Net primary productivity (NPP) is an important component of carbon cycle and regulating ecological processes in terrestrial ecosystems, which can directly reflect the vegetation carbon fixation capabilities. In order to discover the changes in the vegetation carbon fixation capabilities in Micangshan National Nature Reserve, the method of spatio-temporal fusion STARFM were selected to improve the spatial resolution of the data in the study area to 10 meters. Then, the improved CASA model was used to estimate the NPP in 2018, 2020 and 2021, and its spatial distribution, its time-varying characteristics were analyzed and compared with the variation of major climate factors. According to the results, the overall pattern of NPP in different years was relatively stable with little variation. The annual variability is significant, and the annual total amount of NPP is the highest in 2020, followed by 2021, and the lowest in 2018. The monthly variation was mainly controlled by temperature and radiation, and vegetation showed obvious physiological response to these two factors.

The Yellow River Basin is a crucial ecological barrier in China, holding significant strategic importance for ecological security and sustainable development. In recent years, the Yellow River Basin has faced a series of ecological challenges like ecosystem degradation, soil erosion, and desertification, threatening the ecological environment. Timely and quantitative characterization of the spatiotemporal changes in ecological quality is paramount for the protection and sustainable development of the ecological environment in the Yellow River Basin. This study utilized MODIS data from 2023 to extract four ecological indicators: Greenness, Wetness, Dryness, and Heat. The Principal Component Analysis (PCA) method was applied to calculate the Remote Sensing-Based Ecological Index (RSEI) for assessing and analyzing the ecological quality in the Yellow River Basin. The RSEI can effectively reflect the dynamic changes in ecological environment quality across different periods and regions of the Yellow River Basin.

Grassland ecosystem is widely distributed and is an important ecological barrier in China, so it is of great significance for monitoring grassland degradation. Remote sensing data can provide long-term and large-scale records of grassland ecosystem changes. Therefore, remote sensing is an effective way to monitor land degradation in grassland areas. Xilingol grassland is a combination of typical grassland, meadow grassland and desert grassland in arid and semi-arid areas of northern China. We Used NOAA GIMMS NDVI3g data and rainfall time series data from 1983 to 2008 in this article. Meanwhile, time series segmentation and residual trend analysis (TSS-RESTREND) was used to monitor and analyze land degradation in Xilingol grassland. This method can remove the influence of climate factors on NDVI, so as to analyze the change of grassland caused by human activities. The results showed that during 1983-2008, 86.8% of the pixels had no obvious change, 2.5% of the pixels had degradation, 4.7% of the pixels had greenness increase, and 6.0% of the pixels were uncertain. In terms of spatial distribution, land degradation occurred in the eastern part of Xilingol, while land greenness increased in the western part.

Based on the systematic analysis of the current situation and main problems of ecological restoration in China, this paper summarized the technical flow of territorial space ecological protection and restoration project, as well as the core requirements for geospatial data. Then combined with the development status of China's natural resources Landsat and their major technical parameter characteristics, the core supporting product system of satellite remote sensing was designed, and an idea of Lifecycle stage-Theme-Scene-Element was proposed. Finally, by taking some typical cases as examples, the main application modes of the products were explained. The product system and application mode proposed can not only help the government improve the efficiency of ecological restoration management and data consistency by using remote sensing data and Hi-technologies such as artificial intelligence and quantitative remote sensing, but also help to promote widely application of China's natural resources Landsat in government management.

Time series leaf area index (LAI) is essential to studying vegetation dynamics and climate changes. The LAI at current status can be regarded as the accumulative consequence of the counterpart at prior times. Although the deep learning algorithm - Long short-term memory (LSTM) can capture long-time dependencies from sequential satellite data for time series LAI estimation, it only uses the information at prior statuses, and neglects the backward propagation of current vegetation change information. Thus, the LSTM-based LAI quality might be limited. In this letter, the bidirectional LSTM (Bi-LSTM) approach was proposed to integrate the information of multiple satellite products from both the past and future for temporal LAI retrieval. The fused values from GLASS, MODIS, and VIIRS LAI products, as well as MODIS reflectance in 2014-2015, serve as the output response and input for the Bi-LSTM training. Then, we compared the Bi-LSTM predictions with the counterparts from the LSTM, the fused LAI and three products using independent validation datasets in 2016. Results illustrated that our proposed Bi-LSTM method achieved better performance with higher accuracy (R2=0.84, RMSE=0.76) when compared to the LSTM estimation (R2=0.83, RMSE=0.82) and LAI products (R2<0.68, RMSE>1). Furthermore, our proposed method provided smoother and more continuous temporal profiles of LAI than other retrieval approaches.

Spectra bandwidth (SBW) is regarded as an essential parameter of sensors and remote sensing images. It is necessary to carry on the study on SBW index setting of infrared imager based on spectrum simulation. In this paper, based on prior designed index of the sensor, the radiative transfer model and spectral library were used to simulate apparent radiance. On the basis of combining spectral response function of the sensor and the spectra bandwidth initial design values, we simulated apparent radiance data of 10 geo-objects under 11 different SWBs. In the section of spectra bandwidth on object recognition, spectral feature fitting was chosen to compare the fit of simulated spectra with different bandwidths to reference apparent radiance spectra with initial design values. According to the results, the bandwidth setting best suited to the industry is provided as a reasonable scientific basis for sensors' design.

Ecological restoration is an effective method for mitigating environmental degradation, controlling water loss, and diminishing soil erosion. Ecological protection and restoration were carried out in response to the main problems existing in the Fuxian Lake Basin, Vegetation is one of the most critical elements of terrestrial ecosystems and plays an important role in material cycling and energy flow. To understand the ecological transition from 2014 to 2019 in the Fuxian Lake Basin, this study analyzed the spatiotemporal variability of vegetation using the leaf area index as an indicator to monitor the growth of vegetation and evaluate the effectiveness of ecological protection and restoration through the trend analysis of leaf area index. The results showed that LAI in 2.87%, 8.17% and 9.10% of the entire study area increased significantly, with$\mathrm{P} < 0.05$. By contrast, LAI decreased significantly in 5.52%, 1.98% and 5.62% of the entire study area, with$\mathrm{P} < 0.05$.

High resolution thermal infrared satellites can provide important observations for applications in various fields. For mine detection application, high resolution data can better resolve mineralogic boundaries. In this paper, an emissivity image simulation method is proposed for a high resolution thermal infrared satellite concept; and the simulated images can be used for mineral recognition and classification algorithm development using such instrument specification. Using Worldview-3 imagery as the simulation data source, the emissivity image has been generated based on a linear mixing model. In addition, accuracy analysis is conducted through comparison between simulated WorldView-3 images and the actual one. Small relative errors in every WorldView-3 band show it is feasible to use our proposed method for image simulation.

Infrared spectrums play an important role in the information extraction of rock and minerals. Spectrum simulation is a fundamental issue of land surface scene simulation and image simulation of remote sensing systems. Signal to Noise Ratio is regarded as an essential parameter of instrument and remote sensing image. In this study, we used MODTRAN to simulate apparent radiance and different levels of additive white Gaussian noise was added to the simulated spectrum. In the section of noise impact on object recognition, Spectral Feature Fitting was chosen to compare the fit of simulated spectra with different noise levels to reference apparent radiance spectra without noise. Relative error is also calculated for the accuracy assessment which is helpful for validation and improvement of instrument parameters.

This study was conducted in the temperate typical steppe with relatively high species richness in Inner Mongolia. We extracted the red edge position from ground-based spectral data for canopy and leaf using 6 different methods, and analyzed the relationship between the red edge position extracted from canopy spectra and associated nitrogen concentration of canopy. The results showed that the red edge position substantially varied with the extraction methods, as well as the sampling sites (plant species). For nitrogen concentration estimation at the canopy scale, the linear extrapolation method had a relatively high correlation coefficient and the nitrogen concentration estimates at Stipa grandis-dominated sites were slightly better than those of the large sampling sites with multiple dominant plant species.

The requirement for land surface temperature (LST) in environmental studies and resource management makes the remote sensing of LST an important topic. As a result, a number of studies about the development of methodologies for retrieving LST have been carried out. This paper attempts to use both the split-window algorithm and the Linear Spectral Mixed Model for computing vegetation fraction, in order to improve the algorithm to retrieve the land surface temperature from Moderate-resolution Imaging Spectroradiometer (MODIS) data. Compared results of surface temperature of the profile, the LST retrieved by our algorithm ranges from 298K to 308K, which have very consistent trend of temperature distribution with the MODIS LST product. They have very good correlation, and the correlation is significant at the 0.01 level.