Rapid urbanization and continuous loss of rural labor force has resulted in abandonment of large areas of farmland in some regions of China. Remote sensing technology can indirectly help detect abandoned farmland size...Rapid urbanization and continuous loss of rural labor force has resulted in abandonment of large areas of farmland in some regions of China. Remote sensing technology can indirectly help detect abandoned farmland size and quantity, which is of great significance for farmland protection and food security. This study took Qingyun and Wudi counties in Shandong Province as a study area and used CART decision tree classification to compile land use maps of 1990–2017 based on Landsat and HJ-1 A data. We developed rules to identify abandoned farmland, and explored its spatial distribution, duration, and reclamation. CART accuracy exceeded 85% from 1990–2017. The maximum abandoned farmland area was 5503.86 ha during 1992–2017, with the maximum rate being 5.37%. Farmland abandonment rate was the highest during 1996–1998, and abandonment trend decreased year by year after 2006. Maximum abandonment duration was 15 years(1992–2017), mostly within 4 years and only a few exceeded 10 years. From 1993–2017, the maximum reclaimed abandoned farmland was 2022.3 ha, and the minimum ～20 ha. The maximum reclamation rate was 67.44%m, with annual average rate being 31.83%. This study will help analyze farmland abandonment driving forces in the study area and also provide references to identify abandoned farmland in other areas.展开更多
Impervious surface(IS) is often recognized as the indicator of urban environmental changes. Numerous research efforts have been devoted to studying its spatio-temporal dynamics and ecological effects, especially for t...Impervious surface(IS) is often recognized as the indicator of urban environmental changes. Numerous research efforts have been devoted to studying its spatio-temporal dynamics and ecological effects, especially for the IS in Beijing metropolitan region. However, most previous studies primarily considered the Beijing metropolitan region as a whole without considering the differences and heterogeneity among the function zones. In this study, the subpixel impervious surface results in Beijing within a time series(1991, 2001, 2005, 2011 and 2015) were extracted by means of the classification and regression tree(CART) model combined with change detection models. Then based on the method of standard deviation ellipse, Lorenz curve, contribution index(CI) and landscape metrics, the spatio-temporal dynamics and variations of IS(1991, 2001, 2011 and 2015) in different function zones and districts were analyzed. It is found that the total area of impervious surface in Beijing increased dramatically during the study period, increasing about 144.18%. The deflection angle of major axis of standard deviation ellipse decreased from 47.15° to 38.82°, indicating the major development axis in Beijing gradually moved from northeast-southwest to north-south. Moreover, the heterogeneity of impervious surface’s distribution among 16 districts weakened gradually, but the CI values and landscape metrics in four function zones differed greatly. The urban function extended zone(UFEZ), the main source of the growth of IS in Beijing, had the highest CI values. Its lowest CI value was 1.79 that is still much higher than the highest CI value in other function zones. The core function zone(CFZ), the traditional aggregation zone of impervious surface, had the highest contagion index(CONTAG) values, but it contributed less than UFEZ due to its small area. The CI value of the new urban developed zone(NUDZ) increased rapidly, and it increased from negative to positive and multiplied, becoming animportant contributor to the rise of urban impervious surface. However, the ecological conservation zone(ECZ) had a constant negative contribution all the time, and its CI value decreased gradually. Moreover, the landscape metrics and centroids of impervious surface in different density classes differed greatly. The high-density impervious surface had a more compact configuration and a greater impact on the eco-environment.展开更多
Taxus wallichiana Zucc.(Himalayan yew)is subject to international and national conservation measures because of its over-exploitation and decline over the last 30 years.Predicting the impact of climate change on T.wal...Taxus wallichiana Zucc.(Himalayan yew)is subject to international and national conservation measures because of its over-exploitation and decline over the last 30 years.Predicting the impact of climate change on T.wallichiana’s distribution might help protect the wild populations and plan effective ex situ measures or cultivate successfully.Considering the complexity of climates and the uncertainty inherent in climate modeling for mountainous regions,we integrated three Representative Concentration Pathways(RCPs)(i.e.,RCP2.6,RCP4.5,RCP8.5)based on datasets from 14 Global Climate Models of Coupled Model Intercomparison Project,Phase 5 to:(1)predict the potential distribution of T.wallichiana under recent past(1960–1990,hereafter‘‘current’’)and future(2050s and 2070s)scenarios with the species distribution model MaxEnt.;and(2)quantify the climatic factors influencing the distribution.In respond to the future warming climate scenarios,(1)highly suitable areas for T.wallichiana would decrease by 31–55%at a rate of 3–7%/10a;(2)moderately suitable areas would decrease by 20–30%at a rate of 2–4%/10a;(3)the average elevation of potential suitable sites for T.wallichiana would shift upslope by 390 m(15%)to 948 m(36%)at a rate of 42–100 m/10a.Average annual temperature(contribution rate ca.61%),isothermality and temperature seasonality(20%),and annual precipitation(17%)were the main climatic variables affecting T.wallichiana habitats.Prior protected areas and suitable planting areas must be delimited from the future potential distributions,especially the intersection areas at different suitability levels.It is helpful to promote the sustainable utilization of this precious resource by prohibiting exploitation and ex situ restoring wild resources,as well as artificially planting considering climate suitability.展开更多
Angstrom-Prescott equation(AP)is the algorithm recommended by the Food and Agriculture Organization(FAO)of the United Nations for calculating the surface solar radiation(R_(s))to support the estimation of crop evapotr...Angstrom-Prescott equation(AP)is the algorithm recommended by the Food and Agriculture Organization(FAO)of the United Nations for calculating the surface solar radiation(R_(s))to support the estimation of crop evapotranspiration.Thus,the a_(s) and b_(s) coefficients in the AP are vital.This study aims to obtain coefficients a_(s) and b_(s) in the AP,which are optimized for Chinas comprehensive agricultural divisions.The average monthly solar radiation and relative sunshine duration data at 121 stations from 1957-2016 were collected.Using data from 1957 to 2010,we calculated the monthly a_(s) and b_(s) coefficients for each subregion by least-squares regression.Then,taking the observation values of R_(s) from 2011 to 2016 as the true values,we estimated and compared the relative accuracy of R_(s) calculated using the regression values of coefficients a_(s) and b_(s) and that calculated with the FAO recommended coefficients.The monthly coefficients,a_(s) and b_(s),of each subregion are significantly different,both temporally and spatially,from the FAO recommended coefficients.The relative error range(0-54%)of R_(s) calculated via the regression values of the a_(s) and b_(s) coefficients is better than the relative error range(0-77%)of R_(s) calculated using the FAO suggested coefficients.The station-mean relative error was reduced by 1% to 6%.However,the regression values of the a_(s) and b_(s) coefficients performed worse in certain months and agricultural subregions during verification.Therefore,we selected the a_(s) and b_(s) coefficients with the minimum R_(s) estimation error as the final coefficients and constructed a coefficient recommendation table for 36 agricultural production and management subregions in China.These coefficient recommendations enrich the case study of coefficient calibration for the AP in China and can improve the accuracy of calculating R_(s) and crop evapotranspiration based on existing data.展开更多
The Palmer drought severity index （PDSI）, standardized precipitation index （SPI）, and standardized precipitation evapotranspiration index （SPEI） are used worldwide for drought assessment and monitoring. However,...The Palmer drought severity index （PDSI）, standardized precipitation index （SPI）, and standardized precipitation evapotranspiration index （SPEI） are used worldwide for drought assessment and monitoring. However, substantial differences exist in the performance for agricultural drought among these indices and among regions. Here, we performed statistical assessments to compare the strengths of different drought indices for agricultural drought in the North China Plain. Small differences were detected in the comparative performances of SPI and SPEI that were smaller at the long-term scale than those at the short-term scale. The correlation between SPI/SPEI and PDSI considerably increased from 1- to 12-month lags, and a slight decreasing trend was exhibited during 12- and 24-month lags, indicating a 12-month scale in the PDSI, whereas the SPI was strongly correlated with the SPEI at 1- to 24-month lags. Interestingly, the correlation between the trend of temperature and the mean absolute error and its correlation coefficient both suggested stronger relationships between SPI and the SPEI in areas of rapid climate warming. In addition, the yield-drought correlations tended to be higher for the SPI and SPEI than that for the PDSI at the station scale, whereas small differences were detected between the SPI and SPEI in the performance on agricultural systems. However, large differences in the influence of drought conditions on the yields of winter wheat and summer maize were evident among various indices during the crop-growing season. Our findings suggested that multi-indices in drought monitoring are needed in order to acquire robust conclusions.展开更多
Background: The stem curve of standing trees is an essential parameter for accurate estimation of stem volume.This study aims to directly quantify the occlusions within the single-scan terrestrial laser scanning(TLS) ...Background: The stem curve of standing trees is an essential parameter for accurate estimation of stem volume.This study aims to directly quantify the occlusions within the single-scan terrestrial laser scanning(TLS) data,evaluate its correlation with the accuracy of the retrieved stem curves, and subsequently, to assess the capacity of single-scan TLS to estimate stem curves.Methods: We proposed an index, occlusion rate, to quantify the occlusion level in TLS data. We then analyzed three influencing factors for the occlusion rate: the percentage of basal area near the scanning center, the scanning distance and the source of occlusions. Finally, we evaluated the effects of occlusions on stem curve estimates from single-scan TLS data.Results: The results showed that the correlations between the occlusion rate and the stem curve estimation accuracies were strong(r = 0.60–0.83), so was the correlations between the occlusion rate and its influencing factors(r = 0.84–0.99). It also showed that the occlusions from tree stems were the main factor of the low detection rate of stems, while the non-stem components mainly influenced the completeness of the retrieved stem curves.Conclusions: Our study demonstrates that the occlusions significantly affect the accuracy of stem curve retrieval from the single-scan TLS data in a typical-size(32 m × 32 m) forest plot. However, the single-scan mode has the capacity to accurately estimate the stem curve in a small forest plot(< 10 m × 10 m) or a plot with a lower occlusion rate, such as less than 35% in our tested datasets. The findings from this study are useful for guiding the practice of retrieving forest parameters using single-scan TLS data.展开更多
Background:The universal occurrence of randomly distributed dark holes(i.e.,data pits appearing within the tree crown)in LiDAR-derived canopy height models(CHMs)negatively affects the accuracy of extracted forest inve...Background:The universal occurrence of randomly distributed dark holes(i.e.,data pits appearing within the tree crown)in LiDAR-derived canopy height models(CHMs)negatively affects the accuracy of extracted forest inventory parameters.Methods:We develop an algorithm based on cloth simulation for constructing a pit-free CHM.Results:The proposed algorithm effectively fills data pits of various sizes whilst preserving canopy details.Our pitfree CHMs derived from point clouds at different proportions of data pits are remarkably better than those constructed using other algorithms,as evidenced by the lowest average root mean square error(0.4981 m)between the reference CHMs and the constructed pit-free CHMs.Moreover,our pit-free CHMs show the best performance overall in terms of maximum tree height estimation(average bias=0.9674 m).Conclusion:The proposed algorithm can be adopted when working with different quality LiDAR data and shows high potential in forestry applications.展开更多
China is a country largely affected by desertification.The main purpose of this article is to analyze interannual and seasonal changes in fractional vegetation cover(FVC)in the Mu Us Sandy Land(MUSL).It uses fused rem...China is a country largely affected by desertification.The main purpose of this article is to analyze interannual and seasonal changes in fractional vegetation cover(FVC)in the Mu Us Sandy Land(MUSL).It uses fused remote sensing data to quantitatively analyze the response of FVC to climate change and human activities.The results showed that desertification in the MUSL had improved over the past 20 years.Grade V desertification decreased from more than 60%in 2000 to about 15%in 2020.In some years,degradation appeared to be affected by climate factors and human activity,especially in the northwestern portion of the study area.The FVC in summer was slightly higher than that in autumn and far higher than recorded in spring and winter.Spatially,the northwestern and central parts of the study area were unstable,with high coefficients of variation.FVC gradually increased from northwest to southeast,and areas with the fastest increase in FVC were concentrated along the eastern and southern edges of the study area.The correlations between FVC and precipitation and dryness were slightly pos-itive,but the correlation between FVC and temperature showed regional differences.The increase of population density is not a key factor limiting the growth of vegetation;the policy of“grazing prohibition,grazing rest,and rotational grazing”has allowed the restoration of vegetation;and afforestation is an effective way to promote the increase in FVC.展开更多
Long-term mapping of winter wheat is vital for assessing food security and formulating agricultural policies.Landsat data are the only available source for long-term winter wheat mapping in the North China Plain due t...Long-term mapping of winter wheat is vital for assessing food security and formulating agricultural policies.Landsat data are the only available source for long-term winter wheat mapping in the North China Plain due to the fragmented landscape in this area.Although various methods,such as index-based methods,curve similarity-based methods and machine learning-based methods,have been developed for winter wheat mapping based on remote sensing,the former two often require satellite data with high temporal resolution,which are unsuitable for Landsat data with sparse time-series.Machine learning is an effective method for crop classification using Landsat data.Yet,applying machine learn-ing for winter wheat mapping in the North China Plain encounters two main issues:1)the lack of adequate and accurate samples for classifier training;and 2)the difficulty of training a single classifier to accomplish the large-scale crop mapping due to the high spatial heterogeneity in this area.To address these two issues,we first designed a sample selection rule to build a large sample set based on several existing crop maps derived from recent Sentinel data,with specific consideration of the confusion error between winter wheat and winter rapeseed in the available crop maps.Then,we developed an optimal zoning method based on the quadtree region splitting algorithm with classification feature consistency criterion,which divided the study area into six subzones with uni-form classification features.For each subzone,a specific random forest classifier was trained and used to generate annual winter wheat maps from 2013 to 2022 using Landsat 8 OLI data.Field sample validation confirmed the high accuracy of the produced maps,with an average overall accuracy of 91.1%and an average kappa coefficient of 0.810 across different years.The derived winter wheat area also has a good correlation(R2=0.949)with census area at the provincial level.The results underscore the reliability of the produced annual winter wheat maps.Additional experiments demonstrate that our proposed optimal zoning method outper-forms other zoning methods,including Köppen climate zoning,wheat planting zoning and non-zoning methods,in enhancing wheat mapping accuracy.It indicates that the proposed zoning is capable of generating more reasonable subzones for large-scale crop mapping.展开更多
Land surface all-wave net radiation(R_(n))is crucial in determining Earth’s climate by contributing to the surface radiation budget.This study evaluated seven satellite and three reanalysis long-term land surface R_(...Land surface all-wave net radiation(R_(n))is crucial in determining Earth’s climate by contributing to the surface radiation budget.This study evaluated seven satellite and three reanalysis long-term land surface R_(n)products under different spatial scales,spatial and temporal variations,and different conditions.The results showed that during 2000-2018,Global Land Surface Satellite Product(GLASS)-Moderate Resolution Imaging Spectroradiometer(MODIS)performed the best(RMSE=25.54 Wm^(-2),bias=-1.26 Wm^(-2)),followed by ERA5(the fifth-generation of European Centre for Medium-Range Weather Forecast Reanalysis)(RMSE=32.17 Wm^(-2),bias=-4.88 Wm^(-2))and GLASS-AVHRR(Advanced Very-High-Resolution Radiometer)(RMSE=33.10 Wm^(-2),bias=4.03 Wm^(-2)).During 1983-2018,GLASS-AVHRR and ERA5 ranked top and performed similarly,with RMSE values of 31.70 and 33.08 Wm^(-2)and biases of-4.56 and 3.48 Wm^(-2),respectively.The averaged multi-annual mean R_(n)over the global land surface of satellite products was higher than that of reanalysis products by about 10~30 Wm^(-2).These products differed remarkably in long-term trends variations,particularly pre-2000,but no significant trends were observed.Discrepancies were more frequent in satellite data,while reanalysis products showed smoother variations.Large discrepancies were found in regions with high latitudes,reflectance,and elevation which could be attributed to input radiative components,meteorological variables(e.g.,cloud properties,aerosol optical thickness),and applicability of the algorithms used.While further research is needed for detailed insights.展开更多
Agricultural applications of remote sensing data typically require high spatial resolution and frequent observations.The increasing availability of high spatial resolution imagery meets the spatial resolution requirem...Agricultural applications of remote sensing data typically require high spatial resolution and frequent observations.The increasing availability of high spatial resolution imagery meets the spatial resolution requirement well.However,the long revisit period and frequent cloud contamination severely compromise their ability to monitor crop growth,which is characterized by high temporal heterogeneity.Many spatiotemporal fusion methods have been developed to produce synthetic images with high spatial and temporal resolutions.However,these existing methods focus on fusing low and medium spatial resolution satellite data in terms of model development and validation.When it comes to fusing medium and high spatial resolution images,the applicability remains unknown and may face various challenges.To address this issue,we propose a novel spatiotemporal fusion method,the dual-stream spatiotemporal decoupling fusion architecture model,to fully realize the prediction of high spatial resolution images.Compared with other fusion methods,the model has distinct advantages:(a)It maintains high fusion accuracy and good spatial detail by combining deep-learning-based super-resolution method and partial least squares regression model through edge and color-based weighting loss function;and(b)it demonstrates improved transferability over time by introducing image gradient maps and partial least squares regression model.We tested the StarFusion model at 3 experimental sites and compared it with 4 traditional methods:STARFM(spatial and temporal adaptive reflectance fusion),FSDAF(flexible spatiotemporal data fusion),Fit-FC(regression model fitting,spatial filtering,and residual compensation),FIRST(fusion incorporating spectral autocorrelation),and a deep learning base method-super-resolution generative adversarial network.In addition,we also investigated the possibility of our method to use multiple pairs of coarse and fine images in the training process.The results show that multiple pairs of images provide better overall performance but both of them are better than other comparison methods.Considering the difficulty in obtaining multiple cloud-free image pairs in practice,our method is recommended to provide high-quality Gaofen-1 data with improved temporal resolution in most cases since the performance degradation of single pair is not significant.展开更多
Spatiotemporal data fusion technologies have been widely used for land surface phenology(LSP)monitoring since it is a low-cost solution to obtain fine-resolution satellite time series.However,the reliability of fused ...Spatiotemporal data fusion technologies have been widely used for land surface phenology(LSP)monitoring since it is a low-cost solution to obtain fine-resolution satellite time series.However,the reliability of fused images is largely affected by land surface heterogeneity and input data.It is unclear whether data fusion can really benefit LSP studies at fine scales.To explore this research question,this study designed a sophisticated simulation experiment to quantify effectiveness of 2 representative data fusion algorithms,namely,pair-based Spatial and Temporal Adaptive Reflectance Fusion Model(STARFM)and time series-based Spatiotemporal fusion method to Simultaneously generate Full-length normalized difference vegetation Index Time series(SSFIT)by fusing Landsat and Moderate Resolution Imaging Spectroradiometer(MODIS)data in extracting pixel-wise spring phenology(i.e.,the start of the growing season,SOS)and its spatial gradient and temporal variation.Our results reveal that:(a)STARFM can improve the accuracy of pixel-wise SOS by up to 74.47%and temporal variation by up to 59.13%,respectively,compared with only using Landsat images,but it can hardly improve the retrieval of spatial gradient.For SSFIT,the accuracy of pixel-wise SOS,spatial gradient,and temporal variation can be improved by up to 139.20%,26.36%,and 162.30%,respectively;(b)the accuracy improvement introduced by fusion algorithms decreases with the number of available Landsat images per year,and it has a large variation with the same number of available Landsat images,and(c)this large variation is highly related to the temporal distributions of available Landsat images,suggesting that fusion algorithms can improve SOS accuracy only when cloud-free Landsat images cannot capture key vegetation growth period.This study calls for caution with the use of data fusion in LSP studies at fine scales.展开更多
Shrub encroachment into arid and semi-arid grasslands has elicited extensive research attention worldwide under the background of climate change and increasing anthropogenic activities.Shrub encroachment may considera...Shrub encroachment into arid and semi-arid grasslands has elicited extensive research attention worldwide under the background of climate change and increasing anthropogenic activities.Shrub encroachment may considerably impact local ecosystems and economies,including the conversion of the structure and function of ecosystems,the shift in ambient conditions,and the weakness of local stock farming capacity.This article reviews recent research progresses on the shrub encroachment process and mechanism,shrub identification and dynamic monitoring using remote sensing,and modeling and simulation of the shrub encroachment process and dynamics.These studies can help to evaluate the ecological effect of shrub encroachment,and thus,practically manage and recover the ecological environment of degraded areas.However,the lack of effective measures and data for monitoring shrub encroachment at a large spatial scale severely limits research on the mechanism,modeling,and simulation of shrub encroachment,and the shrub encroachment stages can hardly be quantitatively defined,resulting in insufficient analysis and simulation of shrub encroachment for different spatiotemporal scales and stages shift.Improvement in remote sensingbased shrub encroachment dynamic monitoring might be crucial for analyzing and understanding the process and mechanism of shrub encroachment,and multi-disciplinary and multi-partnerships are required in the shrub encroachment studies.展开更多
This paper extends a new temperature and emissivity separation(TES)algorithm for retrieving land surface temperature and emissivity(LST and LSE)to the Advanced Geosynchronous Radiation Imager(AGRI)onboard Fengyun-4A,C...This paper extends a new temperature and emissivity separation(TES)algorithm for retrieving land surface temperature and emissivity(LST and LSE)to the Advanced Geosynchronous Radiation Imager(AGRI)onboard Fengyun-4A,China’s newest geostationary meteorological satellite.The extended TES algorithm was named the AGRI TES algorithm.The AGRI TES algorithm employs a modified water vapor scaling(WVS)method and a recalibrated empirical function over vegetated surfaces.In situ validation and cross-validation are utilized to investigate the accuracy of the retrieved LST and LSE.LST validation using the collected field measurements showed that the mean bias and RMSE of AGRI TES LST are 0.58 and 2.93 K in the daytime and−0.30 K and 2.18 K at nighttime,respectively;the AGRI official LST is systematically underestimated.Compared with the MODIS LST and LSE products(MYD21),the average bias and RMSE of AGRI TES LST are−0.26 K and 1.65 K,respectively.The AGRI TES LSE outperforms the AGRI official LSE in terms of accuracy and spatial integrity.This study demonstrates the good performance of the AGRI TES algorithm for the retrieval of high-quality LST and LSE,and the potential of the AGRI TES algorithm in producing operational LST and LSE products.展开更多
Spectral and spatial features in remotely sensed data play an irreplaceable role in classifying crop types for precision agriculture. Despite the thriving establishment of the handcrafted features, designing or select...Spectral and spatial features in remotely sensed data play an irreplaceable role in classifying crop types for precision agriculture. Despite the thriving establishment of the handcrafted features, designing or selecting such features valid for specific crop types requires prior knowledge and thus remains an open challenge. Convolutional neural networks(CNNs) can effectively overcome this issue with their advanced ability to generate high-level features automatically but are still inadequate in mining spectral features compared to mining spatial features. This study proposed an enhanced spectral feature called Stacked Spectral Feature Space Patch(SSFSP) for CNN-based crop classification. SSFSP is a stack of twodimensional(2 D) gridded spectral feature images that record various crop types’ spatial and intensity distribution characteristics in a 2 D feature space consisting of two spectral bands. SSFSP can be input into2 D-CNNs to support the simultaneous mining of spectral and spatial features, as the spectral features are successfully converted to 2 D images that can be processed by CNN. We tested the performance of SSFSP by using it as the input to seven CNN models and one multilayer perceptron model for crop type classification compared to using conventional spectral features as input. Using high spatial resolution hyperspectral datasets at three sites, the comparative study demonstrated that SSFSP outperforms conventional spectral features regarding classification accuracy, robustness, and training efficiency. The theoretical analysis summarizes three reasons for its excellent performance. First, SSFSP mines the spectral interrelationship with feature generality, which reduces the required number of training samples.Second, the intra-class variance can be largely reduced by grid partitioning. Third, SSFSP is a highly sparse feature, which reduces the dependence on the CNN model structure and enables early and fast convergence in model training. In conclusion, SSFSP has great potential for practical crop classification in precision agriculture.展开更多
The all-wave net radiation(Rn)at the land surface represents surface radiation budget and plays an important role in the Earth's energy and water cycles.Many studies have been conducted to estimate from satellite ...The all-wave net radiation(Rn)at the land surface represents surface radiation budget and plays an important role in the Earth's energy and water cycles.Many studies have been conducted to estimate from satellite top-of-atmosphere(TOA)data using various methods,particularly the application of machine learning(ML)and deep learning(DL).However,few studies have been conducted to provide a comprehensive evaluation about various ML and DL methods in retrieving.Based on extensive in situ measurements distributed at mid-low latitudes,the corresponding Moderate Resolution Imaging Spectroradiometer(MODIS)TOA observations,and the daily from the fifth generation of European Centre for Medium-Range Weather Forecasts Reanalysis 5(ERA5)used as a priori knowledge,this study assessed nine models for daily estimation,including six classic ML methods(random forest-RF,adaptive boosting-Adaboost,extreme gradient boosting-XGBoost,multilayer perceptron-MLP,radial basis function neural network-RBF,and support vector machine-SVM)and three DL methods(multilayer perceptron neural network with stacked autoencoders-SAE,deep belief network-DBN and residual neural network-ResNet).The validation results showed that the three DL methods were generally better than the six ML methods except XGBoost,although they all performed poorly in certain conditions such as winter days,rugged terrain,and high elevation.ResNet had the most robust performance across different land cover types,elevations,seasons,and latitude zones,but it has disadvantages in practice because of its highly configurable implementation environment and low computational efficiency.The estimated daily values from all nine models were more accurate than the corresponding Global LAnd Surface Satellite(GLASS)product.展开更多
Downward shortwave radiation(DSR)is a critical variable in energy balance driving Earth’s surface processes.Satellite-derived and reanalysis DSR products have been developed and continuously improved during the last ...Downward shortwave radiation(DSR)is a critical variable in energy balance driving Earth’s surface processes.Satellite-derived and reanalysis DSR products have been developed and continuously improved during the last decades.However,as those products have different temporal resolutions,their performances in different time scales have not been well-documented,particularly in China.This study intended to evaluate several DSR products across multiple time scales(i.e.instantaneous,1-hourly,daily,and monthly average)and ecosystems in China.Six DSR products,including GLASS,BESS,CLARA-A2,MCD18A1,ERA5 and MERRA-2,were evaluated against ground measurements at Chinese Ecosystem Research Network(CERN)and integrated land-atmosphere interaction observation(TPDC)sites from 2009 to 2012.The instantaneous DSR of MCD18 showed a root mean square error(RMSE)of 146.02 W/m^(2).The hourly RMSE of ERA5(155.52 W/m^(2))was largely smaller than MERRA-2(188.53 W/m^(2)).On the daily and monthly scale,BESS had the most optimized accuracy among the six products(RMSE of 36.82 W/m^(2)).For the satellite-derived DSR products,the monthly accuracy at CERN can meet the threshold accuracy requirement set by World Meteorological Organization(WMO)for Global Numerical Weather Prediction(20 W/m^(2)).展开更多
Generating canopy-reflectance datasets using radiative transfer models under various leaf and optical property combinations is important for remote sensing retrieval of vegetation parameters.Onedimensional radiative t...Generating canopy-reflectance datasets using radiative transfer models under various leaf and optical property combinations is important for remote sensing retrieval of vegetation parameters.Onedimensional radiative transfer models have been frequently used.However,three-dimensional(3D)models usually require detailed 3D information that is difficult to obtain and long model execution time,limiting their use in remote sensing applications.This study aims to address these limitations for practical use of 3D models,proposing a semi-empirical speed-up method for canopy-reflectance simulation based on a LargE-Scale remote sensing data and image Simulation model(LESS),called Semi-LESS.The speed-up method is coupled with 3D LESS to describe the dependency of canopy reflectance on the wavelength,leaf,soil,and branch optical properties for a scene with fixed 3D structures and observation/illumination configurations,allowing fast generating accurate reflectance images under various wavelength-dependent optical parameters.The precomputed dataset stores simulated multispectral coefficient images under few predefined soil,branch,and leaf optical properties for each RAdiation transfer Model Intercomparison-V scene,which can then be used alone to compute reflectance images on the fly without the participation of LESS.Semi-LESS has been validated with full 3D radiative-transfer-simulated images,showing very high accuracy(root mean square error<0.0003).The generation of images using Semi-LESS is much more efficient than full LESS simulations with an acceleration of more than 320 times.This study is a step further to promote 3D radiative transfer models in practical remote sensing applications such as vegetation parameter inversions.展开更多
Tree growth is an important indicator of forest health and can reflect changes in forest structure.Traditional tree growth estimates use easy-to-measure parameters,including tree height,diameter at breast height,and c...Tree growth is an important indicator of forest health and can reflect changes in forest structure.Traditional tree growth estimates use easy-to-measure parameters,including tree height,diameter at breast height,and crown diameter,obtained via forest in situ measurements,which are labor intensive and time consuming.Some new technologies measure the diameter of trees at different positions to monitor the growth trend of trees,but it is difficult to take into account the growth changes at different tree levels.The combination of terrestrial laser scanning and quantitative structure modeling can accurately estimate tree structural parameters nondestructively and has the potential to estimate tree growth from different tree levels.In this context,this paper estimates tree growth from stem-,crown-,and branch-level attributes observed by terrestrial laser scanning.Specifically,tree height,diameter at breast height,stem volume,crown diameter,crown volume,and first-order branch volume were used to estimate the growth of 55-year-old larch trees in Saihanba of China,at the stem,crown,and branch levels.The experimental results showed that tree growth is mainly reflected in the growth of the crown,i.e.,the growth of branches.Compared to onedimensional parameter growth(tree height,diameter at breast height,or crown diameter),three-dimensional parameter growth(crown,stem,and first-order branch volumes)was more obvious,in which the absolute growth of the first-order branch volume is close to the stem volume.Thus,it is necessary to estimate tree growth at different levels for accurate forest inventory.展开更多
Soil water content(SWC)is a crucial parameter in ecology,agriculture,hydrology,and engineering studies.Research on non-invasive monitoring of SWC has been a long-lasting topic in these fields.Ground penetrating radar(...Soil water content(SWC)is a crucial parameter in ecology,agriculture,hydrology,and engineering studies.Research on non-invasive monitoring of SWC has been a long-lasting topic in these fields.Ground penetrating radar(GPR),a non-destructive geophysical technique,has the advantages of high resolution,deep detection depth,and high efficiency in SWC measurements at medium scale.It has been successfully applied in field investigations.This paper summarizes the recent progress in developing GPR-based SWC measurement methods,including reflected wave,ground wave,surface reflection,borehole GPR,full waveform inversion,average envelope amplitude,and frequency shift methods.The principles,advantages,limitations,and applications of these methods are described in detail.A comprehensive technical framework,which comprises the seven methods,is proposed to understand their principles and applicability.Two key procedures,namely,data acquisition and data processing,are emphasized as crucial to method applications.The suitable methods that will satisfy diverse application demands and field conditions are recommended.Future development,potential applications,and advances in hardware and data processing techniques are also highlighted.展开更多
基金The National High Resolution Earth Observation System(The Civil Part)Technology Projects of ChinaState Key Laboratory of Earth Surface Processes and Resource Ecology,No.2017-FX-01(1)
文摘Rapid urbanization and continuous loss of rural labor force has resulted in abandonment of large areas of farmland in some regions of China. Remote sensing technology can indirectly help detect abandoned farmland size and quantity, which is of great significance for farmland protection and food security. This study took Qingyun and Wudi counties in Shandong Province as a study area and used CART decision tree classification to compile land use maps of 1990–2017 based on Landsat and HJ-1 A data. We developed rules to identify abandoned farmland, and explored its spatial distribution, duration, and reclamation. CART accuracy exceeded 85% from 1990–2017. The maximum abandoned farmland area was 5503.86 ha during 1992–2017, with the maximum rate being 5.37%. Farmland abandonment rate was the highest during 1996–1998, and abandonment trend decreased year by year after 2006. Maximum abandonment duration was 15 years(1992–2017), mostly within 4 years and only a few exceeded 10 years. From 1993–2017, the maximum reclaimed abandoned farmland was 2022.3 ha, and the minimum ～20 ha. The maximum reclamation rate was 67.44%m, with annual average rate being 31.83%. This study will help analyze farmland abandonment driving forces in the study area and also provide references to identify abandoned farmland in other areas.
基金National Basic Research Program of China,No.2015CB953603National Natural Science Foundation of China,No.41671339State Key Laboratory of Earth Surface Processes and Resource Ecology,No.2017-FX-01(1)
文摘Impervious surface(IS) is often recognized as the indicator of urban environmental changes. Numerous research efforts have been devoted to studying its spatio-temporal dynamics and ecological effects, especially for the IS in Beijing metropolitan region. However, most previous studies primarily considered the Beijing metropolitan region as a whole without considering the differences and heterogeneity among the function zones. In this study, the subpixel impervious surface results in Beijing within a time series(1991, 2001, 2005, 2011 and 2015) were extracted by means of the classification and regression tree(CART) model combined with change detection models. Then based on the method of standard deviation ellipse, Lorenz curve, contribution index(CI) and landscape metrics, the spatio-temporal dynamics and variations of IS(1991, 2001, 2011 and 2015) in different function zones and districts were analyzed. It is found that the total area of impervious surface in Beijing increased dramatically during the study period, increasing about 144.18%. The deflection angle of major axis of standard deviation ellipse decreased from 47.15° to 38.82°, indicating the major development axis in Beijing gradually moved from northeast-southwest to north-south. Moreover, the heterogeneity of impervious surface’s distribution among 16 districts weakened gradually, but the CI values and landscape metrics in four function zones differed greatly. The urban function extended zone(UFEZ), the main source of the growth of IS in Beijing, had the highest CI values. Its lowest CI value was 1.79 that is still much higher than the highest CI value in other function zones. The core function zone(CFZ), the traditional aggregation zone of impervious surface, had the highest contagion index(CONTAG) values, but it contributed less than UFEZ due to its small area. The CI value of the new urban developed zone(NUDZ) increased rapidly, and it increased from negative to positive and multiplied, becoming animportant contributor to the rise of urban impervious surface. However, the ecological conservation zone(ECZ) had a constant negative contribution all the time, and its CI value decreased gradually. Moreover, the landscape metrics and centroids of impervious surface in different density classes differed greatly. The high-density impervious surface had a more compact configuration and a greater impact on the eco-environment.
文摘Taxus wallichiana Zucc.(Himalayan yew)is subject to international and national conservation measures because of its over-exploitation and decline over the last 30 years.Predicting the impact of climate change on T.wallichiana’s distribution might help protect the wild populations and plan effective ex situ measures or cultivate successfully.Considering the complexity of climates and the uncertainty inherent in climate modeling for mountainous regions,we integrated three Representative Concentration Pathways(RCPs)(i.e.,RCP2.6,RCP4.5,RCP8.5)based on datasets from 14 Global Climate Models of Coupled Model Intercomparison Project,Phase 5 to:(1)predict the potential distribution of T.wallichiana under recent past(1960–1990,hereafter‘‘current’’)and future(2050s and 2070s)scenarios with the species distribution model MaxEnt.;and(2)quantify the climatic factors influencing the distribution.In respond to the future warming climate scenarios,(1)highly suitable areas for T.wallichiana would decrease by 31–55%at a rate of 3–7%/10a;(2)moderately suitable areas would decrease by 20–30%at a rate of 2–4%/10a;(3)the average elevation of potential suitable sites for T.wallichiana would shift upslope by 390 m(15%)to 948 m(36%)at a rate of 42–100 m/10a.Average annual temperature(contribution rate ca.61%),isothermality and temperature seasonality(20%),and annual precipitation(17%)were the main climatic variables affecting T.wallichiana habitats.Prior protected areas and suitable planting areas must be delimited from the future potential distributions,especially the intersection areas at different suitability levels.It is helpful to promote the sustainable utilization of this precious resource by prohibiting exploitation and ex situ restoring wild resources,as well as artificially planting considering climate suitability.
基金National High Resolution Earth Observation System(the Civil Part)Technology Projects of ChinaLocal Scientific&Technological Development Projects of Qinghai Guided by Central Government of ChinaDisaster Research Foundation of PICC P&C,No.2017D24-03。
文摘Angstrom-Prescott equation(AP)is the algorithm recommended by the Food and Agriculture Organization(FAO)of the United Nations for calculating the surface solar radiation(R_(s))to support the estimation of crop evapotranspiration.Thus,the a_(s) and b_(s) coefficients in the AP are vital.This study aims to obtain coefficients a_(s) and b_(s) in the AP,which are optimized for Chinas comprehensive agricultural divisions.The average monthly solar radiation and relative sunshine duration data at 121 stations from 1957-2016 were collected.Using data from 1957 to 2010,we calculated the monthly a_(s) and b_(s) coefficients for each subregion by least-squares regression.Then,taking the observation values of R_(s) from 2011 to 2016 as the true values,we estimated and compared the relative accuracy of R_(s) calculated using the regression values of coefficients a_(s) and b_(s) and that calculated with the FAO recommended coefficients.The monthly coefficients,a_(s) and b_(s),of each subregion are significantly different,both temporally and spatially,from the FAO recommended coefficients.The relative error range(0-54%)of R_(s) calculated via the regression values of the a_(s) and b_(s) coefficients is better than the relative error range(0-77%)of R_(s) calculated using the FAO suggested coefficients.The station-mean relative error was reduced by 1% to 6%.However,the regression values of the a_(s) and b_(s) coefficients performed worse in certain months and agricultural subregions during verification.Therefore,we selected the a_(s) and b_(s) coefficients with the minimum R_(s) estimation error as the final coefficients and constructed a coefficient recommendation table for 36 agricultural production and management subregions in China.These coefficient recommendations enrich the case study of coefficient calibration for the AP in China and can improve the accuracy of calculating R_(s) and crop evapotranspiration based on existing data.
基金supported by the Fundamental Research Funds for the Central Universities (GK201703049)the Major Project of High Resolution Earth Observation System, China
文摘The Palmer drought severity index （PDSI）, standardized precipitation index （SPI）, and standardized precipitation evapotranspiration index （SPEI） are used worldwide for drought assessment and monitoring. However, substantial differences exist in the performance for agricultural drought among these indices and among regions. Here, we performed statistical assessments to compare the strengths of different drought indices for agricultural drought in the North China Plain. Small differences were detected in the comparative performances of SPI and SPEI that were smaller at the long-term scale than those at the short-term scale. The correlation between SPI/SPEI and PDSI considerably increased from 1- to 12-month lags, and a slight decreasing trend was exhibited during 12- and 24-month lags, indicating a 12-month scale in the PDSI, whereas the SPI was strongly correlated with the SPEI at 1- to 24-month lags. Interestingly, the correlation between the trend of temperature and the mean absolute error and its correlation coefficient both suggested stronger relationships between SPI and the SPEI in areas of rapid climate warming. In addition, the yield-drought correlations tended to be higher for the SPI and SPEI than that for the PDSI at the station scale, whereas small differences were detected between the SPI and SPEI in the performance on agricultural systems. However, large differences in the influence of drought conditions on the yields of winter wheat and summer maize were evident among various indices during the crop-growing season. Our findings suggested that multi-indices in drought monitoring are needed in order to acquire robust conclusions.
基金supported by the National Natural Science Foundation of China(Grant Nos.41671414,41971380,41331171 and 41171265)the National Key Research and Development Program of China(No.2016YFB0501404)
文摘Background: The stem curve of standing trees is an essential parameter for accurate estimation of stem volume.This study aims to directly quantify the occlusions within the single-scan terrestrial laser scanning(TLS) data,evaluate its correlation with the accuracy of the retrieved stem curves, and subsequently, to assess the capacity of single-scan TLS to estimate stem curves.Methods: We proposed an index, occlusion rate, to quantify the occlusion level in TLS data. We then analyzed three influencing factors for the occlusion rate: the percentage of basal area near the scanning center, the scanning distance and the source of occlusions. Finally, we evaluated the effects of occlusions on stem curve estimates from single-scan TLS data.Results: The results showed that the correlations between the occlusion rate and the stem curve estimation accuracies were strong(r = 0.60–0.83), so was the correlations between the occlusion rate and its influencing factors(r = 0.84–0.99). It also showed that the occlusions from tree stems were the main factor of the low detection rate of stems, while the non-stem components mainly influenced the completeness of the retrieved stem curves.Conclusions: Our study demonstrates that the occlusions significantly affect the accuracy of stem curve retrieval from the single-scan TLS data in a typical-size(32 m × 32 m) forest plot. However, the single-scan mode has the capacity to accurately estimate the stem curve in a small forest plot(< 10 m × 10 m) or a plot with a lower occlusion rate, such as less than 35% in our tested datasets. The findings from this study are useful for guiding the practice of retrieving forest parameters using single-scan TLS data.
基金the National Natural Science Foundation of China(Grant Nos.41671414,41971380 and 41171265)the National Key Research and Development Program of China(No.2016YFB0501404).
文摘Background:The universal occurrence of randomly distributed dark holes(i.e.,data pits appearing within the tree crown)in LiDAR-derived canopy height models(CHMs)negatively affects the accuracy of extracted forest inventory parameters.Methods:We develop an algorithm based on cloth simulation for constructing a pit-free CHM.Results:The proposed algorithm effectively fills data pits of various sizes whilst preserving canopy details.Our pitfree CHMs derived from point clouds at different proportions of data pits are remarkably better than those constructed using other algorithms,as evidenced by the lowest average root mean square error(0.4981 m)between the reference CHMs and the constructed pit-free CHMs.Moreover,our pit-free CHMs show the best performance overall in terms of maximum tree height estimation(average bias=0.9674 m).Conclusion:The proposed algorithm can be adopted when working with different quality LiDAR data and shows high potential in forestry applications.
基金This work was supported by the National Natural Science Foundation of China(Grant No.41871231)and the National Key Research and Development Program of China(Grant No.2016YFB0501502).
文摘China is a country largely affected by desertification.The main purpose of this article is to analyze interannual and seasonal changes in fractional vegetation cover(FVC)in the Mu Us Sandy Land(MUSL).It uses fused remote sensing data to quantitatively analyze the response of FVC to climate change and human activities.The results showed that desertification in the MUSL had improved over the past 20 years.Grade V desertification decreased from more than 60%in 2000 to about 15%in 2020.In some years,degradation appeared to be affected by climate factors and human activity,especially in the northwestern portion of the study area.The FVC in summer was slightly higher than that in autumn and far higher than recorded in spring and winter.Spatially,the northwestern and central parts of the study area were unstable,with high coefficients of variation.FVC gradually increased from northwest to southeast,and areas with the fastest increase in FVC were concentrated along the eastern and southern edges of the study area.The correlations between FVC and precipitation and dryness were slightly pos-itive,but the correlation between FVC and temperature showed regional differences.The increase of population density is not a key factor limiting the growth of vegetation;the policy of“grazing prohibition,grazing rest,and rotational grazing”has allowed the restoration of vegetation;and afforestation is an effective way to promote the increase in FVC.
基金supported by the National Key Research and Development Program of China[No.2022YFD2001100 and No.2017YFD0300201].
文摘Long-term mapping of winter wheat is vital for assessing food security and formulating agricultural policies.Landsat data are the only available source for long-term winter wheat mapping in the North China Plain due to the fragmented landscape in this area.Although various methods,such as index-based methods,curve similarity-based methods and machine learning-based methods,have been developed for winter wheat mapping based on remote sensing,the former two often require satellite data with high temporal resolution,which are unsuitable for Landsat data with sparse time-series.Machine learning is an effective method for crop classification using Landsat data.Yet,applying machine learn-ing for winter wheat mapping in the North China Plain encounters two main issues:1)the lack of adequate and accurate samples for classifier training;and 2)the difficulty of training a single classifier to accomplish the large-scale crop mapping due to the high spatial heterogeneity in this area.To address these two issues,we first designed a sample selection rule to build a large sample set based on several existing crop maps derived from recent Sentinel data,with specific consideration of the confusion error between winter wheat and winter rapeseed in the available crop maps.Then,we developed an optimal zoning method based on the quadtree region splitting algorithm with classification feature consistency criterion,which divided the study area into six subzones with uni-form classification features.For each subzone,a specific random forest classifier was trained and used to generate annual winter wheat maps from 2013 to 2022 using Landsat 8 OLI data.Field sample validation confirmed the high accuracy of the produced maps,with an average overall accuracy of 91.1%and an average kappa coefficient of 0.810 across different years.The derived winter wheat area also has a good correlation(R2=0.949)with census area at the provincial level.The results underscore the reliability of the produced annual winter wheat maps.Additional experiments demonstrate that our proposed optimal zoning method outper-forms other zoning methods,including Köppen climate zoning,wheat planting zoning and non-zoning methods,in enhancing wheat mapping accuracy.It indicates that the proposed zoning is capable of generating more reasonable subzones for large-scale crop mapping.
基金funded by the National Natural Science Foundation of China[grant numbers 42090012 and 41971291].
文摘Land surface all-wave net radiation(R_(n))is crucial in determining Earth’s climate by contributing to the surface radiation budget.This study evaluated seven satellite and three reanalysis long-term land surface R_(n)products under different spatial scales,spatial and temporal variations,and different conditions.The results showed that during 2000-2018,Global Land Surface Satellite Product(GLASS)-Moderate Resolution Imaging Spectroradiometer(MODIS)performed the best(RMSE=25.54 Wm^(-2),bias=-1.26 Wm^(-2)),followed by ERA5(the fifth-generation of European Centre for Medium-Range Weather Forecast Reanalysis)(RMSE=32.17 Wm^(-2),bias=-4.88 Wm^(-2))and GLASS-AVHRR(Advanced Very-High-Resolution Radiometer)(RMSE=33.10 Wm^(-2),bias=4.03 Wm^(-2)).During 1983-2018,GLASS-AVHRR and ERA5 ranked top and performed similarly,with RMSE values of 31.70 and 33.08 Wm^(-2)and biases of-4.56 and 3.48 Wm^(-2),respectively.The averaged multi-annual mean R_(n)over the global land surface of satellite products was higher than that of reanalysis products by about 10~30 Wm^(-2).These products differed remarkably in long-term trends variations,particularly pre-2000,but no significant trends were observed.Discrepancies were more frequent in satellite data,while reanalysis products showed smoother variations.Large discrepancies were found in regions with high latitudes,reflectance,and elevation which could be attributed to input radiative components,meteorological variables(e.g.,cloud properties,aerosol optical thickness),and applicability of the algorithms used.While further research is needed for detailed insights.
基金supported by High-Resolution Earth Observation System(09-Y30F01-9001-20/22).
文摘Agricultural applications of remote sensing data typically require high spatial resolution and frequent observations.The increasing availability of high spatial resolution imagery meets the spatial resolution requirement well.However,the long revisit period and frequent cloud contamination severely compromise their ability to monitor crop growth,which is characterized by high temporal heterogeneity.Many spatiotemporal fusion methods have been developed to produce synthetic images with high spatial and temporal resolutions.However,these existing methods focus on fusing low and medium spatial resolution satellite data in terms of model development and validation.When it comes to fusing medium and high spatial resolution images,the applicability remains unknown and may face various challenges.To address this issue,we propose a novel spatiotemporal fusion method,the dual-stream spatiotemporal decoupling fusion architecture model,to fully realize the prediction of high spatial resolution images.Compared with other fusion methods,the model has distinct advantages:(a)It maintains high fusion accuracy and good spatial detail by combining deep-learning-based super-resolution method and partial least squares regression model through edge and color-based weighting loss function;and(b)it demonstrates improved transferability over time by introducing image gradient maps and partial least squares regression model.We tested the StarFusion model at 3 experimental sites and compared it with 4 traditional methods:STARFM(spatial and temporal adaptive reflectance fusion),FSDAF(flexible spatiotemporal data fusion),Fit-FC(regression model fitting,spatial filtering,and residual compensation),FIRST(fusion incorporating spectral autocorrelation),and a deep learning base method-super-resolution generative adversarial network.In addition,we also investigated the possibility of our method to use multiple pairs of coarse and fine images in the training process.The results show that multiple pairs of images provide better overall performance but both of them are better than other comparison methods.Considering the difficulty in obtaining multiple cloud-free image pairs in practice,our method is recommended to provide high-quality Gaofen-1 data with improved temporal resolution in most cases since the performance degradation of single pair is not significant.
基金supported by the National Natural Science Foundation of China(Project Nos.42271331 and 42022060)The Hong Kong Polytechnic University(Project Nos.4-ZZND and Q-CDBP).
文摘Spatiotemporal data fusion technologies have been widely used for land surface phenology(LSP)monitoring since it is a low-cost solution to obtain fine-resolution satellite time series.However,the reliability of fused images is largely affected by land surface heterogeneity and input data.It is unclear whether data fusion can really benefit LSP studies at fine scales.To explore this research question,this study designed a sophisticated simulation experiment to quantify effectiveness of 2 representative data fusion algorithms,namely,pair-based Spatial and Temporal Adaptive Reflectance Fusion Model(STARFM)and time series-based Spatiotemporal fusion method to Simultaneously generate Full-length normalized difference vegetation Index Time series(SSFIT)by fusing Landsat and Moderate Resolution Imaging Spectroradiometer(MODIS)data in extracting pixel-wise spring phenology(i.e.,the start of the growing season,SOS)and its spatial gradient and temporal variation.Our results reveal that:(a)STARFM can improve the accuracy of pixel-wise SOS by up to 74.47%and temporal variation by up to 59.13%,respectively,compared with only using Landsat images,but it can hardly improve the retrieval of spatial gradient.For SSFIT,the accuracy of pixel-wise SOS,spatial gradient,and temporal variation can be improved by up to 139.20%,26.36%,and 162.30%,respectively;(b)the accuracy improvement introduced by fusion algorithms decreases with the number of available Landsat images per year,and it has a large variation with the same number of available Landsat images,and(c)this large variation is highly related to the temporal distributions of available Landsat images,suggesting that fusion algorithms can improve SOS accuracy only when cloud-free Landsat images cannot capture key vegetation growth period.This study calls for caution with the use of data fusion in LSP studies at fine scales.
基金supported by the National Natural Science Foundation of China[grant number 41571406]the Fund for Creative Research Groups of National Natural Science Foundation of China[grant number 41621061]the State Key Laboratory of Earth Surface Processes and Resource Ecology at Beijing Normal University[grant number 2015-ZDTD-011].
文摘Shrub encroachment into arid and semi-arid grasslands has elicited extensive research attention worldwide under the background of climate change and increasing anthropogenic activities.Shrub encroachment may considerably impact local ecosystems and economies,including the conversion of the structure and function of ecosystems,the shift in ambient conditions,and the weakness of local stock farming capacity.This article reviews recent research progresses on the shrub encroachment process and mechanism,shrub identification and dynamic monitoring using remote sensing,and modeling and simulation of the shrub encroachment process and dynamics.These studies can help to evaluate the ecological effect of shrub encroachment,and thus,practically manage and recover the ecological environment of degraded areas.However,the lack of effective measures and data for monitoring shrub encroachment at a large spatial scale severely limits research on the mechanism,modeling,and simulation of shrub encroachment,and the shrub encroachment stages can hardly be quantitatively defined,resulting in insufficient analysis and simulation of shrub encroachment for different spatiotemporal scales and stages shift.Improvement in remote sensingbased shrub encroachment dynamic monitoring might be crucial for analyzing and understanding the process and mechanism of shrub encroachment,and multi-disciplinary and multi-partnerships are required in the shrub encroachment studies.
基金supported in part by the National Natural Science Foundation of China under Grants 42192581,42090012,and 42071308in part by the Second Tibetan Plateau Scientific Expedition and Research Program(STEP)under Grant 2019QZKK0206in part by the open fund of Beijing Engineering Research Center for Global Land Remote Sensing Products.
文摘This paper extends a new temperature and emissivity separation(TES)algorithm for retrieving land surface temperature and emissivity(LST and LSE)to the Advanced Geosynchronous Radiation Imager(AGRI)onboard Fengyun-4A,China’s newest geostationary meteorological satellite.The extended TES algorithm was named the AGRI TES algorithm.The AGRI TES algorithm employs a modified water vapor scaling(WVS)method and a recalibrated empirical function over vegetated surfaces.In situ validation and cross-validation are utilized to investigate the accuracy of the retrieved LST and LSE.LST validation using the collected field measurements showed that the mean bias and RMSE of AGRI TES LST are 0.58 and 2.93 K in the daytime and−0.30 K and 2.18 K at nighttime,respectively;the AGRI official LST is systematically underestimated.Compared with the MODIS LST and LSE products(MYD21),the average bias and RMSE of AGRI TES LST are−0.26 K and 1.65 K,respectively.The AGRI TES LSE outperforms the AGRI official LSE in terms of accuracy and spatial integrity.This study demonstrates the good performance of the AGRI TES algorithm for the retrieval of high-quality LST and LSE,and the potential of the AGRI TES algorithm in producing operational LST and LSE products.
基金supported by the National Natural Science Foundation of China (67441830108 and 41871224)。
文摘Spectral and spatial features in remotely sensed data play an irreplaceable role in classifying crop types for precision agriculture. Despite the thriving establishment of the handcrafted features, designing or selecting such features valid for specific crop types requires prior knowledge and thus remains an open challenge. Convolutional neural networks(CNNs) can effectively overcome this issue with their advanced ability to generate high-level features automatically but are still inadequate in mining spectral features compared to mining spatial features. This study proposed an enhanced spectral feature called Stacked Spectral Feature Space Patch(SSFSP) for CNN-based crop classification. SSFSP is a stack of twodimensional(2 D) gridded spectral feature images that record various crop types’ spatial and intensity distribution characteristics in a 2 D feature space consisting of two spectral bands. SSFSP can be input into2 D-CNNs to support the simultaneous mining of spectral and spatial features, as the spectral features are successfully converted to 2 D images that can be processed by CNN. We tested the performance of SSFSP by using it as the input to seven CNN models and one multilayer perceptron model for crop type classification compared to using conventional spectral features as input. Using high spatial resolution hyperspectral datasets at three sites, the comparative study demonstrated that SSFSP outperforms conventional spectral features regarding classification accuracy, robustness, and training efficiency. The theoretical analysis summarizes three reasons for its excellent performance. First, SSFSP mines the spectral interrelationship with feature generality, which reduces the required number of training samples.Second, the intra-class variance can be largely reduced by grid partitioning. Third, SSFSP is a highly sparse feature, which reduces the dependence on the CNN model structure and enables early and fast convergence in model training. In conclusion, SSFSP has great potential for practical crop classification in precision agriculture.
基金supported by National Natural Science Foundation of China:[grant no 41971291,42090012]National Key Research and Development Program of China:[grant no 2020YFA0608704].
文摘The all-wave net radiation(Rn)at the land surface represents surface radiation budget and plays an important role in the Earth's energy and water cycles.Many studies have been conducted to estimate from satellite top-of-atmosphere(TOA)data using various methods,particularly the application of machine learning(ML)and deep learning(DL).However,few studies have been conducted to provide a comprehensive evaluation about various ML and DL methods in retrieving.Based on extensive in situ measurements distributed at mid-low latitudes,the corresponding Moderate Resolution Imaging Spectroradiometer(MODIS)TOA observations,and the daily from the fifth generation of European Centre for Medium-Range Weather Forecasts Reanalysis 5(ERA5)used as a priori knowledge,this study assessed nine models for daily estimation,including six classic ML methods(random forest-RF,adaptive boosting-Adaboost,extreme gradient boosting-XGBoost,multilayer perceptron-MLP,radial basis function neural network-RBF,and support vector machine-SVM)and three DL methods(multilayer perceptron neural network with stacked autoencoders-SAE,deep belief network-DBN and residual neural network-ResNet).The validation results showed that the three DL methods were generally better than the six ML methods except XGBoost,although they all performed poorly in certain conditions such as winter days,rugged terrain,and high elevation.ResNet had the most robust performance across different land cover types,elevations,seasons,and latitude zones,but it has disadvantages in practice because of its highly configurable implementation environment and low computational efficiency.The estimated daily values from all nine models were more accurate than the corresponding Global LAnd Surface Satellite(GLASS)product.
基金supported by National Natural Science Foundation of China Grant(42090012)the Hubei Provincial Natural Science Foundation(2021CFA082)+1 种基金National Key Research and Development Program of China(2020YF A0608704)the Fundamental Research Funds for the Central Universities through Wuhan University under Grant 2042022dx0001.
文摘Downward shortwave radiation(DSR)is a critical variable in energy balance driving Earth’s surface processes.Satellite-derived and reanalysis DSR products have been developed and continuously improved during the last decades.However,as those products have different temporal resolutions,their performances in different time scales have not been well-documented,particularly in China.This study intended to evaluate several DSR products across multiple time scales(i.e.instantaneous,1-hourly,daily,and monthly average)and ecosystems in China.Six DSR products,including GLASS,BESS,CLARA-A2,MCD18A1,ERA5 and MERRA-2,were evaluated against ground measurements at Chinese Ecosystem Research Network(CERN)and integrated land-atmosphere interaction observation(TPDC)sites from 2009 to 2012.The instantaneous DSR of MCD18 showed a root mean square error(RMSE)of 146.02 W/m^(2).The hourly RMSE of ERA5(155.52 W/m^(2))was largely smaller than MERRA-2(188.53 W/m^(2)).On the daily and monthly scale,BESS had the most optimized accuracy among the six products(RMSE of 36.82 W/m^(2)).For the satellite-derived DSR products,the monthly accuracy at CERN can meet the threshold accuracy requirement set by World Meteorological Organization(WMO)for Global Numerical Weather Prediction(20 W/m^(2)).
基金supported by the National Natural Science Foundation of China programs(Grant No.42001279 and Grant No.42101329)Open Fund of State Key Laboratory of Remote Sensing Science(Grant No.OFSLRSS202014 and Grant No.OFSLRSS202115).
文摘Generating canopy-reflectance datasets using radiative transfer models under various leaf and optical property combinations is important for remote sensing retrieval of vegetation parameters.Onedimensional radiative transfer models have been frequently used.However,three-dimensional(3D)models usually require detailed 3D information that is difficult to obtain and long model execution time,limiting their use in remote sensing applications.This study aims to address these limitations for practical use of 3D models,proposing a semi-empirical speed-up method for canopy-reflectance simulation based on a LargE-Scale remote sensing data and image Simulation model(LESS),called Semi-LESS.The speed-up method is coupled with 3D LESS to describe the dependency of canopy reflectance on the wavelength,leaf,soil,and branch optical properties for a scene with fixed 3D structures and observation/illumination configurations,allowing fast generating accurate reflectance images under various wavelength-dependent optical parameters.The precomputed dataset stores simulated multispectral coefficient images under few predefined soil,branch,and leaf optical properties for each RAdiation transfer Model Intercomparison-V scene,which can then be used alone to compute reflectance images on the fly without the participation of LESS.Semi-LESS has been validated with full 3D radiative-transfer-simulated images,showing very high accuracy(root mean square error<0.0003).The generation of images using Semi-LESS is much more efficient than full LESS simulations with an acceleration of more than 320 times.This study is a step further to promote 3D radiative transfer models in practical remote sensing applications such as vegetation parameter inversions.
基金This work was supported in part by the Guangxi Natural Science Fund for Innovation Research Team under Grant 2019GXNSFGA245001in part by the National Natural Science Foundation of China under Grant 41971380+1 种基金in part by the Open Fund of State Key Laboratory of Remote Sensing Science under Grant OFSLRSS201920partially by the Hong Kong Polytechnic University under Project 1-YXAQ.
文摘Tree growth is an important indicator of forest health and can reflect changes in forest structure.Traditional tree growth estimates use easy-to-measure parameters,including tree height,diameter at breast height,and crown diameter,obtained via forest in situ measurements,which are labor intensive and time consuming.Some new technologies measure the diameter of trees at different positions to monitor the growth trend of trees,but it is difficult to take into account the growth changes at different tree levels.The combination of terrestrial laser scanning and quantitative structure modeling can accurately estimate tree structural parameters nondestructively and has the potential to estimate tree growth from different tree levels.In this context,this paper estimates tree growth from stem-,crown-,and branch-level attributes observed by terrestrial laser scanning.Specifically,tree height,diameter at breast height,stem volume,crown diameter,crown volume,and first-order branch volume were used to estimate the growth of 55-year-old larch trees in Saihanba of China,at the stem,crown,and branch levels.The experimental results showed that tree growth is mainly reflected in the growth of the crown,i.e.,the growth of branches.Compared to onedimensional parameter growth(tree height,diameter at breast height,or crown diameter),three-dimensional parameter growth(crown,stem,and first-order branch volumes)was more obvious,in which the absolute growth of the first-order branch volume is close to the stem volume.Thus,it is necessary to estimate tree growth at different levels for accurate forest inventory.
基金supported by the National Natural Science Foundation of China(Grant No.41571404)on project of State Key Laboratory of Earth Surface Processes and Resource Ecology.
文摘Soil water content(SWC)is a crucial parameter in ecology,agriculture,hydrology,and engineering studies.Research on non-invasive monitoring of SWC has been a long-lasting topic in these fields.Ground penetrating radar(GPR),a non-destructive geophysical technique,has the advantages of high resolution,deep detection depth,and high efficiency in SWC measurements at medium scale.It has been successfully applied in field investigations.This paper summarizes the recent progress in developing GPR-based SWC measurement methods,including reflected wave,ground wave,surface reflection,borehole GPR,full waveform inversion,average envelope amplitude,and frequency shift methods.The principles,advantages,limitations,and applications of these methods are described in detail.A comprehensive technical framework,which comprises the seven methods,is proposed to understand their principles and applicability.Two key procedures,namely,data acquisition and data processing,are emphasized as crucial to method applications.The suitable methods that will satisfy diverse application demands and field conditions are recommended.Future development,potential applications,and advances in hardware and data processing techniques are also highlighted.
