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The meiotic entry of undifferentiated germ cells is sexually specific and strictly regulated by the testicular or ovarian environment. Germline stem cells with a set of abnormal sex chromosomes and associated autosomes undergo defective meiotic processes and are eventually eliminated by yet to be defined post‐transcriptional modifications. Herein, we report the role of gsdf, a member of BMP/TGFβ family uniquely found in teleost, in the regulation of meiotic entry in medaka (Oryzias latipes) via analyses of gametogenesis in gsdf‐deficient XX and XY gonads in comparison with their wild‐type siblings. Several differentially expressed genes, including the FKB506‐binding protein 7 (fkbp7), were significantly upregulated in pubertal gsdf‐deficient gonads. The increase in alternative pre‐mRNA isoforms of meiotic synaptonemal complex gene sycp3 was visualized using Integrative Genomics Viewer and confirmed by real‐time qPCR. Nevertheless, immunofluorescence analysis showed that Sycp3 protein products reduced significantly in gsdf‐deficient XY oocytes. Transmission electron microscope observations showed that normal synchronous cysts were replaced by asynchronous cysts in gsdf‐deficient testis. Breeding experiments showed that the sex ratio deviation of gsdf−/− XY gametes in a non‐Mendelian manner might be due to the non‐segregation of XY chromosomes. Taken together, our results suggest that gsdf plays a role in the proper execution of cytoplasmic and nuclear events through receptor Smad phosphorylation and Sycp3 dephosphorylation to coordinate medaka gametogenesis, including sex‐specific mitotic divisions and meiotic recombination. [ABSTRACT FROM AUTHOR]

Charcoal rot caused by the fungal pathogen Macrophomina phaseolina is an important disease of soybean and the use of resistant cultivars is recommended to manage the disease. Since symptoms, including leaf wilt, typically occur as soybeans reach maturity, screening varieties for tolerance to charcoal rot can be time-consuming, requiring nearly an entire growing season. In this study, soybean seedlings (V1) were exposed to a culture filtrate of M. phaseolina containing toxin(s) produced by the fungal pathogen. The effect on the seedlings was measured with hyperspectral spectroscopy on leaves. Two spectrometers integrated with a fiber optic light source and a 6.35 mm diameter probe yielding 480–850 nm and 900–2400 nm ranges after preprocessing were used. The spectra of the untreated plants measured at 0 h, 4 h, 8 h, 12 h, and 24 h post-exposure to the fungal extract were nearly indistinguishable. In contrast, the toxin-treated plants had significantly different spectra from the untreated plants at each of the 4 h, 8 h, 12 h, and 24 h measurements. Reflectance increased in the NIR (900–2400 nm) region with extended exposure to the fungal extract. This change was most noticeable in the 1450 nm and 1940 nm wavebands. Across the spectra, the 24 h reflectance was significantly higher than that of 12 h, which was significantly higher than those of 8 h, 4 h, and 0 h. Jeffries-Matusita (JM) distance, quantifying class separability, was used as a feature selection method and the 24 h measurement had the highest JM distance values, which indicated good separability. Based on JM Distance the most sensitive wavebands were in the regions of 1370–2400 nm. A ratio of the reflectance at 0 h to reflectance at the other times for each of the wavebands was calculated. The ratio curves had two noticeable troughs centered on 1450 nm and 1940 nm, with respective ratios of 0.47 and 0.32 for the 24 h measurement. The 1940 nm ratio at 24 h was proposed as a relative measure of charcoal rot susceptibility of soybean varieties. A ratio of 1.0 indicated no susceptibility with lower ratios indicating greater susceptibility to charcoal rot toxin(s). This study has implications in terms of developing tools to screen for soybean varieties tolerant to charcoal rot and potentially for other biotic or abiotic factors that induce foliar wilting. [ABSTRACT FROM AUTHOR]

Soil organic carbon (SOC) is an important soil property relating to soil formation, structure, and water-holding capacity. Remote sensing has been used for predicting SOC but remains a challenge due to the complex and indirect relationship between SOC and remote sensing variables. In this research we explored the approaches of predicting SOC distribution in a hickory plantation region using random forest (RF) and geographically weighted regression (GWR) based on Landsat and ancillary data, analyzed SOC spatial distribution and dynamic change between 2008 and 2013 through a thresholding approach, and examined major factors that resulted in SOC degradation in the young and mature hickory plantations using a logistic regression. The results showed that RF outperformed GWR in the prediction of SOC and provided stable and reliable SOC predictions with root mean squared errors of 4.6 g kg −1 in 2008 and 4.4 g kg −1 in 2013. A large area of hickory plantation was experiencing SOC decrease. The analysis of major factors causing SOC degradation indicated that steep slope and high proportion of silt component in soil resulted in SOC decrease in the young hickory plantations, and high elevation, high proportion of silt component in the soil, and the increase of soil fraction in the ground cover led to SOC decrease in the mature hickory plantations. This research provides valuable approaches to spatially predict SOC and identify major factors driving SOC degradation, which will be useful for adopting better measures to improve management of hickory plantations. [ABSTRACT FROM AUTHOR]

Quickly and accurately mapping soil properties is critical for agricultural, forestry and environmental management. In this study, a new hyperspectral remote sensing method of soil property prediction was developed and validated in Stipa purpurea dominated alpine grasslands located in Shenzha County of the Qiangtang Plateau, northwestern Qinghai-Tibet Plateau. Hyperspectral data were collected in a total of 67 sample points. At the same time, soil samples were obtained at the locations and soil properties including organic carbon, total nitrogen, total potassium and total phosphorus were measured. The correlations of the soil properties with original bands and enhanced spectral variables derived from both field and satellite hyperspectral data were analyzed. Regression models that explained the relationships were further developed to map the soil properties. The results showed that the stepwise regression models based on the satellite hyperspectral image derived enhanced spectral variables produced reasonable spatial distributions of the soil properties and the relative RMSE values of 68.9, 46.3, 31.4 and 45.5% for soil organic carbon, total nitrogen, total phosphorus and total potassium, respectively. Thus, this study implied that the hyperspectral data based method provided great potential to predict the soil properties. [ABSTRACT FROM AUTHOR]

There is a large amount of remote sensing data available for land use and land cover (LULC) classification and thus optimizing selection of remote sensing variables is a great challenge. Although many methods such as Jeffreys-Matusita (JM) distance and random forests (RF) have been developed for this purpose, the existing methods ignore correlation and information duplication among remote sensing variables. In this study, a novel approach was proposed to improve the measures of potential class separability for the selection of remote sensing variables by taking into account correlations among the variables. The proposed method was examined with a total of thirteen spectral variables from a Gaofen-1 image, three class separability measures including JM distance, transformed divergence and B-distance and three classifiers including Bayesian discriminant (BD), Mahalanobis distance (MD) and RF for classification of six LULC types at the East Dongting Lake of Hunan, China. The results showed that (1) The proposed approach selected the first three spectral variables and resulted in statistically stable classification accuracies for three improved class separability measures. That is, the classification accuracies using three or more spectral variables statistically did not significantly differ from each other at a significant level of 0.05; (2) The statistically stable classification accuracies obtained by integrating MD and BD classifiers with the improved class separability measures were also statistically not significantly different from those by RF; (3) The numbers of the selected spectral variables using the improved class separability measures to create the statistically stable classification accuracies by MD and BD classifiers were much smaller than those from the original class separability measures and RF; and (4) Three original class separability measures and RF led to similar ranks of importance of the spectral variables, while the ranks achieved by the improved class separability measures were different due to the consideration of correlations among the variables. This indicated that the proposed method more effectively and quickly selected the spectral variables to produce the statistically stable classification accuracies compared with the original class separability measures and RF and thus improved the selection of the spectral variables for the classification. [ABSTRACT FROM AUTHOR]

Considering the complexity of the urban rail transit system (URTS), it is necessary to systematically identify the critical factors and their relationships to avoid or prevent urban rail transit accidents. In response to the problems few previous studies have modeled networks for the whole system components of URTS and have focused too much on the identification of specific component states or behaviors without quantitative analysis of inter-component relationships. This paper proposed a risk network construction and analysis method based on complex network theory to identify the critical accident causative factors and their relationships of the URTS system. Firstly, the accident is defined as the combination of a sequence of risk incidents occurring in a certain order and their resulting consequences, and the definitions of risk point and risk incident were given. Secondly, a risk network construction and analysis method based on the combination of accident reports and complex network theory was proposed to obtain the critical factors and their relationships in the process of the accident. At last, this paper constructed the urban rail transit risk point set containing 39 risk points, built the risk network based on 201 accident reports of URTS in China and analyzed the critical risk points and risk relationships. The results show that this study can provide a new perspective for identifying the critical causative factors of URTS accidents and their relationships for practical application in risk analysis and accident prevention. • The new definitions of risk points and incidents are put forward. • A critical risk analysis framework are proposed by complex network theory. • Critical risk points and relationships are identified in the urban rail transit system by the risk network. [ABSTRACT FROM AUTHOR]

In remote sensing–based forest aboveground biomass (AGB) estimation research, data saturation in Landsat and radar data is well known, but how to reduce this problem for improving AGB estimation has not been fully examined. Different vegetation types have their own species composition and stand structure, thus they have different data saturation values in Landsat or radar data. Optical and radar data also have different characteristics in representing forest stand structures, thus effective use of their features may improve AGB estimation. This research examines the effects of Landsat Thematic Mapper (TM) and ALOS PALSAR L-band data and their integrations in forest AGB estimation of Zhejiang Province, China, and the roles of textural images from both datasets. The linear regression models of AGB were conducted by using (1) Landsat TM alone, (2) ALOS PALSAR data alone, (3) their combination as extra bands, and (4) their data fusion, based on non-stratification and stratification of vegetation types, respectively. The results show that (1) overall, Landsat TM data perform better than PALSAR data, but the latter can produce more accurate estimates for bamboo and shrub, and for forests with AGB values less than 60 Mg/ha; (2) the combination of TM and PALSAR data as extra bands can greatly improve AGB estimation performance, but their fusion using the modified high-pass filter resolution-merging technique cannot; (3) textures are indeed valuable in AGB estimation, especially for forests with complex stand structures such as mixed forests and pine forests with understories of broadleaf species; (4) stratification of vegetation types can improve AGB estimation performance; and (5) the results from the linear regression models are characterized by overestimation and underestimation for the smaller and larger AGB values, respectively, and thus, selecting non-linear models or non-parametric algorithms may be needed in future research. [ABSTRACT FROM AUTHOR]

Precise point positioning (PPP) can measure ground motions with a centimetre-level accuracy using only one receiver. It has been widely used in earthquake monitoring and earthquake early warning (EEW) systems. However, traditional PPP highly depends on well-established and robust internet infrastructure for data communication. Along with the broadcast ephemeris, the orbit and clock corrections transmitted by the B2b signals of BeiDou Navigation Satellite System (BDS) can be used to recover the precise products of GPS and BDS. Since the B2b products, namely the satellite state parameter messages, are accessible even without internet infrastructure, PPP with B2b signals is more suitable for real-time applications. This study witnesses the application of PPP with B2b products in seismic monitoring for the first time. The shake table experiments demonstrate that PPP with B2b can reach a millimetre-level accuracy in horizontal in earthquake monitoring for GPS-only, BDS-only and GPS/BDS combination. The application to the 2021 M w 7.4 Maduo earthquake shows that the accuracies of seismic displacements derived from GPS-only PPP with B2b products are 1.7, 2.4 and 1.7 cm in east, north and vertical components, compared with those from GPS-only PPP with final precise products. We therefore conclude that PPP with B2b products has a promising prospect in seismogeodesy and related applications such as EEW and rapid hazard response. [ABSTRACT FROM AUTHOR]

Background: Delta‐like homologue 1 (DLK1), a transmembrane protein, is highly expressed in hepatocellular carcinoma (HCC). We explored whether DLK1‐directed chimeric antigen receptor (CAR) T cells can specifically eliminate DLK1‐positive HCC cells and serve as a therapeutic strategy for HCC immunotherapy. Methods: We first characterized a homemade anti‐human DLK1 monoclonal antibody, sequenced the single‐chain Fragment variable (scFv) and integrated it into the second‐generation CAR lentiviral vector, and then developed the DLK1‐directed CAR‐T cells. The cytotoxic activities of DLK1‐directed CAR‐T cells against different HCC cells were evaluated in vitro and in vivo. Results: The genetically modified human T cells with the DLK1‐directed CARs produced cytotoxic activity against DLK1‐positive HCC cells. Additionally, the DLK1‐directed CARs enhanced T cell proliferation and activation in a DLK1‐dependent manner. Interestingly, the DLK1‐targeted CAR‐T cells significantly inhibited both subcutaneous and peritoneal xenograft tumours derived from human liver cancer cell lines HepG2 or Huh‐7. Conclusion: DLK1‐directed CAR‐T cells specifically suppresses DLK1‐positive HCC cells in vitro and in vivo. This study provides a novel transmembrane antigen DLK1 as a potential therapeutic target appropriate for CAR‐T cell therapy, which may be further developed as a clinical therapeutic strategy for HCC immunotherapy. [ABSTRACT FROM AUTHOR]

Remote sensing-based methods of aboveground biomass (AGB) estimation in forest ecosystems have gained increased attention, and substantial research has been conducted in the past three decades. This paper provides a survey of current biomass estimation methods using remote sensing data and discusses four critical issues – collection of field-based biomass reference data, extraction and selection of suitable variables from remote sensing data, identification of proper algorithms to develop biomass estimation models, and uncertainty analysis to refine the estimation procedure. Additionally, we discuss the impacts of scales on biomass estimation performance and describe a general biomass estimation procedure. Although optical sensor and radar data have been primary sources for AGB estimation, data saturation is an important factor resulting in estimation uncertainty. LIght Detection and Ranging (lidar) can remove data saturation, but limited availability of lidar data prevents its extensive application. This literature survey has indicated the limitations of using single-sensor data for biomass estimation and the importance of integrating multi-sensor/scale remote sensing data to produce accurate estimates over large areas. More research is needed to extract a vertical vegetation structure (e.g. canopy height) from interferometry synthetic aperture radar (InSAR) or optical stereo images to incorporate it into horizontal structures (e.g. canopy cover) in biomass estimation modeling. [ABSTRACT FROM PUBLISHER]

• CS IM-CT (collagen score of the tumor margin minus tumor center) was proposed. • Collagen score was analyzed using multiphoton imaging. • CS IM-CT was an effective prognostic predictor in advanced rectal cancer. • Nomogram with the CS IM-CT can individually predict prognosis in rectal cancer. Little is known about the relationship between collagen and the prognosis of patients with locally advanced rectal cancer (LARC) after neoadjuvant chemoradiotherapy (nCRT). This study aimed to quantitatively analyze collagen alterations, establish a collagen score (CS) in the tumor microenvironment, and evaluate and validate the relationship of the CS with prognosis in these patients. A total of 365 primary patients diagnosed with LARC after nCRT between 2011 and 2018 were retrospectively reviewed (training cohort: 210; independent validation cohort: 155). Multiple collagen features of two fields in the tumor microenvironment, the core of the tumor (CT) and the invasive margin (IM), were derived from multiphoton imaging, and the CS IM-CT was generated using least absolute shrinkage and selection operator (LASSO) Cox regression analysis. The CS IM-CT was created based on 3 features: collagen area, number of collagen fibers and a Gabor textural feature. In the training cohort, the CS IM-CT predicted 3-year disease-free survival (DFS) with an area under the receiver operating characteristic curve (AUROC) of 0.765 (0.675–0.854) and an overall survival (OS) with AUROC of 0.822 (0.734–0.909). Additionally, the CS IM-CT was significantly associated with DFS and OS in the two cohorts. A nomogram with the CS IM-CT was developed and showed good prognostic value predicting a 3-year DFS with an AUROC of 0.826 (0.748–0.905) and an OS with AUROC of 0.882 (0.803–0.960). The CS IM-CT is an effective prognostic marker in patients with LARC after nCRT, and the nomogram with the CS IM-CT can be used to accurately predict the individual prognosis of these patients. [ABSTRACT FROM AUTHOR]

Objective: The primary aim was to assess associations between total steps per day and incident diabetes, whereas the secondary aim was to assess whether the intensity and/or cadence of steps is associated with incident diabetes.Research Design and Methods: Women without physician-diagnosed diabetes (n = 4,838; mean [SD] age 78.9 [6.7] years) were followed up to 6.9 years; 395 developed diabetes. Hip-worn ActiGraph GT3X+ accelerometers worn for 1 week enabled measures of total, light-intensity, and moderate- to vigorous-intensity (MV-intensity) steps per day. Using Cox proportional hazards analysis we modeled adjusted change in the hazard rate for incident diabetes associated with total, light-intensity, and MV-intensity steps per day. We further estimated the proportion of the steps-diabetes association mediated by BMI.Results: On average, participants took 3,729 (SD 2,114) steps/day, of which 1,875 (791) were light-intensity steps and 1,854 ± 1,762 were MV-intensity. More steps per day were associated with a lower hazard rate for incident diabetes. Confounder-adjusted models for a 2,000 steps/day increment yielded hazard ratio (HR) 0.88 (95% CI 0.78-1.00; P = 0.046). After further adjustment for BMI, HR was 0.90 (95% CI 0.80-1.02; P = 0.11). BMI did not significantly mediate the steps-diabetes association (proportion mediated = 17.7% [95% CI -55.0 to 142.0]; P = 0.09]). The relationship between MV-intensity steps per day (HR 0.86 [95% CI 0.74-1.00]; P = 0.04) and incident diabetes was stronger than for light-intensity steps per day (HR 0.97 [95% CI 0.73-1.29]; P = 0.84).Conclusions: These findings suggest that for older adults, more steps per day are associated with lower incident diabetes and MV-intensity steps are most strongly associated with a lower hazard of diabetes. This evidence supports that regular stepping is an important risk factor for type 2 diabetes prevention in older adults. [ABSTRACT FROM AUTHOR]

For earthquake and tsunami early warning and emergency response, the earthquake epicenter and magnitude should be determined rapidly and correctly. Using high-rate GPS observations, we can readily obtain precise and high resolution displacement time series and the seismic waveforms during the earthquake. In this paper, a new algorithm is proposed for estimating the earthquake epicenter and magnitude with the seismic waveforms derived from high-rate GPS data during the earthquake. A case study of the 2008 Wenchuan earthquake is conducted from 1 Hz GPS data and the epicenter and magnitude are determined. Compared with the results issued by the China Seismological Bureau, the estimation error of the epicenter and the magnitude is about 12 km and 0.1 magnitude unit, respectively. It has shown that high-rate GPS could be a new tool feasible for estimating the earthquake epicenter and magnitude, independent of or combined with seismometers. [ABSTRACT FROM AUTHOR]

For the past three decades, ecologists and biogeographers have increasingly incorporated remote sensing and geographical information systems (GISs) to inventory and analyse spatially organized data. Although there are many studies exploring the effects of fine resolution on remote sensing and GIS mapping, there is still a gap on how to identify the most appropriate spatial resolutions for studying landscapes and their structures and dynamics. This study investigated the effects of landscape changes over a 64-year study period at different spatial resolutions using four resampling schemes. The study was conducted on a national wildlife refuge of five land-use and land-cover (LULC) categories using aerial photos recorded in three distant years (1938, 1971, and 2001). This refuge has undergone major landscape changes in the last 64 years. Among the five LULC categories studied, the one that lost the most surface is agriculture; the most gain was made in forest and water. In terms of net change and swapping, agriculture and forest were the most dynamic categories in the National Wildlife Refuge. Our findings showed considerable spatial variability in landscape dynamics at different scales. We specifically observed that hard-classified maps with spatial resolutions of 30 m or finer provided better analysis of landscape dynamics, whereas with soft-classified maps it could go up to 90 m or finer. This implies that there is a range of optimum resolution that could allow for the use of medium-resolution data, such as Landsat, for reliable land change analysis. These findings offer further insight on landscape change analysis at different spatial resolutions and advance our understanding and knowledge on the effect of scale on landscape ecology. [ABSTRACT FROM AUTHOR]

This study aimed to propose a new convolutional neural network (CNN)-based model for spatial resolution enhancement of Landsat images without finer resolution bands for training the CNN-based model. Its performance was assessed using Landsat 8 images from eight different regions that were dominated by various topographic features, and land use and land cover types by comparison with bicubic (BIC), nearest neighbor (NN) and Lanczos (LZ) resampling. It was found that for spatial resolution enhancement of 30 m resolution images to 15 m, 10 m and 7.5 m, the proposed model increased the average peak-signal-to-noise-ratio (PSNR) values by 1.7% to 11.5% compared with the compared methods. The PSNR increases were statistically significantly different from zero at the significant level of.05, but the improvement decreased as the spatial resolution of the input images became finer. Moreover, the deeper the CNN model, the better the performance, but after nine layers, the gain of performance slowed down. This indicates that the proposed algorithm is promising for spatial resolution enhancement of optical images without input of finer spatial resolution images. [ABSTRACT FROM AUTHOR]

Delineating accurate flowlines using digital elevation models is a critical step for overland flow modeling. However, extracting surface flowlines from high‐resolution digital elevation models (HRDEMs) can be biased, partly due to the absence of information on the locations of anthropogenic drainage structures (ADS) such as bridges and culverts. Without the ADS, the roads may act as "digital dams" that prevent accurate delineation of flowlines. However, it is unclear what variables for terrain‐based hydrologic modeling can be used to mitigate the effect of "digital dams." This study assessed the impacts of ADS locations, spatial resolution, depression processing methods, and flow direction algorithms on hydrologic connectivity in an agrarian landscape of Nebraska. The assessment was conducted based on the offset distances between modeled drainage crossings and actual ADS on the road. Results suggested that: (a) stream burning in combination with the D8 or D‐Infinity flow direction algorithm is the best option for modeling surface flowlines from HRDEMs in an agrarian landscape; (b) increasing the HRDEM resolution was found significant for facilitating accurate drainage crossing near ADS locations; and (c) D8 and D‐Infinity flow direction algorithms resulted in similar patterns of drainage crossing at ADS locations. This research is expected to result in improved parameter settings for HRDEMs‐based hydrologic modeling. [ABSTRACT FROM AUTHOR]

Moso bamboo is an evergreen plant that extensively distributes in subtropical regions. Comparing to other forest types, Moso bamboo forest has some unique characteristics: high growth rate, short harvesting rotation, and on/off-year phenomenon. Plant phenology plays an important role in regulating carbon sequestration of the bamboo forest ecosystem. However, it is a challenge task to capture the phenological features of Moso bamboo forests on a regional scale due to frequent change of canopy structures and lack of high spatiotemporal remotely sensed data. The Vegetation and Environment monitoring on a New Micro-Satellite (VENµS) data with high spatiotemporal resolution provide the potential to examine the seasonal change of Moso bamboo forests. This research employs the VENµS time-series data (from January 2018 to December 2019) to analyse the spectral characteristics of on-year/off-year Moso bamboo forests and other two evergreen forest types (i.e., broadleaf forest and coniferous forest). The optimal spectral ranges for examining the seasonal variation of bamboo forests were determined. Three red-edge-based vegetation indices were reconstructed using the Harmonic analysis of time series (Hants) and compared. Red-edge position index (REPI) was selected to identify different phenological periods of Moso bamboo forests and other evergreen forest types. The results show that the spectral range of 730–920 nm in the VENμS data is sensitive to seasonal variation of Moso bamboo forests. The REPI can more effectively identify the two-year growing cycle of the bamboo forests than other vegetation indices, especially the bamboo shoots period. The start of the growing season of the off-year bamboo forest is approximately 50 to 60 days earlier than on-year bamboo forest. The results provided time-series phenological datasets of on-year and off-year Moso bamboo forests, which is valuable for local governments to conduct better ecological management and decision-making. [ABSTRACT FROM AUTHOR]

Increasing the area of planted forests is rather important for compensation the loss of natural forests and slowing down the global warming. Forest growing stem volume (GSV) is a key indicator for monitoring and evaluating the quality of planted forest. To improve the accuracy of planted forest GSV located in south China, four L-band ALOS PALSAR-2 quad-polarimetric synthetic aperture radar (SAR) images were acquired from June to September with short intervals. Polarimetric characteristics (un-fused and fused) derived by the Yamaguchi decomposition from time series SAR images with different intervals were considered as independent variables for the GSV estimation. Then, the general linear model (GLM) obeyed the exponential distribution were proposed to retrieve the stand-level GSV in plantation. The results show that the un-fused power of double bounce scatters and four fused variables derived from single SAR image is highly sensitive to the GSV, and these polarimeric characteristics derived from the time series images more significantly contribute to improved estimation of GSV. Moreover, compared with the estimated GSV using the semi-exponential model, the employed GLM model with less limitations and simple algorithm has a higher saturation level (nearly to 300 m3/ha) and higher sensitivity to high forest GSV values than the semi-exponential model. Furthermore, by reducing the external disturbance with the help of time average, the accuracy of estimated GSV is improved using fused polarimeric characteristics, and the estimation accuracy of forest GSV was improved as the images increase. Using the fused polarimetric characteristics (Dbl×Vol/Odd) and the GLM, the minimum RRMSE was reduced from 33.87% from single SAR image to 24.42% from the time series SAR images. It is implied that the GLM is more suitable for polarimetric characteristics derived from the time series SAR images and has more potential to improve the planted forest GSV. [ABSTRACT FROM AUTHOR]

China has been facing serious land degradation and desertification in its north and northwest arid and semi-arid areas. Monitoring the dynamics of percentage vegetation cover (PVC) using remote sensing imagery in these areas has become critical. However, because these areas are large, remote, and sparsely populated, and also because of the existence of mixed pixels, there have been no accurate and cost-effective methods available for this purpose. Spectral unmixing methods are a good alternative as they do not need field data and are low cost. However, traditional linear spectral unmixing (LSU) methods lack the ability to capture the characteristics of spectral reflectance and scattering from endmembers and their interactions within mixed pixels. Moreover, existing nonlinear spectral unmixing methods, such as random forest (RF) and radial basis function neural network (RBFNN), are often costly because they require field measurements of PVC from a large number of training samples. In this study, a cost-effective approach to mapping PVC in arid and semi-arid areas was proposed. A method for selection and purification of endmembers mainly based on Landsat imagery was first presented. A probability-based spectral unmixing analysis (PBSUA) and a probability-based optimized k nearest-neighbors (PBOkNN) approach were then developed to improve the mapping of PVC in Duolun County in Inner Mongolia, China, using Landsat 8 images and field data from 920 sample plots. The proposed PBSUA and PBOkNN methods were further validated in terms of accuracy and cost-effectiveness by comparison with two LSU methods, with and without purification of endmembers, and two nonlinear approaches, RF and RBFNN. The cost-effectiveness was defined as the reciprocal of cost timing relative root mean square error (RRMSE). The results showed that (1) Probability-based spectral unmixing analysis (PBSUA) was most cost-effective and increased the cost-effectiveness by 29.3% 29.3%, 33.5%, 50.8%, and 53.0% compared with two LSU methods, PBOkNN, RF, and RBFNN, respectively; (2) PBSUA, RF, and RBFNN gave RRMSE values of 22.9%, 21.8%, and 22.8%, respectively, which were not significantly different from each other at the significance level of 0.05. Compatibly, PBOkNN and LSU methods with and without purification of endmembers resulted in significantly greater RRMSE values of 27.5%, 32.4%, and 43.3%, respectively; (3) the average estimates of the sample plots and predicted maps from PBSUA, PBOkNN, RF, and RBFNN fell in the confidence interval of the test plot data, but those from two LSU methods did not, although the LSU with purification of endmembers improved the PVC estimation accuracy by 25.2% compared with the LSU without purification of endmembers. Thus, this study indicated that the proposed PBSUA had great potential for cost-effectively mapping PVC in arid and semi-arid areas. [ABSTRACT FROM AUTHOR]

For the planning and sustainable management of forest resources, well-managed plantations are of great significance to mitigate the decrease of forested areas. Monitoring these planted forests is essential for forest resource inventories. In this study, two ALOS PALSAR-2 quad-polarimetric synthetic aperture radar (SAR) images and ground measurements were employed to estimate growing stem volume (GSV) of Chinese fir plantations located in a hilly area of southern China. To investigate the relationships between forest GSV and polarization characteristics, single and fused variables were derived by the Yamaguchi decomposition and the saturation value of GSV was estimated using a semi-exponential empirical model as a base model. Based on the estimated saturation values and relative root mean square error (RRMSE), the single and fused characteristics and corresponding models were selected and integrated, which led to a novel saturation-based multivariate method used to improve the GSV estimation and mapping of Chinese fir plantations. The new findings included: (1) All the original polarimetric characteristics, statistically, were not significantly correlated with the forest GSV, and their logarithm and ratio transformation fused variables greatly improved the correlations, thus the estimation accuracy of the forest GSV. (2) The logarithm transformation of surface scattering resulted in the greatest saturation, value but the logarithm transformation of double-bounce scattering resulted in the smallest RRMSE of the GSV estimates. (3) Compared with the single transformations, the fused variables led to more reasonable saturation values and obviously reduced the values of RRMSE. (4) The saturation-based multivariate method led to more accurate estimates of the forest GSV than the univariate method, with the smallest value (29.64%) of RRMSE achieved using the set of six variables. This implied that the novel saturation-based multivariate method provided greater potential to improve the estimation and mapping of the forest GSV. [ABSTRACT FROM AUTHOR]

We aimed to investigate whether geniposide, a main component extracted from Gardenia jasminoides Ellis fruit, could exert neuroprotective functions against traumatic brain injury (TBI). Enzyme-linked immunosorbent assay (ELISA) was used for detection of plasma cytokines. Real-time polymerase chain reaction (RT-PCR) was employed for measurements of mRNA levels of cytokines. Neurological outcomes were evaluated by modified neurological severity score (mNSS) and Rota-Rod. Blood–brain barrier (BBB) integrity and brain edema were assessed. Protein expression was tested by Western blot. The plasma levels of interleukin (IL)-1β, IL-6, IL-8 and IL-10 were all elevated in patients with TBI compared to those of healthy controls. TBI rats treated with geniposide showed lower mNSS and longer fall latency time than untreated TBI rats. BBB integrity was maintained and brain edema was reduced by geniposide treatment in TBI rats. Plasma levels of IL-1β, IL-6 and IL-8 were significantly repressed by geniposide treatment in TBI rats, whereas IL-10 level was upregulated. mRNA expression levels of these cytokines in the brain tissues of TBI rats exhibited the same trends of changes. By testing p38 mitogen-activated protein kinase and NF-κB p65 activities, it was observed that phosphorylated (p)-p38 and p-p65 were dramatically inhibited by geniposide. In conclusion, geniposide exerts neuroprotective functions against TBI by inhibiting p-p38 and p-p65. [ABSTRACT FROM AUTHOR]

Elevated expression and genetic aberration of IRTKS, also named as BAIAP2L1, have been observed in many tumors, especially in tumor progression. however, the molecular and cellular mechanisms involved in the IRTKS-enhanced tumor progression are obscure. Here we show that higher IRTKS level specifically increases histone H3 lysine 9 trimethylation (H3K9me3) by promoting accumulation of the histone methyltransferase SETDB1. Furthermore, we reveal that IRTKS recruits the deubiquitinase OTUD4 to remove Lys48-linked polyubiquitination at K182/K1050 sites of SETDB1, thus blocking SETDB1 degradation via the ubiquitin-proteasome pathway. Interestingly, the enhanced IRTKS–OTUD4–SETDB1–H3K9me3 axis leads to a general decrease in chromatin accessibility, which inhibits transcription of CDH1 encoding E-cadherin, a key molecule essential for maintaining epithelial cell phenotype, and therefore results in epithelial-mesenchymal transition (EMT) and malignant cell metastasis. Clinically, the elevated IRTKS levels in tumor specimens correlate with SETDB1 levels, but negatively associate with survival time. Our data reveal a novel mechanism for the IRTKS-enhanced tumor progression, where IRTKS cooperates with OTUD4 to enhance SETDB1-mediated H3K9 trimethylation that promotes tumor metastasis via suppressing E-cadherin expression. This study also provides a potential approach to reduce the activity and stability of the known therapeutic target SETDB1 possibly through regulating IRTKS or deubiquitinase OTUD4. • Higher levels of IRTKS increase H3K9me3 by promoting SETDB1 accuumulation. • IRTKS recruits the deubiquitinase OTUD4 to blocking SETDB1 degradation. • The enhanced IRTKS–OTUD4–SETDB1–H3K9me3 axis decreases chromatin accessibility, and inhibits E-cadherin transcription, resulting in EMT and tumor metastasis. • Clinically, elevated IRTKS levels in tumor correlate with SETDB1 levels, and negatively associate with survival time. [ABSTRACT FROM AUTHOR]

Estimating the net primary production (NPP) of vegetation is essential for eco-environment conservation and carbon cycle research. Remote sensing techniques, combined with algorithm models, have been proven to be promising methods for NPP estimation. High-precision and real-time NPP monitoring in heterogeneous areas requires high spatio-temporal resolution remote sensing data, which are not easy to acquire by single remote sensors, especially in cloudy weather. This study proposes to fuse images of different sensors to provide high spatio-temporal resolution data for NPP estimation in cloud-prone areas. Firstly, the time series Normalized Difference Vegetation Index (NDVI) with a spatial resolution of 30 m and a temporal resolution of 16 days, are obtained by the enhanced spatial and temporal adaptive reflectance fusion model (ESTARFM). Then, the time series NDVI data, combined with meteorological data are input into an improved Carnegie–Ames–Stanford Approach (CASA) model for NPP estimation. This method is validated by a case study of a heavily urbanized area, in the middle reaches of the Yangtze River in China. The results indicate that the NPP estimated by the fused NDVI data has more detailed spatial information than by using the MODIS data. The results show a strong correlation between the actual Landsat8 NDVI and the fused NDVI images, which means that the accuracy of synthetic NDVI images (a 16 day interval and a 30 m resolution) is reliable, and it can provide superior inputs for accurate estimations of a NPP time series. The correlation coefficient (R) and root mean square error between the NPP, based on the fused NDVI and the measured NPP, are 0.66 and 14.280 g C/(m2·yr), respectively, indicating a good consistency. The small discrepancy is caused by the uncertainties of fused NDVI, measurement errors, conversion errors, and other factors in the CASA model. In this study, we achieved NPP with high spatial and temporal resolutions, which can provide higher accuracies of NPP data for analyzing the carbon cycling heavily urbanized areas, compared with similar studies using mono-temporal NPP data. The spatio-temporal fusion technique is an effective way of generating high spatio-temporal resolution images from different sensors, thereby providing enough data for NPP monitoring in urbanized areas. [ABSTRACT FROM AUTHOR]

Rice is one of the world's major staple foods, especially in China. Highly accurate monitoring on rice-producing land is, therefore, crucial for assessing food supplies and productivity. Recently, the deep-learning convolutional neural network (CNN) has achieved considerable success in remote-sensing data analysis. A CNN-based paddy-rice mapping method using the multitemporal Landsat 8, phenology data, and land-surface temperature (LST) was developed during this study. First, the spatial–temporal adaptive reflectance fusion model (STARFM) was used to blend the moderate-resolution imaging spectroradiometer (MODIS) and Landsat data for obtaining multitemporal Landsat-like data. Subsequently, the threshold method is applied to derive the phenological variables from the Landsat-like (Normalized difference vegetation index) NDVI time series. Then, a generalized single-channel algorithm was employed to derive LST from the Landsat 8. Finally, multitemporal Landsat 8 spectral images, combined with phenology and LST data, were employed to extract paddy-rice information using a patch-based deep-learning CNN algorithm. The results show that the proposed method achieved an overall accuracy of 97.06% and a Kappa coefficient of 0.91, which are 6.43% and 0.07 higher than that of the support vector machine method, and 7.68% and 0.09 higher than that of the random forest method, respectively. Moreover, the Landsat-derived rice area is strongly correlated (R2 = 0.9945) with government statistical data, demonstrating that the proposed method has potential in large-scale paddy-rice mapping using moderate spatial resolution images. [ABSTRACT FROM AUTHOR]

Land degradation and desertification in arid and semi-arid areas is of great concern. Accurately mapping percentage vegetation cover (PVC) of the areas is critical but challenging because the areas are often remote, sparsely vegetated, and rarely populated, and it is difficult to collect field observations of PVC. Traditional methods such as regression modeling cannot provide accurate predictions of PVC in the areas. Nonparametric constant k-nearest neighbors (Cons_kNN) has been widely used in estimation of forest parameters and is a good alternative because of its flexibility. However, using a globally constant k value in Cons_kNN limits its ability of increasing prediction accuracy because the spatial variability of PVC in the areas leads to spatially variable k values. In this study, a novel method that spatially optimizes determining the spatially variable k values of Cons_kNN, denoted with Opt_kNN, was proposed to map the PVC in both Duolun and Kangbao County located in Inner Mongolia and Hebei Province of China, respectively, using Landsat 8 images and sample plot data. The Opt_kNN was compared with Cons_kNN, a linear stepwise regression (LSR), a geographically weighted regression (GWR), and random forests (RF) to improve the mapping for the study areas. The results showed that (1) most of the red and near infrared band relevant vegetation indices derived from the Landsat 8 images had significant contributions to improving the mapping accuracy; (2) compared with LSR, GWR, RF and Cons_kNN, Opt_kNN resulted in consistently higher prediction accuracies of PVC and decreased relative root mean square errors by 5%, 11%, 5%, and 3%, respectively, for Duolun, and 12%, 1%, 23%, and 9%, respectively, for Kangbao. The Opt_kNN also led to spatially variable and locally optimal k values, which made it possible to automatically and locally optimize k values; and (3) the RF that has become very popular in recent years did not perform the predictions better than the Opt_kNN for the both areas. Thus, the proposed method is very promising to improve mapping the PVC in the arid and semi-arid areas. [ABSTRACT FROM AUTHOR]

Traditional parametric methods for classification of land use and land cover (LULC) types using remote sensing imagery assume a global distribution model and fail to consider local variation of categorical variables. Differently, non-parametric methods do not make any statistical assumptions but are typically sensitive to the sample sizes of training sample data that usually require a high cost to collect in the field. Geostatistical classifiers, such as indicator kriging and simulation, are local variability-based methods that exhibit great potential for image-based classification of LULC types. However, variogram models required are highly sensitive to the spatial configuration of training samples as well as sample size given a study area. Moreover, when a large number of spectral variables are considered into kriging systems, modeling the variograms and cross-variograms would be problematic. To circumvent these issues, this study extended the geostatistical methods from a 2-dimensional geographic space to a m -dimensional image feature space to derive feature-space indicator variograms (FSIVs). Moreover, a novel stochastic simulation classification algorithm, Feature-Space Indicator Simulation (FSIS), was proposed and examined for classification of LULC types in Duolun County located in Inner Mongolia and in Huang-Feng-Qiao (HFQ) forest farm, Hunan of China. In Duolun, six LULC types were involved and in HFQ a complicated forest landscape consisting of nine forest types plus water, built-up area, and agricultural/bare soil, was classified. The classification results of FSIS were compared with another feature-space geostatistical classifier – feature-space indicator kriging (FSIK), a traditional parametric method – maximum likelihood (ML), a widely used nonparametric method – support vector machine (SVM), and a recently popular method – random forest (RF). The results showed that compared with ML, SVM and RF, in both study areas FSIS statistically significantly increased the accuracy of the classifications by 10.0–29.9% for percentage correct and 19.0–47.6% for Kappa statistic. Compared with FSIK, FSIS also improved the classification accuracy but the accuracy increases were relatively smaller with the percentages correct of 3.5% and 7.6% and the Kappa values of 4.6% and 8.6% for Duolun and HFQ, respectively. Moreover, FSIS led to the spatial uncertainties of the classification estimates as the quality measure of the estimates. In addition, the results also demonstrated that FSIVs were sensitive to the within-class heterogeneity but not very much to the size of training samples. Overall, FSIS exhibited the greater potential to improve the classification accuracy of LULC and forest types using remote sensing image. [ABSTRACT FROM AUTHOR]

To increase the accuracy of predicting net primary productivity (NPP), in this study, Carnegie–Ames–Stanford Approach (CASA) model was modified by developing new methods to estimate absorbed photosynthetically active radiation or fraction of photosynthetically active radiation (FPAR) and water stress coefficient (WSC). In the modified model, FPAR was derived based on its non-linear relationship with leaf area index. Moreover, WSC was estimated using leaf water potential from soil moisture instead of a traditional evapotranspiration-based method. This study was conducted in Baiyun District area of Guangzhou, China, using Gaofen-1 (GF-1), Landsat 7, and Moderate Resolution Imaging Spectroradiometer (MODIS) satellite images. The predictions from the original and three modified CASA models and MODIS NPP product MOD17A3 were compared with field observations. The results showed that all the CASA-based models led to similar spatial distributions of forest aboveground NPP estimates. Overall, the estimates increased with elevation because the valley bottoms were dominated by developed or urbanized areas whereas the hillslopes and hilltops were largely vegetated. Based on root mean square error (RMSE) and relative RMSE between the observed and predicted values, the CASA model that integrated the modifications of both FPAR and WSC increased the estimation accuracy of NPP by 8.1% over the original one. The increase in accuracy was mainly contributed by the modification of FPAR. This suggested that the modification of FPAR provided greater potential than that of WSC for improving the predictions of CASA model. Compared to the CASA models, MOD17A3 had lower accuracy of aboveground NPP estimates. This study also showed that the fine spatial resolution GF-1 image provided a new source of data used to estimate NPP of forest ecosystems. [ABSTRACT FROM PUBLISHER]

Estimating individual tree diameters at breast height (DBH) from delineated crowns and tree heights on the basis of airborne light detection and ranging (LiDAR) data provides a good opportunity for large-scale forest inventory. Generally, ground-based measurements are more accurate, but LiDAR data and derived DBH values can be obtained over larger areas for a relatively smaller cost if a right procedure is developed. A nonlinear least squares (NLS) regression is not an appropriate approach to predict the aboveground biomass (AGB) of individual trees from the estimated DBH because both the response variable and the regressor are subject to measurement errors. In this study, a system of compatible individual tree DBH and AGB error-in-variable models was developed using error-in-variable regression techniques based on both airborne LiDAR and field-measured datasets of individual Picea crassifolia Kom. trees, collected in northwestern China. Two parameter estimation algorithms, i.e., the two-stage error-in-variable model (TSEM) and the nonlinear seemingly unrelated regression (NSUR), were proposed for estimating the parameters in the developed system of compatible individual tree DBH and AGB error-in-variable models. Moreover, two model structures were applied to estimate AGB for comparison purposes: NLS with AGB estimation depending on DBH (NLS&DD) and NLS with AGB estimation not depending on DBH (NLS&NDD). The results showed that both TSEM and NSUR led to almost the same parameter estimates for the developed system. Moreover, the developed system effectively accounted for the inherent correlation between DBH and AGB as well as for the effects of measurement errors in the DBH on the predictions of AGB, whereas NLS&DD and NLS&NDD did not. A leave-one-out cross-validation indicated that the prediction accuracy of the developed system of compatible individual tree DBH and AGB error-in-variable models with NSUR was the highest among those estimated by the four methods evaluated, but, statistically, the accuracy improvement was not significantly different from zero. The main reason might be that, except for the measurement errors, other source errors were ignored in the modeling. This study implies that, overall, the proposed method provides the potential to expand the estimations of both DBH and AGB from individual trees to stands by combining the error-in-variable modeling and LiDAR data and improve their estimation accuracies, but its application needs to be further validated by conducting a systematical uncertainty analysis of various source errors in a future study. [ABSTRACT FROM AUTHOR]

In this study, an individual tree crown ratio (CR) model was developed with a data set from a total of 3134 Mongolian oak (Quercus mongolica) trees within 112 sample plots allocated in Wangqing Forest Bureau of northeast China. Because of high correlation among the observations taken from the same sampling plots, the random effects at levels of both blocks defined as stands that have different site conditions and plots were taken into account to develop a nested two-level nonlinear mixed-effect model. Various stand and tree characteristics were assessed to explore their contributions to improvement of model prediction. Diameter at breast height, plot dominant tree height and plot dominant tree diameter were found to be significant predictors. Exponential model with plot dominant tree height as a predictor had a stronger ability to account for the heteroskedasticity. When random effects were modeled at block level alone, the correlations among the residuals remained significant. These correlations were successfully reduced when random effects were modeled at both block and plot levels. The random effects from the interaction of blocks and sample plots on tree CR were substantially large. The model that took into account both the block effect and the interaction of blocks and sample plots had higher prediction accuracy than the one with the block effect and population average considered alone. Introducing stand density into the model through dummy variables could further improve its prediction. This implied that the developed method for developing tree CR models of Mongolian oak is promising and can be applied to similar studies for other tree species. [ABSTRACT FROM AUTHOR]

Abstract: Zoigê alpine wetland located at the Qinghai–Tibet Plateau, China, is the largest alpine wetland in the world and very sensitive to global climate changes. However, little is known of the dynamics of soil water soluble organic carbon (WSOC), light fraction organic carbon (LFOC), soil organic carbon (SOC) and its indicators in this alpine wetland. In this study, four wetland sites and one degraded wetland site in the Zoigê alpine wetland were selected to investigate the relationships of stable isotope and labile organic carbon dynamics with groundwater elevation or water table level. We also examine if the δ13C abundance, WSOC, and LFOC can be used as potential indicators of SOC dynamics in these wetland sites. The results showed that the plant dominant species in the degraded wetland site were different from those in the non-degraded wetland sites. Moreover, the declining water table did not significantly lower foliar δ13C, C and N concentrations and C/N ratio of dominant plant species, but led to a significant decrease of soil SOC, WSOC and LFOC concentrations and an increase of soil δ13C. In the wetland sites, SOC was significantly correlated with soil δ13C, WSOC, and LFOC. Our regression analysis showed that LFOC was the best potential indicator of SOC dynamics with WSOC and soil δ13C being less useful as indicators, respectively. In addition, the absolute slope coefficients of the regression models increased as the soil depth increased, which indicated that the SOC turnover increased with soil depth. [Copyright &y& Elsevier]

The degradation of permafrost in the Earth's cold regions due to climate warming and human activities is of increasing concern because of its impact on ecosystems and land management, thus, monitoring the degradation becomes critical. This study aimed to demonstrate a method in which Sentinel-1 DInSAR and MODIS data were combined to estimate and map seasonal and long-term land surface deformation (subsidence/uplifting) and permafrost active layer thickness (ALT) in Donnelly Training Area (DTA) in Alaska. The coherence (similarity) between the paired SAR images was analyzed for their applicability. A sensitivity analysis and accuracy assessment of the estimates were conducted to account for their quality. Results showed that the estimated seasonal subsidence mainly occurring in June and July was in the range of 0 to −0.43 m, while the estimated uplifting mainly happening from September to May of next year was as great as +0.34 m. Moreover, the estimated long-term (2015 to 2018) subsidence and uplifting were mostly distributed in the southern and northern parts of DTA, respectively. The spatially variable time delays led to the ALT estimates greater along the east river and in the west and south parts, and smaller in the north parts. At the significance level of 0.05, the coherence estimates of the paired images were significantly different from zero and compared with the referenced predictions from a widely used annual prediction model, the average residual of the ALT estimates did not significantly differ from zero at the significance level of 0.05. The spatial distributions of the uncertainties for the seasonal surface deformation estimates were similar to those of the input uncertainties from the estimates of model coefficients, the phase change, the modelling error, and the image pairs. Thus, the DInSAR image based method coupled with MODIS data offered the potential of mapping and monitoring the dynamics of permafrost environment for the cold regions such as DTA in which collecting field observations is difficult and costly. This study also enhanced understanding spatiotemporal variability of permafrost deformation in DTA and provided guidelines for developing a near real-time monitoring system of the permafrost environment. • Permafrost degradation due to climate warming and human activity is of great concern. • Land surface deformation of an installation in Alaska is mapped by SAR&MODIS images. • A quality assessment of the land surface deformation estimates is conducted. • The accuracy of the estimates is compatible with that from a widely used model. • This study provides guidelines for developing a near real-time monitoring system. [ABSTRACT FROM AUTHOR]

For the same antenna type, numerical differences among the phase center correction (PCC) models released by different institutes can reach several millimeters, which is far beyond the nominal calibration accuracy. This contribution introduces a new method to evaluate the consistency of these PCC models. We first investigated the coupling of phase center offset (PCO) and phase center variation (PCV) through simulation experiments, and the results show that the calibrated PCO values under different strategies may result in large differences, and so do PCV values due to strong coupling with PCO. This is further confirmed by field calibration experiments. Moreover, a new datum parameter is introduced to equivalently transform the PCC models to assess the consistency of PCC models of the same antenna type calibrated under different strategies or by different facilities. It is also essential to perform consistency analysis of PCC models in the coordinate domain. We further investigated these PCC models through a simulated positioning experiment. The results show that millimeter-level consistency of PCC models will lead to the same level of positioning precision in the coordinate domain. Moreover, as a comparison, both baseline positioning and PPP were performed with an antenna-type JAV_RINGANT_G3T NONE based on real observations. Multiday results showed that the average RMS of the positioning differences between PCC models from robot and anechoic chamber calibration is less than 1 mm for the baseline solution and 4 mm for the PPP solution, although the PCC model differences can reach 6 mm in L1 and 4 mm in L2, respectively. Finally, we also investigated the distribution of position biases without PCC or with inaccurate PCC. Considering the actual GPS constellation, we found that position biases have a strong correlation with latitude, if PCV values fluctuate greatly with the users' elevation angle. [ABSTRACT FROM AUTHOR]

For earthquake and tsunami early warning and emergency response, earthquake magnitude is the crucial parameter to be determined rapidly and correctly. However, a reliable and rapid measurement of the magnitude of an earthquake is a challenging problem, especially for large earthquakes (M > 8). Here, the magnitude is determined based on the GPS displacement waveform derived from real-time precise point positioning (RTPPP). RTPPP results are evaluated with an accuracy of 1 cm in the horizontal components and 2–3 cm in the vertical components, indicating that the RTPPP is capable of detecting seismic waves with amplitude of 1 cm horizontally and 2–3 cm vertically with a confidence level of 95 per cent. In order to estimate the magnitude, the unique information provided by the GPS displacement waveform is the horizontal peak displacement amplitude. We show that the empirical relation of Gutenberg (1945) between peak displacement and magnitude holds up to nearly magnitude 9.0 when displacements are measured with GPS. We tested the proposed method for three large earthquakes. For the 2010 Mw 7.2 El Mayor-Cucapah earthquake, our method provides a magnitude of M7.18 ± 0.18. For the 2011 Mw 9.0 Tohoku-oki earthquake the estimated magnitude is M8.74 ± 0.06, and for the 2010 Mw 8.8 Maule earthquake the value is M8.7 ± 0.1 after excluding some near-field stations. We, therefore, conclude that depending on the availability of high-rate GPS observations, a robust value of magnitude up to 9.0 for a point source earthquake can be estimated within tens of seconds or a few minutes after an event using a few GPS stations close to the epicentre. The rapid magnitude could be as a pre-requisite for tsunami early warning, fast source inversion and emergency response is feasible. [ABSTRACT FROM AUTHOR]

Abstract: This paper explores the geography of ecosystem service benefits, the journey from ecosystem source to human beneficiary, using a comparative case study of the 2555 ha Cache River wetlands in rural southern Illinois and the 362 ha Des Plaines River wetlands in urban northeastern Illinois. Applying log-linear and exponential distance decay functions from source ecosystems to residents of zip code units yields annual economic values for the Cache River of $28,258–$2,548,793,956 and for the Des Plaines of $531,926–$216,284,749, with the Cache having greater value at shallow rates of spatial discounting and the Des Plaines at steeper rates. These extraordinarily wide ranges in estimated value highlight the essential importance of spatial discounting in ecosystem service valuation and, to a lesser extent, the importance of the spatial unit to which they are applied. They also highlight the dearth of empirical knowledge on the geographic context of ecosystem service values and the need to address critical research questions. [ABSTRACT FROM AUTHOR]

Northern China has been long threatened by aeolian desertification. In recent years, all levels of the Chinese government have performed a series of ecological protection and sand control projects. To grasp the implementation effects of these projects and adjust policies in time, it is necessary to understand the process of aeolian desertification quickly and accurately. Remote sensing technologies play an irreplaceable role in aeolian desertification monitoring. In this study, the Zhenglan Banner, which is in the hinterland of the Hunshandake Sandy Land, was considered as the research area. Based on unmanned aerial vehicle (UAV) images, ground survey data, and Landsat images called in Google Earth Engine (GEE), the aeolian desertified land (ADL) in 2000, 2004, 2010, 2015, and 2019 was extracted using spectral mixture analysis. A desertification index (DI) was constructed to evaluate the spatial and temporal dynamics of the ADL in the Zhenglan Banner. Finally, a residual analysis explored the driving forces of aeolian desertification. The results showed that (1) the ADL area in the Zhenglan Banner has been trending downwards over the past 20 years but rebounded from 2004 to 2010; (2) over the past 20 years, the area of slightly, moderately, and severely desertified land has decreased at annual rates of 0.4%, 2.7%, and 3.4%, respectively; (3) human activities had significantly positive and negative impacts on the aeolian desertification trend for 20.0% and 21.0% of the study area, respectively, but not for the rest. This paper explored new techniques for rapid aeolian desertification monitoring and is of great significance for controlling and managing aeolian desertification in this region. [ABSTRACT FROM AUTHOR]

Forest growing stem volume (GSV) reflects the richness of forest resources as well as the quality of forest ecosystems. Remote sensing technology enables robust and efficient GSV estimation as it greatly reduces the survey time and cost while facilitating periodic monitoring. Given its red edge bands and a short revisit time period, Sentinel-2 images were selected for the GSV estimation in Wangyedian forest farm, Inner Mongolia, China. The variable combination was shown to significantly affect the accuracy of the estimation model. After extracting spectral variables, texture features, and topographic factors, a stepwise random forest (SRF) method was proposed to select variable combinations and establish random forest regressions (RFR) for GSV estimation. The linear stepwise regression (LSR), Boruta, Variable Selection Using Random Forests (VSURF), and random forest (RF) methods were then used as references for comparison with the proposed SRF for selection of predictors and GSV estimation. Combined with the observed GSV data and the Sentinel-2 images, the distributions of GSV were generated by the RFR models with the variable combinations determined by the LSR, RF, Boruta, VSURF, and SRF. The results show that the texture features of Sentinel-2's red edge bands can significantly improve the accuracy of GSV estimation. The SRF method can effectively select the optimal variable combination, and the SRF-based model results in the highest estimation accuracy with the decreases of relative root mean square error by 16.4%, 14.4%, 16.3%, and 10.6% compared with those from the LSR-, RF-, Boruta-, and VSURF-based models, respectively. The GSV distribution generated by the SRF-based model matched that of the field observations well. The results of this study are expected to provide a reference for GSV estimation of coniferous plantations. [ABSTRACT FROM AUTHOR]

Forest canopy height is one of the most important spatial characteristics for forest resource inventories and forest ecosystem modeling. Light detection and ranging (LiDAR) can be used to accurately detect canopy surface and terrain information from the backscattering signals of laser pulses, while photogrammetry tends to accurately depict the canopy surface envelope. The spatial differences between the canopy surfaces estimated by LiDAR and photogrammetry have not been investigated in depth. Thus, this study aims to assess LiDAR and photogrammetry point clouds and analyze the spatial differences in canopy heights. The study site is located in the Jigongshan National Nature Reserve of Henan Province, Central China. Six data sets, including one LiDAR data set and five photogrammetry data sets captured from an unmanned aerial vehicle (UAV), were used to estimate the forest canopy heights. Three spatial distribution descriptors, namely, the effective cell ratio (ECR), point cloud homogeneity (PCH) and point cloud redundancy (PCR), were developed to assess the LiDAR and photogrammetry point clouds in the grid. The ordinary neighbor (ON) and constrained neighbor (CN) interpolation algorithms were used to fill void cells in digital surface models (DSMs) and canopy height models (CHMs). The CN algorithm could be used to distinguish small and large holes in the CHMs. The optimal spatial resolution was analyzed according to the ECR changes of DSMs or CHMs resulting from the CN algorithms. Large negative and positive variations were observed between the LiDAR and photogrammetry canopy heights. The stratified mean difference in canopy heights increased gradually from negative to positive when the canopy heights were greater than 3 m, which means that photogrammetry tends to overestimate low canopy heights and underestimate high canopy heights. The CN interpolation algorithm achieved smaller relative root mean square errors than the ON interpolation algorithm. This article provides an operational method for the spatial assessment of point clouds and suggests that the variations between LiDAR and photogrammetry CHMs should be considered when modeling forest parameters. [ABSTRACT FROM AUTHOR]

The forest growth and yield models, which are used as important decision-support tools in forest management, are commonly based on the individual tree characteristics, such as diameter at breast height (DBH), crown ratio, and height to crown base (HCB). Taking direct measurements for DBH and HCB through the ground-based methods is cumbersome and costly. The indirect method of getting such information is possible from remote sensing databases, which can be used to build DBH and HCB prediction models. The DBH and HCB of the same trees are significantly correlated, and so their inherent correlations need to be appropriately accounted for in the DBH and HCB models. However, all the existing DBH and HCB models, including models based on light detection and ranging (LiDAR) have ignored such correlations and thus failed to account for the compatibility of DBH and HCB estimates, in addition to disregarding measurement errors. To address these problems, we developed a compatible simultaneous equation system of DBH and HCB error-in-variable (EIV) models using LiDAR-derived data and ground-measurements for 510 Picea crassifolia Kom trees in northwest China. Four versatile algorithms, such as nonlinear seemingly unrelated regression (NSUR), two-stage least square (2SLS) regression, three-stage least square (3SLS) regression, and full information maximum likelihood (FIML) were evaluated for their estimating efficiencies and precisions for a simultaneous equation system of DBH and HCB EIV models. In addition, two other model structures, namely, nonlinear least squares with HCB estimation not based on the DBH (NLS and NBD) and nonlinear least squares with HCB estimation based on the DBH (NLS and BD) were also developed, and their fitting precisions with a simultaneous equation system compared. The leave-one-out cross-validation method was applied to evaluate all estimating algorithms and their resulting models. We found that only the simultaneous equation system could illustrate the effect of errors associated with the regressors on the response variables (DBH and HCB) and guaranteed the compatibility between the DBH and HCB models at an individual level. In addition, such an established system also effectively accounted for the inherent correlations between DBH with HCB. However, both the NLS and BD model and the NLS and NBD model did not show these properties. The precision of a simultaneous equation system developed using NSUR appeared the best among all the evaluated algorithms. Our equation system does not require the stand-level information as input, but it does require the information of tree height, crown width, and crown projection area, all of which can be readily derived from LiDAR imagery using the delineation algorithms and ground-based DBH measurements. Our results indicate that NSUR is a more reliable and quicker algorithm for developing DBH and HCB models using large scale LiDAR-based datasets. The novelty of this study is that the compatibility problem of the DBH model and the HCB EIV model was properly addressed, and the potential algorithms were compared to choose the most suitable one (NSUR). The presented method and algorithm will be useful for establishing similar compatible equation systems of tree DBH and HCB EIV models for other tree species. [ABSTRACT FROM AUTHOR]

As an important vegetation canopy parameter, the leaf area index (LAI) plays a critical role in forest growth modeling and vegetation health assessment. Estimating LAI is helpful for understanding vegetation growth and global ecological processes. Machine learning methods such as k-nearest neighbors (kNN) and random forest (RF) with remote sensing images have been widely used for mapping LAI. However, the accuracy of mapping LAI in arid and semi-arid areas using these methods is limited due to remote and large areas, the high cost of collecting field data, and the great spatial variability of the vegetation canopy. Here, a novel and modified kNN method was presented for mapping LAI in arid and semi-arid areas of China using Sentinel-2 and Landsat 8 images with field data collected in Ganzhou and Kangbao of China. The modified kNN was developed by integrating the traditional kNN estimation and RF classification. The results were compared with those from kNN and RF regression alone using three sets of input predictors: (i) spectral reflectance bands (input 1); (ii) vegetation indices (input 2); and (iii) a combination of spectral reflectance bands and vegetation indices (input 3). Our analysis showed that in Ganzhou, the red-edge bands of the Sentinel-2 image had a high correlation with LAI. Using the red-edge band-derived vegetation indices increased the accuracy of mapping LAI compared with using other spectral variables. Among the three sets of input predictors, input 3 resulted in the highest prediction accuracy. Based on the combination, the values of RMSE obtained by the traditional kNN, RF, and modified kNN were 0.526, 0.523, and 0.372, respectively, and the modified kNN significantly improved the accuracy of LAI prediction by 29.3% and 28.9% compared with the kNN and RF alone, respectively. A similar improvement was achieved for input 1 and input 2. In Kangbao, the improvement of the prediction accuracy obtained by the modified kNN was 31.4% compared with both the kNN and RF. Therefore, this study implied that the modified kNN provided the potential to improve the accuracy of mapping LAI in arid and semi-arid regions using the images. [ABSTRACT FROM AUTHOR]

There has been substantial research for estimating and mapping soil moisture content (SMC) of large areas using remotely sensed images by developing models of soil thermal inertia (STI). However, it is still a great challenge to accurately estimate SMC because of the impact of vegetation canopies and vegetation-induced shadows in mixed pixels on the estimates. In this study, a new method was developed to increase the estimation accuracy of SMC for an irrigated area located in YingKe of Heihe, China, using ASTER data. In the method, an original model of estimating bare STI was modified by decomposing a mixed pixel into three components, bare soil, vegetated soil, and shaded soil, as well as extracting their fractions using a spectral unmixing analysis and then deriving their fluxes. Moreover, the 90 m spatial resolution thermal images were scaled down to the 15 m spatial resolution by data fusion of a discrete wavelet transform (DWT) and re-sampling using the nearest neighbor method (NNM). The modified model was compared with the original model based on the mean absolute error (MAE) and relative root mean square error (RRMSE) between the SMC estimates and observations from 30 validation soil samples. The results indicated that compared to the original model based on the parallel dual layer, the modified STI model based on the serial dual layer statistically significantly decreased the MAE and RRMSE of the SMC estimates by 63.0–63.2% and 63.0–63.5%, respectively. The 15 m spatial resolution thermal bands obtained by the DWT data fusion provided more detailed information of SMC but did not significantly improve its estimation accuracy than the 15 m spatial resolution thermal bands by re-sampling using NNM. This implied that the novel method offered insights on how to increase the accuracy of retrieving SMC estimates in vegetated areas. [ABSTRACT FROM AUTHOR]

Unplanned urban settlements exist worldwide. The geospatial information of these areas is critical for urban management and reconstruction planning but usually unavailable. Automatically characterizing individual buildings in the unplanned urban village using remote sensing imagery is very challenging due to complex landscapes and high-density settlements. The newly emerging deep learning method provides the potential to characterize individual buildings in a complex urban village. This study proposed an urban village mapping paradigm based on U-net deep learning architecture. The study area is located in Guangzhou City, China. The Worldview satellite image with eight pan-sharpened bands at a 0.5-m spatial resolution and building boundary vector file were used as research purposes. There are ten sites of the urban villages included in this scene of the Worldview image. The deep neural network model was trained and tested based on the selected six and four sites of the urban village, respectively. Models for building segmentation and classification were both trained and tested. The results indicated that the U-net model reached overall accuracy over 86% for building segmentation and over 83% for the classification. The F1-score ranged from 0.9 to 0.98 for the segmentation, and from 0.63 to 0.88 for the classification. The Interaction over Union reached over 90% for the segmentation and 86% for the classification. The superiority of the deep learning method has been demonstrated through comparison with Random Forest and object-based image analysis. This study fully showed the feasibility, efficiency, and potential of the deep learning in delineating individual buildings in the high-density urban village. More importantly, this study implied that through deep learning methods, mapping unplanned urban settlements could further characterize individual buildings with considerable accuracy. [ABSTRACT FROM AUTHOR]

Rapidly advancing airborne laser scanning technology has become greatly useful to estimate tree- and stand-level variables at a large scale using high spatial resolution data. Compared with that of ground measurements, the accuracy of the inferred information of diameter at breast height (DBH) from a remotely sensed database and the models developed with traditional regression approaches (e.g., ordinary least square regression) may not be sufficient. Thus, this regression approach is no longer appropriate to develop accurate models and predict DBH from remotely sensed-related variables because DBH is subject to the random effects of forest stands. This study developed a generalized nonlinear mixed-effects DBH estimation model from remotely sensed imagery data. The light detection and ranging (LiDAR)-derived stand canopy density, crown projection area, and tree height were used as predictors in the DBH estimation model. These variables can be more readily measured over an extensive forest area with higher accuracy compared to the conventional field-based methods. The airborne LiDAR data for a total of 402 Picea crassifolia Kom trees on a sample plot that were divided into 16 sub-sample plots and located in the most important distribution region of western China were used. The leave-one sub-sample plot-out cross-validation method was applied to evaluate the model's prediction accuracy. The results indicated that the random effects of the sub-sample plot on the prediction of DBH were large and their inclusion into the DBH model significantly improved the prediction accuracy. The prediction accuracy of the proposed model at the mean (M) response was also substantially improved relative to the accuracy obtained from the base model. Among several tree selection alternatives evaluated, a sample size of the two largest trees per sub-sample plot used in estimating the random effects showed a significantly higher accuracy compared to other sampling alternatives. This sample size would balance both the measurement cost and potential prediction errors. The nonlinear mixed-effects DBH estimation model at the M response can also be applied if obtaining the estimates of individual tree DBH with a relatively lower cost, and a lower prediction accuracy was the purpose of the study. [ABSTRACT FROM AUTHOR]

Accurately estimating growing stem volume (GSV) is very important for forest resource management. The GSV estimation is affected by remote sensing images, variable selection methods, and estimation algorithms. Optical images have been widely used for modeling key attributes of forest stands, including GSV and aboveground biomass (AGB), because of their easy availability, large coverage and related mature data processing and analysis technologies. However, the low data saturation level and the difficulty of selecting feature variables from optical images often impede the improvement of estimation accuracy. In this research, two GaoFen-2 (GF-2) images, a Landsat 8 image, and fused images created by integrating GF-2 bands with the Landsat multispectral image using the Gram–Schmidt method were first used to derive various feature variables and obtain various datasets or data scenarios. A DC-FSCK approach that integrates feature variable screening and a combination optimization procedure based on the distance correlation coefficient and k-nearest neighbors (kNN) algorithm was proposed and compared with the stepwise regression analysis (SRA) and random forest (RF) for feature variable selection. The DC-FSCK considers the self-correlation and combination effect among feature variables so that the selected variables can improve the accuracy and saturation level of GSV estimation. To validate the proposed approach, six estimation algorithms were examined and compared, including Multiple Linear Regression (MLR), kNN, Support Vector Regression (SVR), RF, eXtreme Gradient Boosting (XGBoost) and Stacking. The results showed that compared with GF-2 and Landsat 8 images, overall, the fused image (Red_Landsat) of GF-2 red band with Landsat 8 multispectral image improved the GSV estimation accuracy of Chinese pine and larch plantations. The Red_Landsat image also performed better than other fused images (Pan_Landsat, Blue_Landsat, Green_Landsat and Nir_Landsat). For most of the combinations of the datasets and estimation models, the proposed variable selection method DC-FSCK led to more accurate GSV estimates compared with SRA and RF. In addition, in most of the combinations obtained by the datasets and variable selection methods, the Stacking algorithm performed better than other estimation models. More importantly, the combination of the fused image Red_Landsat with the DC-FSCK and Stacking algorithm led to the best performance of GSV estimation with the greatest adjusted coefficients of determination, 0.8127 and 0.6047, and the smallest relative root mean square errors of 17.1% and 20.7% for Chinese pine and larch, respectively. This study provided new insights on how to choose suitable optical images, variable selection methods and optimal modeling algorithms for the GSV estimation of Chinese pine and larch plantations. [ABSTRACT FROM AUTHOR]

Uncertainties in forest aboveground biomass (AGB) estimates resulting from over- and underestimations using remote sensing data have been widely studied. The uncertainties may occur due to the spatial effects of the plot data. In this study, we collected AGB data from a total of 147 Pinus densata forest sample plots in Yunnan of southwestern China and analyzed the spatial effects on the estimation of AGB. An ordinary least squares (OLS) and four spatial regression methods were compared for the estimation using Landsat 8-OLI images. Through the spatial analysis of AGB and residuals of model predictions, it was found that the spatial autocorrelation and heterogeneity of the plot data could not be ignored. Compared with the OLS, the impact of the spatial effects on AGB estimation could be reduced slightly by the spatial lag model (SLM) and the spatial error model (SEM) and greatly reduced by the linear mixed effects model (LMM) and geographically weighted regression (GWR) based on the distributions of prediction residuals, global Moran's I, and Z score. The spatial regression models had better performance for model fitting and prediction because of the reduction in overestimations and underestimations for the forests with small and large AGB values, respectively. However, the reductions in the overestimations and underestimations varied depending on the spatial regression models. The GWR provided the most accurate predictions with the largest R2 (0.665), the smallest root mean square error (34.507), and mean relative error (−9.070%) by greatly reducing the AGB interval for overestimations occurring and significantly increasing the threshold of AGB from 150 Mg/ha to 200 Mg/ha for underestimations. Thus, GWR offered the greatest potential of improving the estimation of Pinus densata forest AGB in Yunnan of southwestern China. [ABSTRACT FROM AUTHOR]

Rapid and efficient assessment of cultivated land quality (CLQ) using remote sensing technology is of great significance for protecting cultivated land. However, it is difficult to obtain accurate CLQ estimates using the current satellite-driven approaches in the pressure-state-response (PSR) framework, owing to the limitations of linear models and CLQ spectral indices. In order to improve the estimation accuracy of CLQ, this study used four evaluation models (the traditional linear model; partial least squares regression, PLSR; back propagation neural network, BPNN; and BPNN with genetic algorithm optimization, GA-BPNN) to evaluate CLQ for determining the accurate evaluation model. In addition, the optimal satellite-derived indicator in the land state index was selected among five vegetation indices (the normalized vegetation index, NDVI; enhanced vegetation index, EVI; modified soil-adjusted vegetation index, MSAVI; perpendicular vegetation index, PVI; and soil-adjusted vegetation index, SAVI) to improve the prediction accuracy of CLQ. This study was conducted in Conghua District of Guangzhou, Guangdong Province, China, based on Gaofen-1 (GF-1) data. The prediction accuracies from the traditional linear model, PLSR, BPNN, and GA-BPNN were compared using observations. The results demonstrated that (1) compared with other models (the traditional linear model: R2 = 0.14 and RMSE = 91.53; PLSR: R2 = 0.33 and RMSE = 74.58; BPNN: R2 = 0.50 and RMSE = 61.75), the GA-BPNN model based on EVI in the land state index provided the most accurate estimates of CLQ, with the R2 of 0.59 and root mean square error (RMSE) of 56.87, indicating a nonlinear relationship between CLQ and the prediction indicator; and (2) the GA-BPNN-based evaluation approach of CLQ in the PSR framework was driven to map CLQ of the study area using the GF-1 data, leading to an RMSE of 61.44 at the regional scale, implying that the GA-BPNN-based evaluation approach has the potential to map CLQ over large areas. This study provides an important reference for the high-accuracy prediction of CLQ based on remote sensing technology. [ABSTRACT FROM AUTHOR]

Spectral reflectance distortions caused by terrain and solar illumination seriously reduce the accuracy of mapping forest carbon density, especially in mountainous regions. Many models have been developed for mitigating or eliminating the terrain effects on the quality of remote sensing images in hilly and mountainous areas. However, these models usually use global parameters, which may lead to overcorrections for regions with poor illumination and steep slopes. In this study, we present a local parameter estimation (LPE) method based on a pixel-moving window for topographic correction (TC), which can be considered as a general optimization framework for most semiempirical TC models. We set seven kernel sizes for the presented framework, which are 15 pixels, 25 pixels, 50 pixels, 100 pixels, 250 pixels, 500 pixels, and 1000 pixels, respectively. The proposed method was then applied to four traditional TC models, Minnaert (MIN), C Correction (CC), Sun Canopy Sensor + C (SCSC) and Statistical Empirical Correction (SEC), to form four new TC models. These new models were used to estimate forest carbon density of a mountainous area in Southern China using field plot data and a Landsat 8 image. Four evaluation methods, including correlation analysis, the stability of land covers, comparison of reflectance between sunlit and shaded slopes, and accuracy assessment of forest carbon density, were employed to evaluate the contributions of moving window sizes, and assess the performance of the TC models for forest carbon density estimation. The results show that the four TC models with LPE perform much better than the traditional TC models in reducing the topographic effects and improving the estimation accuracy of forest carbon density for the study area. Among the traditional TC models, SEC performs slightly better than SCSC, CC, and MIN. Therefore, the SEC-based model with LPE, that is, LPE-SEC, gets greater R2 and smaller relative RMSE values in estimating forest carbon density than other models. Moreover, all the means of the predicted forest carbon density values fall in the confidence interval of the validation data at a significant level of 0.05. Overall, this study implies that the proposed method with LPE provides great potential to improve the performance of TC and forest carbon density estimation for the study area. It is expected that the improved TC method can be applied to other mountainous areas to improve the quality of remotely sensed images. [ABSTRACT FROM AUTHOR]

This study proposes a method for determining the optimal image date to improve the evaluation of cultivated land quality (CLQ). Five vegetation indices: leaf area index (LAI), difference vegetation index (DVI), enhanced vegetation index (EVI), normalized difference vegetation index (NDVI), and ratio vegetation index (RVI) are first retrieved using the PROSAIL model and Gaofen-1 (GF-1) images. The indices are then introduced into four regression models at different growth stages for assessing CLQ. The optimal image date of CLQ evaluation is finally determined according to the root mean square error (RMSE). This method is tested and validated in a rice growth area of Southern China based on 115 sample plots and five GF-1 images acquired at the tillering, jointing, booting, heading to flowering, and milk ripe and maturity stage of rice in 2015, respectively. The results show that the RMSEs between the measured and estimated CLQ from four vegetation index-based regression models at the heading to flowering stage are smaller than those at the other growth stages, indicating that the image date corresponding with the heading to flowering stage is optimal for CLQ evaluation. Compared with other vegetation index-based models, the LAI-based logarithm model provides the most accurate estimates of CLQ. The optimal model is also driven using the GF-1 image at the heading to flowering stage to map CLQ of the study area, leading to a relative RMSE of 14.09% at the regional scale. This further implies that the heading to flowering stage is the optimal image time for evaluating CLQ. This study is the first effort to provide an applicable method of selecting the optimal image date to improve the estimation of CLQ and thus advanced the literature in this field. [ABSTRACT FROM AUTHOR]

Quickly and efficiently monitoring soil nutrient contents using remote sensing technology is of great significance for farmland soil productivity, food security and sustainable agricultural development. Current research has been conducted to estimate and map soil nutrient contents in large areas using hyper-spectral techniques, however, it is difficult to obtain accurate estimates. In order to improve the estimation accuracy of soil nutrient contents, we introduced a GA-BPNN method, which combined a back propagation neural network (BPNN) with the genetic algorithm optimization (GA). This study was conducted in Guangdong, China, based on soil nutrient contents and hyperspectral data. The prediction accuracies from a partial least squares regression (PLSR), BPNN and GA-BPNN were compared using field observations. The results showed that (1) Among three methods, the GA-BPNN provided the most accurate estimates of soil total nitrogen (TN), total phosphorus (TP) and total potassium (TK) contents; (2) Compared with the BPNN models, the GA-BPNN models significantly improved the estimation accuracies of the soil nutrient contents by decreasing the relative root mean square error (RRMSE) values by 15.9%, 5.6% and 20.2% at the sample point level, and 20.1%, 16.5% and 47.1% at the regional scale for TN, TP and TK, respectively. This indicated that by optimizing the parameters of BPNN, the GA-BPNN provided greater potential to improving the estimation; and (3) Soil TK content could be more accurately mapped by the GA-BPNN method using HuanJing-1A Hyperspectral Imager (HJ-1A HSI) (manufacturer: China Aerospace Science and Technology Corporation; Beijing, China) data with a RRMSE value of 20.37% than the soil TN and TP with the RRMSE values of 40.41% and 34.71%, respectively. This implied that the GA-BPNN model provided the potential to map the soil TK content for the large area. The research results provided an important reference for high-accuracy prediction of soil nutrient contents. [ABSTRACT FROM AUTHOR]

Quickly and efficiently monitoring soil heavy metal content is crucial for protecting the natural environment and for human health. Estimating heavy metal content in soils using hyperspectral data is a cost-efficient method but challenging due to the effects of complex landscapes and soil properties. One of the challenges is how to make a lab-derived model based on soil samples applicable to mapping the contents of heavy metals in soil using air-borne or space-borne hyperspectral imagery at a regional scale. For this purpose, our study proposed a novel method using hyperspectral data from soil samples and the HuanJing-1A (HJ-1A) HyperSpectral Imager (HSI). In this method, estimation models were first developed using optimal relevant spectral variables from dry soil spectral reflectance (DSSR) data and field observations of soil heavy metal content. The relationship of the ratio of DSSR to moisture soil spectral reflectance (MSSR) with soil moisture content was then derived, which built up the linkage of DSSR with MSSR and provided the potential of applying the models developed in the laboratory to map soil heavy metal content at a regional scale using hyperspectral imagery. The optimal relevant spectral variables were obtained by combining the Boruta algorithm with a stepwise regression and variance inflation factor. This method was developed, validated, and applied to estimate the content of heavy metals in soil (As, Cd, and Hg) in Guangdong, China, and the Conghua district of Guangzhou city. The results showed that based on the validation datasets, the content of Cd could be reliably estimated and mapped by the proposed method, with relative root mean square error (RMSE) values of 17.41% for the point measurements of soil samples from Guangdong province and 17.10% for the Conghua district at the regional scale, while the content of heavy metals As and Hg in soil were relatively difficult to predict with the relative RMSE values of 32.27% and 28.72% at the soil sample level and 51.55% and 36.34% at the regional scale. Moreover, the relationship of the DSSR/MSSR ratio with soil moisture content varied greatly before the wavelength of 1029 nm and became stable after that, which linked DSSR with MSSR and provided the possibility of applying the DSSR-based models to map the soil heavy metal content at the regional scale using the HJ-1A images. In addition, it was found that overall there were only a few soil samples with the content of heavy metals exceeding the health standards in Guangdong province, while in Conghua the seriously polluted areas were mainly distributed in the cities and croplands. This study implies that the new approach provides the potential to map the content of heavy metals in soil, but the estimation model of Cd was more accurate than those of As and Hg. [ABSTRACT FROM AUTHOR]