabs in the West Bank that had the lowest brightness. At the statistical zone level (n = 299), more than 50% of the variability in night-time brightness, was explained by land cover properties (NDVI), infrastructure (roads and built volume) and the ethnic/religious composition. In addition, we found that the spectral ratio index which was based on the red and green bands, enabled to better distinguish between land use classes, than the spectral ratio index which was based on the green and blue bands. The availability of night-time multi-spectral imagery at fine spatial resolution now enables to study urban land-use and spatial inequality, and to better understand the factors explaining night-time brightness. [ABSTRACT FROM AUTHOR]

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