Soil moisture and salinity are two crucial coastal saline soil variables, which influence the soil quality and agricultural productivity in the reclaimed coastal region. Accurately characterizing the spatial variabili...Soil moisture and salinity are two crucial coastal saline soil variables, which influence the soil quality and agricultural productivity in the reclaimed coastal region. Accurately characterizing the spatial variability of these soil parameters is critical for the rational development and utilization of tideland resources. In the present study, the spatial variability of soil moisture and salinity in the reclaimed area of Hangzhou gulf, Shangyu City, Zhejiang Province, China, was detected using the data acquired from radar image and the proximal sensor EM38. Soil moisture closely correlates radar scattering coefficient, and a simplified inversion model was built based on a backscattering coefficient extracted from multi-polarization data of ALOS/PALSAR and in situ soil moisture measured by a time domain reflectometer to detect soil moisture variations. The result indicated a higher accuracy of soil moisture inversion by the HH polarization mode than those by the HV mode. Soil salinity is reflected by soil apparent electrical conductivity （ECa）. Further, ECa can be rapidly detected by EM38 equipment in situ linked with GPS for characterizing the spatial variability of soil salinity. Based on the strong spatial variability and interactions of soil moisture and salinity, a cokriging interpolation method with auxiliary variable of backscattering coefficient was adopted to map the spatial variability of ECa. When compared with a map of ECa interpolated by the ordinary kriging method, detail was revealed and the accuracy was increased by 15.3%. The results conclude that the integrating active remote sensing and proximal sensors EM38 are effective and acceptable approaches for rapidly and accurately detecting soil moisture and salinity variability in coastal areas, especially in the subtropical coastal zones of China with frequent heavy cloud cover.展开更多
A coastal saline field of 10.5 ha was selected as the study site and 122 bulk electrical conductivity (ECb) measurements were performed thrice in situ in the topsoil (0-20 cm) across the field using a hand held device...A coastal saline field of 10.5 ha was selected as the study site and 122 bulk electrical conductivity (ECb) measurements were performed thrice in situ in the topsoil (0-20 cm) across the field using a hand held device to assess the spatial variability and temporal stability of the distribution of soil electrical conductivity (EC), to identify the management zones using cluster analysis based on the spatiotemporal variability of soil EC, and to evaluate the probable potential for site-specific management in coastal regions with conventional statistics and geostatistical techniques. The results indicated high coefficients of variation for topsoil salinity over all the three samplings. The spatial structure of the salinity variability remained relatively stable with time. Kriged contour maps, drawn on the basis of spatial variance structure of the data, showed the spatial trend of the salinity distribution and revealed areas of consistently high or consistently low salinity, while a temporal stability map indicated stable and unstable regions. On the basis of the spatiotemporal characteristics, cluster analysis divided the site into three potential management zones, each with different characteristics that could have an impact on the way the field was managed. On the basis of the clearly defined management zones it was concluded that coastal saline land could be managed in a site-specific way.展开更多
The spatial estimation for soil properties was improved and sampling intensities also decreased in terms of incorporated auxiliary data. In this study, kriging and two interpolation methods were proven well to estimat...The spatial estimation for soil properties was improved and sampling intensities also decreased in terms of incorporated auxiliary data. In this study, kriging and two interpolation methods were proven well to estimate auxiliary variables： cokriging and regression-kriging, and using the salinity data from the first two stages as auxiliary variables, the methods both improved the interpolation of soil salinity in coastal saline land. The prediction accuracy of the three methods was observed under different sampling density of the target variable by comparison with another group of 80 validation sample points, from which the root-mean-square error （RMSE） and correlation coefficient （r） between the predicted and measured values were calculated. The results showed, with the help of auxiliary data, whatever the sample size of the target variable may be, cokriging and regression-kriging performed better than ordinary kriging. Moreover, regression-kriging produced on average more accurate predictions than cokriging. Compared with the kriging results, cokriging improved the estimations by reducing RMSE from 23.3 to 29% and increasing r from 16.6 to 25.5%, regression-kriging improved the estimations by reducing RMSE from 25 to 41.5% and increasing r from 16.8 to 27.2%. Therefore, regression-kriging shows promise for improved prediction for soil salinity and reduction of soil sampling intensity considerably while maintaining high prediction accuracy. Moreover, in regression-kriging, the regression model can have any form, such as generalized linear models, non-linear models or tree-based models, which provide a possibility to include more ancillary variables.展开更多
Popular science tourism is a new tourism mode, and geopark scenic area is an important carrier of popular science tourism. It is the premise and foundation for the popular science tourism development of geoparks to es...Popular science tourism is a new tourism mode, and geopark scenic area is an important carrier of popular science tourism. It is the premise and foundation for the popular science tourism development of geoparks to establish a scientific evaluation index system and evaluation model. On the basis of analyzing relevant evaluation models, this paper explored the evaluation system and model of geopark popular science tourism in following steps. First, establish the index system. Based on the theory method, frequency method, and expert consultation method, this paper established the popular science tourism evaluation index system of geopark scenic area. The evaluation index system includes 3 primary indexes, 8 secondary indexes and 30 tertiary indexes. Second, define index weight and scoring standards. Index weights were defined using Analytic Hierarchy Process(AHP), the measuring method of each index and the scoring standards were specified. Third, evaluation model and grade. The evaluation model of popular science tourism development was constructed, and the evaluation results were classified into 4 grades. Fourth, empirical study. The evaluation model was applied to measure and investigate popular science tourism of Henan Yuntain Mountain World Geopark, and the final score was 7.619, indicating a higher evaluation grade of popular science tourism of the park.展开更多
One approach to apply precision agriculture to optimize crop production and environmental quality is identifying management zones. In this paper,the variables of soil electrical conductivity (EC) data,cotton yield dat...One approach to apply precision agriculture to optimize crop production and environmental quality is identifying management zones. In this paper,the variables of soil electrical conductivity (EC) data,cotton yield data and normalized differ-ence vegetation index (NDVI) data in an about 15 ha field in a coastal saline land were selected as data resources,and their spatial variabilities were firstly analyzed and spatial distribution maps constructed with geostatistics technique. Then fuzzy c-means clustering algorithm was used to define management zones,fuzzy performance index (FPI) and normalized classification entropy (NCE) were used to determine the optimal cluster numbers. Finally one-way variance analysis was performed on 224 georefer-enced soil and yield sampling points to assess how well the defined management zones reflected the soil properties and produc-tivity level. The results reveal that the optimal number of management zones for the present study area was 3 and the defined management zones provided a better description of soil properties and yield variation. Statistical analyses indicate significant differences between the chemical properties of soil samples and crop yield in each management zone,and management zone 3 presented the highest nutrient level and potential crop productivity,whereas management zone 1 the lowest. Based on these findings,we conclude that fuzzy c-means clustering approach can be used to delineate management zones by using the given three variables in the coastal saline soils,and the defined management zones form an objective basis for targeting soil samples for nutrient analysis and development of site-specific application strategies.展开更多
Landslide susceptibility map is one of the study fields portraying the spatial distribution of future slope failure sus- ceptibility. This paper deals with past methods for producing landslide susceptibility map and d...Landslide susceptibility map is one of the study fields portraying the spatial distribution of future slope failure sus- ceptibility. This paper deals with past methods for producing landslide susceptibility map and divides these methods into 3 types. The logistic linear regression approach is further elaborated on by crosstabs method, which is used to analyze the relationship between the categorical or binary response variable and one or more continuous or categorical or binary explanatory variables derived from samples. It is an objective assignment of coefficients serving as weights of various factors under considerations while expert opinions make great difference in heuristic approaches. Different from deterministic approach, it is very applicable to regional scale. In this study, double logistic regression is applied in the study area. The entire study area is first analyzed. The logistic regression equation showed that elevation, proximity to road, river and residential area are main factors triggering land- slide occurrence in this area. The prediction accuracy of the first landslide susceptibility map was showed to be 80%. Along the road and residential area, almost all areas are in high landslide susceptibility zone. Some non-landslide areas are incorrectly divided into high and medium landslide susceptibility zone. In order to improve the status, a second logistic regression was done in high landslide susceptibility zone using landslide cells and non-landslide sample cells in this area. In the second logistic regression analysis, only engineering and geological conditions are important in these areas and are entered in the new logistic regression equation indicating that only areas with unstable engineering and geological conditions are prone to landslide during large scale engineering activity. Taking these two logistic regression results into account yields a new landslide susceptibility map. Double logistic regression analysis improved the non-landslide prediction accuracy. During calculation of parameters for logistic regres- sion, landslide density is used to transform nominal variable to numeric variable and this avoids the creation of an excessively high number of dummy variables.展开更多
Soil spatial information has traditionally been presented as polygon maps at coarse scales. Solving global and local issues, including food security, water regulation, land degradation, and climate change requires hig...Soil spatial information has traditionally been presented as polygon maps at coarse scales. Solving global and local issues, including food security, water regulation, land degradation, and climate change requires higher quality, more consistent and detailed soil information. Accurate prediction of soil variation over large and complex areas with limited samples remains a challenge, which is especially significant for China due to its vast land area which contains the most diverse soil landscapes in the world. Here, we integrated predictive soil mapping paradigm with adaptive depth function fitting, state-of-the-art ensemble machine learning and high-resolution soil-forming environment characterization in a highperformance parallel computing environment to generate 90-m resolution national gridded maps of nine soil properties(pH, organic carbon, nitrogen, phosphorus, potassium, cation exchange capacity, bulk density, coarse fragments, and thickness) at multiple depths across China. This was based on approximately5000 representative soil profiles collected in a recent national soil survey and a suite of detailed covariates to characterize soil-forming environments. The predictive accuracy ranged from very good to moderate(Model Efficiency Coefficients from 0.71 to 0.36) at 0–5 cm. The predictive accuracy for most soil properties declined with depth. Compared with previous soil maps, we achieved significantly more detailed and accurate predictions which could well represent soil variations across the territory and are a significant contribution to the GlobalSoilMap.net project. The relative importance of soil-forming factors in the predictions varied by specific soil property and depth, suggesting the complexity and non-stationarity of comprehensive multi-factor interactions in the process of soil development.展开更多
We developed a sophisticated method to depict the spatial and seasonal characterization of net primary productivity (NPP) and climate variables. The role of climate variability in the seasonal variation of NPP exerts ...We developed a sophisticated method to depict the spatial and seasonal characterization of net primary productivity (NPP) and climate variables. The role of climate variability in the seasonal variation of NPP exerts delayed and continuous effects. This study expands on this by mapping the seasonal characterization of NPP and climate variables from space using geographic information system (GIS) technology at the pixel level. Our approach was developed in southeastern China using moderate-resolution imaging spectroradiometer (MODIS) data. The results showed that air temperature,precipitation and sunshine percentage contributed significantly to seasonal variation of NPP. In the northern portion of the study area,a significant positive 32-d lagged correlation was observed between seasonal variation of NPP and climate (P<0.01),and the influences of changing climate on NPP lasted for 48 d or 64 d. In central southeastern China,NPP showed 16-d,48-d,and 96-d lagged correlation with air temperature,precipitation,and sunshine percentage,respectively (P<0.01); the influences of air temperature and precipitation on NPP lasted for 48 d or 64 d,while sunshine influence on NPP only persisted for 16 d. Due to complex topography and vegetation distribution in the southern part of the study region,the spatial patterns of vegetation-climate relationship became complicated and diversiform,especially for precipitation influences on NPP. In the northern part of the study area,all vegetation NPP had an almost similar response to seasonal variation of air temperature except for broad crops. The impacts of seasonal variation of precipitation and sunshine on broad and cereal crop NPP were slightly different from other vegetation NPP.展开更多
Detection of crop health conditions plays an important role in making control strategies of crop disease and insect damage and gaining high-quality production at late growth stages. In this study, hyperspectral reflec...Detection of crop health conditions plays an important role in making control strategies of crop disease and insect damage and gaining high-quality production at late growth stages. In this study, hyperspectral reflectance of rice panicles was measured at the visible and near-infrared regions. The panicles were divided into three groups according to health conditions： healthy panicles, empty panicles caused by Nilaparvata lugens St^l, and panicles infected with Ustilaginoidea virens. Low order derivative spectra, namely, the first and second orders, were obtained using different techniques. Principal component analysis （PCA） was performed to obtain the principal component spectra （PCS） of the foregoing derivative and raw spectra to reduce the reflectance spectral dimension. Support vector classification （SVC） was employed to discriminate the healthy, empty, and infected panicles, with the front three PCS as the in- dependent variables. The overall accuracy and kappa coefficient were used to assess the classification accuracy of SVC. The overall accuracies of SVC with PCS derived from the raw, first, and second reflectance spectra for the testing dataset were 96.55%, 99.14%, and 96.55%, and the kappa coefficients were 94.81%, 98.71%, and 94.82%, respectively. Our results demonstrated that it is feasible to use visible and near-infrared spectroscopy to discriminate health conditions of rice panicles.展开更多
The winter oilseed rape（Brassica napus L.） accounts for about 90% of the total acreage of oilseed rape in China. However, it suffers the risk of freeze injury during the winter. In this study, we used Chinese HJ-1A/...The winter oilseed rape（Brassica napus L.） accounts for about 90% of the total acreage of oilseed rape in China. However, it suffers the risk of freeze injury during the winter. In this study, we used Chinese HJ-1A/1B CCD sensors, which have a revisit frequency of 2 d as well as 30 m spatial resolution, to monitor the freeze injury of oilseed rape. Mahalanobis distance-derived growing regions in a normal year were taken as the benchmark, and a mask method was applied to obtain the growing regions in the 2010–2011 growing season. The normalized difference vegetation index（NDVI） was chosen as the indicator of the degree of damage. The amount of crop damage was determined from the difference in the NDVI before and after the freeze. There was spatial variability in the amount of crop damage, so we examined three factors that may affect the degree of freeze injury： terrain, soil moisture, and crop growth before the freeze. The results showed that all these factors were significantly correlated with freeze injury degree（P0.01, two-tailed）. The damage was generally more serious in low-lying and drought-prone areas; in addition, oilseed rape planted on south- and west-oriented facing slopes and those with luxuriant growth status tended to be more susceptible to freeze injury. Furthermore, land surface temperature（LST） of the coldest day, soil moisture, pre-freeze growth and altitude were in descending order of importance in determining the degree of damage. The findings proposed in this paper would be helpful in understanding the occurrence and severity distribution of oilseed rape freeze injury under certain natural or vegetation conditions, and thus help in mitigation of this kind of meteorological disaster in southern China.展开更多
The acquisition of precise soil data representative of the entire survey area, is a critical issue for many treatments such as irrigation or fertilization in precision agriculture. The aim of this study was to investi...The acquisition of precise soil data representative of the entire survey area, is a critical issue for many treatments such as irrigation or fertilization in precision agriculture. The aim of this study was to investigate the spatial variability of soil bulk electrical conductivity （ECb） in a coastal saline field and design an optimized spatial sampling scheme of ECb based on a sampling design algorithm, the variance quad-tree （VQT） method. Soil ECb data were collected from the field at 20 m interval in a regular grid scheme. The smooth contour map of the whole field was obtained by ordinary kriging interpolation, VQT algorithm was then used to split the smooth contour map into strata of different number desired, the sampling locations can be selected within each stratum in subsequent sampling. The result indicated that the probability of choosing representative sampling sites was increased significantly by using VQT method with the sampling number being greatly reduced compared to grid sampling design while retaining the same prediction accuracy. The advantage of the VQT method is that this scheme samples sparsely in fields where the spatial variability is relatively uniform and more intensive where the variability is large. Thus the sampling efficiency can be improved, hence facilitate an assessment methodology that can be applied in a rapid, practical and cost-effective manner.展开更多
Characterizing spatial variability of soil attributes, using traditional soil sampling and laboratory analysis, is cost prohibitive. The potential benefit of managing soils on a site-specific basis is well established...Characterizing spatial variability of soil attributes, using traditional soil sampling and laboratory analysis, is cost prohibitive. The potential benefit of managing soils on a site-specific basis is well established. High variations in glacial till soil render detailed soil mapping difficult with limited number of soil samples. To overcome this problem, this paper demonstrates the feasibility of soil carbon and clay mapping using the newly developed on-the-go near-infrared reflectance spectroscopy (NIRS). Compared with the geostatistics method, the partial least squares regression (PLSR), with NIRS measurements, could yield a more detailed map for both soil carbon and clay. Further, by using independent validation dataset, the accuracy of predicting could be improved significantly for soil clay content and only slightly for soil carbon content. Owing to the complexity of field conditions, more work on data processing and calibration modeling might be necessary for using on-the-go NIRS measurements.展开更多
Soil organic carbon density(SOCD)and soil organic carbon sequestration potential(SOCP)play an important role in carbon cycle and mitigation of greenhouse gas emissions.However,the majority of studies focused on a two-...Soil organic carbon density(SOCD)and soil organic carbon sequestration potential(SOCP)play an important role in carbon cycle and mitigation of greenhouse gas emissions.However,the majority of studies focused on a two-dimensional scale,especially lacking of field measured data.We employed the interpolation method with gradient plane nodal function(GPNF)and Shepard(SPD)across a range of parameters to simulate SOCD with a 40 cm soil layer depth in a dryland farming region(DFR)of China.The SOCP was estimated using a carbon saturation model.Results demonstrated the GPNF method was proved to be more effective in simulating the spatial distribution of SOCD at the vertical magnification multiple and search point values of 3.0×106 and 25,respectively.The soil organic carbon storage(SOCS)of 40 cm and 20 cm soil layers were estimated as 22.28×10^(11)kg and 13.12×10^(11)kg simulated by GPNF method in DFR.The SOCP was estimated as 0.95×10^(11)kg considered as a carbon sink at the 20–40 cm soil layer.Furthermore,the SOCP was estimated as–2.49×10^(11)kg considered as a carbon source at the 0–20 cm soil layer.This research has important values for the scientific use of soil resources and the mitigation of greenhouse gas emissions.展开更多
The chlorophyll fluorescence （CF） signature emitted from vegetation provides an abundance of information regarding photosynthetics activity and has been used as a powerful tool to obtain physiological information of...The chlorophyll fluorescence （CF） signature emitted from vegetation provides an abundance of information regarding photosynthetics activity and has been used as a powerful tool to obtain physiological information of plant leaves in a non-invasive manner. CF is difficult to quantify because the CF signal is obscured by reflected light. In the present study, the apparent reflectance spectra of wheat （Triticum aestivum L.） leaves were measured under illuminations with and without filtering by three specially designed long-wave pass edge filters; the cut-off wavelengths of the three filters were 653.8, 678.2, and 694. l nm at 50% of maximum transmittance. The CF spectra could be derived as the reflectance difference spectra of the leaves under illuminations with and without the long wave pass edge filters. The ratio of the reflectance difference at 685 and 740 nm （Dif685/Dif740） was linear correlated with the CF parameters （maximal photochemical efficiency Fv/Fm, and the yield of quantum efficiency） measured by the modulated fluorometer. In addition, the ratio reflected the water stress status of the wheat leaf, which was very high when water deficiency was serious. This method provides a new approach for detecting CF and the physiological state of crops.展开更多
e The objective of this study was to investigate the tempo-spatial distribution of paddy rice in Northeast China using moderate resolution imaging spectroradiometer （MODIS） data. We developed an algorithm for detect...e The objective of this study was to investigate the tempo-spatial distribution of paddy rice in Northeast China using moderate resolution imaging spectroradiometer （MODIS） data. We developed an algorithm for detection and estimation of the transplanting and flooding periods of paddy rice with a combination of enhanced vegetation index （EVI） and land surface water index with a central wavelength at 2130 nm （LSW12130）. In two intensive sites in Northeast China, fine resolution satellite imagery was used to validate the performance of the algorithm at pixel and 3x3 pixel window levels, respectively. The commission and omission errors in both of the intensive sites were approximately less than 20%. Based on the algorithm, annual distribution of paddy rice in Northeast China from 2001 to 2009 was mapped and analyzed. The results demonstrated that the MODIS-derived area was highly correlated with published agricultural statistical data with a coefficient of determination （R^2） value of 0.847. It also revealed a sharp decline in 2003, especially in the Sanjiang Plain located in the northeast of Heilongjiang Province, due to the oversupply and price decline of rice in 2002. These results suggest that the approaches are available for accurate and reliable monitoring of rice cultivated areas and variation on a large scale.展开更多
The oilseed rape growing in the lower reaches of Yangtze River in China belongs to winter varieties and suffers the risk of freezing injury.In this research,a typical freezing injury event occurred in Anhui Province w...The oilseed rape growing in the lower reaches of Yangtze River in China belongs to winter varieties and suffers the risk of freezing injury.In this research,a typical freezing injury event occurred in Anhui Province was taken as a case study,the freezing damage degree of oilseed rape was assessed,and its development characteristics based on the vegetation metrics derived from MODIS and MERIS data were investigated.The oilseed rape was mapped according to the decline of greenness from bud stage to full-bloom period,with the phenological phases identified adopting time-series analyses.NDVI was more sensitive to freezing injury compared with other commonly used vegetation indices(VIs)calculated using MODIS bands,e.g.,EVI,GNDVI and SAVI.The freezing damage degree employing the difference between post-freeze growth and the baseline level in adjacent damage-free growing seasons was determined.The remote sensing-derived damage levels were supported by their correlation with the cold accumulated temperatures at the county level.The performance of several remote sensing indicators of plant biophysical and biochemical parameters was also investigated,i.e.,the photosynthetic rate,canopy water status,canopy chlorophyll content,leaf area index(LAI)and the red edge position(REP),in response to the advance of the freezing damage.It was found that the photosynthetic rate indicator—Photochemical Reflectance Index(PRI)responded strongly to freezing stress.Freezing injury caused canopy water loss,which could be detected though the magnitude was not very large.MERIS-LAI showed a slow and lagging response to low temperature and restored rapidly in the recovery phase;additionally,REP and the indicator of canopy chlorophyll content—MERIS Terrestrial Chlorophyll Index(MTCI),did not appear to be influenced by freezing injury.It was concluded that the physiological functions,canopy structure,and organic content metrics showed a descending order of vulnerabilities to freezing injury.展开更多
The tasseled cap transformation of remote sensing data has been widely used in agriculture,forest,ecology,and landscape.In this paper,tasseled cap transformation coefficients appropriate for data from a new sensor(Chi...The tasseled cap transformation of remote sensing data has been widely used in agriculture,forest,ecology,and landscape.In this paper,tasseled cap transformation coefficients appropriate for data from a new sensor(China & Brazil Earth Resource Satellite(CBERS-02B)) are presented.The first three components after transformation captured 98% of the four-band variance,and represent the physical characteristics of brightness(coefficients:0.509,0.431,0.330,and 0.668),greenness(coefficients:0.494,0.318,0.324,and 0.741),and blueness(coefficients:0.581,0.070,0.811,and 0.003),respectively.We hope these results will enhance the application of CBERS-02B charge-coupled device(CCD) data in the areas of agriculture,forest,ecology,and landscape.展开更多
基金funded by the Zhejiang Provincial Natural Science Foundation of China (R5100140)the National Natural Science Foundation of China (40871100)the Science and Technology Project of Zhejiang Province, China(2011C13010)
文摘Soil moisture and salinity are two crucial coastal saline soil variables, which influence the soil quality and agricultural productivity in the reclaimed coastal region. Accurately characterizing the spatial variability of these soil parameters is critical for the rational development and utilization of tideland resources. In the present study, the spatial variability of soil moisture and salinity in the reclaimed area of Hangzhou gulf, Shangyu City, Zhejiang Province, China, was detected using the data acquired from radar image and the proximal sensor EM38. Soil moisture closely correlates radar scattering coefficient, and a simplified inversion model was built based on a backscattering coefficient extracted from multi-polarization data of ALOS/PALSAR and in situ soil moisture measured by a time domain reflectometer to detect soil moisture variations. The result indicated a higher accuracy of soil moisture inversion by the HH polarization mode than those by the HV mode. Soil salinity is reflected by soil apparent electrical conductivity （ECa）. Further, ECa can be rapidly detected by EM38 equipment in situ linked with GPS for characterizing the spatial variability of soil salinity. Based on the strong spatial variability and interactions of soil moisture and salinity, a cokriging interpolation method with auxiliary variable of backscattering coefficient was adopted to map the spatial variability of ECa. When compared with a map of ECa interpolated by the ordinary kriging method, detail was revealed and the accuracy was increased by 15.3%. The results conclude that the integrating active remote sensing and proximal sensors EM38 are effective and acceptable approaches for rapidly and accurately detecting soil moisture and salinity variability in coastal areas, especially in the subtropical coastal zones of China with frequent heavy cloud cover.
基金Project supported by the National Natural Science Foundation of China (Nos. 40001008 and 40571066)German Federal Ministry of Education and Research (BMBF) (No. AZ39742)the Postdoctoral Science Foundation o China (No. 20060401048).
文摘A coastal saline field of 10.5 ha was selected as the study site and 122 bulk electrical conductivity (ECb) measurements were performed thrice in situ in the topsoil (0-20 cm) across the field using a hand held device to assess the spatial variability and temporal stability of the distribution of soil electrical conductivity (EC), to identify the management zones using cluster analysis based on the spatiotemporal variability of soil EC, and to evaluate the probable potential for site-specific management in coastal regions with conventional statistics and geostatistical techniques. The results indicated high coefficients of variation for topsoil salinity over all the three samplings. The spatial structure of the salinity variability remained relatively stable with time. Kriged contour maps, drawn on the basis of spatial variance structure of the data, showed the spatial trend of the salinity distribution and revealed areas of consistently high or consistently low salinity, while a temporal stability map indicated stable and unstable regions. On the basis of the spatiotemporal characteristics, cluster analysis divided the site into three potential management zones, each with different characteristics that could have an impact on the way the field was managed. On the basis of the clearly defined management zones it was concluded that coastal saline land could be managed in a site-specific way.
基金the National Natural Science Foundation of China （40571066, 40001008）the Postdoctoral Science Foundation of China （20060401048） the Key Program of Science and Technology Bureau of Zhejiang Province, China 030523）.
文摘The spatial estimation for soil properties was improved and sampling intensities also decreased in terms of incorporated auxiliary data. In this study, kriging and two interpolation methods were proven well to estimate auxiliary variables： cokriging and regression-kriging, and using the salinity data from the first two stages as auxiliary variables, the methods both improved the interpolation of soil salinity in coastal saline land. The prediction accuracy of the three methods was observed under different sampling density of the target variable by comparison with another group of 80 validation sample points, from which the root-mean-square error （RMSE） and correlation coefficient （r） between the predicted and measured values were calculated. The results showed, with the help of auxiliary data, whatever the sample size of the target variable may be, cokriging and regression-kriging performed better than ordinary kriging. Moreover, regression-kriging produced on average more accurate predictions than cokriging. Compared with the kriging results, cokriging improved the estimations by reducing RMSE from 23.3 to 29% and increasing r from 16.6 to 25.5%, regression-kriging improved the estimations by reducing RMSE from 25 to 41.5% and increasing r from 16.8 to 27.2%. Therefore, regression-kriging shows promise for improved prediction for soil salinity and reduction of soil sampling intensity considerably while maintaining high prediction accuracy. Moreover, in regression-kriging, the regression model can have any form, such as generalized linear models, non-linear models or tree-based models, which provide a possibility to include more ancillary variables.
基金Sponsored by National Natural Science Foundation of China(41301641)National Social Science Foundation of China(15BJY130)+1 种基金Open Project of Henan Collaborative Innovation Center of Central Plain Economic Zone Smart Tourism(2015-ZHLV-006,2015-ZHLV-002)2015"10,000 Tourism Talents Plan"of National Tourism Administration(WMYC20151037)
文摘Popular science tourism is a new tourism mode, and geopark scenic area is an important carrier of popular science tourism. It is the premise and foundation for the popular science tourism development of geoparks to establish a scientific evaluation index system and evaluation model. On the basis of analyzing relevant evaluation models, this paper explored the evaluation system and model of geopark popular science tourism in following steps. First, establish the index system. Based on the theory method, frequency method, and expert consultation method, this paper established the popular science tourism evaluation index system of geopark scenic area. The evaluation index system includes 3 primary indexes, 8 secondary indexes and 30 tertiary indexes. Second, define index weight and scoring standards. Index weights were defined using Analytic Hierarchy Process(AHP), the measuring method of each index and the scoring standards were specified. Third, evaluation model and grade. The evaluation model of popular science tourism development was constructed, and the evaluation results were classified into 4 grades. Fourth, empirical study. The evaluation model was applied to measure and investigate popular science tourism of Henan Yuntain Mountain World Geopark, and the final score was 7.619, indicating a higher evaluation grade of popular science tourism of the park.
基金Project supported by the National Natural Science Foundation of China (Nos. 40701007 and 40571066)the Postdoctoral Science Foundation of China (No. 20060401048)
文摘One approach to apply precision agriculture to optimize crop production and environmental quality is identifying management zones. In this paper,the variables of soil electrical conductivity (EC) data,cotton yield data and normalized differ-ence vegetation index (NDVI) data in an about 15 ha field in a coastal saline land were selected as data resources,and their spatial variabilities were firstly analyzed and spatial distribution maps constructed with geostatistics technique. Then fuzzy c-means clustering algorithm was used to define management zones,fuzzy performance index (FPI) and normalized classification entropy (NCE) were used to determine the optimal cluster numbers. Finally one-way variance analysis was performed on 224 georefer-enced soil and yield sampling points to assess how well the defined management zones reflected the soil properties and produc-tivity level. The results reveal that the optimal number of management zones for the present study area was 3 and the defined management zones provided a better description of soil properties and yield variation. Statistical analyses indicate significant differences between the chemical properties of soil samples and crop yield in each management zone,and management zone 3 presented the highest nutrient level and potential crop productivity,whereas management zone 1 the lowest. Based on these findings,we conclude that fuzzy c-means clustering approach can be used to delineate management zones by using the given three variables in the coastal saline soils,and the defined management zones form an objective basis for targeting soil samples for nutrient analysis and development of site-specific application strategies.
基金Project supported by the Natural Science Foundation of ZhejiangProvince (No. 30295) and the Key Project of Zhejiang Province (No.011103192), China
文摘Landslide susceptibility map is one of the study fields portraying the spatial distribution of future slope failure sus- ceptibility. This paper deals with past methods for producing landslide susceptibility map and divides these methods into 3 types. The logistic linear regression approach is further elaborated on by crosstabs method, which is used to analyze the relationship between the categorical or binary response variable and one or more continuous or categorical or binary explanatory variables derived from samples. It is an objective assignment of coefficients serving as weights of various factors under considerations while expert opinions make great difference in heuristic approaches. Different from deterministic approach, it is very applicable to regional scale. In this study, double logistic regression is applied in the study area. The entire study area is first analyzed. The logistic regression equation showed that elevation, proximity to road, river and residential area are main factors triggering land- slide occurrence in this area. The prediction accuracy of the first landslide susceptibility map was showed to be 80%. Along the road and residential area, almost all areas are in high landslide susceptibility zone. Some non-landslide areas are incorrectly divided into high and medium landslide susceptibility zone. In order to improve the status, a second logistic regression was done in high landslide susceptibility zone using landslide cells and non-landslide sample cells in this area. In the second logistic regression analysis, only engineering and geological conditions are important in these areas and are entered in the new logistic regression equation indicating that only areas with unstable engineering and geological conditions are prone to landslide during large scale engineering activity. Taking these two logistic regression results into account yields a new landslide susceptibility map. Double logistic regression analysis improved the non-landslide prediction accuracy. During calculation of parameters for logistic regres- sion, landslide density is used to transform nominal variable to numeric variable and this avoids the creation of an excessively high number of dummy variables.
基金the National Key Basic Research Special Foundation of China(2008FY110600 and 2014FY110200)the National Natural Science Foundation of China(41930754 and42071072)+1 种基金the 2nd Comprehensive Scientific Survey of the Qinghai-Tibet Plateau(2019QZKK0306)the Project of “OneThree-Five”Strategic Planning&Frontier Sciences of the Institute of Soil Science,Chinese Academy of Sciences(ISSASIP1622)。
文摘Soil spatial information has traditionally been presented as polygon maps at coarse scales. Solving global and local issues, including food security, water regulation, land degradation, and climate change requires higher quality, more consistent and detailed soil information. Accurate prediction of soil variation over large and complex areas with limited samples remains a challenge, which is especially significant for China due to its vast land area which contains the most diverse soil landscapes in the world. Here, we integrated predictive soil mapping paradigm with adaptive depth function fitting, state-of-the-art ensemble machine learning and high-resolution soil-forming environment characterization in a highperformance parallel computing environment to generate 90-m resolution national gridded maps of nine soil properties(pH, organic carbon, nitrogen, phosphorus, potassium, cation exchange capacity, bulk density, coarse fragments, and thickness) at multiple depths across China. This was based on approximately5000 representative soil profiles collected in a recent national soil survey and a suite of detailed covariates to characterize soil-forming environments. The predictive accuracy ranged from very good to moderate(Model Efficiency Coefficients from 0.71 to 0.36) at 0–5 cm. The predictive accuracy for most soil properties declined with depth. Compared with previous soil maps, we achieved significantly more detailed and accurate predictions which could well represent soil variations across the territory and are a significant contribution to the GlobalSoilMap.net project. The relative importance of soil-forming factors in the predictions varied by specific soil property and depth, suggesting the complexity and non-stationarity of comprehensive multi-factor interactions in the process of soil development.
基金Project supported by the National High-Tech Research and Development Program (863) of China (No. 2006AA120101)the National Natural Science Foundation of China (Nos. 40871158 and 40875070)the Key Technologies Research and Development Program of China (No. 2006BAD10A01)
文摘We developed a sophisticated method to depict the spatial and seasonal characterization of net primary productivity (NPP) and climate variables. The role of climate variability in the seasonal variation of NPP exerts delayed and continuous effects. This study expands on this by mapping the seasonal characterization of NPP and climate variables from space using geographic information system (GIS) technology at the pixel level. Our approach was developed in southeastern China using moderate-resolution imaging spectroradiometer (MODIS) data. The results showed that air temperature,precipitation and sunshine percentage contributed significantly to seasonal variation of NPP. In the northern portion of the study area,a significant positive 32-d lagged correlation was observed between seasonal variation of NPP and climate (P<0.01),and the influences of changing climate on NPP lasted for 48 d or 64 d. In central southeastern China,NPP showed 16-d,48-d,and 96-d lagged correlation with air temperature,precipitation,and sunshine percentage,respectively (P<0.01); the influences of air temperature and precipitation on NPP lasted for 48 d or 64 d,while sunshine influence on NPP only persisted for 16 d. Due to complex topography and vegetation distribution in the southern part of the study region,the spatial patterns of vegetation-climate relationship became complicated and diversiform,especially for precipitation influences on NPP. In the northern part of the study area,all vegetation NPP had an almost similar response to seasonal variation of air temperature except for broad crops. The impacts of seasonal variation of precipitation and sunshine on broad and cereal crop NPP were slightly different from other vegetation NPP.
基金supported by the National Basic Research Program (973) of China (No.2010CB126200)China Postdoctoral Science Foundation Project (No.20090451437)
文摘Detection of crop health conditions plays an important role in making control strategies of crop disease and insect damage and gaining high-quality production at late growth stages. In this study, hyperspectral reflectance of rice panicles was measured at the visible and near-infrared regions. The panicles were divided into three groups according to health conditions： healthy panicles, empty panicles caused by Nilaparvata lugens St^l, and panicles infected with Ustilaginoidea virens. Low order derivative spectra, namely, the first and second orders, were obtained using different techniques. Principal component analysis （PCA） was performed to obtain the principal component spectra （PCS） of the foregoing derivative and raw spectra to reduce the reflectance spectral dimension. Support vector classification （SVC） was employed to discriminate the healthy, empty, and infected panicles, with the front three PCS as the in- dependent variables. The overall accuracy and kappa coefficient were used to assess the classification accuracy of SVC. The overall accuracies of SVC with PCS derived from the raw, first, and second reflectance spectra for the testing dataset were 96.55%, 99.14%, and 96.55%, and the kappa coefficients were 94.81%, 98.71%, and 94.82%, respectively. Our results demonstrated that it is feasible to use visible and near-infrared spectroscopy to discriminate health conditions of rice panicles.
基金Project supported by the National Natural Science Foundation of China(No.41171276)
文摘The winter oilseed rape（Brassica napus L.） accounts for about 90% of the total acreage of oilseed rape in China. However, it suffers the risk of freeze injury during the winter. In this study, we used Chinese HJ-1A/1B CCD sensors, which have a revisit frequency of 2 d as well as 30 m spatial resolution, to monitor the freeze injury of oilseed rape. Mahalanobis distance-derived growing regions in a normal year were taken as the benchmark, and a mask method was applied to obtain the growing regions in the 2010–2011 growing season. The normalized difference vegetation index（NDVI） was chosen as the indicator of the degree of damage. The amount of crop damage was determined from the difference in the NDVI before and after the freeze. There was spatial variability in the amount of crop damage, so we examined three factors that may affect the degree of freeze injury： terrain, soil moisture, and crop growth before the freeze. The results showed that all these factors were significantly correlated with freeze injury degree（P0.01, two-tailed）. The damage was generally more serious in low-lying and drought-prone areas; in addition, oilseed rape planted on south- and west-oriented facing slopes and those with luxuriant growth status tended to be more susceptible to freeze injury. Furthermore, land surface temperature（LST） of the coldest day, soil moisture, pre-freeze growth and altitude were in descending order of importance in determining the degree of damage. The findings proposed in this paper would be helpful in understanding the occurrence and severity distribution of oilseed rape freeze injury under certain natural or vegetation conditions, and thus help in mitigation of this kind of meteorological disaster in southern China.
基金We thank the financial support from the National Natural Science Foundation of China(40701007,40571066)the Postdoctoral Science Foundation of China(20060401048).
文摘The acquisition of precise soil data representative of the entire survey area, is a critical issue for many treatments such as irrigation or fertilization in precision agriculture. The aim of this study was to investigate the spatial variability of soil bulk electrical conductivity （ECb） in a coastal saline field and design an optimized spatial sampling scheme of ECb based on a sampling design algorithm, the variance quad-tree （VQT） method. Soil ECb data were collected from the field at 20 m interval in a regular grid scheme. The smooth contour map of the whole field was obtained by ordinary kriging interpolation, VQT algorithm was then used to split the smooth contour map into strata of different number desired, the sampling locations can be selected within each stratum in subsequent sampling. The result indicated that the probability of choosing representative sampling sites was increased significantly by using VQT method with the sampling number being greatly reduced compared to grid sampling design while retaining the same prediction accuracy. The advantage of the VQT method is that this scheme samples sparsely in fields where the spatial variability is relatively uniform and more intensive where the variability is large. Thus the sampling efficiency can be improved, hence facilitate an assessment methodology that can be applied in a rapid, practical and cost-effective manner.
基金Supported by the Agricultural S&T Cooperation Program of Zhejiang Province, China (No. N20100015)
文摘Characterizing spatial variability of soil attributes, using traditional soil sampling and laboratory analysis, is cost prohibitive. The potential benefit of managing soils on a site-specific basis is well established. High variations in glacial till soil render detailed soil mapping difficult with limited number of soil samples. To overcome this problem, this paper demonstrates the feasibility of soil carbon and clay mapping using the newly developed on-the-go near-infrared reflectance spectroscopy (NIRS). Compared with the geostatistics method, the partial least squares regression (PLSR), with NIRS measurements, could yield a more detailed map for both soil carbon and clay. Further, by using independent validation dataset, the accuracy of predicting could be improved significantly for soil clay content and only slightly for soil carbon content. Owing to the complexity of field conditions, more work on data processing and calibration modeling might be necessary for using on-the-go NIRS measurements.
基金Youth Innovation Promotion Association CAS,No.2021119Future Star Talent Program of Aerospace Information Research Institute,Chinese Academy of Sciences,No.2020KTYWLZX08National Special Support Program for High-level Personnel Recruitment。
文摘Soil organic carbon density(SOCD)and soil organic carbon sequestration potential(SOCP)play an important role in carbon cycle and mitigation of greenhouse gas emissions.However,the majority of studies focused on a two-dimensional scale,especially lacking of field measured data.We employed the interpolation method with gradient plane nodal function(GPNF)and Shepard(SPD)across a range of parameters to simulate SOCD with a 40 cm soil layer depth in a dryland farming region(DFR)of China.The SOCP was estimated using a carbon saturation model.Results demonstrated the GPNF method was proved to be more effective in simulating the spatial distribution of SOCD at the vertical magnification multiple and search point values of 3.0×106 and 25,respectively.The soil organic carbon storage(SOCS)of 40 cm and 20 cm soil layers were estimated as 22.28×10^(11)kg and 13.12×10^(11)kg simulated by GPNF method in DFR.The SOCP was estimated as 0.95×10^(11)kg considered as a carbon sink at the 20–40 cm soil layer.Furthermore,the SOCP was estimated as–2.49×10^(11)kg considered as a carbon source at the 0–20 cm soil layer.This research has important values for the scientific use of soil resources and the mitigation of greenhouse gas emissions.
文摘The chlorophyll fluorescence （CF） signature emitted from vegetation provides an abundance of information regarding photosynthetics activity and has been used as a powerful tool to obtain physiological information of plant leaves in a non-invasive manner. CF is difficult to quantify because the CF signal is obscured by reflected light. In the present study, the apparent reflectance spectra of wheat （Triticum aestivum L.） leaves were measured under illuminations with and without filtering by three specially designed long-wave pass edge filters; the cut-off wavelengths of the three filters were 653.8, 678.2, and 694. l nm at 50% of maximum transmittance. The CF spectra could be derived as the reflectance difference spectra of the leaves under illuminations with and without the long wave pass edge filters. The ratio of the reflectance difference at 685 and 740 nm （Dif685/Dif740） was linear correlated with the CF parameters （maximal photochemical efficiency Fv/Fm, and the yield of quantum efficiency） measured by the modulated fluorometer. In addition, the ratio reflected the water stress status of the wheat leaf, which was very high when water deficiency was serious. This method provides a new approach for detecting CF and the physiological state of crops.
基金Project supported by the National High-Tech R&D Program (863) of China(No.2012AA12A30703)the Meteorology Industry Special Project of China Meteorological Administration(CMA)(No.GYHY 201306036)the Ph.D Programs Foundation of the Ministry of Education of China(No.20100101110035)
文摘e The objective of this study was to investigate the tempo-spatial distribution of paddy rice in Northeast China using moderate resolution imaging spectroradiometer （MODIS） data. We developed an algorithm for detection and estimation of the transplanting and flooding periods of paddy rice with a combination of enhanced vegetation index （EVI） and land surface water index with a central wavelength at 2130 nm （LSW12130）. In two intensive sites in Northeast China, fine resolution satellite imagery was used to validate the performance of the algorithm at pixel and 3x3 pixel window levels, respectively. The commission and omission errors in both of the intensive sites were approximately less than 20%. Based on the algorithm, annual distribution of paddy rice in Northeast China from 2001 to 2009 was mapped and analyzed. The results demonstrated that the MODIS-derived area was highly correlated with published agricultural statistical data with a coefficient of determination （R^2） value of 0.847. It also revealed a sharp decline in 2003, especially in the Sanjiang Plain located in the northeast of Heilongjiang Province, due to the oversupply and price decline of rice in 2002. These results suggest that the approaches are available for accurate and reliable monitoring of rice cultivated areas and variation on a large scale.
基金the National Natural Science Foundation of China(No.41171276)。
文摘The oilseed rape growing in the lower reaches of Yangtze River in China belongs to winter varieties and suffers the risk of freezing injury.In this research,a typical freezing injury event occurred in Anhui Province was taken as a case study,the freezing damage degree of oilseed rape was assessed,and its development characteristics based on the vegetation metrics derived from MODIS and MERIS data were investigated.The oilseed rape was mapped according to the decline of greenness from bud stage to full-bloom period,with the phenological phases identified adopting time-series analyses.NDVI was more sensitive to freezing injury compared with other commonly used vegetation indices(VIs)calculated using MODIS bands,e.g.,EVI,GNDVI and SAVI.The freezing damage degree employing the difference between post-freeze growth and the baseline level in adjacent damage-free growing seasons was determined.The remote sensing-derived damage levels were supported by their correlation with the cold accumulated temperatures at the county level.The performance of several remote sensing indicators of plant biophysical and biochemical parameters was also investigated,i.e.,the photosynthetic rate,canopy water status,canopy chlorophyll content,leaf area index(LAI)and the red edge position(REP),in response to the advance of the freezing damage.It was found that the photosynthetic rate indicator—Photochemical Reflectance Index(PRI)responded strongly to freezing stress.Freezing injury caused canopy water loss,which could be detected though the magnitude was not very large.MERIS-LAI showed a slow and lagging response to low temperature and restored rapidly in the recovery phase;additionally,REP and the indicator of canopy chlorophyll content—MERIS Terrestrial Chlorophyll Index(MTCI),did not appear to be influenced by freezing injury.It was concluded that the physiological functions,canopy structure,and organic content metrics showed a descending order of vulnerabilities to freezing injury.
基金Project (No. 40871158/D0106) supported by the National Natural Science Foundation of China
文摘The tasseled cap transformation of remote sensing data has been widely used in agriculture,forest,ecology,and landscape.In this paper,tasseled cap transformation coefficients appropriate for data from a new sensor(China & Brazil Earth Resource Satellite(CBERS-02B)) are presented.The first three components after transformation captured 98% of the four-band variance,and represent the physical characteristics of brightness(coefficients:0.509,0.431,0.330,and 0.668),greenness(coefficients:0.494,0.318,0.324,and 0.741),and blueness(coefficients:0.581,0.070,0.811,and 0.003),respectively.We hope these results will enhance the application of CBERS-02B charge-coupled device(CCD) data in the areas of agriculture,forest,ecology,and landscape.
