最新刊期
卷 25 ， 期 4 ， 2021
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摘要：The space Earth science is a comprehensive and interdisciplinary discipline that studies the interactions, mechanisms and evolutions of the Earth system and Earth’s subsystems through the means of space observation (satellite remote sensing). It brings new means, new ideas and new perspectives to scientists for Earth science studies. The development of space Earth science will promote the development of a series of disciplines including aerospace technology, remote sensing science, Earth system science, meteorology, hydrology, ecology etc. It is an important cornerstone for the cooperation to jointly build a community of common destiny for all mankind. This article mainly reviews the development of space Earth science, and discusses the role of space observation in the study of key subsystems (e.g., water cycle) of the Earth system. It also looks forward to the future development of space Earth science in China.Space Earth science has emerged as a powerful tool to investigate the Earth as a system, expanding the research of Earth science from local to the global dimensions, from static to dynamic queues, and will continue to expand our knowledge about how the Earth has changed. The current space Earth sciences are showing some new development trends, such as (1) paying more attention to the key cyclic processes in the Earth system, and (2) Combining of satellite remote sensing and Earth system model. Taking the observation of Earth’s water cycle from space as an example, the key elements and process variables related to the global water cycle include precipitation, evapotranspiration, runoff, soil moisture, sea temperature and salinity, surface-water bodies, glaciers, snow, frozen soil, sea ice, polar ice caps and ice sheets, atmospheric water vapor, and groundwater etc. Satellite remote sensing has the unique advantage to provide information on the water status, migration, exchange, and phase change processes, which are essential information involved in the global climate change. At present, meteorological, oceanic and Earth observation satellites that have been launched internationally and have been able to measure many water cycle elements in the atmosphere, ocean, and land. These space observation data have greatly enhanced scientists’ knowledge and understanding of the global water cycle process.The outlook for future space Earth observation in the field of water cycle is as follows.(1) Develop satellites for detecting surface state variables of water cycle, including the synthetic aperture microwave radiometer technology, active and passive integrated detection technology, to enable multi-element, high-accuracy, high-resolution simultaneous observation of key state variables such as soil moisture, soil freeze/thaw, snow properties and sea surface salinity, etc.(2) Develop satellites for estimating land-atmosphere water fluxes of water cycle (precipitation and evapotranspiration) by enhancing the atmospheric detection abilities to distinguish between rainfall and snowfall, and combining the thermal infrared and microwave measurements to improve the quantities and qualities of variables related to evapotranspiration.(3) Develop thematic satellites of the cryosphere (water in solid form), including the development of long-wavelength (such as P-band) and wide-band microwave radiometer for the measurement of ice density and temperature profile of ice sheets, the measurement of glacier thickness, and the observation of the thickness of permafrost active layer, etc., and the development of synthetic aperture radar interferometry and lidar for high-precision surveying of ice sheet surface elevation and ice volume.关键词：space earth science;earth system science;remote sensing;global change;water cycle- 851
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发布时间：2021-04-20
Scholar's View Point
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摘要：A series of large remote sensing experiments that began in the 1980s systematically studied the exchanges of matter and energy at the land surface, which played an important role in the integration of remote sensing and Earth system science. However, there are few effective solutions to comprehensively solve energy balance, carbon, and water cycles by using multi-source remote sensing data. The State Key Laboratory of Remote Sensing Science organized a fundamental, multi-disciplinary, multi-scale “Comprehensive Remote Sensing Experiment of Carbon Cycle, Water Cycle and Energy Balance in Luan River Basin” in the upper stream of Luan River. This experiment is oriented to the newly challenging requirements of Earth system science for remote sensing, and it is aimed to the scientific issue that how remote sensing can improve the Earth-atmosphere processes modelling. During the experiment, the spaceborne, airborne and ground-based (multi-scale) remote sensing and ground measurements were carried out to demonstrate China’s self-designed satellite missions related to the carbon cycle, water cycle and energy balance. The experimental data would be used to verify the full-band remote sensing models and the complex surface radiative transfer mechanism based on the real structure simulation over large-scale scenes. The core experimental area includes a relatively flat Shandian River basin and a complex Xiaoluan River basin. The experiments conducted in the Shandian River basin are mainly focused on comprehensive remote sensing observations of water cycle and energy balance with the main land cover types of cropland and grassland. While the experiments in the Xiaoluan River basin are mainly aimed to comprehensive remote sensing observations of carbon cycle with the main land cover types of forest and grassland. Systematic multi-sort airborne experiments and simultaneously full-band, active and passive ground-based observations were carried out at both experimental areas. A 165 km long-span flight, which spans the two experimental areas, was specially designed to cover the gradual transition of land cover types and altitude. Starting from the preliminary experiment in 2017, the entire experiment will last for five years. This experiment is scientific goal-driven, open, collaborative, and shared, and attracted 10 national-level scientific research projects, four satellite mission project teams, and 200 participants from 19 research institutes/universities. It is a China’s self-led scientific research on remote sensing and Earth system science, and it would promote multidisciplinary collaboration to address science challenges in Earth system science.关键词：remote sensing experiment;carbon cycle;water cycle;energy balance;luan River basin;full-band;active and passive synergy- 803
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发布时间：2021-04-20 -
摘要：Remote sensing experiment is an important tool for the verification of remote sensing principles, development of radiative transfer models and retrieval algorithms, and calibration/validation of satellite products. It can help the demonstration of new satellite missions and the promotion of its application in Earth system science. The comprehensive remote sensing experiment of water cycle and energy balance in the Shandian River (the upper stream of Luan River) integrates the space, airborne and ground based remote sensing technologies to conduct a full-band and active-passive observation of typical elements related to water cycle and energy balance processes of the Earth system. It is aimed to study the spatial-temporal variability and observation strategy of those hydro-thermal elements at various remote sensing scales, to study the remote sensing methodologies of those elements and their application in land surface and hydrology models, and to support the design and feasibility studies of new satellite missions (Terrestrial Water Resources Satellite, Energy Budget Observation Mission) in China.The paper describes the general design of this experiment, its scientific objectives and main compositions including the airborne missions, ground sampling strategies, ground-based observation experiments and key variables measurement through wireless sensor networks. Airborne experiments were conducted to obtain multi-resolution, multi-angle observations of both active and passive microwave together with infrared, multispectral, and hyperspectral data. It enables us to explore the remote sensing of various parameters and the impacts of scaling issues, as well as the incidence angle effects associated with the synthetic aperture radiometer system. Ground-based synchronous observation experiments were carried out based on microwave radiometer, radar and spectroradiometer. Concurrent ground data included soil moisture, ground temperature, vegetation water content and surface roughness, etc. are sampled based on large-medium-small quadrats to cover a wide range of land surface conditions. Moreover, ground observation networks were established to monitor meteorological parameters, soil temperature and moisture, surface radiation, evapotranspiration, and precipitation, etc.This experiment provides a unique platform to explore the synergy of active, passive microwave and optical data for water cycle and energy balance remote sensing at improved accuracy and resolution. The experiment overview and preliminary analysis of remote sensing and ground data have confirmed that the data set will help to address a variety of science questions of land-atmosphere energy and water exchanges under global change.关键词：remote sensing experiment;water cycle;energy balance;Shandian river basin;airborne observation;watershed observation network- 865
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发布时间：2021-04-20 -
摘要：Comprehensive remote sensing experiments play an important role in the development of remote sensing science and technology. Both the fundamental research and application of remote sensing need to be supported by experiments. The State Key Laboratory of Remote Sensing Science (SLRSS) has organized a large remote sensing experiment for the studies of carbon cycle over complex land surfaces in the upper reaches of the Xiaoluan River basin since 2018. This paper is targeted to introduce the objectives, study regions, observation parameters, methods and prospects of the experiment and to provide a useful reference for the design of remote sensing experiments.The experiment adopted the satellite, airborne, and ground-based remote sensing, collected the data from the satellites in orbit and the remote sensing products covering the study region. The aerial and Unmanned Aerial Vehicle (UAV) remote sensing experiments were carried out with optical sensors to obtain key parameters of water cycle, carbon cycle and energy flow. Ground observation experiments were synchronously carried out to monitor the key parameters of atmosphere, vegetation and soil.Rich amount of remote sensing data were collected from ground observation experiments, UAV and aerial remote sensing experiments. Driven by the experiment, the SLRSS set up a number of comprehensive observation towers in the experimental area in 2020, equipped with a variety of observation instruments and started long time series observation task. The construction of the large scale virtual scenery for remote sensing experiment and the operation of the BEPS (Boreal Ecosystem Productivity Simulator) model are being carried out.The comprehensive experiment on carbon cycle at complex surfaces in Xiaoluan River basin has effectively obtained the key parameters of surface water, energy and carbon cycles by using the satellite, airborne, and ground-based remote sensing. The experiment provides the important basic data for the development of remote sensing mechanism model, inversion method and scale transformation research. It has been used to establish a comprehensive validation platform for remote sensing mechanism models, to improve the applicability of remote sensing products at complex surfaces, and to clarify the physical process of carbon-water coupling on watershed scale.关键词：remote sensing;comprehensive experiment;watershed scale;carbon cycle;Xiaoluan River;Saihanba- 702
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发布时间：2021-04-20 -
摘要：This paper introduces the “Saihanba comprehensive airborne remote sensing experiment of forest resources” (The carbon cycle airborne experiment), which is part of “The comprehensive experiment of carbon cycle, water cycle and energy balance”. This paper described the purpose, design scheme, flight mission execution, data processing and products of airborne remote sensing experiment of forest resources, respectively. The experiment focused on Saihanba Forest Farm, launched out with the monitoring of forest resources and energy balance of carbon-water cycle. 10 flights were flown from August 31 to September 20, 2018 using the Chinese Academy of Forestry’s LiDAR，CCD and Hyperspectral airborne observation system (CAF-LiCHy). The raw data volume was about 1568 GB. High-level remote sensing products were produced after further data processing. The POS position has a difference within 2 cm in both horizontal and vertical directions. The LiDAR point density is larger than 4 pts/m2. The horizontal and vertical differences of LiDAR point cloud data are within 0.2 m. The spatial resolution of digital elevation model product is 2 m. The spectral resolution of hyperspectral image is better than 10 nm with the spatial resolution of 1 m. The spatial resolution of CCD image is 0.2 m. The overall geolocation accuracy is about 1 m among these three type sensors. This study provided high-quality datasets for carbon-water cycle and forest resources monitoring, reflected the advantages of active and passive integrated observation system in collecting forest resources simultaneously.关键词：airborne remote sensing;lidar;hyperspectral;CCD;forest resources monitoring;saihanba forest farm- 870
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发布时间：2021-04-20
Design of Experiment
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摘要：The remote sensing experiment is an important way to verify the principle of remote sensing, develop remote sensing models and retrieval algorithms, and to validate remote sensing products. It is also important to promote the demonstration of satellite missions and the application of its observations in earth system science. In the ShanDian River comprehensive remote sensing experiment, a large-scale aerial remote sensing experiment with soil moisture as the primary target was carried out using the airborne L-band active and passive microwave sounder. The passive detection part, the microwave radiometer, uses a microstrip antenna, supplemented by a mechanical scanning method for multi-angle imaging observation. In order to effectively support the development of remote sensing models and algorithms, real-time calibration of brightness temperature must be performed. This paper introduces a distributed calibration method combining internal and external calibration.Due to the limitation of the space and weight of the aircraft, the end-to-end calibration method cannot be used to calibrate the system. Therefore, the step-by-step calibration method is used in the test. First use Standard noise generator on the ground to calibrate the brightness temperature of the internal noise source, and then measure the antenna radiation efficiency, main beam efficiency, cable loss and other parameters. During the flight, use the reference load and the internal noise source as the two-point standard sources. The relationship between the receiver and the input brightness temperature is obtained, and finally the parameters of the ground measurement system are substituted to obtain the calibration equation of the airborne radiometer. During the flight, the water body in the Hulunnao’er was selected as the reference point for external calibration target to correct the equation.The results show that the airborne radiation brightness temperature is relatively consistent with the ground reference point (grassland) simulated brightness temperature. The comparison shows that the minimum root mean square error is 0.93 K (September 26, 2018, H polarization), and the unbiased average The minimum square root error is 0.96K (September 24, 2018, V polarization), which effectively supports the demonstration of the domestic-made L-band microwave radiometer satellite program and the subsequent development of related research work such as quantitative inversion and downscaling.The calibration method adopted in this paper can accurately describe the complex relationship between the radiometer electrical signal and the brightness temperature. However, this study did not consider the effects of antenna port matching, aircraft pod loss, antenna pattern error, etc. During the calibration of internal noise sources on the ground, the impact of the environment and the system nonlinearity were not considered. It is necessary to take these factors into consideration in subsequent research, and the calibration accuracy can be further improved.关键词：remote sensing;active and passive microwave detector;airborne remote sensing experiment;airborne microwave radiometer;calibration- 719
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发布时间：2021-04-20 -
摘要：In the process of the earth's water cycle, the status of soil moisture is very important. Soil moisture is an important parameter that controls the exchange of water, heat and energy within the various layers of the earth. In recent years, microwave remote sensing has become one of the important methods for monitoring surface soil moisture. There is a mathematical correlation between the amount of soil moisture and the dielectric constant of the soil. At the same time, the dielectric constant information of the soil directly has a systematic influence on the microwave backscattering intensity information, so by obtaining the backscattering information of the radar microwave signal, the dielectric information of the observation surface can be estimated, so as to carry out the soil moisture monitoring. It can be seen that using mathematical models to describe the correlation between backscatter information and soil moisture is helpful to obtain soil moisture information. A ground-based radar observation experiment is conducted at Xinyuan Ranch Station in the lightning river basin of Inner Mongolia to investigate the temporal and spatial variations of backscattered signals of ground-based synthetic aperture radar and study the influencing factors of radar soil moisture inversion. The radar backscattering coefficients are analyzed on the basis of the ground-based radar data from the above observation tests, including radar bands, incident angles, polarization channels, and other radar parameters. Then, the results of the preceding analysis are used to select a surface microwave surface scattering model. Lastly, an artificial neural network data set is constructed using the selected surface microwave surface scattering model to retrieve surface soil moisture. The results are as follows: (1) In the ground-based radar field of view, the simulation results of the surface microwave surface scattering model and the L-band full polarization data measured using the ground-based radar are the best fit for the AIEM-Oh model. (2) The absolute residual analysis of the AIEM-Oh model simulation results of radar incident angles in the range of 20°–60° indicated that the simulation results are closest to the radar measured values when the radar incident angles are 25°, 41°, and 53°. (3) The results of soil moisture inversion show that when the radar incident angle is 41°, the soil moisture inversion accuracy is highest, the correlation coefficient R is 0.8080, and the RMSE is 0.0385 m³/ m³. The conclusion of this paper is that the radar backscatter signal is affected by the combination of the radar incident angle and surface roughness. Therefore, a reasonable selection of radar incident angle by considering surface roughness can improve the accuracy of soil moisture retrieval. On the one hand, this research uses the surface microwave surface scattering model to simulate the neural network training data set, which is equivalent to using the practical physical simulation data set to embed the mathematical model (neural network) with the physical foundation, so as to reasonably explain the effectiveness of the training data set. On the other hand, through the sensitivity analysis of radar measurement data, the law of backscattering strength with the radar incident angle is obtained, which weakens the inversion error caused by the spatial heterogeneity of radar data. The improvement of soil moisture retrieval methods also provides new ideas for improving soil moisture retrieval.关键词：remote sensing;ground-based radar observation test;surface microwave scattering model;neural network;soil moisture;cGBSAR- 621
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发布时间：2021-04-20 -
摘要：Soil moisture plays an important role in the survival of animals and surface plants, the energy and material cycle between atmosphere and surface. Meanwhile, the large-scale monitoring of soil moisture is a critical indicator for water cycle, climate change, agricultural monitoring, ecological environment, geological disasters, fire and other applications. However, because of the influence of soil types, soil structure conditions, terrain characteristics, vegetation environment and human activities, the distribution of soil moisture has spatial heterogeneity characteristic, so it is still hard to find an optimal method to monitor the distribution of soil moisture in large areas (such as watershed scale). In the comprehensive experiments, the Shandian River Basin was selected as the experiment area. Vegetation Indices (VIs) retrieved from MODIS reflectance data and Land Surface Temperature (LST) were calculated as the input parameters, and then the soil moisture measured was taken as the expected output parameters. Further, the random forest model is used to calculate the feature importance of each input parameters to complete feature selection, and a soil moisture inversion model based on extreme random tree was constructed. Compared with empirical models, the soil moisture retrieval model based on extreme random tree has stronger nonlinear expression ability, and can introduce more input parameters; compared with traditional machine learning methods such as Support Vector Machine(SVM) and Neural Network(NN), extreme random tree can achieve better retrieval accuracy on small sample set by integrating several “weak learners” into “strong learners”; Compared with random forest, extreme random tree can reduce the variance and the deviation of the model to achieve better integration effects. Considering the difficulty of surface temperature measurements and the requirements of regional soil moisture monitoring, the research selected Short wave infrared Transformed Reflectance (STR) instead of LST to establish the extreme random tree model, and inversed the soil moisture map of 2°× 2° area covering experiment area of Shandian River Basin. The results show that: (1) when LST is used as the input parameter, the soil moisture retrieval model based on extreme random tree, (root mean square error 0.054 m3m-3 and correlation coefficient 0.69), has better performance than other models (support vector machine and random forest); (2) when STR is used as the input parameter, the root mean square error of prediction result is 0.060 m3m-3 and the correlation coefficient is 0.66. So it is feasible to introduce STR instead of LST to predict soil moisture in large area. Further, the spatial distribution of soil moisture is basically consistent with the actual situation, which can meet the requirements of general application. Focused on the limitation of MODIS data quality and the scale difference between single point measurement data and satellite data, the accuracy of soil moisture estimation still need to be improved. At the same time, due to the lack of long-term measured data of soil moisture, the spatiotemporal scalability of soil moisture inversion model needs to be further verified. Optical remote sensing is greatly affected by the weather, while microwave remote sensing can be the alternative choices for all-weather observation. The multi-source remote sensing data fusion should be attribute more attention to soil moisture retrieval model construction.关键词：soil moisture;the Shandian river;extreme random trees;MODIS;STR- 628
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发布时间：2021-04-20 -
摘要：Soil moisture derived from passive microwave remote sensing has a low spatial resolution, which cannot meet the requirement for the application of regional hydrological and agricultural activities, such as meteorological forecast, flood forecast, and irrigation management. The integrated microwave load of radar and radiometer loaded on “land-water resource satellite,” which is planned and demonstrated by Chinese civil space infrastructure, will obtain high-resolution soil moisture with the combination of active and passive microwave observation by using a one-dimensional synthetic aperture imaging observation mode. However, the incidence angle changes during observation. Present downscaling algorithms with combination of active and passive microwave observations are developed under the condition of fixed incident angles of radar and radiometer. In this study, downscaling algorithms based on time series regression analysis and spectral analysis are tested using the air flight experimental data of a lightning river basin to demonstrate that the feasibility of each for application to the incidence angles of radiometer and radar differs.The downscaling method based on time series regression analysis is the basic algorithm for Soil Moisture Active and Passive. The principal theoretical basis for this method is the linear relationship between the brightness temperature observation of radiometer and the backscatter coefficient observation of radar, and the linear relationship is related to surface roughness, vegetation, and incident angle. The spectral analysis downscaling method was first proposed and applied to remote sensing water vapor downscaling. Its theoretical basis is to obtain a high-resolution image by correctly simulating the spatial characteristics of the image spectral domain.The downscaling results based on active and passive observation regression analysis can reproduce spatial details, but the root-mean-square error (RMSE) is large. The minimum RMSE of V polarization is 7.57 K, and the minimum RMSE of H polarization is 7.46 K. The downscaling results based on spectral analysis can basically reflect the spatial distribution of the original observation. Nevertheless, evident plate phenomena occur in some areas, the spatial transition is not smooth, and the traces of downscaling are obvious. The minimum RMSE of H polarization is 7.13 K, and that of V polarization is 6.61 K. In accordance with the RMSE, the overall accuracy of the spectral downscaling method is higher than that of the time series regression analysis.The downscaling method based on the regression analysis of active and passive observation depends on the time series observation of active and passive microwave observation. The method of spectrum analysis does not need to regress to determine the relationship between active and passive microwave. It directly uses low-resolution passive radiometer observation and high-resolution radar copolarization (vv) observation for downscaling and does not need to rely on long-term time observation. The experimental results indicate that the time series regression analysis downscaling method can obtain the best results when the incident angle of radiometer is 27.5° and that of radar is 52.5° or 55°. The minimum RMSEs of V and H polarizations are 7.57 K and 7.46 K, respectively. The minimum RMSEs of V and H polarizations are 7.13 K and 6.61 K, respectively, for the spectral analysis downscaling method, which are 0.44k and 0.85 K, respectively, lower than those of the time series regression analysis downscaling method.关键词：microwave remote sensing;radiometer;radar;downscaling;soil water- 651
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发布时间：2021-04-20 -
摘要：Soil Moisture (SM) is not only an important variable in land surface processes, but also a key parameter in global water cycle. In this paper, the objectives are: (1) downscaling SMAP (Soil Moisture Active Passive) SM (SMAP SM) from spatial resolution of 9 km to 1 km, with the using of the auxiliary data from MODIS (Moderate-Resolution Imaging Spectroradiometer) products (land surface temperature and normalized difference vegetation index) by a downscaling method based on self-adaptive window in Shandian river basin; (2) validating the downscaled SM with the in-situ SM and the airborne passive microwave SM (airborne SM); and (3) analyzing the uncertainty caused by auxiliary data and SM estimated model in the downscaling process. The downscaling method used in this paper involves two steps. The SM model was established by using geographically weighted regression model between SMAP SM and the auxiliary data to calculate the 1-km estimated model SM (SMR). Then the 9-km residual (RC) generated by the SM estimated model is downscaled to 1-km spatial resolution (RF) by area-to-point kriging. Finally, the downscaled SM (SMF) is the sum of SMR and RF. It’s worth noting that to derive the robust downscaled SM, self-adaptive windows are adopted in these two steps. Visual assessment shows that the downscaling method can not only improve the spatial resolution of SMAP SM, but also retain the consistency between the spatial distributions of the downscaled SM and of the original SMAP SM. The validation results of the airborne SM, the SMAP SM and the downscaled SM against the in-situ SM are not satisfactory. On Sep 24, the correlation coefficient (R) between the three SM data and the in-situ SM are less than 0.5, and on Sep 26, the root mean squared errors (RMSE) are greater than 0.08 m3/m3. By analyzing these data, we found that the limited amount of valid data used in validation was one of the reasons for the poor validation. In addition, the different spatial representativeness and the inconsistent spatial matching of point-scale data and pixel-scale data are also the factors caused the uncertainty in the validation results. Compared with the in-situ SM, the SMAP SM and the downscaled SM have better correlations with the airborne SM. The RMSEs between the downscaled SM and the airborne SM are about 0.04 m3/m3, while the RMSEs between the SMAP SM and the airborne SM are less than 0.04 m3/m3. The correlation between the SMAP SM and auxiliary data (the absolute values of Rs are greater than 0.6) is higher than that between the airborne SM and the auxiliary data (the absolute values of Rs are less than 0.53). It can be seen that there are some differences between the SMAP SM and the airborne SM, which is mainly affected by different spatial scales, observation configurations, SM derived algorithms and auxiliary data using in algorithms of these two SM data. However, more studies are needed on the mechanism of the relationship between auxiliary data and SM in the downscaling process. By adding auxiliary data (land surface albedo) or changing the SM estimation model, the validated results of the downscaled SM against the airborne SM did not improve obviously. This is mainly because more auxiliary data and higher polynomials caused overfitting in the downscaling process, which will be still the focus of future research.关键词：soil moisture;spatial downscaling;airborne passive microwave soil moisture;uncertainty analysis;SMAP;MODIS- 702
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发布时间：2021-04-20 -
摘要：SMAP (Soil Moisture Active and Passive) with a grid resolution of 9 km, ASCAT (Advanced Scatterometer) with a grid resolution of 0.1D (Degree), FY-3B and ESA-CCI (European Space Agency-Climate Change Initiative) with a grid resolution of 25 km, these satellite soil moisture (SM) products were widely used in drought monitoring, evapotranspiration estimation etc. applications. It is very important to evaluate these satellite SM products before using them. In this study, the approximate synchronous in-situ SM measurements (point-scale) and airborne observation-based soil moisture data (area-scale airborne SM with a grid resolution of 1 km) from the Comprehensive Remote Sensing Experiment of Water Cycle and Energy Balance in the Shandian river basin of Inner Mongolia in September, 2018 were used to evaluate the SMAP, ASCAT, FY-3B, ESA-CCI satellite SM products using RMSE (Mean Square Root Error), R (Correlation Coefficient), MAE (Mean Absolute Error), Bias and ubRMSE (unbiased Mean Square Root Error) etc. evaluation indexes. This study achieved the evaluation process from the point-scale in-situ SM measurements to the area-scale (1 km×1 km) airborne SM data to the area-scale (9 km×9 km, 0.1 D×0.1 D, 25 km×25 km) satellite SM products using airborne SM as the bridge between in-situ SM measurements and satellite SM products. The results dedicated that in bare soil area the airborne SM was more consistent with the in-situ SM measurements, the RMSE, MAE, Bias and ubRMSE and R values were 0.033 cm3/cm3, 0.030 cm3/cm3, -0.004 cm3/cm3, 0.033 cm3/cm3 and 0.474 respectively. Comparing with ASCAT, FY-3B and ESA-CCI satellite SM products, the SMAP satellite SM product were more consistent with in-situ SM measurements, RMSE, MAE, Bias, ubRMSE and R values were 0.037 cm3/cm3, 0.032 cm3/cm3, -0.008 cm3/cm3, 0.036 cm3/cm3 and 0.507 respectively. In addition, the R values between the satellite SM products (SMAP, ASCAT, FY-3B and ESA-CCI) and airborne SM data were higher, 0.735, 0.558, 0.558 and 0.575 respectively. Overall, the SMAP satellite SM product was more consistent with in-situ SM measurements and airborne SM in Shandian river basin area, then FY-3B and ESA-CCI SM products.关键词：soil moisture;SMAP (Soil Moisture Active and Passive);ASCAT (Advanced Scatterometer);FY-3B;ESA-CCI- 681
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发布时间：2021-04-20 -
摘要：The actual EvapoTranspiration (ET) is an important ecohydrological process that links the land surface water cycle, energy balance, and carbon budget, and it remains as one of the most uncertainty process in the global water cycle research. One key point to obtain accurate ET is soil water stress, which is also one of the most difficult points. Hence, the current study is proposed to study the impact of soil moisture on the ET estimation, considering the large uncertainty of satellite remote sensing soil moisture products.In the current study, six satellite remote sensing soil moisture products were collected and downscaled to 1 km resolution, including SMAP (Soil Moisture Active and Passive), SMOS (Soil Moisture Ocean Salinity), ASCAT (The Advanced Scatterometer), FY-3B and FY-3C (Chinese Feng Yun satellite 3 B and 3 C), and ESA CCI (European Space Agency Climate Change Initiative). All soil moisture data were input to ETMonitor algorithm to obtain the corresponding ET estimation. The ETMonitor model has been proven to be able to generate accurate regional and global ET estimation, and the surface soil moisture data derived by microwave remote sensing is set as an important input for the ETMonitor model to estimate the surface resistance to account the constraining of soil moisture on ET. Meanwhile, to validate the satellite remote sensing soil moisture and corresponding ET estimation, we collected the ground observation-based ET and soil moisture data and the airborne observation-based soil moisture in September of 2018 based on the Comprehensive Remote Sensing Experiment of Water Cycle and Energy Balance in the Shandian River Basin. The daily observed ET is calculated by the 30-min latent heat flux obtained by an installed close-path eddy covariance system.The satellite remote sensing soil moisture and ET time series were compared with the ground observation during September 2018 to illustrate their accuracy to reflect their temporal variations. It’s found that the Chinese FY-3C soil moisture data shows the highest correlation with the ground observation, while SMAP soil moisture data shows the lowest root mean square error with the ground observation. The ASCAT data overestimates the soil moisture significantly, while SMOS underestimates the soil moisture. Hence, the ET estimated based on ASCAT soil moisture data also overestimates ET significantly, and ET estimated based on SMOS tend to underestimate ET. It’s also noted that ET estimated based on Chinese FY-3C soil moisture, ESA CCI combined soil moisture, and SMAP soil moisture show the highest correlation and lowest root mean square error when comparing with ground observation. While the accuracies of ET estimated based on SMOS and ASCAT soil moisture products are lower based on their relatively large bias. The error of soil moisture is not linearly propagated to estimated ET. When the soil moisture is higher, e.g. higher than the field capacity, soil moisture does not stress the ET progress anymore, and the overestimation of soil moisture will not be propagated to the estimated ET.The spatial variations of satellite remote sensing soil moisture after downscaling were compared with the airborne soil moisture, and general good correlation could be found among them. And ET estimated based on SMAP showed the highest correlation with ET estimated based on airborne soil moisture. Generally, all the satellite remote sensing soil moisture products are higher than the airborne soil moisture except SMOS, which is lower than the airborne soil moisture when soil moisture is high. Hence the ET values estimated based on satellite remote sensing soil moisture products are also higher than the ET estimated based on airborne soil moisture expect for the ET estimated based on SMOS.Overall, the current study contributed to assess the uncertainty of satellite remote sensing soil moisture products and how it is propagated to the estimated ET. And it could also guide to obtain accurate regional and global ET products.关键词：evapotranspiration;soil moisture;airborne measurement;Shandian river basin;ETMonitor- 841
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发布时间：2021-04-20
Water Cycle and Energy Balance
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摘要：A 30 m-spatial-resolution LAI time series estimation method was proposed on the basis of the ensemble Kalman filter (EnKF). Time series LAI of 2000—2018 was produced in the Saihanba area, and vegetation change monitoring was applied. The detected disturbance was consistent with climate condition and field management.Time series LAI is critical for vegetation growth monitoring, surface process simulation, and global change research. Saihanba is an important ecological environment protection area in China, and long-term monitoring of this area is significant for forest management and development.In this study, MODIS LAI products and Landsat surface reflectance data were used to generate time series high-resolution LAI datasets from 2004 to 2018 in Saihanba by using EnKF. Vegetation changes were then monitored on the basis of the generated LAI time series with the Prophet model. First, the multistep Savitzky-Golay filtering algorithm was used to smooth the MODIS LAI data, and the upper envelope of time series LAI was generated. A dynamic model was constructed in accordance with the trend of LAI upper envelope to provide a short-range forecast of LAI. Then, the ground measured LAI data and the corresponding Landsat reflectance data were used to train a Back Propagation (BP) neural network. The high-resolution LAI data from the BP model were used to update the dynamic model in real time to generate high-resolution time series LAI data based on the EnKF method. Lastly, the time series LAI data were used as the input of the Prophet deep learning model to obtain the LAI time series prediction values of a certain year. The correlation coefficient and root-mean-square error distribution maps could be obtained from the comparison of the prediction results with the LAI of the current year. A Support Vector Machine (SVM) method was used to classify the disturbed and normal pixels.The EnKF algorithm can generate continuous high-resolution LAI data, and the estimation results are consistent with the field LAI values with R2 of 0.9498 and RMSE of 0.1577. At the regional scale, the estimation LAI maps have high consistency with the Landsat reference LAI maps, the R2 is higher than 0.87, and the RMSE is less than 0.61. The Prophet and SVM models detected that the vegetation in Saihanba was severely disturbed in 2009, 2010, 2013, 2014, and 2015, mainly due to the low annual rainfall and deforestation. The detection results are consistent with the local precipitation and logging data.The algorithm proposed in this paper can be used for time series high-spatial-resolution LAI data inversion on a large scale, and the inversion results can be used for vegetation change detection. This work has important reference significance for the planning and management of Saihanba and even the national forest area.关键词：leaf area index;time series high resolution;the Ensemble Kalman Filter algorithm;deep learning method;change detection- 780
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发布时间：2021-04-20 -
摘要：Multiangle remote sensing observation is critical for the study of the Bidirectional Reflectance Distribution Function (BRDF) of vegetation canopies. However, sampling the bidirectional reflectance of forest canopies at a small fixed angular interval over a certain observation plane is still difficult at present, and capturing hotspot and dark spot images is especially challenging. The objectives of this study are (1) to obtain bidirectional hyperspectral reflectance images, including hotspot and dark spot, at the principal plane for two conifer forest canopies, (2) to analyze the observed canopy BRDF characteristics, and (3) to compare and validate the observation with four-scale geometric optical model simulation.The two coniferous forest sites are located in the Saihanba Mechanical Forest Farm in Hebei Province in northern China. A multiangle hyperspectral observation method was developed on the basis of an Unmanned Aerial Vehicle (UAV) imaging platform. A hyperspectral imaging sensor was mounted on the multirotor UAV with a rotatable gimbal. The View Zenith Angle (VZA) of the hyperspectral sensor was controlled by adjusting the pitch angle of the gimbal at each observation point over the flight of the UAV. The VZAs ranged from 60° backward to 60° forward with an interval of 10°, along with the hotspot and dark spot angles. The observed UAV images were processed from digital numbers to reflectance by using a standard gray panel with known reflectance placed in the observation area. The UAV images were then resampled to 60 m to examine the reflectance at the canopy scale. Canopy reflectance was also simulated with the four-scale model and compared with the observation.Bidirectional reflectance, including hotspot and dark spot, images of conifer forest canopies were effectively observed using the developed multiangle UAV observation method. This method has the advantage of making hyperspectral and multiangular observations at the same time. The canopy reflectance values are higher at backward observation and highest at the hotspot direction compared with those at forward directions. The observed BRDF shapes differ at the two study sites with different canopy structure and leaf optical properties. The bowl effect with increasing reflectance with VZA is observed when the VZAs are larger than 40°. The results are as follows: (1) The BRDF shapes and canopy reflectance simulated using a four-scale model are consistent with the observation, except for a minimal underestimation at the hotspot in the red spectral bands and some deviation in the near infrared (NIR) bands at large VZAs in the forward direction; (2) The BRDF shapes differ in forests with different canopy structure and leaf optical properties; (3) The observed bidirectional reflectance of the two coniferous forests demonstrates distinct spectral variability of BRDF effects. The reflectance anisotropy is highest in the red bands and lowest in the NIR bands; and (4) Anisotropy of vegetation indices, including normalized difference vegetation index, photochemical reflectance index, MERIS terrestrial chlorophyll index, and enhanced vegetation index, is observed due to the spectral variability of BRDF effects.The multiangle UAV platform is able to capture bidirectional reflectance images effectively, especially in the hotspot and dark spot directions. Compared with satellite-based, aerial, and tower- or ground-based multiangle observation methods, the UAV platform is more effective and flexible with a much lower labor and financial cost. However, the application of this UAV platform at a large spatial scale is limited due to the small coverage of UAV images compared with satellite images. The multiangle UAV platform has great potential in the research of bidirectional characteristics of various targets.关键词：anisotropy;BRDF;multi-angle remote sensing;hotspot;Unmanned Aerial Vehicle (UAV);hyperspectral remote sensing;conifer forest- 794
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发布时间：2021-04-20 -
Estimation of water content for short vegetation based on PROSAIL model and vegetation water indices
摘要：As the dominant component by weight of live vegetation, vegetation moisture is one of the main factors determining plant photosynthesis, respiration, and biomass. Based on the type of remote sensed data used, retrieval algorithms for vegetation moisture retrieving algorithms fall into two categories, i.e., microwave-data-based methods and optical-data-based methods. However, microwave-based methods are always characterized by low spatial resolutions and often have difficulty in separating out vegetation and soil signals. On the contrary, because of the high spatial resolution and good sensitivity to green vegetation, optical remote sensing techniques have been the baseline method for estimating Vegetation Water Content (VWC) of short vegetation (i.e., Canopy Water Content, CWC). Here, we try to set up a universal, accurate and easy-to-apply way of retrieving CWC/VWC of short vegetation based on simulations from the PROSAIL model and generalized normalized Difference water index (NDWI), i.e., spectral indices taking the form of the NDWI formula.The new proposed method is based on PROSAIL model and four NDWI variants, i.e., NDWI(860,970), NDWI(860,1240), NDWI(860,1640) and NDWI(1240,1640). First, the parameter sensitivity analysis is carried out to determine their different influence mechanisms on the output reflectance and to optimize the PROSAIL model's input parameters. After that, canopy reflectance simulations are generated for short vegetation. According to the simulated reflectance, simulations of the four NDWI variants are derived, which were used to construct relationships with the simulated CWC and VWC of short vegetation. It is found that, instead of the linear relationship derived in previous studies, the simulated CWC/VWC is best approximated as an exponential function of NDWI. Following the analysis of the PROSAIL-generated results, a newly NDWI-based scheme is proposed for estimating CWC for short vegetation. Furthermore, VWC can also be estimated by combining the empirical relationship between VWC and CWC.Results derived from simulations show that the four NDWI variants are all linear related to ln(CWC), which were further used as CWC retrieving models. Moreover, the CWC retrieving models can also be used for VWC retrieving by combining the empirical relationship between VWC and CWC. Results derived from simulations also indicate that since NDWI(860,1640) and NDWI(1240,1640) are highly correlated (R2=0.99), both of the two variants can provide similar and relatively good CWC estimation accuracy. The validation results based on ground measurements show good consistency with simulated results, i.e., the VWC estimates from NDWI(860,1640) and NDWI(1240,1640) variants have high accuracy with both R2=0.88 and RMSE respectively of 0.4558 kg/m2 and 0.4380 kg/m2. The validation results based on Landsat 5 TM datasets also show that the R2 between CWC estimates and CWC ground measurements is 0.84, with a corresponding RMSE of 0.1342 kg/m2，while the RMSE between VWC estimates and VWC ground measurements is 0.5651 kg/m2.The proposed NDWI-based scheme for retrieving CWC/VWC of short vegetation is easy to implement and highly accurate. It can also be applied to agriculture for crop growth monitoring and drought indication. The estimation framework is also useful for CWC/VWC estimation of other short vegetation types. Moreover, since crop cover remains a challenging land cover for satellite-based soil moisture retrieval, this method can also be used to improve the quality of cropland vegetation information available as an ancillary input data for microwave-based soil moisture retrieval algorithms.关键词：optical remote sensing;PROSAIL;canopy water content;vegetation water content;vegetation water index;short vegetation- 996
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发布时间：2021-04-20
Carbon Cycle
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