After 50 years of development, satellite remote sensing has become one of the most effective tools for detecting the Earth on the regional and global spatial scales, providing a previously unimaginable amount of data. Satellite remote sensing can rapidly monitor the atmosphere, land and sea, and the non-contact observation process effectively avoids the limitations and risks in field measurement. At present, satellite remote sensing data support a large number of practical applications. For example, it supports weather forecasting, agriculture and fishery, surveying and mapping, urban planning and mineral resources exploration, monitoring environmental pollution, climate change, shoreline dynamics, sea surface temperature and salinity, marine ecosystem and biomass, sea level change, vegetation, forests, etc., and provides important remote sensing data support for scientific research such as COVID-19.

Especially in the past two decades, satellite design has developed rapidly and more advanced satellite payloads have been introduced. For example, MOPITT, GOSAT and OCO for monitoring carbon dioxide and methane in the environmental field, AIRS, TES, IASI, IMG and CRIS for weather prediction and climate change through monitoring the atmospheric state, as well as multi-purpose MODIS, MERIS and SGLI, as well as relatively special ATSR, AATSR, MISR and POLDER loads. In addition to the traditional passive observation, active observation loads such as CloudSat, CALIPSO and Aeolus were also launched to monitor the vertical structure of clouds, aerosols and wind fields.

The prosperous development of space instrument technology and informatics has greatly weakened the restrictions on satellite observation in terms of hardware, data acquisition and processing. The academic community has also accumulated rich experience in the management and analysis of satellite data, constantly tapping the potential of existing satellite data sets, and gradually understanding how to improve future satellite data. These advances have helped people understand the true value and potential of satellite remote sensing, but also recognized its limitations. The academic community has never stopped trying to solve the basic difficulties existing in remote sensing technology, such as the separation of signal and noise, accurate instrument calibration, small amount of observation data and insufficient information to fully describe geophysical, chemical and biological processes, and the growing number of observable objects and objects. These fundamental problems of satellite remote sensing often need to be properly constrained by theoretical models, prior knowledge and auxiliary observation in the actual processing process, and have not been completely solved to a certain extent.

At present, the main challenges faced by satellite remote sensing technology include the challenges of space-time coverage, increasing the amount of information and collaborative observation, remote sensing inversion algorithms, and the consistency and continuity of high-quality long-term data. With a view to the future, it is necessary to sort out the challenges from satellite design, observation, data processing and monitoring applications.

1.The challenge of space-time coverage

One of the main advantages of satellite remote sensing is that it can quickly observe most areas of the earth, but the coverage of currently available satellite data has obvious limitations. The earth polar orbit load in low orbit usually needs at least one day or more to achieve global coverage, so many natural phenomena with high spatiotemporal variability cannot be fully captured, and the geostationary observation high orbit (GEO) load can solve this limitation by high-frequency day and night observation of the same region. However, there is still a trade-off between the spatial coverage and the resolution of satellite images (generally, higher coverage will lead to lower spatial resolution).

The best case is that satellite remote sensing has a wide range of high-resolution space-time coverage, but this is very challenging. To this end, the design of satellite observation needs to be innovative, and play the synergy of auxiliary data and complementary observation to increase the coverage and resolution of spatiotemporal data records.

2.The challenge of increasing information and collaborative observation

Although the existing satellite observation performance has been high, the amount of information provided by these observations is still limited. In a complex environment, no single payload can provide comprehensive information about the target, so it is still necessary to develop and deploy new sensors or their combinations with enhanced functions. For example, multi-angle polarization loads can provide the most appropriate data to characterize the detailed characteristics of atmospheric aerosols and clouds. However, due to the limited sensitivity to the vertical changes of aerosols and clouds, even the most advanced multi-angle polarization loads can not ensure the complete and reliable three-dimensional characterization of aerosol characteristics. The active remote sensing payload represented by spaceborne lidar and radar can provide detailed information about the vertical changes of the atmosphere.

Therefore, while deploying satellite instruments with enhanced capabilities, it is necessary to explore complementary cooperative observation, synthesize the load observation values with different sensitivity to the target at different time or space scales and in different spectral ranges, and combine satellite observation with suborbital observation and chemical transmission model results to increase the total amount of observation information. As shown in the figure, when observing highly heterogeneous targets such as clouds, passive observation data (spectrum, polarization and microwave) and active observation (lidar and radar) are integrated to reduce observation uncertainty.

Passive observation load (spectrum, polarization and microwave) and active observation load (lidar and radar) are observed in cooperation

3.Challenges of remote sensing inversion algorithm

Algorithm performance is a key factor affecting the quality of satellite remote sensing products. In fact, once a payload is deployed, the quality of the observed data obtained cannot be improved from the hardware, while the inversion algorithm can still be improved continuously. In the past decade, new remote sensing inversion algorithms have made significant progress. For example, the new algorithm tends to use a more accurate atmospheric state model (rather than a pre-calculated lookup table) to achieve the simultaneous acquisition of aerosol and land surface characteristics and cloud characteristics, and can simultaneously retrieve a large number of parameters. In the face of collaborative observation tasks, it is necessary to develop appropriate algorithms, apply them to different loads or their combinations, and analyze the results of multi-source data. More and more researchers take this as the goal of algorithm development, and the evolution process of the algorithm does show that there is great potential to improve the accuracy of inversion by using cooperative observation.

At the same time, the experience gained from the continuous application of machine learning methods in algorithm development shows that machine learning is very suitable for the analysis and interpretation of Earth observation data. Machine learning can "learn" from data and make decisions with minimal human intervention. In recent years, the emerging deep learning and deep neural network technologies in this method are not only used to process and analyze large amounts of data in remote sensing research, but also expected to further improve the prediction and modeling of physical phenomena.

4.Challenges of high quality long-term data consistency and continuity

Long-term and high-quality observation records of basic variables are essential for monitoring and studying the Earth (for example, climate change, etc.). In order to ensure the continuity of high-quality observation, it is critical to correctly interpret and interpret the differences and consistency in the multi-load data records. For this reason, the absolute calibration of loads and the mutual calibration of multiple related loads are very important. Calibration and calibration are challenging for many new instruments, especially the Xiaowei constellation itself. After solving the problems of calibration and data authenticity testing, the continuity and compatibility of the data generated by the future satellite load and the current load are equally important. In this way, the continuity of satellite observation and high-quality long-term data can be achieved, and the accumulated records can meet the needs of global major issues such as climate change.

Since the launch of the first satellite for more than half a century, satellite remote sensing has developed into a highly complex tool, providing a large amount of data to support all aspects of human activities from basic science to daily life. These major challenges faced by satellite remote sensing also gave birth to a huge space for future development in this field. Here, we sincerely invite readers to contribute what they know and learn to solve these challenges together.