This study proposes a new technique for retrieving temperature and humidity profiles based on Artificial Neural Networks (ANNs) using data acquired from the GIIRS (Geosynchronous Interferometric Infrared Sounder) L1 and ERA-Interim (European Centre for Medium-Range Weather Forecasts Reanalysis). The approach is also compared against another method that uses simulated data from the radiative transfer model to construct the retrieval network. Furthermore, the two methods of network construction are evaluated in the North China Plain for July and August 2018, for which ground validated observations concurrent to the satellite data were available. In summary, the results showed that: (1) the ANN built with the GIIRS L1 and the EC data is superior to that built with the forward simulation and EC data in retrieval accuracy; (2) the retrieval accuracy for the troposphere exceeds that for the stratosphere; (3) the root mean square errors (RMSEs) of the relative humidity in the troposphere as retrieved by the two ANNs are 6.003% and 10.608%, respectively; (4) a relatively low correlation (R) between the simulated and observed radiance of the GIIRS is found, ranging between 720 and 736.875 cm⁻¹, and the correlation between the simulated and observed radiance of the water vapor channels exceeds that between the temperature channels; (5) compared with Atmospheric Infrared Sounder’s (AIRS’) products, our retrieved temperature profiles exhibit preferable consistency and the humidity retrievals also show an acceptable accuracy. Our study offers important insights towards improving our ability to retrieve atmospheric temperature and humidity profiles from the most sophisticated Earth Observation instruments such as the GIIRS of the FY-4 satellite, which could assist in expanding the use of those products globally.

Measuring chlorophyll fluorescence is a direct and non-destructive way to monitor vegetation. In this paper, the fluorescence retrieval methods from multiple scales, ranging from near the ground to the use of space-borne sensors, are analyzed and summarized in detail. At the leaf-scale, the chlorophyll fluorescence is measured using active and passive technology. Active remote sensing technology uses a fluorimeter to measure the chlorophyll fluorescence, and passive remote sensing technology mainly depends on the sun-induced chlorophyll fluorescence filling in the Fraunhofer lines or oxygen absorptions bands. Based on these retrieval principles, many retrieval methods have been developed, including the radiance-based methods and the reflectance-based methods near the ground, as well as physically and statistically-based methods that make use of satellite data. The advantages and disadvantages of different approaches for sun-induced chlorophyll fluorescence retrieval are compared and the key issues of the current sun-induced chlorophyll fluorescence retrieval algorithms are discussed. Finally, conclusions and key problems are proposed for the future research.

Alpine wetlands in the Tibetan Plateau (TP) play a crucial role in the regional hydrological cycle due to their strong influence on surface ecohydrological processes; therefore, understanding how TP wetlands respond to climate change is essential for projecting their future condition and potential vulnerability. We investigated the hydrological responses of a large TP wetland complex to recent climate change, by combining multiple satellite observations and in-situ hydro-meteorological records. We found different responses of runoff production to regional warming trends among three basins with similar climate, topography and vegetation cover but different wetland proportions. The basin with larger wetland proportion (40.1%) had a lower mean runoff coefficient (0.173 ± 0.006), and also showed increasingly lower runoff level (−3.9% year⁻¹, p = 0.002) than the two adjacent basins. The satellite-based observations showed an increasing trend of annual non-frozen period, especially in the wetland-dominated region (2.64 day·year⁻¹, p < 0.10), and a strong extension of vegetation growing-season (0.26–0.41 day·year⁻¹, p < 0.10). Relatively strong increasing trends in evapotranspiration (ET) (~1.00 mm·year⁻¹, p < 0.01) and the vertical temperature gradient above ground surface (0.043 °C·year⁻¹, p < 0.05) in wetland-dominant areas were documented from satellite-based ET observations and weather station records. These results indicate recent surface drying and runoff reduction of alpine wetlands, and their potential vulnerability to degradation with continued climate warming.

Zoige Peatland in the eastern Tibetan Plateau, the largest alpine peatland in China, was widely ditched in 1970s for pasture expansion. The ditching is believed to have caused peatland degradation, but there is still no widespread agreement on this due to the absence of essential regional and temporal information about ditch drainage. Therefore, this study used both remote-sensing observations and field surveys to examine the ecological influences of ditching for this alpine peatland. In the study, ditch distribution was interpreted with remote-sensing imagery and the ecological responses were investigated with temporal observation by Moderate Resolution Imaging Spectroradiometer (MODIS) and field surveys. The results showed that there were ~1200 km ditches interpreted, mainly in three spatial patterns depending on hydro-geomorphologies. The MODIS enhanced vegetation index (EVI) was more sensitive to peatland surface water depth (R² = 0.678, P < 0.001) than the normalized difference water index (NDWI) (R² = 0.583, P < 0.001), because the latter would become saturated at a certain surface water depth (~50 cm in Zoige). The temporal MODIS imagery reflected the ecological responses of ditched peatland to drainage in terms of vegetation density and water conditions. This study indicated that ditching depressed the surface water depth of the Zoige Peatland in summer, but not to the extent of completely transforming peatland into steppe due to the recharging of local beneficial hydro-geomorphologies. The MODIS indices investigated in the study could be used to monitor the annual regional status of vegetation cover and surface water for Zoige peatland.

Precipitation is the basic component of the Earth’s water cycle. Obtaining high-resolution and high-precision precipitation data is of great significance. This paper establishes a precipitation retrieval model based on a random forest classification and regression model during the day and at night with FY-4B/AGRI Level1 data on China from July to August 2022. To evaluate the retrieval effect of the model, the GPM IMERG product is used as a reference, and the retrieval results are compared against those of the FY-4B/AGRI operational precipitation product. In addition, the retrieval results are analyzed according to different underlying surfaces. The results showed that compared with the FY-4B/AGRI operational precipitation product, the retrieval model can better identify precipitation and capture precipitation areas of light rain, moderate rain, heavy rain and torrential rain. Among them, the probability of detection (POD) of the day model increased from 0.328 to 0.680, and the equitable threat score (ETS) increased from 0.252 to 0.432. The POD of the night model increased from 0.337 to 0.639, and the ETS score increased from 0.239 to 0.369. Meanwhile, the precipitation estimation accuracy of the day model increased by 38.98% and that of the night model increased by 40.85%. Our results also showed that due to the surface uniformity of the ocean, the model can identify precipitation better on the ocean than on the land. Our findings also indicated that for the different underlying surfaces of the land, there is no significant difference in each evaluation index of the model. This is a strong argument for the universal applicability of the model. Notably, the results showed that, especially for more vegetated areas and areas covered by water, the model is capable of estimating precipitation. In conclusion, the precipitation retrieval model that is proposed herein can better determine precipitation regions and estimate precipitation intensities compared with the FY-4B/AGRI operational precipitation product. It can provide some reference value for future precipitation retrieval research on FY-4B/AGRI.

The hyperspectral infrared atmospheric sounder (HIRAS), the first Chinese hyperspectral infrared instrument, was launched in 2017 on board the fourth polar orbiter of the Feng Yun 3 series, FY-3D. The instrument is a Fourier transform spectrometer with 2275 channels covering three spectral bands (650–1136, 1210–1750, and 2155–2550 cm⁻¹) with 0.625 cm⁻¹ spectral resolution. The first data quality assessment of HIRAS observations at full and normal spectral resolutions is presented. Comparisons with short-range forecasts from the Met Office numerical weather prediction (NWP) global system have revealed biases (standard deviation) generally less than 2.6 K (2 K) in the spectral regions mostly unaffected by trace gases where the confidence in the NWP model is largest. Of particular concern, HIRAS detector 3 seems to suffer from sunlight contamination of its calibration towards the end of the descending node. This, together with an obstruction of the detector field of view by an element of the platform, results in accentuated bias and noise in the observations from this detector. At normal spectral resolution, a background departure double difference analysis has been conducted between HIRAS and the NOAA-20 crosstrack infrared sounder (CrIS). The results show that HIRAS and CrIS are in good agreement with a mean difference across the three bands of −0.05 K (±0.26 K at 1σ) and 75.2% of the channels within CrIS radiometric uncertainty, noting though that HIRAS is noisier than CrIS with, on average, a standard deviation 0.34 K larger.