Validation of AVHRR Land Surface Temperature with MODIS and In Situ LST—A TIMELINE Thematic Processor

Understanding vegetation phenology responses to easily ignored climate factors in china's mid-high latitudes

Previous studies have primarily focused on the influence of temperature and precipitation on phenology. It is unclear if the easily ignored climate factors with drivers of vegetation growth can effect on vegetation phenology. In this research, we conducted an analysis of the start (SOS) and end (EOS) of the growing seasons in the northern region of China above 30°N from 1982 to 2014, focusing on two-season vegetation phenology. We examined the response of vegetation phenology of different vegetation types to preseason climatic factors, including relative humidity (RH), shortwave radiation (SR), maximum temperature (Tmax), and minimum temperature (Tmin). Our findings reveal that the optimal preseason influencing vegetation phenology length fell within the range of 0-60 days in most areas. Specifically, SOS exhibited a significant negative correlation with Tmax and Tmin in 44.15% and 42.25% of the areas, respectively, while EOS displayed a significant negative correlation with SR in 49.03% of the areas. Additionally, we identified that RH emerged as the dominant climatic factor influencing the phenology of savanna (SA), whereas temperature strongly controlled the SOS of deciduous needleleaf forest (DNF) and deciduous broadleaf forest (DBF). Meanwhile, the EOS of DNF was primarily influenced by Tmax. In conclusion, this study provides valuable insights into how various vegetation types adapt to climate change, offering a scientific basis for implementing effective vegetation adaptation measures.

Retrieving land surface temperatures from IASI hyperspectral thermal infrared data using an AFNO-transformer model

An adaptive Fourier neural operator (AFNO)-transformer model was developed to retrieve land surface temperature (LST) data from infrared atmospheric sounding interferometer (IASI) observations. A weight selection scheme based on linearization of the radiative transfer equation was proposed to solve the hyperspectral data channel redundancy problem. The IASI brightness temperatures and Advanced Very High Resolution Radiometer onboard MetOp (AVHRR/MetOp) LST product were selected to construct the training and test datasets. The AFNO-transformer performed effective token mixing through self-attention and effectively solved the global convolution problem in the Fourier domain, which can better learn complex nonlinear equations and achieve time-series forecasting. The root mean square error indicated that the LST in Eastern Spain and North Africa could be retrieved with an error of less than 2.5 K compared with the AVHRR/MetOp LST product. Moreover, the validation results from other time period data showed that the retrieval accuracy of this model can be less than 3 K. The proposed model provides a novel approach for hyperspectral LST retrieval.

Consistency of spatiotemporal variability of MODIS and ERA5-Land surface warming trends over complex topography

In this study, the trend of widely used MODIS MxD11 and MxD21 Land Surface Temperature (LST) and ERA5-Land Skin Temperature (SKT) and 2 m air temperature products were validated using 2 m air temperature trends obtained by ground observations from 266 stations in 2000-2021 over Turkey, known to have complex topography. The results show that colder regions have substantially higher temporal temperature variability than warmer ones. MxD21 and MxD11 products are 4.4 °C and 2.9 °C warmer than ERA5-Land products, respectively, while ERA5-Land products (SKT and 2 m) have nearly similar averages (12.5 °C). The consistency between MODIS and ERA5-Land data is significantly lower over areas with more complex topography and irrigation activities, despite the fact that the products show a high linear relationship over the study area. While February trends are consistently much higher than other months (2.2 and 1.4 °C/decade for MODIS and ERA5-Land, respectively), overall MODIS skin temperature products (0.7 °C/decade) generally exhibit smaller trends than ERA5-Land skin and air temperature trends (0.94 °C/decade). The results suggested that MODIS and ERA5-Land trends, which are highly consistent with observations, might replace observations in the absence of long-term station-based records.

Spatial and Temporal Assessment of Remotely Sensed Land Surface Temperature Variability in Afghanistan during 2000–2021

The dynamics of land surface temperature (LST) in Afghanistan in the period 2000–2021 were investigated, and the impact of the factors such as soil moisture, precipitation, and vegetation coverage on LST was assessed. The remotely sensed soil moisture data from Land Data Assimilation System (FLDAS), precipitation data from Climate Hazards Group Infra-Red Precipitation with Station (CHIRPS), and NDVI and LST from Moderate-Resolution Imaging Spectroradiometer (MODIS) were used. The correlations between these data were analyzed using the regression method. The result shows that the LST in Afghanistan has a slightly decreasing but insignificant trend during the study period (R = 0.2, p-value = 0.25), while vegetation coverage, precipitation, and soil moisture had an increasing trend. It was revealed that soil moisture has the highest impact on LST (R = −0.71, p-value = 0.0007), and the soil moisture, precipitation, and vegetation coverage explain almost 80% of spring (R2 = 0.73) and summer (R2 = 0.76) LST variability in Afghanistan. The LST variability analysis performed separately for Afghanistan’s river subbasins shows that the LST of the Amu Darya subbasin had an upward trend in the study period, while for the Kabul subbasin, the trend was downward.