Accurate estimation of the surface emission longwave radiation (SELR) has important scientific significance for understanding its spatiotemporal dynamics and surface thermal environment. High spatial resolution thermal infrared images provide better data support for studying SELR of complex surfaces such as urban surface. This paper focus on proposing a new urban-oriented hybrid method to estimate urban surface emission longwave radiation from top-of-atmosphere thermal radiance images, by taking the GF-5/VIMI thermal image as an example, and conduct the parameter sensitive analysis of the model as well as application over Beijing city. The experimental results of the simulation dataset showed that the developed method has relatively high precision, with SELR errors of less than 12.0 W/m2under low water vapor conditions and less than 17.0 W/m2under high water vapor conditions. The application of method in GF-5 image also demonstrated the rationality and effectiveness of the method.

As an essential input parameter, Leaf Area Index (LAI) plays significant value in global climate models.. Acquisition of LAI products with extensive long-term series is crucial for various applications. Presently, numerous remote sensing inversion algorithms have been proposed for the generation of LAI products. However, the low temporal resolution is a huge challenge for producing LAI at a medium to high spatial resolution scale. In addition, the cloud has resulted in a significant reduction of accessible data. In this study, a LAI reconstruction method considering vegetation phenological period was developed. A total of 241 Sentinel-2 images from Saihanba in northern China and cloud probability data provided by Sentinel Hub were collected. The results indicate that the proposed method can effectively reconstruct LAI value in the data-missing period and areas. This study evaluated the performance of LAI reconstruction by implementing the "leave-one-out" method. The results demonstrate that the RMSE of LAI reconstruction is 0.5509 when using July 31st data as the validation dataset, and 0.3933 when using August 7th data as the validation dataset. In summary, this study demonstrates the potential for the LAI time series reconstruction from high-resolution data.

The fraction of absorbed photosynthetically active radiation (FAPAR) is directly linked to the estimation of canopy gross primary production and is a key input parameter in terrestrial ecosystem models. Diffuse FAPAR calculation is necessary for cloudy conditions. Direct and diffuse FAPAR require distinct simulation methods due to different transfer paths, yet most models do not consider diffuse FAPAR independently. In this study, an improved FAPAR-P model （ FAPAR-Pro ） based on the spectral invariant theory, which considers direct and diffuse FAPAR separately, was proposed to simulate all-sky vegetation FAPAR. Based on the proposed FAPAR-Pro model, along with the hourly ratio of diffuse radiance data calculated from Himawari-8 products, the all-sky vegetation FAPAR was calculated. Validation performed with ground measurements showed that the overall RMSE and MAE values are 0.056 and 0.067 for diverse types of vegetation canopies and different diffuse radiation conditions. The results indicate that the model was applicable to diverse types of vegetation canopies and different radiation conditions, and therefore, may provide support for continuous FAPAR time series simulation and various ecological applications in the future.

The Advanced Geostationary Radiation Imager (AGRI) on board of the Chinese geostationary satellite FengYun-4B (FY4B) designs four thermal infrared channels, which has the characteristics of wide observation range, high observation frequency and fixed point observation, with a spatial resolution of 4 km at nadir and a full-disk observation every 15 minutes. Therefore, it can monitor the surface temperature changes on a large time scale, providing important data support for agricultural drought monitoring and climate change. However, there is currently no algorithm for land surface temperature retrieval with this sensor. This paper proposed a three-channel temperature–emissivity separation (TES) algorithm that estimates the LST and emissivity from three thermal-infrared (TIR) images. The analysis shows that the algorithm can theoretically retrieve the LST and emissivity with errors less than 0.8 K and 0.016, respectively.

Leaf chlorophyll content (LCC) is a critical indicator for precision agriculture. Accurately estimating winter wheat LCC based on remote sensing at high spatial resolution is of great significance for agricultural management and decision. In this study, a two-step method was used to retrieve wheat LCC from UAV hyperspectral imagery. The first step is converting canopy reflectance to leaf reflectance using Look-up tables (LUTs) generated from the unified model of bidirectional reflectance distribution function (BRDF). The second step is retrieving wheat LCC from derived leaf reflectance using the PROSPECT-PRO model. Retrieved wheat LAI, leaf reflectance, and LCC were validated against field measurements. The results indicate a good agreement between retrieved and measured LAI with RMSE of 0.092 and R2of 0.605. Leaf reflectance retrieved from UAV canopy reflectance exhibit good consistency with measured leaf reflectance with RMSE of 0.017 and R2of 0.962. Retrieved wheat LCC is of good quality compared to measured LCC, with R2of 0.7675 and RMSE of 4.33 μg/cm2. In conclusion, this study holds potential in estimating wheat LCC from UAV hyperspectral imagery.

Leaf area index (LAI) is a critical indicator for modeling global biosphere–climate interactions. Accurately measuring forest LAI significantly affects the objectivity and accuracy of LAI remote sensing products. DHP is the most widely used instrument to measure forest LAI. However, it is challenging to measure forest LAI accurately using DHP at meter-scale as the wide viewing angle range of DHP is not appropriate for validation of high-resolution pixels. To address this issue, a geometric-based method was proposed to obtain the optimal view zenith range for DHP-based LAI field measurements at meter-scale in this study. Three virtual forest scenes were generated by LESS (LargE-Scale remote sensing data and image Simulation framework) and field measurements at Saihanba, in northern China was collected. The validation results indicate a good agreement between LAI estimation using proposed method and reference LAI datasets. The R2between the LESS-derived LAI and DHP-derived LAI are all greater than 0.9, and the RMSE are all less than 0.1 in simulated scenes. Meanwhile, the R2between the UAV lidar-derived LAI and DHP-derived LAI is 0.668 with RMSE of 0.376. In conclusion, this study holds potential in measuring forest LAI at meter-scale.

Forests are essential for stabilizing the biosphere and providing ecological service functions that are significant to human beings. However, current forest landcover products cannot effectively reflect their ecological service functions. This study focused on the north Tianshan Mountain, China and proposed an ecological service function-based forest classification method using multi-source landcover products and Digital Elevation Model (DEM) data. The proposed method included spatial superposition, clustering, and window sliding techniques. The resulting forest classification map accurately portrayed the spatial distribution of three ecological service function-based forests: water and soil conservation (WSC), farmland shelter (FS), and windbreak and sand-fixation (WSF) forests. Results indicated that WSC forests dominate the forest distribution in the north Tianshan Mountain, constituting over half of the total forest area, while FS forests are mostly spread across the western part of the region, accounting for 34.1% of the entire forest area. WSF forests are relatively small and mainly distributed in the central part of the region. This study provides valuable insights into forest classification based on their ecological service functions, which is critical for understanding the spatial distribution and dynamic changes of forests, promoting sustainable development and preserving their ecological service values.

Leaf area index (LAI) field measurements based on digital hemispherical photography (DHP) and LAI-2200 instruments have been widely used for remote sensing validation in forestry. Both DHP and LAI-2200 utilize fish-eye lens to capture the largest footprint of a canopy with a wide range of view zenith angle (VZA). However, accurately measuring field LAI at high spatial resolution poses a significant challenge since the view scope of fish-eye sensor is much larger than the size of high spatial resolution pixel. Therefore, selecting appropriate VZA ranges is crucial to address this issue. In this letter, we propose an improved geometry-based method that considers the average tree height, crown depth, and high-resolution pixel size. To validate this method, we designed four simulated forest scenes with different crown shapes through the LargE-Scale Remote Sensing Data and Image Simulation Framework (LESS) model and conducted field measurements. The results indicate that our proposed method significantly enhances the accuracy of fisheye-based LAI field measurements at high spatial resolution compared to previous method, with an almost 70% reduction in RMSE. In addition, our method exhibits greater improvement in measuring forest LAI at high resolution with DHP (RMSE < 0.3) compared to LAI-2200 (RMSE < 0.5). In conclusion, our method holds great potential in accurately measuring fisheye-based forest LAI for remote sensing validation at high spatial resolution.

On the basis of the spectral shape of thermal infrared (TIR) emissivity for sandy land, a remote sensing sand index called the sand differential emissivity index (SDEI) is proposed in this article to simply and conveniently detect sandy land over large areas. The SDEI is evaluated on ground, airborne, and spaceborne thermal emissivity data, and it shows good characterization of sandy land and performs better in sandy land identification than two previous indices. The SDEI was also evaluated in the transition zones of China's four mega-sandy lands and was applied to long-term land surface emissivity to obtain the spatial distribution and variation in China's sandy land from 2000 to 2016. The findings showed that a mean accuracy of 96% and a mean kappa coefficient of 0.83 were obtained in the transition zones, and the sandy land in the transition zone exhibited a decreasing trend over the past 17 years and a significant decline in the Mu Us sandy land. Meanwhile, the sandy land area in China decreased by 3.6×104km2(1.53%) by the end of 2016 compared with that in early 2000.

Spatial distribution and disappearance of sandy land is important for ecosystem management of desert regions and provides highly valuable information on desertification and climate change studies in arid environments. Based on the field measurement in the Gurbantonggut Desert, Xinjiang, China and the analysis of the spectral features of sandy land, a new sand differential emissivity index (SDEI) was proposed first for sandy land detection. Compared with the previous vegetation index, which can only distinguish green plants from bare land, SDEI can make a distinction well between sandy land and dry vegetation. For large regional mapping of sandy land, SDEI was applied on the ASTER Global Emissivity Dataset based on the Google Earth Engine platform. And then, four emissivity simulation schemes of different mixed pixels were conducted to determine the best threshold of sandy land mapping. The results show that when the threshold value is larger than 0.041, the sand distribution can be well extracted. Finally, the sandy land area of China extracted by SDEI is 160.67×104km2for year 2008, which is close to the data released by the China’s State Forestry Administration. These experimental results indicated that SDEI is applicable to identification of sandy land, and therefore satellite remotely-sensed thermal infrared observations have good potential in sandy land detection.