Mapping Spatial and Temporal Variations of Leaf Area Index for Winter Wheat in North China

Leaf area index (LAI) is an important parameter in a number of models related to ecosystem functioning, carbon budgets, climate, hydrology, and crop growth simulation. Mapping and monitoring the spatial and temporal variations of LAI are necessary for understanding crop growth and development at regional level. In this study, the relationships between LAI of winter wheat and Landsat TM spectral vegetation indices (SVIs) were analyzed by using the curve estimation procedure in North China Plain. The series of LAI maps retrieved by the best regression model were used to assess the spatial and temporal variations of winter wheat LAI. The results indicated that the general relationships between LAI and SVIs were curvilinear, and that the exponential model gave a better fit than the linear model or other nonlinear models for most SVIs. The best regression model was constructed using an exponential model between surface-reflectance-derived difference vegetation index (DVI) and LAI, with the adjusted R2 (0.82) and the RMSE (0.77). The TM LAI maps retrieved from DVI- LAI model showed the significant spatial and temporal variations. The mean TM LAI value (30 m) for winter wheat of the study area increased from 1.29 (March 7, 2004) to 3.43 (April 8, 2004), with standard deviations of 0.22 and 1.17, respectively. In conclusion, spectral vegetation indices from multi-temporal Landsat TM images can be used to produce fine-resolution LAI maps for winter wheat in North China Plain.

Repurposing EV Batteries for Storing Solar Energy

1.Introduction Carbon neutrality has emerged as a global goal due to its pivotal role in addressing the challenges of global climate change.Before the United Nations Climate Summit was held in November 2020,124 countries promised to reach net-zero emissions[1].Solar energy is one of the important renewable energy sources that significantly curtail carbon emissions originating from fossil fuels.

Comparison between TVDI and CWSI for drought monitoring in the Guanzhong Plain,China

Temperature vegetation dryness index(TVDI) and crop water stress index(CWSI) are two commonly used remote sensing-based agricultural drought indicators. This study explored the applicability of monthly moderate resolution imaging spectroradiometer(MODIS) normalized difference vegetation index(NDVI) and land surface temperature(LST) data for agricultural drought monitoring in the Guanzhong Plain,China in 2003. The data were processed using TVDI,calculated by parameterizing the relationship between the MODIS NDVI and LST data. We compared the effectiveness of TVDI against CWSI,derived from the MOD16 products,for drought monitoring. In addition,the surface soil moisture and monthly precipitation were collected and used for verification of the results. Results from the study showed that:(1) drought conditions measured by TVDI and CWSI had a number of similarities,which indicated that both CWSI and TVDI can be used for drought monitoring,although they had some discrepancies in the spatiotemporal characteristics of drought intensity of this region; and(2) both standardized precipitation index(SPI) and SM contents at the depth of 10 and 20 cm had better correlations to CWSI than to TVDI,indicating that there were more statistically significant relationships between CWSI and SPI/SM,and that CWSI is a more reliable indicator for assessing and monitoring droughts in this region.

Assessment for soil loss by using a scheme of alterative sub-models based on the RUSLE in a Karst Basin of Southwest China

Accurate assessment of soil loss caused by rainfall is essential for natural and agricultural resources management. Soil erosion directly affects the environment and human sustainability. In this work,the empirical and contemporary model of revised universal soil loss equation(RUSLE) was applied for simulating the soil erosion rate in a karst catchment using remote sensing data and geographical information systems. A scheme of alterative sub-models was adopted to calculate the rainfall erosivity(R),soil erodibility(K),slope length and steepness(LS),cover management(C) and conservation practice(P) factors in the geographic information system(GIS) environment. A map showing the potential of soil erosion rate was produced by the RUSLE and it indicated the severe soil erosion in the study area. Six classes of erosion rate are distinguished from the map: 1) minimal,2) low,3) medium,4) high,5) very high,and 6) extremely high. The RUSLE gave a mean annual erosion rate of 30.24 Mg ha–1 yr–1 from the 1980 s to 2000 s. The mean annual erosion rate obtained using RUSLE is consistent with the result of previous research based on in situ measurement from 1980 to 2009. The high performance of the RUSLE model indicates the reliability of the sub-models and possibility of applying the RUSLE on quantitative estimation. The result of the RUSLE model is sensitive to the slope steepness,slope length,vegetation factors and digital elevation model(DEM) resolution. The study suggests that attention should be given to the topographic factors and DEM resolution when applying the RUSLE on quantitative estimation of soil loss.

Characterizing Spatial Patterns of Phenology in Cropland of China Based on Remotely Sensed Data

This study used time-series of global inventory modeling and mapping studies(GIMMS) normalized difference vegetation index(NDVI) datasets at a spatial resolution of 8 km and 15-d interval to investigate the spatial patterns of cropland phenology in China.A smoothing algorithm based on an asymmetric Gaussian function was first performed on NDVI dataset to minimize the effects of anomalous values caused by atmospheric haze and cloud contamination.Subsequent processing for identifying cropping systems and extracting phenological parameters,the starting date of growing season(SGS) and the ending date of growing season(EGS) was based on the smoothed NVDI time-series data.The results showed that the cropping systems in China became complex as moving from north to south of China.Under these cropping systems,the SGS and EGS for the first growing season varied largely over space,and those regions with multiple cropping systems generally presented a significant advanced SGS and EGS than the regions with single cropping patterns.On the contrary,the phenological events of the second growing season including both the SGS and EGS showed little difference between regions.The spatial patterns of cropping systems and phenology in Chinese cropland were highly related to the geophysical environmental factors.Several anthropogenic factors,such as crop variety,cultivation levels,irrigation,and fertilizers,could profoundly influence crop phenological status.How to discriminate the impacts of biophysical forces and anthropogenic drivers on phenological events of cultivation remains a great challenge for further studies.

Developing crop specific area frame stratifications based on geospatial crop frequency and cultivation data layers

Area Sampling Frames(ASFs) are the basis of many statistical programs around the world. To improve the accuracy,objectivity and efficiency of crop survey estimates,an automated stratification method based on geospatial crop planting frequency and cultivation data is proposed. This paper investigates using 2008–2013 geospatial corn,soybean and wheat planting frequency data layers to create three corresponding single crop specific and one multi-crop specific South Dakota(SD) U.S. ASF stratifications. Corn,soybeans and wheat are three major crops in South Dakota. The crop specific ASF stratifications are developed based on crop frequency statistics derived at the primary sampling unit(PSU) level based on the Crop Frequency Data Layers. The SD corn,soybean and wheat mean planting frequency strata of the single crop stratifications are substratified by percent cultivation based on the 2013 Cultivation Layer. The three newly derived ASF stratifications provide more crop specific information when compared to the current National Agricultural Statistics Service(NASS) ASF based on percent cultivation alone. Further,a multi-crop stratification is developed based on the individual corn,soybean and wheat planting frequency data layers. It is observed that all four crop frequency based ASF stratifications consistently predict corn,soybean and wheat planting patterns well as verified by the 2014 Farm Service Agency(FSA) Common Land Unit(CLU) and 578 administrative data. This demonstrates that the new stratifications based on crop planting frequency and cultivation are crop type independent and applicable to all major crops. Further,these results indicate that the new crop specific ASF stratifications have great potential to improve ASF accuracy,efficiency and crop estimates.

Landslide initiation and runout susceptibility modeling in the context of hill cutting and rapid urbanization: a combined approach of weights of evidence and spatial multicriteria

Rainfall induced landslides are a common threat to the communities living on dangerous hillslopes in Chittagong Metropolitan Area, Bangladesh. Extreme population pressure, indiscriminate hill cutting, increased precipitation events due to global warming and associated unplanned urbanization in the hills are exaggerating landslide events. The aim of this article is to prepare a scientifically accurate landslide susceptibility map by combining landslide initiation and runout maps. Land cover, slope, soil permeability, surface geology, precipitation, aspect, and distance to hill cut, road cut, drainage and stream network factor maps were selected by conditional independence test. The locations of 56 landslides were collected by field surveying. A weight of evidence(Wo E) method was applied to calculate the positive(presence of landslides) and negative(absence of landslides) factor weights. A combination of analytical hierarchical process(AHP) and fuzzymembership standardization(weighs from 0 to 1) was applied for performing a spatial multi-criteria evaluation. Expert opinion guided the decision rule for AHP. The Flow-R tool that allows modeling landslide runout from the initiation sources was applied. The flow direction was calculated using the modified Holmgren's algorithm. The AHP landslide initiation and runout susceptibility maps were used to prepare a combined landslide susceptibility map. The relative operating characteristic curve was used for model validation purpose. The accuracy of Wo E, AHP, and combined susceptibility map was calculated 96%, 97%, and 98%, respectively.

RF-CLASS： A remote-sensing-based flood crop loss assessment cyber-service system for supporting crop statistics and insurance decision-making

Floods often cause significant crop loss in the United States. Timely and objective information on flood-related crop loss,such as flooded acreage and degree of crop damage,is very important for crop monitoring and risk management in agricultural and disaster-related decision-making at many concerned agencies. Currently concerned agencies mostly rely on field surveys to obtain crop loss information and compensate farmers' loss claim. Such methods are expensive,labor intensive,and time consumptive,especially for a large flood that affects a large geographic area. The results from such methods suffer from inaccuracy,subjectiveness,untimeliness,and lack of reproducibility. Recent studies have demonstrated that Earth observation(EO) data could be used in post-flood crop loss assessment for a large geographic area objectively,timely,accurately,and cost effectively. However,there is no operational decision support system,which employs such EO-based data and algorithms for operational flood-related crop decision-making. This paper describes the development of an EO-based flood crop loss assessment cyber-service system,RF-CLASS,for supporting flood-related crop statistics and insurance decision-making. Based on the service-orientated architecture,RF-CLASS has been implemented with open interoperability specifications to facilitate the interoperability with EO data systems,particularly the National Aeronautics and Space Administration(NASA) Earth Observing System Data and Information System(EOSDIS),for automatically fetching the input data from the data systems. Validated EO algorithms have been implemented as web services in the system to operationally produce a set of flood-related products from EO data,such as flood frequency,flooded acreage,and degree of crop damage,for supporting decision-making in flood statistics and flood crop insurance policy. The system leverages recent advances in the remote sensing-based flood monitoring and assessment,the near-real-time availability of EO data,the service-oriented architecture,geospatial interoperability standards,and the standard-based geospatial web service technology. The prototypical system has automatically generated the flood crop loss products and demonstrated the feasibility of using such products to improve the agricultural decision-making. Evaluation of system by the end-user agencies indicates that significant improvement on flood-related crop decision-making has been achieved with the system.

Remote sensing for agricultural applications

Agricultural geospatial information is critical for agricultural policy formulation and decision making,land use monitoring,agricultural sustainability,crop acreage and yield estimation,disaster assessment,bioenergy crop inventory,food security policy,environmental assessment,carbon accounting,and other research topics that are of vital importance to agriculture and economy.Remote sensing technology enables

A public Cloud-based China's Landslide Inventory Database(Cs LID): development, zone, and spatiotemporal analysis for significant historical events, 1949-2011

Landslide inventory plays an important role in recording landslide events and showing their temporal-spatial distribution. This paper describes the development, visualization, and analysis of a China's Landslide Inventory Database(Cs LID) by utilizing Google's public cloud computing platform. Firstly, Cs LID(Landslide Inventory Database) compiles a total of 1221 historical landslide events spanning the years 1949-2011 from relevant data sources. Secondly, the Cs LID is further broken down into six zones for characterizing landslide cause-effect, spatiotemporal distribution, fatalities, and socioeconomic impacts based on the geological environment and terrain. The results show that among all the six zones, zone V, located in Qinba and Southwest Mountainous Area is the most active landslide hotspot with the highest landslide hazard in China. Additionally, the Google public cloud computing platform enables the Cs LID to be easily accessible, visually interactive, and with the capability of allowing new data input to dynamically augment the database. This work developed a cyber-landslide inventory and used it to analyze the landslide temporal-spatial distribution in China.

MILP formulations for highway petrol station replenishment in initiative distribution mode

To investigate highway petrol station replenishment in initiative distribution mode,this paper develops a mixed-integer linear programming(MILP)model with minimal operational costs that includes loading costs,unloading costs,transport costs and the costs caused by unpunctual distribution.Based on discrete representation,the working day is divided into equal time intervals,and the truck distribution process is decomposed into a pair of tasks including driving,standby,rest,loading and unloading.Each truck must execute one task during a single interval,and the currently executing task is closely related to the preceding and subsequent tasks.By accounting for predictive time-varying sales at petrol stations,real-time road congestion and a series of operational constraints,the proposed model produces the optimal truck dispatch,namely,a detailed task assignment for all trucks during each time interval.The model is tested on a real-world case of a replenishment system comprising eight highway petrol stations,one depot,one garage and eight trucks to demonstrate its applicability and accuracy.

Multi-Resolution Landslide Susceptibility Analysis Using a DEM and Random Forest

Landslide susceptibility (LS) mapping is a requisite for safety against sediment related disasters, and considerable effort has been exerted in this discipline. However, the size heterogeneity and distribution of landslides still impose challenges in selecting an appropriate scale for LS studies. This requires identification of an optimal scale for landslide causative parameters. In this study, we propose a method to identify the optimum scale for each parameter and use multiple optimal parameter-scale combinations for LS mapping. A random forest model was used, together with 16 geomorphological parameters extracted from 10, 30, 60, 90, 120, 150, and 300 m digital elevation models (DEMs) and an inventory of historical landslides. Experiments in two equal-sized (625 km2) areas in Niigata and Ehime, Japan, with different geological and environmental settings and landslide density, demonstrated the efficiency of the proposed method. It outperformed all other single scale LS analysis with a prediction accuracy of 79.7% for Niigata and 78.62% for Ehime. Values of areas under receiver operating characteristics (ROC) curves (AUC) of 0.877 and 0.870 validate the application of the multi-scale model.

广州SARS流行过程的空间模式与分异特征

根据广州市SARS感染者的家庭住址信息进行空间化处理,得到了不同SARS感染人群在空间上的分布,并用数据模型定量表达了广州市SARS疫区的传播中心及其置信范围,结合SARS接收医院中心点的分布及其变化规律,对不同感染人群传播中心的异质性及其变化规律进行了分析,结果表明:随着疫情的发展,广州市SARS传播中心具有明显的向SARS接收医院中心点接近的趋势,且变化规律与SARS接收医院中心点的迁移规律大致相同,医院传播对广州市SARS传播过程有重要影响,且经历了由强到弱再到强的变化过程。本文研究有助于更好地了解SARS及其他新型传染病在都市区的空间传播扩散过程,并为公共卫生应急预案提供科学依据。

作物长势遥感监测指标的改进与比较分析

为改善归一化植被指数(NDVI)作为遥感监测作物长势指标的性能,该文分析了归一化植被指数的内在设计缺陷,在不增加额外波段的情况下,以近红外波段和红色波段为基础引入一种新的作物长势遥感监测指标——GRNDVI。通过在像素和区域层次上同其他4种指数进行比较发现:GRNDVI能够改善归一化植被指数在低植被覆盖度时期/地区容易受到作物冠层土壤背景的影响,而在高植被覆盖度时期/地区又容易发生饱和现象的设计缺陷,可以作为遥感监测作物长势过程中替代归一化植被指数的指标。

基于线性光谱模型的城市不透水面遥感估算

城市不透水面是评估城市生态环境和社会经济的关键指示性因子,对于城市规划和资源管理有着重要意义。本研究以长春市为例,使用2014年Landsat 8影像,基于"植被-不透水面-土壤"理论模型,采用多端元优化的提取方法,依据研究区实际土地覆被特点,选取了高反照度、低反照度、植被、裸土、耕地等五个端元,利用线性光谱模型求算长春市不透水面,利用高分辨率遥感影像高分一号对估算结果进行验证,并对其空间分布格局进行分析。结果表明:基于几何顶点的端元提取方法得到的城市不透水面比例的RMSE为0.126,误差范围在-0.366—0.387,而基于多端元优化提取方法获取结果的RMSE为0.079,误差范围在-0.319—0.265,且超过80%样本的绝对误差小于0.1,精度有显著提升;长春市绕城高速范围内平均城市不透水面比例为47.4%,整体分布呈现"三角形"特征,南部不透水面分布面积明显高于北部区域。从城市外环到内部一环,城市不透水面比例有明显的递增趋势,三环内比例超过66.7%,不透水面分布密集。总体来说,在城市区域尺度上,采用多端元优化提取方法,利用中等空间分辨率多光谱遥感数据提取城市不透水面精度令人满意。

Regression model to estimate flood impact on corn yield using MODIS NDVI and USDA cropland data layer

Flood events and their impact on crops are extremely significant scientific research issues; however,flood monitoring is an exceedingly complicated process. Flood damages on crops are directly related to yield change,which requires accurate assessment to quantify the damages. Various remote sensing products and indices have been used in the past for this purpose. This paper utilizes the moderate resolution imaging spectroradiometer(MODIS) weekly normalized difference vegetation index(NDVI) product to detect and further quantify flood damages on corn within the major corn producing states in the Midwest region of the US. County-level analyses were performed by taking weighted average of all pure corn pixels(>90%) masked by the United States Department of Agriculture(USDA) Cropland Data Layer(CDL). The NDVI-based time-series difference between flood years and normal year(median of years 2000–2014) was used to detect flood occurrences. To further measure the impact of the flood on corn yield,regression analysis between change in NDVI and change in corn yield as independent and dependent variables respectively was performed for 30 different flooding events within growing seasons of the corn. With the R2 value of 0.85,the model indicates statistically significant linear relation between the NDVI and corn yield. Testing the predictability of the model with 10 new cases,the average relative error of the model was only 4.47%. Furthermore,small error(4.8%) of leave-one-out cross validation(LOOCV) along with smaller statistical error indicators(root mean square error(RMSE),mean absolute error(MAE),and mean bias error(MBE)),further validated the accuracy of the model. Utilizing the linear regression approach,change in NDVI during the growing season of corn appeared to be a good indicator to quantify the yield loss due to flood. Additionally,with the 250 m MODIS-based NDVI,these yield losses can be estimated up to field level.

Landslide Susceptibility Assessment of the Youfang Catchment using Logistic Regression

A detailed landslide susceptibility map was produced in the Youfang catchment using logistic regression method with datasets developed for a geographic information system(GIS).Known as one of the most landslide-prone areas in China, the Youfang catchment of Longnan mountain region,which lies in the transitional area among QinghaiTibet Plateau, loess Plateau and Sichuan Basin, was selected as a representative case to evaluate the frequency and distribution of landslides.Statistical relationships for landslide susceptibility assessment were developed using landslide and landslide causative factor databases.Logistic regression(LR)was used to create the landslide susceptibility maps based on a series of available data sources: landslide inventory; distance to drainage systems, faults and roads; slope angle and aspect; topographic elevation and topographical wetness index, and land use.The quality of the landslide susceptibility map produced in this paper was validated and the result can be used fordesigning protective and mitigation measures against landslide hazards.The landslide susceptibility map is expected to provide a fundamental tool for landslide hazards assessment and risk management in the Youfang catchment.

Inventory of Atmospheric Pollutant Emissions from Burning of Crop Residues in China Based on Satellite-retrieved Farmland Data

The burning of crop residues emits large quantities of atmospheric aerosols.Published studies have developed inventories of emissions from crop residue burning based on statistical data.In contrast,this study used satellite-retrieved land-cover data(1 km×1 km)as activity data to compile an inventory of atmospheric pollutants emitted from the burning of crop residues in China in 2015.The emissions of PM10,PM2.5,VOCs,NOx,SO2,CO,and NH3 from burning crop straw on nonirrigated farmland in China in 2015 were 610.5,598.4,584.4,230.6,35.4,3329.3,and 36.1 Gg(1 Gg=109 g),respectively;the corresponding emissions from burning paddy rice residues were 234.1,229.7,342.3,57.5,57.5,1122.1,and 21.5 Gg,respectively.The emissions from crop residue burning showed large spatial and temporal variations.The emissions of particulate matter and gaseous pollutants from crop residue burning in nonirrigated farmland were highest in east China,particularly in Shandong,Henan,Anhui,and Sichuan provinces.Emissions from burning paddy rice residue were highest in east and central China,with particularly high levels in Shandong,Jiangsu,Zhejiang,and Hunan provinces.The monthly variations in atmospheric pollutant emissions were similar among different regions,with the highest levels observed in October in north,northeast,northwest,east,and southwest China and in June and July in central and south China.The developed inventory of emissions from crop residue burning is expected to help improve air quality models by providing high-resolution spatial and temporal data.

Monthly gravity field solution from GRACE range measurements using modified short arc approach

In this paper we present a series of monthly gravity field solutions from Gravity Recovery and Climate Experiment(GRACE) range measurements using modified short arc approach,in which the ambiguity of range measurements is eliminated via differentiating two adjacent range measurements.The data used for developing our monthly gravity field model are same as Tongji-GRACEOl model except that the range measurements are used to replace the range rate measurements,and our model is truncated to degree and order 60,spanning Jan.2004 to Dec.2010 also same as Tongji-GRACE01 model.Based on the comparison results of the C_(2,0),C_(2,1),S_(2,1),and C_(15,15),S_(15,15),time series and the global mass change signals as well as the mass change time series in Amazon area of our model with those of Tongji-GRACE01 model,we can conclude that our monthly gravity field model is comparable with Tongji-GRACE01 monthly model.

GRACE RL05-based ice mass changes in the typical regions of antarctica from 2004 to 2012

The Antarctic ice sheet is the largest block of ice on Earth,a tiny change of its ice sheet will have a significant impact on sea level change,so it plays an important role in global climate change. The Gravity Recovery and Climate Experiment( GRACE) mission,launched in 2002,provides an alternative method to monitor the Antarctic ice mass variation. The latest Release Level 05( RL05) version of GRACE time-variable gravity( TVG) data,derived from GRACE observations with improved quality and time-span over 10 years,were released by three GRACE data centers( CSR,JPL and GFZ) in April 2012,which gives us a chance to re-estimate the ice mass change over Antarctic more accurately. In this paper,we examine ice mass changes in regional scale,including Antarctic Peninsula( AP,West Antarctica),Amundsen Sea Embayment( ASE,West Antarctica),Lambert-Amery System( LAS,East Antarctica) and 27 drainage basins based on three data sets.The AP mass change rates are- 12. 03 ± 0. 74 Gt / a( CSR,2004- 2012),- 13. 92 ± 2. 33 Gt / a( JPL,2004- 2012),- 12. 28 ± 0. 76 Gt / a( GFZ,2005- 2012),with an acceleration of- 1. 50 ± 0. 25 Gt / a2,-1. 54 ± 0. 26 Gt / a2,- 0. 46 ± 0. 28 Gt/a2 respectively,the ASE mass change rates are- 89. 22 ± 1. 93 Gt / a,- 86. 28 ± 2. 20 Gt / a,- 83. 67 ± 1. 76 Gt / a with an acceleration of- 10. 03 ± 0. 65 Gt / a2,- 8. 74 ± 0. 74 Gt / a2and- 5. 69 ± 0. 68 Gt / a2,and the LAS mass change rates are- 4. 31 ± 1. 95 Gt/a,- 7. 29 ± 2. 84 Gt/a,1. 20 ± 1. 35 Gt / a with an acceleration of- 0. 18 ± 0. 62 Gt / a2,3. 55 ± 0. 95 Gt/a2 and 0. 97 ± 0. 49 Gt /a2. The mass change rates derived from the three RL05 data are very close to each other both in AP and ASE with the uncertainties much smaller than the change rates,and mass losses are significantly accelerated since2007 in AP and 2006 in ASE,respectively. However,the mass change rates are significantly different in LAS,negative rate from CSR and JPL data,but positive rate from GFZ data,the uncertainties are even larger than the correspondent change rates. With regard to the 27 drainage basins,seven basins( basin 3- 9) located in the east Antarctica show positive mass change rates,and the rest twenty basins are characterized by negative mass change rates during the time span of the three RL05 data.