Spatial and temporal variation of water clarity in typical reservoirs in the Beijing-Tianjin-Hebei region observed by GF 1-WFV satellite data

Rapidly monitoring regional water quality and the changing trend is of great practical and scientific significance,especially for the Beijing-Tianjin-Hebei(BTH)region of China where water resources are relatively scarce and inland water bodies are generally small.The remote sensing data of the GF 1 satellite launched in 2013 have characteristics of high spatial and temporal resolution,which can be used for the dynamic monitoring of the water environment in small lakes and reservoirs.However,the water quality remote sensing monitoring model based on the GF 1 satellite data for lakes and reservoirs in BTH is still lacking because of the considerable differences in the optical characteristics of the lakes and reservoirs.In this paper,the typical reservoirs in BTH-Guanting Reservoir,Yuqiao Reservoir,Panjiakou Reservoir,and Daheiting Reservoir are taken as the study areas.In the atmospheric correction of GF 1-WFV,the relative radiation normalized atmospheric correction was adopted after comparing it with other methods,such as 6 S and FLAASH.In the water clarity retrieval,a water color hue angle based model was proposed and outperformed other available published models,with the R 2 of 0.74 and MRE of 31.7%.The clarity products of the four typical reservoirs in the BTH region in 2013-2019 were produced using the GF 1-WFV data.Based on the products,temporal and spatial changes in clarity were analyzed,and the main influencing factors for each water body were discussed.It was found that the clarity of Guanting,Daheiting,and Panjiakou reservoirs showed an upward trend during this period,while that of Yuqiao Reservoir showed a downward trend.In the influencing factors,the water level of the water bodies can be an important factor related to the water clarity changes in this region.

Development of Spectral Features for Monitoring Rice Bacterial Leaf Blight Disease Using Broad-Band Remote Sensing Systems

As an important rice disease, rice bacterial leaf blight (RBLB, caused by the bacterium Xanthomonas oryzae pv.oryzae), has become widespread in east China in recent years. Significant losses in rice yield occurred as a result ofthe disease’s epidemic, making it imperative to monitor RBLB at a large scale. With the development of remotesensing technology, the broad-band sensors equipped with red-edge channels over multiple spatial resolutionsoffer numerous available data for large-scale monitoring of rice diseases. However, RBLB is characterized by rapiddispersal under suitable conditions, making it difficult to track the disease at a regional scale with a single sensorin practice. Therefore, it is necessary to identify or construct features that are effective across different sensors formonitoring RBLB. To achieve this goal, the spectral response of RBLB was first analyzed based on the canopyhyperspectral data. Using the relative spectral response (RSR) functions of four representative satellite or UAVsensors (i.e., Sentinel-2, GF-6, Planet, and Rededge-M) and the hyperspectral data, the corresponding broad-bandspectral data was simulated. According to a thorough band combination and sensitivity analysis, two novel spectralindices for monitoring RBLB that can be effective across multiple sensors (i.e., RBBRI and RBBDI) weredeveloped. An optimal feature set that includes the two novel indices and a classical vegetation index was formed.The capability of such a feature set in monitoring RBLB was assessed via FLDA and SVM algorithms. The resultdemonstrated that both constructed novel indices exhibited high sensitivity to the disease across multiple sensors.Meanwhile, the feature set yielded an overall accuracy above 90% for all sensors, which indicates its cross-sensorgenerality in monitoring RBLB. The outcome of this research permits disease monitoring with different remotesensing data over a large scale.

Remote sensing of quality traits in cereal and arable production systems:A review

Cereal is an essential source of calories and protein for the global population.Accurately predicting cereal quality before harvest is highly desirable in order to optimise management for farmers,grading harvest and categorised storage for enterprises,future trading prices,and policy planning.The use of remote sensing data with extensive spatial coverage demonstrates some potential in predicting crop quality traits.Many studies have also proposed models and methods for predicting such traits based on multiplatform remote sensing data.In this paper,the key quality traits that are of interest to producers and consumers are introduced.The literature related to grain quality prediction was analyzed in detail,and a review was conducted on remote sensing platforms,commonly used methods,potential gaps,and future trends in crop quality prediction.This review recommends new research directions that go beyond the traditional methods and discusses grain quality retrieval and the associated challenges from the perspective of remote sensing data.

Progress of Earth Observation in China

China is expanding and sharing its capacity for Earth observation by developing sensors,platforms,and launch capabilities in tandem with growing lunar and deep space exploration.China is considering the Moon as a viable Earth observation platform to provide high-quality,planetary-scale data.The platform would produce consistent spatiotemporal data because of its long operational life and the geological stability of the Moon.China is also quickly improving its capabilities in processing and transforming Earth observation data into useful and practical information.Programs such as the Big Earth Data Science Engineering Program(CASEarth)provide opportunities to integrate data and develop“Big Earth Data”platforms to add value to data through analysis and integration.Such programs can offer products and services independently and in collaboration with international partners for data-driven decision support and policy development.With the rapid digital transformation of societies,and consequently increasing demand for big data and associated products,Digital Earth and the Digital Belt and Road Program(DBAR)allow Chinese experts to collaborate with international partners to integrate valuable Earth observation data in regional and global sustainable development.

SDGSAT-1: Capabilities for Monitoring and Evaluating SDG Indicators

SDGSAT-1,the world's first science satellite dedicated to assisting the United Nations 2030 Sustainable Development Agenda,has been operational for over two and a half years.It provides valuable data to aid in implementing the Sustainable Development Goals internationally.Through its Open Science Program,the satellite has maintained consistent operations and delivered free data to scientific and technological users from 88 countries.This program has produced a wealth of scientific output,with 72 papers,including 28 on data processing methods and 44 on applications for monitoring progress toward SDGs related to sustainable cities,clean energy,life underwater,climate action,and clean water and sanitation.SDGSAT-1 is equipped with three key instruments:a multispectral imager,a thermal infrared spectrometer,and a glimmer imager,which have enabled ground-breaking research in a variety of domains such as water quality analysis,identification of industrial heat sources,assessment of environmental disaster impacts,and detection of forest fires.The precise measurements and ongoing monitoring made possible by this invaluable data significantly advance our understanding of various environmental phenomena.They are essential for making well-informed decisions on a local and global scale.Beyond its application to academic research,SDGSAT-1 promotes global cooperation and strengthens developing countries'capacity to accomplish their sustainable development goals.As the satellite continues to gather and distribute data,it plays a pivotal role in developing strategies for environmental protection,disaster management and relief,and resource allocation.These initiatives highlight the satellite's vital role in fostering international collaboration and technical innovation to advance scientific knowledge and promote a sustainable future.

Assessing changes in nighttime lighting in the aftermath of the Turkey-Syria earthquake using SDGSAT-1 satellite data

On February 6,2023,a powerful earthquakewith amagnitude of 7.8MW struck southeastern Turkey and northwestern Syria.The epicenter of the earthquake was in Gaziantep,Turkey.The earthquake was followed by a devastating aftershock 9 h later,which registered a magnitude of 7.5 MW and which had an epicenter located 100 km farther north in Kahramanmaras,Turkey.Thousands of subsequent tremors and aftershocks since the initial earthquake have further exacerbated the impacts on lives and infrastructure,resulting in more than 50,000 deaths and damage to hundreds of thousands of buildings in the region.

Earth observation big data for climate change research

Earth observation technology has provided highly useful information in global climate change research over the past few decades and greatly promoted its development,especially through providing biological,physical,and chemical parameters on a global scale.Earth observation data has the 4V features(volume,variety,veracity,and velocity) of big data that are suitable for climate change research.Moreover,the large amount of data available from scientific satellites plays an important role.This study reviews the advances of climate change studies based on Earth observation big data and provides examples of case studies that utilize Earth observation big data in climate change research,such as synchronous satelliteeaerialeground observation experiments,which provide extremely large and abundant datasets; Earth observational sensitive factors(e.g.,glaciers,lakes,vegetation,radiation,and urbanization); and global environmental change information and simulation systems.With the era of global environment change dawning,Earth observation big data will underpin the Future Earth program with a huge volume of various types of data and will play an important role in academia and decisionmaking.Inevitably,Earth observation big data will encounter opportunities and challenges brought about by global climate change.

Detection of Internal Leaf Structure Deterioration Using a New Spectral Ratio Index in the Near-Infrared Shoulder Region

Spectral reflectance in the near-infrared （NIR） shoulder （750-900 nm） region is affected by internal leaf structure, but it has rarely been investigated. In this study, a dehydration treatment and three paraquat herbicide applications were conducted to explore how spectral reflectance and shape in the NIR shoulder region responded to various stresses. A new spectral ratio index in the NIR shoulder region （NSRI）, defined by a simple ratio of reflectance at 890 nm to reflectance at 780 nm, was proposed for assessing leaf structure deterioration. Firstly, a wavelength-independent increase in spectral reflectance in the NIR shoulder region was observed from the mature leaves with slight dehydration. An increase in spectral slope in the NIR shoulder would be expected only when water stress developed sufficiently to cause severe leaf dehydration resulting in an alteration in cell structure. Secondly, the alteration of leaf cell structure caused by Paraquat herbicide applications resulted in a wavelength-dependent variation of spectral reflectance in the NIR shoulder region. The NSRI in the NIR shoulder region increased significantly under an herbicide application. Although the dehydration process also occurred with the herbicide injury, NSRI is more sensitive to herbicide injury than the water-related indices （water index and normalized difference water index） and normalized difference vegetation index. Finally, the sensitivity of NSRI to stripe rust in winter wheat was examined, yielding a determination coefficient of 0.61, which is more significant than normalized difference vegetation index （NDVI）, water index （WI） and normalized difference water index （NDWI）, with a determination coefficient of 0.45, 0.36 and 0.13, respectively. In this study, all experimental results demonstrated that NSRI will increase with internal leaf structure deterioration, and it is also a sensitive spectral index for herbicide injury or stripe rust in winter wheat.

Dynamic Monitoring of Soil Erosion for Upper Stream of Miyun Reservoir in the Last 30 Years

The Revised Universal Soil Loss Equation （RUSLE） was applied to assess the spatial distribution and dynamic properties of soil loss with geographic information system （GIS） and remote sensing （RS） technologies. To improve the accuracy of soil-erosion estimates, a new C-factor estimation model was developed based on land cover and time series normalized difference vegetation index （NDVI） datasets. The new C-factor was then applied in the RUSLE to integrate rainfall, soil, vegetation, and topography data of different periods, and thus monitor the distribution of soil erosion patterns and their dynamics during a 3o-year period of the upstream watershed of Miynn Reservoir （UWMR）, China. The results showed that the new C-factor estimation method, which considers land cover status and dynamics, and explicitly incorporates within-land cover variability, was more rational, quantitative, and reliable. An average annual soil loss in UWMR of 25.68, 21.04, and 16.8o t ha-1 a-1 was estimated for 1990, 2000 and 2010, respectively, corroborated by comparing spatial and temporal variation in sediment yield. Between 2000 and 2010, a 1.38% average annual increase was observed in the area of lands that lost less than 5 t ha-1 a^-1, while during 1990-2000 such lands only increased on average by o.46%. Areas that classified as severe, very severe and extremely severe accounted for 5.68% of the total UWMR in 2010, and primarily occurred in dry areas or grasslands of sloping fields. The reason for the change in rate of soil loss is explained by an increased appreciation of soil conservation by developers and planners. Moreover,we recommend that UWMR watershed adopt further conservation measures such as terraced plowing of dry land, afforestation, or grassland enclosures as part of a concerted effort to reduce on-going soil erosion.

Monitoring elevation change of glaciers on Geladandong Mountain using Tan DEM-X SAR interferometry

Glaciers play an important role in the climate system. The elevation change of a glacier is an important parameter in studies of glacier dynamics. Only a few ground-based measurements of high mountain glaciers are available due to their remoteness, high elevation, and complex topography. The acquisition from the German Tan DEM-X(Terra SAR-X add-on for Digital Elevation Measurement) SAR imaging configuration provides a reliable data sources for studying the elevation change of glaciers. In this study, the bistatic Tan DEM-X data that cover the Geladandong Mountain on the Tibetan Plateau were processed with SAR interferometry technique and the elevation changes of the mountain's glaciers during 2000–2014 were obtained. The results indicated that although distinct positive and negative elevation changes were found for different glacier tongues, the mean elevation change was about-0.14±0.26 m a-1. Geoscience Laser Altimeter System(GLAS) data were obtained for comparison and verification. The investigation using GLAS data demonstrated the efficacy of the proposed method in determining glacier elevation change. Thus, the presented approach is appropriate for monitoring glacier elevation change and it constitutes a valuable tool for studies of glacier dynamics.

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.

Preliminary validation of SMOS sea surface salinity measurements in the South China Sea

The SMOS(soil moisture and ocean salinity) mission undertaken by the European Space Agency(ESA) has provided sea surface salinity(SSS) measurements at global scale since 2009.Validation of SSS values retrieved from SMOS data has been done globally and regionally.However,the accuracy of SSS measurements by SMOS in the China seas has not been examined in detail.In this study,we compared retrieved SSS values from SMOS data with in situ measurements from a South China Sea(SCS) expedition during autumn 2011.The comparison shows that the retrieved SSS values using ascending pass data have much better agreement with in situ measurements than the result derived from descending pass data.Accuracy in terms of bias and root mean square error(RMS) of the SSS retrieved using three different sea surface roughness models is very consistent,regardless of ascending or descending orbits.When ascending and descending measurements are combined for comparison,the retrieved SSS using a semi-empirical model shows the best agreement with in situ measurements,with bias-0.33 practical salinity units and RMS 0.74.We also investigated the impact of environmental conditions of sea surface wind and sea surface temperature on accuracy of the retrieved SSS.The SCS is a semi-closed basin where radio frequencies transmitted from the mainland strongly interfere with SMOS measurements.Therefore,accuracy of retrieved SSS shows a relationship with distance between the validation sites and land.

Spatial-temporal evolution of vegetation evapotranspiration in Hebei Province,China

Evapotranspiration （ET） is the sum of soil or water body evaporation and plant transpiration from the earth surface and ocean to the atmosphere, and thus plays a significant role in regulating carbon and water resource cycles. The time-series data set from the remote sensing MOLDS product （MOD16） was used to study the spatial-temporal evolution of vegetation evapotranspiration in salinized areas during 2000-2014 by analyzing the variability, spatial patterns and Mann-Kendall （MK） nonparametric trends for the time series. The results indicate that inter-annual and intra-annual variations of ET across various vegetated areas show seasonal changes, with the abnormal months identified. The Cultivated land displays a greater degree of spatial heterogeneity and the spatial pattern of ET in the area covered by broadleaved deciduous forests corresponds to a higher ET rate and increased water consumption. Awidespread decline of ET is observed only in cultivated areas. However, agricultural cultivation doesn＇t worsen water shortage and soil salinization problems in the region, and water shortage problems are worsening for other vegetated areas. This research provides a basis of reference for the reasonable allocation of water resources and restructuring of vegetation patterns in salinized areas.

Temporal and spatial characteristics of dissolved organic carbon in the Wujiang River,Southwest China

River systems play an important role in the global carbon cycle. Rivers transport carbon to the ocean and also affect the carbon cycle in the coastal ocean. The flux from land to the ocean is thought to be a very important part of the land carbon budget. To investigate the effect of dam-building on dissolved organic carbon(DOC)in rivers, three reservoirs of different trophic states in the Wujiang basin, Guizhou Province, were sampled twice per month between May 2011 and May 2012. Temporal and spatial distributions of DOC in the reservoirs and their released waters were studied. It was found that different factors controlled DOC in river water, reservoir water, and released water. DOC in the rivers tended to be affected by primary production. For reservoirs, the main controlling factors of DOC concentration varied by trophic state. For the mesotrophic Hongjiadu Reservoir, the effect of primary production on DOC concentration was obvious. For the eutrophic Dongfengdu Reservoir and the hypereutrophic Wujiangdu Reservoir, primary production was not significant and DOC came instead from soil and plant litter.

Mapping favorable groundwater potential recharge zones using a GIS-based analytical hierarchical process and probability frequency ratio model:A case study from an agro-urban region of Pakistan

In Punjab(Pakistan),the increasing population and expansion of land use for agriculture have severely exploited the regional groundwater resources.Intensive pumping has resulted in a rapid decline in the level of the water table as well as its quality.Better management practices and artificial recharge are needed for the development of sustainable groundwater resources.This study proposes a methodology to delineate favorable groundwater potential recharge zones(FPRI)by integrating maps of groundwater potential recharge index(PRI)with the DRASTIC-based groundwater vulnerability index(VI).In order to evaluate both indexes,different thematic layers corresponding to each index were overlaid in ArcGIS.In the overlay analysis,the weights(for various thematic layers)and rating values(for sub-classes)were allocated based on a review of published literature.Both were then normalized and modified using the analytical hierarchical process(AHP)and a frequency ratio model respectively.After evaluating PRI and FPRI,these maps were validated using the area under the curve(AUC)method.The PRI map indicates that 53%of the area assessed exists in very low to low recharge zones,22%in moderate,and 25%in high to excellent potential recharge zones.The VI map indicates that 38%of the area assessed exists in very low to low vulnerability,33%in moderate,and 29%in high to very high vulnerability zones.The FPRI map shows that the central region of Punjab is moderately-to-highly favorable for recharge due to its low vulnerability and high recharge potential.During the validation process,it was found that the AUC estimated with modified weights and rating values was 79%and 67%,for PRI and VI indexes,respectively.The AUC was less when evaluated using original weights and rating values taken from published literature.Maps of favorable groundwater potential recharge zones are helpful for planning and implementation of wells and hydraulic structures in this region.

Influence of voxel size on forest canopy height estimates using full-waveform airborne LiDAR data

Background: Forest canopy height is a key forest structure parameter. Precisely estimating forest canopy height is vital to improve forest management and ecological modelling. Compared with discrete-return LiDAR(Light Detection and Ranging), small-footprint full-waveform airborne LiDAR(FWL) techniques have the capability to acquire precise forest structural information. This research mainly focused on the influence of voxel size on forest canopy height estimates.Methods: A range of voxel sizes(from 10.0 m to 40.0 m interval of 2 m) were tested to obtain estimation accuracies of forest canopy height with different voxel sizes. In this study, all the waveforms within a voxel size were aggregated into a voxel-based LiDAR waveform, and a range of waveform metrics were calculated using the voxelbased LiDAR waveforms. Then, we established estimation model of forest canopy height using the voxel-based waveform metrics through Random Forest(RF) regression method.Results and conclusions: The results showed the voxel-based method could reliably estimate forest canopy height using FWL data. In addition, the voxel sizes had an important influence on the estimation accuracies(R2 ranged from 0.625 to 0.832) of forest canopy height. However, the R2 values did not monotonically increase or decrease with the increase of voxel size in this study. The best estimation accuracy produced when the voxel size was 18 m(R2= 0.832, RMSE = 2.57 m, RMSE% = 20.6%). Compared with the lowest estimation accuracy, the R2 value had a significant improvement(33.1%) when using the optimal voxel size. Finally, through the optimal voxel size, we produced the forest canopy height distribution map for this study area using RF regression model. Our findings demonstrate that the optimal voxel size need to be determined for improving estimation accuracy of forest parameter using small-footprint FWL data.

A two-scale approach for estimating forest aboveground biomass with optical remote sensing images in a subtropical forest of Nepal

Forests account for 80%of the total carbon exchange between the atmosphere and terrestrial ecosystems.Thus,to better manage our responses to global warming,it is important to monitor and assess forest aboveground carbon and forest aboveground biomass(FAGB).Different levels of detail are needed to estimate FAGB at local,regional and national scales.Multi-scale remote sensing analysis from high,medium and coarse spatial resolution data,along with field sampling,is one approach often used.However,the methods developed are still time consuming,expensive,and inconvenient for systematic monitoring,especially for developing countries,as they require vast numbers of field samples for upscaling.Here,we recommend a convenient two-scale approach to estimate FAGB that was tested in our study sites.The study was conducted in the Chitwan district of Nepal using GeoEye-1(0.5 m),Landsat(30 m)and Google Earth very high resolution(GEVHR)Quickbird(0.65 m)images.For the local scale(Kayerkhola watershed),tree crowns of the area were delineated by the object-based image analysis technique on GeoEye images.An overall accuracy of 83%was obtained in the delineation of tree canopy cover(TCC)per plot.A TCC vs.FAGB model was developed based on the TCC estimations from GeoEye and FAGB measurements from field sample plots.A coefficient of determination(R2)of 0.76 was obtained in the modelling,and a value of 0.83 was obtained in the validation of the model.To upscale FAGB to the entire district,open source GEVHR images were used as virtual field plots.We delineated their TCC values and then calculated FAGB based on a TCC versus FAGB model.Using the multivariate adaptive regression splines machine learning algorithm,we developed a model from the relationship between the FAGB of GEVHR virtual plots with predictor parameters from Landsat 8 bands and vegetation indices.The model was then used to extrapolate FAGB to the entire district.This approach considerably reduced the need for field data and commercial very high resolution imagery while achieving two-scale forest information and FAGB estimates at high resolution(30 m)and accuracy(R2=0.76 and 0.7)with minimal error(RMSE=64 and 38 tons ha-1)at local and regional scales.This methodology is a promising technique for cost-effective FAGB and carbon estimations and can be replicated with limited resources and time.The method is especially applicable for developing countries that have low budgets for carbon estimations,and it is also applicable to the Reducing Emissions from Deforestation and Forest Degradation(REDD?)monitoring reporting and verification processes.

Evaluation of effective spectral features for glacial lake mapping by using Landsat-8 OLI imagery

Glacial lake mapping provides the most feasible way for investigating the water resources and monitoring the flood outburst hazards in High Mountain Region.However,various types of glacial lakes with different properties bring a constraint to the rapid and accurate glacial lake mapping over a large scale.Existing spectral features to map glacial lakes are diverse but some are generally limited to the specific glaciated regions or lake types,some have unclear applicability,which hamper their application for the large areas.To this end,this study provides a solution for evaluating the most effective spectral features in glacial lake mapping using Landsat-8 imagery.The 23 frequently-used lake mapping spectral features,including single band reflectance features,Water Index features and image transformation features were selected,then the insignificant features were filtered out based on scoring calculated from two classical feature selection methods-random forest and decision tree algorithm.The result shows that the three most prominent spectral features(SF)with high scores are NDWI1,EWI,and NDWI3(renamed as SF8,SF19 and SF12 respectively).Accuracy assessment of glacial lake mapping results in five different test sites demonstrate that the selected features performed well and robustly in classifying different types of glacial lakes without any influence from the mountain shadows.SF8 and SF19 are superior for the detection of large amount of small glacial lakes,while some lake areas extracted by SF12 are incomplete.Moreover,SF8 achieved better accuracy than the other two features in terms of both Kappa Coefficient(0.8812)and Prediction(0.9025),which further indicates that SF8 has great potential for large scale glacial lake mapping in high mountainous area.

Effects of land use change on the spatiotemporal variability of soil organic carbon in an urban-rural ecotone of Beijing,China

Understanding the effects of land use changes on the spatiotemporal variation of soil organic carbon （SOC） can provide guidance for low carbon and sustainable agriculture. In this paper, based on the large-scale datasets of soil surveys in 1982 and 2009 for Pinggu District -- an urban-rural ecotone of Beijing, China, the effects of land use and land use changes on both temporal variation and spatial variation of SOC were analyzed. Results showed that from 1982 to 2009 in Pinggu District, the following land use change mainly occurred： Grain cropland converted to orchard or vegetable land, and grassland converted to forestland. The SOC content decreased in region where the land use type changed to grain cropland （e.g., vegetable land to grain cropland decreased by 0.7 g kg-1; orchard to grain cropland decreased by 0.2 g kg-l）. In contrast, the SOC content increased in region where the land use type changed to either orchard （excluding forestland） or forestland （e.g., grain cropland to orchard and forestland increased by 2.7 and 2.4 g kg-1, respectively; grassland to orchard and forestland increased by 4.8 and 4.9 g kg-1, respectively）. The organic carbon accumulation capacity per unit mass of the soil increased in the following order： grain cropland soil〈vegetable land/grassland soil〈orchard soil〈forestland soil. Therefore, to both secure supply of agricultural products and develop low carbon agriculture in a modern city, orchard has proven to be a good choice for land using.

Sensitivity analysis of Biome-BGCMuSo for gross and net primary productivity of typical forests in China

Background:Process-based models are widely used to simulate forest productivity,but complex parameterization and calibration challenge the application and development of these models.Sensitivity analysis of numerous parameters is an essential step in model calibration and carbon flux simulation.However,parameters are not dependent on each other,and the results of sensitivity analysis usually vary due to different forest types and regions.Hence,global and representative sensitivity analysis would provide reliable information for simple calibration.Methods:To determine the contributions of input parameters to gross primary productivity(GPP)and net primary productivity(NPP),regression analysis and extended Fourier amplitude sensitivity testing(EFAST)were conducted for Biome-BGCMuSo to calculate the sensitivity index of the parameters at four observation sites under climate gradient from ChinaFLUX.Results:Generally,GPP and NPP were highly sensitive to C:Nleaf(C:N of leaves),Wint(canopy water interception coefficient),k(canopy light extinction coefficient),FLNR(fraction of leaf N in Rubisco),MRpern(coefficient of linear relationship between tissue N and maintenance respiration),VPDf(vapor pressure deficit complete conductance reduction),and SLA1(canopy average specific leaf area in phenological phase 1)at all observation sites.Various sensitive parameters occurred at four observation sites within different climate zones.GPP and NPP were particularly sensitive to FLNR,SLA1 and Wint,and C:Nleaf in temperate,alpine and subtropical zones,respectively.Conclusions:The results indicated that sensitivity parameters of China's forest ecosystems change with climate gradient.We found that parameter calibration should be performed according to plant functional type(PFT),and more attention needs to be paid to the differences in climate and environment.These findings contribute to determining the target parameters in field experiments and model calibration.