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The unceasing quest for a profound comprehension of the Earth system propels the continuous evolution of novel methods for Earth observation. Of these, the Lagrange points situated in the cislunar space proffer noteworthy prospects for space-based Earth observation. Although extant research predominantly centers on Moon-based Earth observation and the L1 point within the Sun-Earth system, the realm of cislunar space remains relatively unexplored. This paper scrutinizes the overarching characteristics of the L1 point within the Earth-Moon system concerning Earth observation. A pivotal enhancement is introduced through the incorporation of the halo orbit. This research comprehensively analyzes the relative motion between the halo orbiter and the Earth, achieved via orbit determination within a rotating coordinate system, followed by a transformation into the Earth coordinate system. Subsequently, numerical simulations employing ephemeris data unveil the observing geometry and Earth observation characteristics, encompassing the distribution of nadir points, viewing angles, and the spatiotemporal ground coverage. As a point of reference, we also present a case study involving a Moon-based platform. Our findings reveal that the motion of the halo orbit, perpendicular to the lunar orbital plane, results in a broader range of nadir point latitudes, which can extend beyond 42°N/S, contingent upon the orbit’s size. Additionally, it manifests a more intricate latitude variation, characterized by the bimodal peaks of the proposed temporal complexity curve. The viewing angles and the spatiotemporal ground coverage closely resemble those of Moon-based platforms, with a marginal enhancement in coverage frequency for polar regions. Consequently, it can be deduced that the Earth observation characteristics of the L1 point within the Earth-Moon system bear a close resemblance to those of Moon-based platforms. Nevertheless, considering the distinct advantages of Moon-based platforms, the lunar surface remains the paramount choice, boasting the highest potential for Earth observation within cislunar space. In summation, this study demonstrates the Earth observation characteristics of the L1 point within the Earth-Moon system, emphasizing the distinctions between this and Moon-based platforms.

Observation angles are of great importance with respect to Earth observation platforms. The richness of angular combination, i.e., the combination of three observational angles (viewing zenith angle, solar zenith angle, and relative azimuth angle), is an important parameter to illustrate the angle sampling capability of an Earth observation platform. Here, the angular combination characteristics of a Moon-based platform were investigated and compared with existing artificial satellites. Furthermore, the effects of the time sampling interval on the angular combination characteristics were analyzed using a newly established angular combination number index (ACNI). Results show that a Moon-based platform can complement angular sampling observations from existing satellites. We found that the time sampling interval has different effects on the angular combination for different observed points. Accordingly, the Earth’s surface can be divided into two zones with respect to its sensitivity to the time sampling interval. When the time sampling interval increased from 10 min to 2 h, the maximum loss of the angular combination reached 50% for the observed points in the mid–low latitude zone.

Geological disasters are responsible for the loss of human lives and for significant economic and financial damage every year. Considering that these disasters may occur anywhere—both in remote and/or in highly populated areas—and anytime, continuously monitoring areas known to be more prone to geohazards can help to determine preventive or alert actions to safeguard human life, property and businesses. Remote sensing technology—especially satellite-based—can be of help due to its high spatial and temporal coverage. Indeed, data acquired from the most recent satellite missions is considered suitable for a detailed reconstruction of past events but also to continuously monitor sensitive areas on the lookout for potential geohazards. This work aims to apply different techniques and methods for extensive exploitation and analysis of remote sensing data, with special emphasis given to landslide hazard, risk management and disaster prevention. Multi-temporal SAR (Synthetic Aperture Radar) interferometry, SAR tomography, high-resolution image matching and data modelling are used to map out landslides and other geohazards and to also monitor possible hazardous geological activity, addressing different study areas: (i) surface deformation of mountain slopes and glaciers; (ii) land surface displacement; and (iii) subsidence, landslides and ground fissure. Results from both the processing and analysis of a dataset of earth observation (EO) multi-source data support the conclusion that geohazards can be identified, studied and monitored in an effective way using new techniques applied to multi-source EO data. As future work, the aim is threefold: extend this study to sensitive areas located in different countries; monitor structures that have strategic, cultural and/or economical relevance; and resort to artificial intelligence (AI) techniques to be able to analyse the huge amount of data generated by satellite missions and extract useful information in due course.

Moon-based Earth observations have attracted significant attention across many large-scale phenomena. As the only natural satellite of the Earth, and having a stable lunar surface as well as a particular orbit, Moon-based Earth observations allow the Earth to be viewed as a single point. Furthermore, in contrast with artificial satellites, the varied inclination of Moon-based observations can improve angular samplings of specific locations on Earth. However, the potential for estimating the global outgoing longwave radiation (OLR) from the Earth with such a platform has not yet been fully explored. To evaluate the possibility of calculating OLR using specific Earth observation geometry, we constructed a model to estimate Moon-based OLR measurements and investigated the potential of a Moon-based platform to acquire the necessary data to estimate global mean OLR. The primary method of our study is the discretization of the observational scope into various elements and the consequent integration of the OLR of all elements. Our results indicate that a Moon-based platform is suitable for global sampling related to the calculation of global mean OLR. By separating the geometric and anisotropic factors from the measurement calculations, we ensured that measured values include the effects of the Moon-based Earth observation geometry and the anisotropy of the scenes in the observational scope. Although our results indicate that higher measured values can be achieved if the platform is located near the center of the lunar disk, a maximum difference between locations of approximately 9 × 10−4 W m−2 indicates that the effect of location is too small to remarkably improve observation performance of the platform. In conclusion, our analysis demonstrates that a Moon-based platform has the potential to provide continuous, adequate, and long-term data for estimating global mean OLR.

To study the Earth’s energy balance and to extend exoplanet research, the Earth’s outward radiative flux and its radiance in the Moon-based view were simulated according to the Earth–Sun–Moon geometry model, with the help of ERA5. A framework was developed to identify the angular distribution model (ADM) of Earth’s surface and its scene types, according to the surface and atmospheric data from ERA5. Our simulation shows that the specific viewing geometry controls the periodical variations in the Moon-based view radiative flux and its radiance, which reflect the orbital period of the Moon. The seasonal variations in shortwave and longwave radiative flux follow the energy balance in general, which is probably influenced by the Earth albedo. The derived global ADM would help to identify the anisotropic factor of observations at DSCOVR. Our simulations prove that Moon-based observation is a valuable source for Earth observation and that the orbital information of exoplanets could be derived from the radiance observation.

Spatio-temporal characteristics are the crucial conditions for Moon-based Earth observations. In this study, we established a Moon-based Earth observation geometric model by considering the intervisibility condition between a Moon-based platform and observed points on the Earth, which can analyze the spatio-temporal characteristics of the observations of Earth’s hemisphere. Furthermore, a formula for the spherical cap of the Earth visibility region on the Moon is analytically derived. The results show that: (1) the observed Earth spherical cap has a diurnal period and varies with the nadir point. (2) All the annual global observation durations in different years show two lines that almost coincide with the Arctic circle and the Antarctic circle. Regions between the two lines remain stable, but the observation duration of the South pole and North pole changes every 18.6 years. (3) With the increase of the line-of-sight minimum observation elevation angle, the area of an intervisible spherical cap on the lunar surface is obviously decreased, and this cap also varies with the distance between the barycenter of the Earth and the barycenter of the Moon. In general, this study reveals the effects of the elevation angle on the spatio-temporal characteristics and additionally determines the change of area where the Earth’s hemisphere can be observed on the lunar surface; this information can provide support for the accurate calculation of Moon-based Earth hemisphere observation times.

The Karakoram has had an overall slight positive glacier mass balance since the end of 20th century, which is anomalous given that most other regions in High Mountain Asia have had negative changes. A large number of advancing, retreating, and surging glaciers are heterogeneously mixed in the Karakoram increasing the difficulties and inaccuracies to identify glacier surges. We found two adjacent glaciers in the eastern Karakoram behaving differently from 1995 to 2019: one was surging and the other was advancing. In order to figure out the differences existing between them and the potential controls on surges in this region, we collected satellite images from Landsat series, ASTER, and Google Earth, along with two sets of digital elevation model. Utilizing visual interpretation, feature tracking of optical images, and differencing between digital elevation models, three major differences were observed: (1) the evolution profiles of the terminus positions occupied different change patterns; (2) the surging glacier experienced a dramatic fluctuation in the surface velocities during and after the event, while the advancing glacier flowed in a stable mode; and (3) surface elevation of the surging glacier decreased in the reservoir and increased in the receiving zone. However, the advancing glacier only had an obvious elevation increase over its terminus part. These differences can be regarded as standards for surge identification in mountain ranges. After combining the differences with regional meteorological conditions, we suggested that changes of thermal and hydrological conditions could play a role in the surge occurrence, in addition, geomorphological characteristics and increasing warming climate might also be part of it. This research strongly contributes to the literatures of glacial motion and glacier mass change in the eastern Karakoram through remote sensing.

In this study, backward and forward fluorescence radiance within the emission spectrum of 640–850 nm were measured for leaves of soybean, cotton, peanut and wheat using a hyperspectral spectroradiometer coupled with an integration sphere. Fluorescence parameters of crop leaves were retrieved from the leaf hyperspectral measurements by inverting the FluorMODleaf model, a leaf-level fluorescence model able to simulate chlorophyll fluorescence spectra for both sides of leaves. This model is based on the widely used and validated PROSPECT (leaf optical properties) model. Firstly, a sensitivity analysis of the FluorMODleaf model was performed to identify and quantify influential parameters to assist the strategy for the inversion. Implementation of the Extended Fourier Amplitude Sensitivity Test (EFAST) method showed that the leaf chlorophyll content and the fluorescence lifetimes of photosystem I (PSI) and photosystem II (PSII) were the most sensitive parameters among all eight inputs of the FluorMODleaf model. Based on results of sensitivity analysis, the FluorMODleaf model was inverted using the leaf fluorescence spectra measured from both sides of crop leaves. In order to achieve stable inversion results, the Bayesian inference theory was applied. The relative absorption cross section of PSI and PSII and the fluorescence lifetimes of PSI and PSII of the FluorMODleaf model were retrieved with the Bayesian inversion approach. Results showed that the coefficient of determination (R2) and root mean square error (RMSE) between the fluorescence signal reconstructed from the inverted fluorescence parameters and measured in the experiment were 0.96 and 3.14 × 10−6 W·m−2·sr−1·nm−1, respectively, for backward fluorescence, and 0.92 and 3.84 × 10−6 W·m−2·sr−1·nm−1 for forward fluorescence. Based on results, the inverted values of the fluorescence parameters were analyzed, and the potential of this method was investigated.

We obtained accurate, detailed motion distribution of glaciers in Central Asia by applying digital elevation model (DEM) assisted pixel-tracking method to L-band synthetic aperture radar imagery. The paper firstly introduces and analyzes each component of the offset field briefly, and then describes the method used to efficiently and precisely compensate the topography-related offset caused by the large spatial baseline and rugged terrain with the help of DEM. The results indicate that the rugged topography not only forms the complex shapes of glaciers, but also affects the glacier velocity estimation, especially with large spatial baseline. The maximum velocity, 0.85 m∙d−1, was observed in the middle part on the Fedchenko Glacier, which is the world’s longest mountain glacier. The motion fluctuation on its main trunk is apparently influenced by mass flowing in from tributaries, as well as angles between tributaries and the main stream. The approach presented in this paper was proved to be highly appropriate for monitoring glacier motion and will provide valuable sensitive indicators of current and future climate change for environmental analysis.

The Moon provides us with a long-term, stable, and unique place for Earth observation. Space agencies of various countries, including the United States, China, and Italy, have made the realization of Moon-based Earth observation an objective of their lunar missions. To date, although some conceptual studies have been presented, an accurate geometric model for Moon-based Earth observation has not yet been described. This paper introduces such a geometric model, which connects the attitude of a Moon-based sensor with a corresponding field of view on the Earth’s surface. The aberration and light time correction are involved. Due to the lack of high-quality experimental data, one qualitative comparison with Chang’E lander images and another quantitative comparison with software output are made. The comparison results show good similarity. The overall model accuracy is evaluated to be better than 4″ at current stage and will be better than 1.5″ if the seleno-graphic position is accurately determined. In direct geolocation process, the aberration and light time will cause a total 0.7 km deviation on the ground near the sublunar point. In SAR range history simulation, the light time effect will lead to a linear error, as large as tens of meters, throughout the integration time.

The offset tracking technique based on synthetic aperture radar (SAR) image intensity information can estimate glacier displacement even when glacier velocities are high and the time interval between images is long, allowing for the broad use of this technique in glacier velocity monitoring. Terrestrial laser scanners, a non-contact measuring system, can measure the velocity of a glacier even if there are no control points arranged on a glacier. In this study, six COSMO-SkyMed images acquired between 31 July and 22 December 2016 were used to obtain the glacial movements of five glaciers on the northern slope of the central Himalayas using the offset tracking approach. During the period of image acquirement, a terrestrial laser scanner was used, and point clouds of two periods in a small area at the terminus of the Pingcuoliesa Glacier were obtained. By selecting three fixed areas of the point clouds that have similar shapes across two periods, the displacements of the centers of gravity of the selected areas were calculated by using contrast analyses of feature points. Although the overall low-density point clouds data indicate that the glacial surfaces have low albedos relative to the wavelength of the terrestrial laser scanner and the effect of its application is therefore influenced in this research, the registration accuracy of 0.0023 m/d in the non-glacial areas of the scanner’s measurements is acceptable, considering the magnitude of 0.072 m/d of the minimum glacial velocity measured by the scanner. The displacements from the point clouds broadly agree with the results of the offset tracking technique in the same area, which provides further evidence of the reliability of the measurements of the SAR data in addition to the analyses of the root mean squared error of the velocity residuals in non-glacial areas. The analysis of the movement of five glaciers in the study area revealed the dynamic behavior of these glacial surfaces across five periods. G089972E28213N Glacier, Pingcuoliesa Glacier and Shimo Glacier show increasing surface movement velocities from the terminus end to the upper part with elevations of 1500 m, 4500 m, and 6400 m, respectively. The maximum velocities on the glacial surface profiles were 31.69 cm/d, 62.40 cm/d, and 42.00 cm/d, respectively. In contrast, the maximum velocity of Shie Glacier, 50.60 cm/d, was observed at the glacier’s terminus. For each period, Glacier G090138E28210N exhibited similar velocity values across the surface profile, with a maximum velocity of 39.70 cm/d. The maximum velocities of G089972E28213N Glacier, Pingcuoliesa Glacier, and Shie Glacier occur in the areas where the topography is steepest. In general, glacial surface velocities are higher in the summer than in the winter in this region. With the assistance of a terrestrial laser scanner with optimized wavelengths or other proper ground-based remote sensing instruments, the offset tracking technique based on high-resolution satellite SAR data should provide more reliable and detailed information for local and even single glacial surface displacement monitoring.

Glaciers in high mountain regions play an important role in global climate research. Glacier motion, which is the main characteristic of glacier activity, has attracted much interest and has been widely studied, because an accurate ice motion field is crucial for both glacier activity analysis and ice avalanche prediction. Unfortunately, the serious topographic effects associated with the complex terrain in high mountain regions can result in errors in ice movement estimation. Thus, according to the different characteristics of the results of pixel tracking in the wavelet domain after random sample consensus (RANSAC)-based global deformation removal, a wavelet-based topographic effect compensation operation is presented in this paper. The proposed method is then used for ice motion estimation in the Muztagh Ata region, without the use of synthetic-aperture radar (SAR) imaging geometry parameters. The results show that the proposed method can effectively improve the accuracy of glacier motion estimation by reducing the mean and standard deviation values from 0.32 m and 0.4 m to 0.16 m and 0.23 m, respectively, in non-glacial regions, after precisely compensating the topographic effect with Advanced Land Observing Satellite–Phased Array-type L-band Synthetic Aperture Radar (ALOS–PALSAR) imagery. Therefore, the presented wavelet-based topographic effect compensation method is also effective without requiring the SAR imaging geometry parameters and has the potential to be widely used in the accurate estimation of mountain glacier velocity.

Most bamboo forests grow in humid climates in low-latitude tropical or subtropical monsoon areas, and they are generally located in hilly areas. Bamboo trunks are very straight and smooth, which means that bamboo forests have low structural diversity. These features are beneficial to synthetic aperture radar (SAR) microwave penetration and they provide special information in SAR imagery. However, some factors (e.g., foreshortening) can compromise the interpretation of SAR imagery. The fusion of SAR and optical imagery is considered an effective method with which to obtain information on ground objects. However, most relevant research has been based on two types of remote sensing image. This paper proposes a new fusion scheme, which combines three types of image simultaneously, based on two fusion methods: bidimensional empirical mode decomposition (BEMD) and the Gram-Schmidt transform. The fusion of panchromatic and multispectral images based on the Gram-Schmidt transform can enhance spatial resolution while retaining multispectral information. BEMD is an adaptive decomposition method that has been applied widely in the analysis of nonlinear signals and to the nonstable signal of SAR. The fusion of SAR imagery with fused panchromatic and multispectral imagery using BEMD is based on the frequency information of the images. It was established that the proposed fusion scheme is an effective remote sensing image interpretation method, and that the value of entropy and the spatial frequency of the fused images were improved in comparison with other techniques such as the discrete wavelet, à-trous, and non-subsampled contourlet transform methods. Compared with the original image, information entropy of the fusion image based on BEMD improves about 0.13–0.38. Compared with the other three methods it improves about 0.06–0.12. The average gradient of BEMD is 4%–6% greater than for other methods. BEMD maintains spatial frequency 3.2–4.0 higher than other methods. The experimental results showed the proposed fusion scheme could improve the accuracy of bamboo forest classification. Accuracy increased by 12.1%, and inaccuracy was reduced by 11.0%.

As a sensitive indicator of climate change, mountain glacier dynamics are of great concern, but the ice motion pattern of an entire glacier surface cannot be accurately and efficiently generated by the use of only phase-based or intensity-based methods with synthetic aperture radar (SAR) imagery. To derive the ice movement of the whole glacier surface with a high accuracy, an integrated approach combining differential interferometric SAR (D-InSAR), multi-aperture interferometry (MAI), and a pixel-tracking (PT) method is proposed, which could fully exploit the phase and intensity information recorded by the SAR sensor. The Chongce Glacier surface flow field is estimated with the proposed integrated approach. Compared with the traditional SAR-based methods, the proposed approach can determine the ice motion over a widely varying range of ice velocities with a relatively high accuracy. Its capability is proved by the detailed ice displacement pattern with the average accuracy of 0.2 m covering the entire Chongce Glacier surface, which shows a maximum ice movement of 4.9 m over 46 days. Furthermore, it is shown that the ice is in a quiescent state in the downstream part of the glacier. Therefore, the integrated approach presented in this paper could present us with a novel way to comprehensively and accurately understand glacier dynamics by overcoming the incoherence phenomenon, and has great potential for glaciology study.

Accurate monitoring of grassland biomass at high spatial and temporal resolutions is important for the effective utilization of grasslands in ecological and agricultural applications. However, current remote sensing data cannot simultaneously provide accurate monitoring of vegetation changes with fine temporal and spatial resolutions. We used a data-fusion approach, namely the spatial and temporal adaptive reflectance fusion model (STARFM), to generate synthetic normalized difference vegetation index (NDVI) data from Moderate-Resolution Imaging Spectroradiometer (MODIS) and Landsat data sets. This provided observations at fine temporal (8-d) and medium spatial (30 m) resolutions. Based on field-sampled aboveground biomass (AGB), synthetic NDVI and support vector machine (SVM) techniques were integrated to develop an AGB estimation model (SVM-AGB) for Xilinhot in Inner Mongolia, China. Compared with model generated from MODIS-NDVI (R2 = 0.73, root-mean-square error (RMSE) = 30.61 g/m2), the SVM-AGB model we developed can not only ensure the accuracy of estimation (R2 = 0.77, RMSE = 17.22 g/m2), but also produce higher spatial (30 m) and temporal resolution (8-d) biomass maps. We then generated the time-series biomass to detect biomass anomalies for grassland regions. We found that the synthetic NDVI-derived estimations contained more details on the distribution and severity of vegetation anomalies compared with MODIS NDVI-derived AGB estimations. This is the first time that we have generated time series of grassland biomass with 30-m and 8-d intervals data through combined use of a data-fusion method and the SVM-AGB model. Our study will be useful for near real-time and accurate (improved resolutions) monitoring of grassland conditions, and the data have implications for arid and semi-arid grasslands management.

Most bamboo forests grow in humid climates in low-latitude tropical or subtropical monsoon areas, and they are generally located in hilly areas. Bamboo trunks are very straight and smooth, which means that bamboo forests have low structural diversity. These features are beneficial to synthetic aperture radar (SAR) microwave penetration and they provide special information in SAR imagery. However, some factors (e.g., foreshortening) can compromise the interpretation of SAR imagery. The fusion of SAR and optical imagery is considered an effective method with which to obtain information on ground objects. However, most relevant research has been based on two types of remote sensing image. This paper proposes a new fusion scheme, which combines three types of image simultaneously, based on two fusion methods: bidimensional empirical mode decomposition (BEMD) and the Gram-Schmidt transform. The fusion of panchromatic and multispectral images based on the Gram-Schmidt transform can enhance spatial resolution while retaining multispectral information. BEMD is an adaptive decomposition method that has been applied widely in the analysis of nonlinear signals and to the nonstable signal of SAR. The fusion of SAR imagery with fused panchromatic and multispectral imagery using BEMD is based on the frequency information of the images. It was established that the proposed fusion scheme is an effective remote sensing image interpretation method, and that the value of entropy and the spatial frequency of the fused images were improved in comparison with other techniques such as the discrete wavelet, à-trous, and non-subsampled contourlet transform methods. Compared with the original image, information entropy of the fusion image based on BEMD improves about 0.13-0.38. Compared with the other three methods it improves about 0.06-0.12. The average gradient of BEMD is 4%-6% greater than for other methods. BEMD maintains spatial frequency 3.2-4.0 higher than other methods. The experimental results showed the proposed fusion scheme could improve the accuracy of bamboo forest classification. Accuracy increased by 12.1%, and inaccuracy was reduced by 11.0%. [ABSTRACT FROM AUTHOR]

Accurate monitoring of grassland biomass at high spatial and temporal resolutions is important for the effective utilization of grasslands in ecological and agricultural applications. However, current remote sensing data cannot simultaneously provide accurate monitoring of vegetation changes with fine temporal and spatial resolutions. We used a data-fusion approach, namely the spatial and temporal adaptive reflectance fusion model (STARFM), to generate synthetic normalized difference vegetation index (NDVI) data from Moderate-Resolution Imaging Spectroradiometer (MODIS) and Landsat data sets. This provided observations at fine temporal (8-d) and medium spatial (30 m) resolutions. Based on field-sampled aboveground biomass (AGB), synthetic NDVI and support vector machine (SVM) techniques were integrated to develop an AGB estimation model (SVM-AGB) for Xilinhot in Inner Mongolia, China. Compared with model generated from MODIS-NDVI (R² = 0.73, root-mean-square error (RMSE) = 30.61 g/m²), the SVM-AGB model we developed can not only ensure the accuracy of estimation (R² = 0.77, RMSE = 17.22 g/m²), but also produce higher spatial (30 m) and temporal resolution (8-d) biomass maps. We then generated the time-series biomass to detect biomass anomalies for grassland regions. We found that the synthetic NDVI-derived estimations contained more details on the distribution and severity of vegetation anomalies compared with MODIS NDVI-derived AGB estimations. This is the first time that we have generated time series of grassland biomass with 30-m and 8-d intervals data through combined use of a data-fusion method and the SVM-AGB model. Our study will be useful for near real-time and accurate (improved resolutions) monitoring of grassland conditions, and the data have implications for arid and semi-arid grasslands management. [ABSTRACT FROM AUTHOR]