Influence of varied drought types on soil conservation service within the framework of climate change:insights from the Jinghe River Basin,China

Severe soil erosion and drought are the two main factors affecting the ecological security of the Loess Plateau,China.Investigating the influence of drought on soil conservation service is of great importance to regional environmental protection and sustainable development.However,there is little research on the coupling relationship between them.In this study,focusing on the Jinghe River Basin,China as a case study,we conducted a quantitative evaluation on meteorological,hydrological,and agricultural droughts(represented by the Standardized Precipitation Index(SPI),Standardized Runoff Index(SRI),and Standardized Soil Moisture Index(SSMI),respectively)using the Variable Infiltration Capacity(VIC)model,and quantified the soil conservation service using the Revised Universal Soil Loss Equation(RUSLE)in the historical period(2000-2019)and future period(2026-2060)under two Representative Concentration Pathways(RCPs)(RCP4.5 and RCP8.5).We further examined the influence of the three types of drought on soil conservation service at annual and seasonal scales.The NASA Earth Exchange Global Daily Downscaled Projections(NEX-GDDP)dataset was used to predict and model the hydrometeorological elements in the future period under the RCP4.5 and RCP8.5 scenarios.The results showed that in the historical period,annual-scale meteorological drought exhibited the highest intensity,while seasonal-scale drought was generally weakest in autumn and most severe in summer.Drought intensity of all three types of drought will increase over the next 40 years,with a greater increase under the RCP4.5 scenario than under the RCP8.5 scenario.Furthermore,the intra-annual variation in the drought intensity of the three types of drought becomes smaller under the two future scenarios relative to the historical period(2000-2019).Soil conservation service exhibits a distribution pattern characterized by high levels in the southwest and southeast and lower levels in the north,and this pattern has remained consistent both in the historical and future periods.Over the past 20 years,the intra-annual variation indicated peak soil conservation service in summer and lowest level in winter;the total soil conservation of the Jinghe River Basin displayed an upward trend,with the total soil conservation in 2019 being 1.14 times higher than that in 2000.The most substantial impact on soil conservation service arises from annual-scale meteorological drought,which remains consistent both in the historical and future periods.Additionally,at the seasonal scale,meteorological drought exerts the highest influence on soil conservation service in winter and autumn,particularly under the RCP4.5 and RCP8.5 scenarios.Compared to the historical period,the soil conservation service in the Jinghe River Basin will be significantly more affected by drought in the future period in terms of both the affected area and the magnitude of impact.This study conducted beneficial attempts to evaluate and predict the dynamic characteristics of watershed drought and soil conservation service,as well as the response of soil conservation service to different types of drought.Clarifying the interrelationship between the two is the foundation for achieving sustainable development in a relatively arid and severely eroded area such as the Jinghe River Basin.

Assessment of natural and anthropogenic impacts on terrestrial water storage in the Loess Plateau based on different types of GRACE/GRACE-FO solutions

Changes in water resource storage are inevitable due to climate change and human activities,thus understanding alterations in water storage within a specific region is imperative for the planning and management of water resources.Data from the Gravity Recovery and Climate Experiment(GRACE)satellite mission are extensively employed to analyze large-scale total terrestrial water storage anomalies(TWSA).In this study,we derived a more reliable TWSA using different types of GRACE gravity models,which served as the basis for evaluating spatial and temporal variations in total terrestrial water storage and its hydrological components(soil moisture and groundwater)across the Loess Plateau.Additionally,we analyzed the impact of natural and anthropogenic influences on water storage in the Loess Plateau,categorizing them into primary and secondary influences,utilizing data on climate and human activities.The findings revealed a declining trend in the overall TWSA of the Loess Plateau,with a rate of decrease at-0.65±0.05 cm/yr from 2003 to 2020(P<0.01).As the direct factors affecting TWSA,soil moisture dominated the change of TWSA before 2009,and groundwater dominated the change of TWSA after 2009.Spatially,there was variability in the changes of TWSA in the Loess Plateau.More in-depth studies showed that soil moisture changes in the study area were primarily driven by evapotranspiration and temperature,with precipitation and vegetation cover status playing a secondary role.Human activities had a secondary effect on soil moisture in some sub-regions.Population change and agricultural development were major factors in altering groundwater storage in the study area.Other than that,groundwater was influenced by natural factors to a limited extent.These findings provided valuable insights for local governments to implement proactive water management policies.

Deformation monitoring of long-span railway bridges based on SBAS-InSAR technology

The deformation monitoring of long-span railway bridges is significant to ensure the safety of human life and property.The interferometric synthetic aperture radar(In SAR)technology has the advantage of high accuracy in bridge deformation monitoring.This study monitored the deformation of the Ganjiang Super Bridge based on the small baseline subsets(SBAS)In SAR technology and Sentinel-1A data.We analyzed the deformation results combined with bridge structure,temperature,and riverbed sediment scouring.The results are as follows:(1)The Ganjiang Super Bridge area is stable overall,with deformation rates ranging from-15.6 mm/yr to 10.7 mm/yr(2)The settlement of the Ganjiang Super Bridge deck gradually increases from the bridge tower toward the main span,which conforms to the typical deformation pattern of a cable-stayed bridge.(3)The sediment scouring from the riverbed cause the serious settlement on the bridge’s east side compared with that on the west side.(4)The bridge deformation negatively correlates with temperature,with a faster settlement at a higher temperature and a slow rebound trend at a lower temperature.The study findings can provide scientific data support for the health monitoring of long-span railway bridges.

Parameter-driven Level of Detail Derivation Method for Semantic Building Facade Model

The relentless progress in the research of geographic spatial data models and their application scenarios is propelling an unprecedented rich Level of Detail(LoD)in realistic 3D representation and smart cities.This pursuit of rich details not only adds complexity to entity models but also poses significant computational challenges for model visualization and 3D GIS.This paper introduces a novel method for deriving multi-LOD models,which can enhance the efficiency of spatial computing in complex 3D building models.Firstly,we extract multiple facades from a 3D building model(LoD3)and convert them into individual semantic facade models.Through the utilization of the developed facade layout graph,each semantic facade model is then transformed into a parametric model.Furthermore,we explore the specification of geometric and semantic details in building facades and define three different LODs for facades,offering a unique expression.Finally,an innovative heuristic method is introduced to simplify the parameterized facade.Through rigorous experimentation and evaluation,the effectiveness of the proposed parameterization methodology in capturing complex geometric details,semantic richness,and topological relationships of 3D building models is demonstrated.

Research on the Preservation Method of Traditional Village Roof Information:A Case Study of Gubeikou Village

Traditional Chinese villages serve as crucial repositories of traditional culture.However,In China,the urgent task of preserving information about traditional village architecture has arisen due to the degradation of these villages’appearance caused by rapid urbanization in recent years.This paper proposes a method for preserving information about traditional village rooftops based on high spatial resolution remote sensing imagery.Leveraging an improved Mask R-CNN model,the method conducts target recognition on the rooftops of traditional village buildings and generates vectorized representations of these rooftops.The precision rate,recall rate,and F1-score achieved in the experimental results are 93.26%,86.33%,and 92.02%,respectively.These findings indicate the effectiveness of the proposed method in preserving information about traditional village architecture and providing a viable approach to support the sustainable development of traditional villages in China.

High-Resolution Remote Sensing Imagery for the Recognition of Traditional Villages

Traditional Chinese villages,vital carriers of traditional culture,have faced significant alterations due to urbanization in recent years,urgently necessitating artificial intelligence data updates.This study integrates high spatial resolution remote sensing imagery with deep learning techniques,proposing a novel method for identifying rooftops of traditional Chinese village buildings using high-definition remote sensing images.Using 0.54 m spatial resolution imagery of traditional village areas as the data source,this method analyzes the geometric and spectral image characteristics of village building rooftops.It constructs a deep learning feature sample library tailored to the target types.Employing a semantically enhanced version of the improved Mask R-CNN(Mask Region-based Convolutional Neural Network)for building recognition,the study conducts experiments on localized imagery from different regions.The results demonstrated that the modified Mask R-CNN effectively identifies traditional village building rooftops,achieving an of 0.7520 and an of 0.7400.It improves the current problem of misidentification and missed detection caused by feature heterogeneity.This method offers a viable and effective approach for industrialized data monitoring of traditional villages,contributing to their sustainable development.

Evaluation of surface temperature and pressure derived from MERRA-2 and ERA5 reanalysis datasets and their applications in hourly GNSS precipitable water vapor retrieval over China

Temperature and pressure play key roles in Global Navigation Satellite System(GNSS) precipitable water vapor(PWV) retrieval. The National Aeronautics and Space Administration(NASA) and European Center for Medium-Range Weather Forecasts(ECMWF) have released their latest reanalysis product: the modern-era retrospective analysis for research and applications, version 2(MERRA-2) and the fifthgeneration ECMWF reanalysis(ERA5), respectively. Based on the reanalysis data, we evaluate and analyze the accuracy of the surface temperature and pressure products in China using the the measured temperature and pressure data from 609 ground meteorological stations in 2017 as reference values.Then the accuracy of the two datasets and their performances in estimating GNSS PWV are analyzed. The PWV derived from the pressure and temperature products of ERA5 and MERRA-2 has high accuracy. The annual average biases of pressure and temperature for ERA5 are-0.07 hPa and 0.45 K, with the root mean square error(RMSE) of 0.95 hPa and 2.04 K, respectively. The annual average biases of pressure and temperature for MERRA-2 are-0.01 hPa and 0.38 K, with the RMSE of 1.08 h Pa and 2.66 K, respectively.The accuracy of ERA5 is slightly higher than that of MERRA-2. The two reanalysis data show negative biases in most regions of China, with the highest to lowest accuracy in the following order: the south,north, northwest, and Tibet Plateau. Comparing the GNSS PWV calculated using MERRA-2(GNSS MERRA-2 PWV) and ERA5(GNSS ERA5 PWV) with the radiosonde-derived PWV from 48 co-located GNSS stations and the measured PWV of the co-location radiosonde stations, it is found that the accuracy of GNSS ERA5 PWV is better than that of GNSS MERRA-2 PWV. These results show the different applicability of surface temperature and pressure products from MERRA-2 and ERA5 data, indicating that both have important applications in meteorological research and GNSS water vapor monitoring in China.

Spatiotemporal variations of drought and driving factors based on multiple remote sensing drought indices:A case study in karst areas of southwest China

Droughts are recurrent in southwest China due to the fragility and sensitivity of the karst environment.These events have serious impacts on local agricultural output,ecological diversity,and social stability.Understanding spatiotemporal variations and driving factors of drought in this area is of extreme importance for effective mitigation measures.The karst areas situated in southwest China were spatially divided into seven sub-regions according to the topography and degree of karst development.Drought indices,including vegetation condition index(VCI),temperature condition index(TCI),vegetation health index(VHI),normalized vegetation water supply index(NVSWI),and temperature vegetation drought index(TVDI),were calculated from MODIS data during 2000 and 2018for each sub-region,and drought patterns were examined.The results show that droughts were found to be concentrated in sub-regions such as karst basin,karst plateau,karst gorge,and karst depression areas.Furthermore,there were more drought conditions in karst areas than in non-karst areas.In addition,improvements to drought situation in the study period are significant(p<0.05),and mitigation areas respectively account for 80.1%(NVSWI),74.2%(VCI),74.2%(VHI),30.1%(TCI)and 33.2%(TVDI)of the study area,while drought expands slightly(<3.4%)in areas undergoing urban construction.Pearson's correlation coefficients between drought indices and temperature are generally above 0.5 in all sub-regions.However,the correlation coefficients between drought indices and precipitation mostly fall within the range of 0.3-0.4,indicating a weaker correlation.Our explanation for the spatiotemporal patterns of drought is that karst phenomena are the natural basis of drought and agricultural production is one of important driving forces.Positive changes of drought conditions have benefited from efforts to control rocky desertification and restore ecosystems over the past years.

Optimization of desert lake information extraction from remote sensing images using cellular automata

Desert lakes are important wetland resources in the blown-sand area of western China and play a significant role in maintain-ing the regional ecological environment.However,large-scale coal mining in recent years has considerably impacted the deposition condition of several lakes.Rapid and accurate extraction of lake information based on satellite images is crucial for developing protective measures against desertification.However,the spatial resolution of these images often leads to mixed pixels near water boundaries,affecting extraction precision.Traditional pixel unmixing methods mainly obtain water coverage information in a mixed pixel,making it difficult to accurately describe the spatial distribution.In this paper,the cellular automata(CA)model was adopted in order to realize lake information extraction at a sub-pixel level.A mining area in Shenmu City,Shaanxi Province,China is selected as the research region,using the image of Sentinel-2 as the data source and the high spatial resolution UAV image as the reference.First,water coverage of mixed pixels in the Sentinel-2 image was calculated with the dimidiate pixel model and the fully constrained least squares(FCLS)method.Second,the mixed pixels were subdivided to form the cellular space at a sub-pixel level and the transition rules are constructed based on the water coverage information and spatial correlation.Lastly,the process was implemented using Python and IDL,with the ArcGIS and ENVI software being used for validation.The experiments show that the CA model can improve the sub-pixel positioning accuracy for lake bodies in mixed pixel image and improve classification accuracy.The FCLS-CA model has a higher accuracy and is able to identify most water bodies in the study area,and is therefore suitable for desert lake monitor-ing in mining areas.

Winter Wheat Yield Estimation Based on Sparrow Search Algorithm Combined with Random Forest:A Case Study in Henan Province,China

Precise and timely prediction of crop yields is crucial for food security and the development of agricultural policies.However,crop yield is influenced by multiple factors within complex growth environments.Previous research has paid relatively little attention to the interference of environmental factors and drought on the growth of winter wheat.Therefore,there is an urgent need for more effective methods to explore the inherent relationship between these factors and crop yield,making precise yield prediction increasingly important.This study was based on four type of indicators including meteorological,crop growth status,environmental,and drought index,from October 2003 to June 2019 in Henan Province as the basic data for predicting winter wheat yield.Using the sparrow search al-gorithm combined with random forest(SSA-RF)under different input indicators,accuracy of winter wheat yield estimation was calcu-lated.The estimation accuracy of SSA-RF was compared with partial least squares regression(PLSR),extreme gradient boosting(XG-Boost),and random forest(RF)models.Finally,the determined optimal yield estimation method was used to predict winter wheat yield in three typical years.Following are the findings:1)the SSA-RF demonstrates superior performance in estimating winter wheat yield compared to other algorithms.The best yield estimation method is achieved by four types indicators’composition with SSA-RF)(R^(2)=0.805,RRMSE=9.9%.2)Crops growth status and environmental indicators play significant roles in wheat yield estimation,accounting for 46%and 22%of the yield importance among all indicators,respectively.3)Selecting indicators from October to April of the follow-ing year yielded the highest accuracy in winter wheat yield estimation,with an R^(2)of 0.826 and an RMSE of 9.0%.Yield estimates can be completed two months before the winter wheat harvest in June.4)The predicted performance will be slightly affected by severe drought.Compared with severe drought year(2011)(R^(2)=0.680)and normal year(2017)(R^(2)=0.790),the SSA-RF model has higher prediction accuracy for wet year(2018)(R^(2)=0.820).This study could provide an innovative approach for remote sensing estimation of winter wheat yield.yield.

An improved typhoon monitoring model based on precipitable water vapor and pressure

The potential of monitoring the movement of typhoons using the precipitable water vapor(PWV) has been confirmed. However, monitoring the movement of typhoon is focused on PWV, making it difficult to describe the movement of a typhoon in detail minutely and resulting in insufficient accuracy. Hence,based on PWV and meteorological data, we propose an improved typhoon monitoring mode. First, the European Centre for Medium-Range Weather Forecasts Reanalysis 5-derived PWV(ERA5-PWV) and the Global Navigation Satellite System-derived PWV(GNSS-PWV) were compared with the reference radiosonde PWV(RS-PWV). Then, using the PWV and atmospheric parameters derived from ERA5, we discussed the anomalous variations of PWV, pressure(P), precipitation, and wind speed during different typhoons. Finally, we compiled a list of critical factors related to typhoon movement, PWV and P. We developed an improved multi-factor typhoon monitoring mode(IMTM) with different models(i.e.,IMTM-I and IMTM-II) in different cases with a higher density of GNSS observation or only Numerical Weather Prediction(NWP) data. The IMTM was evaluated through the reference movement speeds of HATO and Mangkhut from the China Meteorological Observatory Typhoon Network(CMOTN). The results show that the root mean square(RMS) of the IMTM-I is 1.26 km/h based on ERA5-P and ERA5-PWV,and the absolute bias values are mostly within 2 km/h. Compared with the models considering the single factor ERA5-P/ERA5-PWV, the RMS of the IMTM-I is improved by 26.3% and 38.5%, respectively. The IMTM-II model manifests a residual of only 0.35 km/h. Compared with the single-factor model based on GNSS-PWV/P, the residual of the IMTM-II model is reduced by 90.8% and 84.1%, respectively. These results propose that the typhoon movement monitoring approach combining PWV and P has evident advantages over the single-factor model and is expected to supplement traditional typhoon monitoring.

Research on mechanism of mountain landslide due to underground mining

The basics of mining landslides were first summed up based on the analysis ofactual materials by the application of mining subsidence and landslide theories. Further,the mechanism of along-layer landslide by mining was studied with the example of theXiangshan mining landslide at Hancheng, Shaanxi Province. Meanwhile, the state ofstress, and the mechanism of deformation and destruction of the Xiangshan mining slopewere analyzed by the Finite Element Method.

Molecular Fingerprints of Soil Organic Matter in a Typical Freshwater Wetland in Northeast China

Natural wetlands are known to store huge amounts of organic carbon in their soils. Despite the importance of this storage,uncertainties remain about the molecular characteristics of soil organic matter(SOM), a key factor governing the stability of soil organic carbon(SOC). In this study, the molecular fingerprints of SOM in a typical freshwater wetland in Northeast China were investigated using pyrolysis gas-chromatography/mass-spectrometry technology(Py-GC/MS). Results indicated that the SOC, total nitrogen(TN),and total sulfur contents of the cores varied between 16.88% and 45.83%, 0.93% and 2.82%, and 1.09% and 3.79%, respectively. The bulk δ^13C and δ^15N varied over a range of 9.85‰, between –26.85‰ and –17.00‰, and between –0.126‰ and 1.002‰, respectively. A total of 134 different pyrolytic products were identified, and they were grouped into alkyl(including n-alkanes(C:0) and n-alkenes(C:1),aliphatics(Al), aromatics(Ar), lignin(Lg), nitrogen-containing compounds(Nc), polycyclic aromatic hydrocarbons(PAHs), phenols(Phs), polysaccharides(Ps), and sulfur-containing compounds(Sc). On average, Phs moieties accounted for roughly 24.11% peak areas of the total pyrolysis products, followed by Lg(19.27%), alkyl(18.96%), other aliphatics(12.39%), Nc compounds(8.08%), Ps(6.49%), aromatics(6.32%), Sc(3.26%), and PAHs(1.12%). Soil organic matter from wetlands had more Phs and Lg and less Nc moieties in pyrolytic products than soil organic matters from forests, lake sediments, pastures, and farmland.δ^13 C distribution patterns implied more C3 plant-derived soil organic matter, but the vegetation was in succession to C4 plant from C3 plant. Significant negative correlations between Lg or Ps proportions and C3 plant proportions were observed. Multiple linear analyses implied that the Ar and Al components had negative effects on SOC. Alkyl and Ar could facilitate ratios between SOC and total nitrogen(C/N), while Al plays the opposite role. Al was positively related to the ratio of dissolved organic carbon(DOC) to SOC. In summary, SOM of wetlands might characterize by more Phs and lignin and less Nc moieties in pyrolytic products. The use of Pyrolysis gas-chromatography/mass-spectrometry(Py-GC/MS) technology provided detailed information on the molecular characteristics of SOM from a typical freshwater wetland.

Three Decades of Sea-Ice Variability in Jiaozhou Bay Revealed by Landsat Observations

In this study, we used Landsat images and meteorological data to examine the spatiotemporal distribution and variability of sea ice in Jiaozhou Bay(JZB) between 1986 and 2016. The results show that JZB is not always covered by sea ice in winter, but in some extreme cases, sea ice has covered more than one-third of the sea area of the bay. Sea ice in JZB has generally formed between January 1 and February 5, primarily along the coast, and gradually expanding to the central area of the bay. Both meteorological and artificial factors have played important roles in modulating the sea ice distribution. We found sea ice coverage to have been strongly correlated with the accumulated freezing-degree days nine days before the occurrence of sea ice(R2 = 0.767). North-northwest surface winds have dominated the freezing period of sea water in the JZB, and wind speed has exerted a more significant influence on the formation of sea ice when the sea ice coverage has been generally small. Additionally, artificial factors began to affect the expansion of sea ice in JZB since 2007. The construction of the Jiao-Zhou-Bay Bridge(JZBB) is believed to have retarded water flow and reduced the tidal prism, thereby leading to the formation of an ice bridge along the JZBB, which effectively prevents the southward expansion of sea ice.

Influence of higher-order ionospheric delay correction on GPS precise orbit determination and precise positioning

At present, Global Navigation Satellite Systems（GNSS） users usually eliminate the influence of ionospheric delay of the first order items by dual-frequency ionosphere-free combination. But there is still residual ionospheric delay error of higher order term. The influence of the higher-order ionospheric corrections on both GPS precision orbit determination and static Precise Point Positioning（PPP） are studied in this paper. The influence of higher-order corrections on GPS precision orbit determination, GPS observations and static PPP are analyzed by neglecting or considering the higher-order ionospheric corrections by using a globally distributed network which is composed of International GNSS Service（IGS） tracking stations. Numerical experimental results show that, the root mean square（RMS） in three dimensions of satellite orbit is 36.6 mme35.5 mm. The maximal second-order ionospheric correction is 9 cm, and the maximal third-order ionospheric correction is 1 cm. Higher-order corrections are influenced by latitude and station distribution. PPP is within 3 mm in the directions of east and up. Furthermore, the impact is mainly visible in the direction of north, showing a southward migration trend, especially at the lower latitudes where the influence value is likely to be bigger than 3 mm.

An entirely new approach based on remote sensing data to calculate the nitrogen nutrition index of winter wheat

The nitrogen nutrition index(NNI)is a reliable indicator for diagnosing crop nitrogen(N)status.However,there is currently no specific vegetation index for the NNI inversion across multiple growth periods.To overcome the limitations of the traditional direct NNI inversion method(NNI_(T1))of the vegetation index and traditional indirect NNI inversion method(NNI_(T2))by inverting intermediate variables including the aboveground dry biomass(AGB)and plant N concentration(PNC),this study proposed a new NNI remote sensing index(NNI_(RS)).A remote-sensing-based critical N dilution curve(Nc_(_RS))was set up directly from two vegetation indices and then used to calculate NNI_(RS).Field data including AGB,PNC,and canopy hyperspectral data were collected over four growing seasons(2012–2013(Exp.1),2013–2014(Exp.2),2014–2015(Exp.3),2015–2016(Exp.4))in Beijing,China.All experimental datasets were cross-validated to each of the NNI models(NNI_(T1),NNI_(T2)and NNI_(RS)).The results showed that:(1)the NNI_(RS)models were represented by the standardized leaf area index determining index(sLAIDI)and the red-edge chlorophyll index(CI_(red edge))in the form of NNI_(RS)=CI_(red edge)/(a×sLAIDI~b),where"a"equals 2.06,2.10,2.08 and 2.02 and"b"equals 0.66,0.73,0.67 and 0.62 when the modeling set data came from Exp.1/2/4,Exp.1/2/3,Exp.1/3/4,and Exp.2/3/4,respectively;(2)the NNI_(RS)models achieved better performance than the other two NNI revised methods,and the ranges of R2 and RMSE were 0.50–0.82 and 0.12–0.14,respectively;(3)when the remaining data were used for verification,the NNI_(RS)models also showed good stability,with RMSE values of 0.09,0.18,0.13 and 0.10,respectively.Therefore,it is concluded that the NNI_(RS)method is promising for the remote assessment of crop N status.

Quantifying the effects of stripe rust disease on wheat canopy spectrum based on eliminating non-physiological stresses

The wheat canopy reflectance spectrum is affected by many internal and external factors such as diseases and growth stage. Separating the effects of disease stress on the crop from the observed mixed signals is crucial for increasing the precision of remote sensing monitoring of wheat stripe rust. The canopy spectrum of winter wheat infected by stripe rust was processed with the difference-in-differences(DID) algorithm used in econometrics. The monitoring accuracies of wheat stripe rust before and after processing with the DID algorithm were compared in the presence of various external factors, disease severity, and several simulated satellite sensors. The correlation between the normalized difference vegetation index processed by the DID algorithm(NDVI-DID) and the disease severity level(SL) increased in comparison with the NDVI before processing. The increase in precision in the natural disease area in the field in the presence of large differences in growth stage, growth, planting, and management of the crop was greater than that in the controlled experiment. For low disease levels(SL < 20%), the R2 of the regression of NDVI-DIDon SL was 38.8% higher than that of the NDVI and the root mean square error(RMSE) was reduced by 11.1%. The increase in precision was greater than that for the severe level(SL > 40%).According to the measured hyperspectral data, the spectral reflectance of three satellite sensor levels was simulated. The wide-band NDVI was calculated. Compared with the wide-band NDVI and vegetation indexes(VI) before DID processing, there were increases in the correlation between SL and the various types of VIS-DID, as well as in the correlation between SL and NDVI-DID. It is feasible to apply the DID algorithm to multispectral satellite data and diverse types of VISfor monitoring wheat stripe rust. Our results improve the quantification of independent effects of stripe rust infection on canopy reflectance spectrum,increase the precision of remote sensing monitoring of wheat stripe rust, and provide a reference for remote sensing monitoring of other crop diseases.

Recognition of Milankovitch cycles in the stratigraphic record: application of the CWT and the FFT to well-log data

The authors applied a the combination of Continuous Wavelet Transform (CWT) and Fast Fourier Transform (FFT) methods to gamma ray well-log data from the Q3, G1 and D2 wells. This high-resolution stratigraphic study was based on Milankovitch's orbital cycle theory. It was found that the CWT scale factors, ‘a,’ of 12, 24 and 60 match the ratios of the periodicities of precession, obliquity and eccentricity very well. Nine intervals of the Permo-carboniferous strata were recognized to have Milankovitch cycles in them. For example, section A of well Q3 has 29 precession cycles, 15 obliquity cycles and 7 short eccentricity cycles. The wavelengths are 2.7, 4.4 and 7.8 m for precession, obliquity and eccentricity, respectively. Important geological parameters such as the stratigraphic completeness and the accumulation rate were also estimated. These results provide basic information for further cyclostratigraphic correlation studies in the area. They are of great significance for the study of ancient and future climate change.

Mining subsidence monitoring using the method of combining InSAR and GPS technology

Attempted to conduct a dynamic monitoring research on coal mining subsidence in western mining areas by using the method of combining D-InSAR and GPS technology. The observation points were installed on the main section and the three-dimensional coordinates of the points were measured by using the method of dynamic differential GPS. Meanwhile, the radar images of this subsidence area were processed by using the method of interferometry with daris software, and the interferogram of the subsidence area was obtained. Through this study, the GPS monitoring data and the InSAR deformation data were integrated and the dynamic subsidence contours of the experimental area were obtained. GPS/InSAR fusion technology provides a new technological means for large-scale dynamic monitoring of coal mining subsidence in western mountainous mining areas and shows good application prospects in coal mining subsidence monitoring and disaster warning.

Spatial and Temporal Evolution of Surface Subsidence in Tianjin from 2015 to 2020 Based on SBAS-InSAR Technology

Tianjin is one of the inland cities with the most severe cases of subsidence hazard in China.The majority of the existing studies have mainly focused on Beijing-Tianjin-Hebei,and little attention has been given to Tianjin.In addition,these existing studies are short-term investigations,lacking long-term monitoring of surface subsidence.In the present study,we use the Small Baseline Subset Interferometric Synthetic Aperture Radar(SBAS-InSAR)technique to monitor the subsidence process in Tianjin between 2015 and 2020 and reveal its spatial and temporal variation.We divided the 44-view Sentinel-1A image data into three periods to avoid the effect of temporal and spatial decoherence by extracting the surface deformation field in Tianjin.We finally verified the accuracy and reliability of the inversion results using second-order leveling data.Results showed that the correlation coefficient r between the two reached 0.89,and the root mean square error was 4.84 mm/y.Obvious subsidence funnels exist in Tianjin,mainly in the towns of Wangqingtuo and Shengfang.These subsidence funnels have a subsidence deformation rate of-136.2 mm/y and a maximum cumulative settlement of-346.3 mm within the study period.The subsidence area tends to extend to the southwest.The analysis of annual rainfall,groundwater resource extraction,spatial location distribution of industrial areas combined with SBAS-InSAR inversion results indicates that overextraction of groundwater resources is the main cause of land subsidence in the area.Therefore,strict control of groundwater extraction is the main approach to mitigate land subsidence effectively.