Currently, various algorithms have been developed to retrieve regional and global Land surface temperature (LST) from satellite thermal infrared (TIR) observations, among which, the split window (SW) algorithm is the most widely used one. However, the LST retrieval accuracy would be affected by the channel centers and channel widths owing to the vast atmospheric conditions and land surface types around the world. The theoretical channel configuration leading to the best performance of the SW algorithm is still not well investigated currently. In this study, the LST retrieval accuracies of the SW algorithm using different channel configurations were studied iteratively through the whole TIR atmospheric window. Consequently, the two channels centered at 10.3 μm and 11.5 μm with the widths of 0.3 μm and 0.4 μm were found to be the optimal channel configuration for applying the SW algorithm. Based on the global atmospheric profiles provided in the ERA5 and SeeBor V5.0 database and the emissivity spectra provided in the ECOSTRESS library, the performance of the SW algorithm using the determined channel configuration was delicately evaluated. Results show that the LST retrieval Root Mean Square Error (RMSE) of the determined channel configuration was 1.09 K, better than that of the MODIS (1.28 K), Landsat-9 (1.24 K), and Sentinel-3A (1.24 K) instruments regarding the global atmospheric profiles provided in the ERA5 database. Similar results were obtained corresponding to SeeBor V5.0 atmospheric profiles with the LST retrieval RMSE of 1.28K (determined channel configuration), 1.49 K (MODIS), 1.96 K (Landsat-9), and 1.94 K (Sentinel-3A).

Aboveground biomass (AGB) of rice is crucial for monitoring growth and predicting yields. While deep learning algorithms, such as deep convolutional neural networks (DCNNs), show compelling performance in estimating crop parameters, gathering sufficient ground-truth samples for model training poses a significant challenge, leading to the “small sample problem.” To address this, we propose a framework that utilizes a hybrid inversion model based on the PROSAIL-PRO radiative transfer model (RTM) combined with machine learning techniques [XGBoost and random forest (RF)]. This framework incorporates active learning optimization and the spectral angle mapper (SAM) method to select simulated samples that closely match real-world conditions, simultaneously assigning geographic location information to the samples. Using these qualified samples, we constructed both single-branch and multibranch DCNN models that integrate uncrewed aerial vehicle (UAV)-based hyperspectral principal components (PCs), canopy height (CH) information from the canopy surface model (CSM), and canopy temperature derived from thermal infrared (TIR) images. The effectiveness of this approach was validated across two experimental sites. The single-branch DCNN achieved the highest accuracy at site A ($R^{2} =0.816$and root-mean-square error (RMSE) =61.608 g/m2) with PCs, TIR, and CSM as inputs, while the multibranch DCNN performed best at site B ($R^{2} =0.784$and RMSE =65.533 g/m2), using PCs and TIR as inputs. Results indicate that simulated samples have considerable potential for practical applications. PCs were the primary contributors to the model, with TIR playing a more significant role than CSM. Overall, this study demonstrates high-precision estimation of rice AGB despite limited measured samples, offering valuable insights for crop monitoring under small sample conditions.

Leaf area index (LAI) and leaf chlorophyll content (LCC) are key indicators for monitoring rice growth dynamics. While UAV-based hyperspectral data is widely used, its high redundancy poses challenges for efficient information extraction. To address this, we propose a two-step generic framework. First, synthetic spectra generated by a field-constrained PROSAIL model are used to train a one-dimensional convolutional neural network with a self-attention mechanism that derives Spectral Composite Variables (SCVs) from redundant hyperspectral data. Then, the SCVs are combined with canopy temperature (from thermal infrared sensors) and crop height (derived from UAV-based LiDAR and RGB imagery) to develop a retrieval model, validated through both within-site and cross-site strategies. Results showed that the SCVs generated exhibited strong correlations with LAI and LCC, averaging 0.83 and 0.85, respectively. Moreover, the proposed framework achieved high retrieval accuracy across all growth stages (e.g., booting, heading, filling), with mean R² values of 0.76 for LAI and 0.71 for LCC. Specifically, both estimations reached peak performance during the heading stage, with an R² of 0.83 and RMSE of 0.47 m²/m² for LAI, and an R² of 0.77 and RMSE of 4.13 μg/cm² for LCC. Cross-site validation confirmed the model’s robustness and transferability, with the best performance consistently observed during the heading stage. Benefiting from this framework, spatial predictions of LAI and LCC at centimeter-level resolution closely aligned with observed patterns, enabling precise monitoring of rice growth. Overall, this study presents a robust and transferable solution for overcoming hyperspectral redundancy and enhancing crop growth estimation accuracy.

The challenges of utilizing the 250-m resolution thermal infrared (TIR) data obtained from the Medium Resolution Spectral Imager-II (MERSI-II) onboard the Chinese Fengyun-3D (FY-3D) satellite are bowtie effect and nonuniform brightness stripe noise. While previous solutions have addressed these issues separately, this article introduced a more integrated two-step image quality enhancement strategy for MERSI-II TIR images. It considered the interactions between the two issues and overcame the excessive or inadequate destriping in existing models due to the ideal stripe-type assumption. Specifically, for the bowtie effect, a rigorous geometric model suitable for MERSI-II was constructed by considering the Earth’s curvature and adjusting the preset image width. For the nonuniform brightness stripe noise, a novel adaptive multiscale frequential (AMSF) algorithm was developed. The multiscale spectral detection effectively captured the anomaly frequency, and the adaptive threshold dynamically adjusted the detection range, profiting in preserving details. The proposed strategy was validated on MERSI-II TIR images, outperforming existing methods in quantitative and qualitative assessments with higher efficiency on both bowtie effect and stripe noise removal. Further experiments conducted on Moderate Resolution Imaging Spectroradiometer (MODIS) data demonstrated the AMSF algorithm’s applicability to different data. In addition, the 250-m MERSI-II FY-3D data can help us understand the rapid variations of Arctic sea ice leads, which are key features within the sea ice. Using the quality-enhanced images to extract the sea ice leads in winter Arctic Baffin Bay improved the overall accuracy from 0.88 to 0.95, thereby providing more accurate and reliable sea ice lead data.

Land surface temperature (LST) is a pivotal parameter in many study areas. At present, numerous algorithms are available to retrieve accurate LST from different satellite thermal infrared (TIR) observations. However, rare studies focus on simultaneous LST and land surface emissivity (LSE) retrieval from the TIR measurements of MERSI-II onboard Chinese FengYun-3D satellite designed with a spatiotemporal resolution of 250 meters and five days, that just bridges the Terra/Aqua-MODIS and Landsat-TIRS observations. Although simultaneous LST and LSE retrieval could be achieved by the existing temperature-emissivity separation (TES) method, only two of the three MERSI-II TIR channels are with the spatial resolution of 250 meters leading to the difficulty of directly applying the traditional TES method. Inspired by the TES method and the theory of temperature-independent spectral indices (TISI), this study proposed a new scheme for deriving the 250-meters LST and LSE simultaneously from the MERSI-II TIR data. T-based validations using the ground measurements indicated that the LST retrieval accuracy was about 3.19 K and 2.51 K in the daytime and nighttime, respectively. Cross-validations taking MODIS LST product as the references showed that errors in the retrieved LST was <2.1 K in the daytime while <1.4 K during the nighttime. Overall, results showed that the proposed method can be used to retrieve global LST and LSE from the MERSI-II data, which can facilitate their applications in relevant fields.

Land surface temperature (LST) is a crucial parameter needed to study the thermal environment in urban areas. Currently, it can be restored from thermal infrared (TIR) measurements based on various LST retrieval algorithms. But the expected urban LST retrieval accuracy of <1 K is difficult to achieve because knowledge is lacking on how to correct the impact from the surface 3-D structures and the sunlit-shadow temperature contrast. Although an Analytical TIR radiative transfer Model Over Urban area (ATIMOU) has been proposed, the temperature contrast between sunlit and shadowed areas has been not well managed yet, thus lead to its inapplicability in daytime TIR observations. This study develops an Extended ATIMOU (E_ATIMOU) that considers the impact from both 3-D structures and sunlit-shadow temperature contrast. According to the simulations based on E_ATIMOU, if such impact is not properly accounted for, a 4.43 K bias can be potentially introduced to the ground brightness temperature of a street canyon under the condition of wavelength of 10 μm, ratio “sunlit-road area/total-road area” of 0.5, shadowed wall and road temperature of 300 K, and the sunlit-shadow temperature contrast of 5 K, which emphasizes the necessity of addressing this impact during the LST retrieval in urban areas. Moreover, E_ATIMOU has also been validated by intercomparing with the discrete anisotropic radiative model (DART). The discrepancy between the two models for the calculated ground brightness temperatures is found to be <0.1 K for various urban scenarios, indicating that the E_ATIMOU is in good agreement with DART.

Ice/snow surface temperature (I/SST) is an essential parameter in many research fields such as the climate change, energy, and matter balance of the South pole regions. Recently, many algorithms have been developed for various satellite observations to derive the I/SST. However, rare studies focus on accurate I/SST retrieval from the observations of Chinese MEdium Resolution Spectral Imager II (MERSI-II) instrument onboard the FY-3D satellite with the spatial resolution of 250 m and temporal resolution of about five days, which just bridges the specifications of the Aqua-MODerate-resolution Imaging Spectroradiometer (MODIS) (1000-m pixel size and 0.5-day revisit cycle) and Landsat-Thermal InfraRed Sensor (TIRS) (100-m pixel size and 16-day revisit cycle) instruments. In this study, a new method with correction of striping noise and consideration of angular emissivity effect is developed for the MERSI-II data to accurately retrieve the I/SST. The performance of the proposed method is assessed by using both MODIS product and ground I/SST measurements. The results show that the FY-3D I/SST retrieval accuracy is comparable to the MODIS product, with a discrepancy of < 1.6 K. Ground-based validation reveals that the proposed method could be used to accurately retrieve the I/SST with a root-mean-square error (RMSE) of < 1.5 K. Overall, this study proposes a method for accurate I/SST retrieval from the FY-3D MERSI-II data with the pixel size of 250 m, implying the possibilities in improving the spatio-temporal resolutions of the current I/SST products. Moreover, the proposed method is also helpful to improve our understandings of the polar regions.

Land surface temperature (LST) is a key parameter for many fields of study. Currently, LST retrieved from satellite thermal infrared (TIR) measurements is attainable with an accuracy of about 1 K for most natural flat surfaces. However, over urban areas, TIR measurements are influenced by 3-D structures and their radiation that could degrade the performance of existing LST retrieval algorithms. Therefore, quantitative models are needed to investigate such impact. Current 3-D radiative transfer models are generally based on time-consuming numerical integrations whose solutions are not analytical, and are therefore difficult to exploit in the methods of physical retrieval of LST in urban areas. This article proposes an analytical TIR radiative transfer model over urban (ATIMOU) areas that considers the impact of 3-D structures and their radiation. The magnitude of this impact on TIR measurements is investigated in detail, using ATIMOU, under various conditions. Simulations show that failure to acknowledge this impact can potentially introduce a 1.87-K bias to the ground brightness temperature for street canyon whose ratio “wall height/road width” is 2, wall and road temperature is 300 K, wall emissivity is 0.906, and road emissivity is 0.950. This bias reaches 4.60 K if road emissivity decreases to 0.921, and road temperature decreases to 260 K. ATIMOU is also compared to the discrete anisotropic radiative transfer (DART) model. Small mean absolute error of 0.10 K was found between the models regarding the simulated ground brightness temperatures, indicating that ATIMOU is in good agreement with DART.

Sensor-observed energy from adjacent pixels, known as the adjacency effect, influences land surface reflectivity retrieval accuracy in optical remote sensing. As the spatial resolution of thermal infrared (TIR) images increases, the adjacency effect may influence land surface temperature (LST) retrieval accuracy in TIR remote sensing. However, to our knowledge, few studies have focused on quantifying this adjacency effect on TIR measurements. In this study, a forward adjacency effect radiative transfer model (FAERTM) was developed to quantify the adjacency effect on high-spatial-resolution TIR measurements. The model was verified to be in good agreement with moderate resolution atmospheric transmission (MODTRAN) code, with a discrepancy3 K in some cases. These findings indicate that the adjacency effect should be considered when retrieving LSTs from TIR measurements, at least in some specific conditions. The proposed FAERTM provides a useful model for quantifying and addressing the adjacency effect on TIR measurements.