Modelling the crop yield gap with a remote sensing-based process model:A case study of winter wheat in the North China Plain

Understanding the spatial distribution of the crop yield gap(YG)is essential for improving crop yields.Recent studies have typically focused on the site scale,which may lead to considerable uncertainties when scaled to the regional scale.To mitigate this issue,this study used a process-based and remote sensing driven crop yield model for winter wheat(PRYM-Wheat),which was derived from the boreal ecosystem productivity simulator(BEPS),to simulate the YG of winter wheat in the North China Plain from 2015 to 2019.Yield validation based on statistical yield data revealed good performance of the PRYM-Wheat Model in simulating winter wheat actual yield(Ya).The distribution of Ya across the North China Plain showed great heterogeneity,decreasing from southeast to northwest.The remote sensing-estimated results show that the average YG of the study area was 6400.6 kg ha^(–1).The YG of Jiangsu Province was the largest,at7307.4 kg ha^(–1),while the YG of Anhui Province was the smallest,at 5842.1 kg ha^(–1).An analysis of the responses of YG to environmental factors showed no obvious correlation between YG and precipitation,but there was a weak negative correlation between YG and accumulated temperature.In addition,the YG was positively correlated with elevation.In general,studying the specific features of the YG can provide directions for increasing crop yields in the future.

Extensive identification of landslide boundaries using remote sensing images and deep learning method

The frequent occurrence of extreme weather events has rendered numerous landslides to a global natural disaster issue.It is crucial to rapidly and accurately determine the boundaries of landslides for geohazards evaluation and emergency response.Therefore,the Skip Connection DeepLab neural network(SCDnn),a deep learning model based on 770 optical remote sensing images of landslide,is proposed to improve the accuracy of landslide boundary detection.The SCDnn model is optimized for the over-segmentation issue which occurs in conventional deep learning models when there is a significant degree of similarity between topographical geomorphic features.SCDnn exhibits notable improvements in landslide feature extraction and semantic segmentation by combining an enhanced Atrous Spatial Pyramid Convolutional Block(ASPC)with a coding structure that reduces model complexity.The experimental results demonstrate that SCDnn can identify landslide boundaries in 119 images with MIoU values between 0.8and 0.9;while 52 images with MIoU values exceeding 0.9,which exceeds the identification accuracy of existing techniques.This work can offer a novel technique for the automatic extensive identification of landslide boundaries in remote sensing images in addition to establishing the groundwork for future inve stigations and applications in related domains.

The Application of Remote Sensing Monitoring Method and Groundwater Model for Water Rights Management in Haihe River basin

Groundwater is an important water resource in Haihe River basin,North China.The number of aquifers that appear to be declining under conditions of groundwater overdraft is increasing.To effectively manage the water resources,there is a strong scientific need to analyze the net use of this important water resource and to quantify the water rights allocation for improved understanding of the future water

Integrating a novel irrigation approximation method with a process-based remote sensing model to estimate multi-years'winter wheat yield over the North China Plain

Accurate estimation of regional winter wheat yields is essential for understanding the food production status and ensuring national food security.However,using the existing remote sensing-based crop yield models to accurately reproduce the inter-annual and spatial variations in winter wheat yields remains challenging due to the limited ability to acquire irrigation information in water-limited regions.Thus,we proposed a new approach to approximating irrigations of winter wheat over the North China Plain(NCP),where irrigation occurs extensively during the winter wheat growing season.This approach used irrigation pattern parameters(IPPs)to define the irrigation frequency and timing.Then,they were incorporated into a newly-developed process-based and remote sensing-driven crop yield model for winter wheat(PRYM–Wheat),to improve the regional estimates of winter wheat over the NCP.The IPPs were determined using statistical yield data of reference years(2010–2015)over the NCP.Our findings showed that PRYM–Wheat with the optimal IPPs could improve the regional estimate of winter wheat yield,with an increase and decrease in the correlation coefficient(R)and root mean square error(RMSE)of 0.15(about 37%)and 0.90 t ha–1(about 41%),respectively.The data in validation years(2001–2009 and 2016–2019)were used to validate PRYM–Wheat.In addition,our findings also showed R(RMSE)of 0.80(0.62 t ha–1)on a site level,0.61(0.91 t ha–1)for Hebei Province on a county level,0.73(0.97 t ha–1)for Henan Province on a county level,and 0.55(0.75 t ha–1)for Shandong Province on a city level.Overall,PRYM–Wheat can offer a stable and robust approach to estimating regional winter wheat yield across multiple years,providing a scientific basis for ensuring regional food security.

Evaluation of the International Vehicle Emission (IVE) model with on-road remote sensing measurements

Intemational Vehicle Emissions （IVE） model funded by U.S. Environmental Protection Agency （USEPA） is designed to estimate emissions from motor vehicles in developing countries. In this study, the IVE model was evaluated by utilizing a dataset available from the remote sensing measurements on a large number of vehicles at five different sites in Hangzhou, China, in 2004 and 2005. Average fuel-based emission factors derived from the remote sensing measurements were compared with corresponding emission factors derived from IVE calculations for urban, hot stabilized condition. The results show a good agreement between the two methods for gasoline passenger cars＇ HC emission for all 1VE subsectors and technology classes. In the case of CO emissions, the modeled results were reasonably good, although systematically underestimate the emissions by almost 12%-50% for different technology classes. However, the model totally overestimated NOx emissions. The IVE NOx emission factors were 1.5-3.5 times of the remote sensing measured ones. The IVE model was also evaluated for light duty gasoline truck, heavy duty gasoline vehicles and motor cycles. A notable result was observed that the decrease in emissions from technology class State II to State I were overestimated by the IVE model compared to remote sensing measurements for all the three pollutants. Finally, in order to improve emission estimation, the adjusted base emission factors from local studies are strongly recommended to be used in the IVE model.

A STUDY ON WETLAND CLASSIFICATION MODEL OF REMOTE SENSING IN THE SANGJIANG PLAIN

The Sanjiang Plain, where nearly 20 kinds of wetlands exist now, is one of the largest wetlands distributed area of wetlands in China. To identify each of them and pick up them separately by means of automatic interpretation of remote sensing from TM Landsat images is extremely important. However, most of the types of wetlands can not be divided each other due to the similarity and the illegibility of the wetland spectrum shown in TM images. Special disposals to remote sensing images include the spectrum enhancement of wetland information, the pseudo color composite of TM images of different bands and the algebra enhancement of TM images. By this way some kinds of wetlands such as Sparganium stoloniferum and Bolboschoenus maritimus can be identified. But in many cases, these methods are still insufficient because of the noise brought from the atmosphere transportation and so on. The physical features of wetlands reflecting the diversification of spectrum information of wetlands, which include the spatial temporal characteristics of the wetlands distribution, the landscape differences of wetlands from season to season, the growing environment and the vertical structure of wetlands vegetation and so on, must be taken into consideration. Besides these, the artificial alteration to spatial structure of wetlands such as the exploitation of some types of them can be also used as important symbols of wetlands identification from remote sensing images. On the basis of the above geographics analysis, a set of wetlands classification models of remote sensing could be established, and many types of wetlands such as paddy field, reed swamp, peat mire, meadow, CAREX marsh and paludification meadow and so on, will be distinguished consequently. All the ways of geographical analysis and model establishment will be given in detail in this article.

The remote sensing composite information of Nansha reef's closed atoll and the model of its spatial structure evolution——Ⅰ

Based on the feature of the growth and the local limitation of the Nansha reef's closed atoll, also the related remote sensing information of it, the composite analysis is made. The closed atoll conception remote sensing system is constructed through the formation mechanism, development law and the space-time distribution of the closed atoll. Solution of the equation consisting of information function value and isostatic value of closed atoll and half-closed atoll using this system, and topologic analysis on the two kinds of atoll's morphologic growth are the basic foundation for the research of their evolutionary comparative model.

Modeling and predicting dengue fever cases in key regions of the Philippines using remote sensing data

Objective: To correlate climatic and environmental factors such as land surface temperature, rainfall, humidity and normalized difference vegetation index with the incidence of dengue to develop prediction models for the Philippines using remote-sensing data.Methods: Timeseries analysis was performed using dengue cases in four regions of the Philippines and monthly climatic variables extracted from Global Satellite Mapping of Precipitation for rainfall, and MODIS for the land surface temperature and normalized difference vegetation index from 2008-2015.Consistent dataset during the period of study was utilized in Autoregressive Integrated Moving Average models to predict dengue incidence in the four regions being studied.Results: The best-fitting models were selected to characterize the relationship between dengue incidence and climate variables.The predicted cases of dengue for January to December 2015 period fitted well with the actual dengue cases of the same timeframe.It also showed significantly good linear regression with a square of correlation of 0.869 5 for the four regions combined.Conclusion: Climatic and environmental variables are positively associated with dengue incidence and suit best as predictor factors using Autoregressive Integrated Moving Average models.This finding could be a meaningful tool in developing an early warning model based on weather forecasts to deliver effective public health prevention and mitigation programs.

Indication of the Expression of Transgene in Rice Plant Based on Hyperspectral Remote Sensing Technique Ⅱ——Growth Monitoring of Samples in the Contrast Experiment

Since the complication of monitoring and evaluating the problems about the transgenic expression and its impacts on the receptor in the transgenic crop breeding and other relevant evaluated works,the authors in the present work tried to assess the differences of spectral parameters of the transgenic rice in contrast with its parent group quantitatively and qualitatively,fulfilling the growth monitoring of the transgenic samples.The spectral parameters(spectral morphological characteristics and indices) chosen are highly related to internal or external stresses to the receipts,and thus could be applied as indicators of biophysical or biochemical processes changes of plant.By ASD portable field spectroradiometer with high-density probe,fine foliar spectra of 8 groups were obtained.By analyzing spectral angle and continuum removal,the spectral morphological differences and their locations of sample spectra were found which could be as auxiliary priori knowledge for quantitative analysis.By investigating spectral indices of the samples,the quantitative differences of spectra were revealed about foliar chlorophyll a+b and carotenoid content.In this study both the spectral differences between transgenic and parent groups and among transgenic groups were investigated.The results show that hyperspectral technique is promising and a helpful auxiliary tool in the study of monitoring the transgenic crop and other relevant researches.By this technique,quantitative and qualitative results of sample spectra could be provided as prior knowledge,as certain orientation,for laboratory professional advanced transgenic breeding study.

The research on the remote sensing＇s information tree model of the Nansha coral islets and reef＇s spatial structure

Based on the Nansha coral islets and reef's time-space attributes,and the intension and extension of the remote sensing information, the concept model and concept system of coral islets and reef are proposed.Then twin-tree remote sensing information model for different kinds of reef is constructed by using abstracted islets and reef's primitive, and the structure recognition system for coral islets and reef type is developed.

Towards a semi-empirical model of the sea ice thickness based on hyperspectral remote sensing in the Bohai Sea

Sea ice thickness is one of the most important input parameters for the prevention and mitigation of sea ice disasters and the prediction of local sea environments and climates. Estimating the sea ice thickness is currently the most important issue in the study of sea ice remote sensing. With the Bohai Sea as the study area, a semiempirical model of the sea ice thickness（SEMSIT） that can be used to estimate the thickness of first-year ice based on existing water depth estimation models and hyperspectral remote sensing data according to an optical radiative transfer process in sea ice is proposed. In the model, the absorption and scattering properties of sea ice in different bands（spectral dimension information） are utilized. An integrated attenuation coefficient at the pixel level is estimated using the height of the reflectance peak at 1 088 nm. In addition, the surface reflectance of sea ice at the pixel level is estimated using the 1 550–1 750 nm band reflectance. The model is used to estimate the sea ice thickness with Hyperion images. The first validation results suggest that the proposed model and parameterization scheme can effectively reduce the estimation error associated with the sea ice thickness that is caused by temporal and spatial heterogeneities in the integrated attenuation coefficient and sea ice surface. A practical semi-empirical model and parameterization scheme that may be feasible for the sea ice thickness estimation using hyperspectral remote sensing data are potentially provided.

Remote sensing-based dynamic monitoring and environmental change of wetlands in southern Mongolian Plateau in 2000‒2018

The environmental change in the wetlands in the southern Mongolian Plateau has important impacts on the environment of North China and even the entire Northeast Asia,from which the global climate change can be understood on a large scale,especially the climate change in the Mongolian Plateau.This study extracted the information on the wetlands from three stages of remote sensing images(also referred to as RS images)of the study area,including Enhanced Thematic Mapper Plus(ETM+)images of 2000,TM images of 2010,and Landsat 8 Operational Land Imager(OLI)images of 2018.As indicated by the extraction results,the area of wetlands decreased from 796.90 km^(2) of 2000 to 666.24 km^(2) of 2018 at a rate of 7.26 km^(2)/a.The reduced area is 130.66 km^(2),which is about 16.4%reduction.And the patch number of wetlands decreased from 731 of 2000 to 316 of 2018 in the study area,approximately 56.8%reduction(415 patches),and the decrease in the area of the wetlands mainly occurred in the northwest endorheic region.In terms of wetland types,the change of the wetlands was dominated by the decrease of lacustrine wetlands,of which the area and patch number decreased by 106.2 km^(2) and 242,respectively.Furthermore,the area of the lacustrine wetlands decreased at the highest rate of 8.70 km^(2)/a in 2010‒2018.From the perspective of spatial distribution,the wetlands in the western part shrunk more notably than those in the eastern part as a whole in the study area.According to local meteorological data,the precipitation gently decreased and the temperature increased(about 1.7℃)from 1975-2018.Overall,the decrease in the area of the wetlands and the temperature rises in the study area were mainly driven by the Mongolian monsoon climate,reduction in precipitation,and human activities.

Satellite remote sensing of sea-ice and its operational monitoring method along the coast of China

Sea-ice is an important operational item for real timely monitoring and forecasting marine environment of China. This paper introduces an operational method of satellite remote sensing to monitor sea- ice using quantitative data of NOAA, and its contents include computer processing of AVHRR sounding data of NOAA and its program design, imagery processing of sea-ice imagery from satellite and their thematic analysis. The sea-ice satellite colour imageries processed via this software system are able to interpret sea-ice pattern, characterizing it by thickness, maximum position of ice boundary, floe concentration and dynamic process of ice changing. At the same time, analyses of the ice condition of the Bohai Sea for the two-year period (1986-1988) as monitored by satellite have been summarized.

Empirical-Statistical Models Based on Remote Sensing for Estimating the Volume of Landslides Induced by the Wenchuan Earthquake

The Wenchuan Ms 8.0 earthquake on May 12, 2008 induced a huge number of landslides. The distribution and volume of the landslides are very important for assessing risks and understanding the landslide - debris flow - barrier lake - bursts flood disaster chain. The number and the area of landslides in a wide region can be easily obtained by remote sensing technique, while the volume is relatively difficult to obtain because it requires some detailed geometric information of slope failure surface and sub-surface. Different empirical models for estimating landslide volume were discussed based on the data of 107 landslides in the earthquake-stricken area. The volume data of these landslides were collected by field survey. Their areas were obtained by interpreting remote sensing images while their apparent friction coefficients and height were extracted from the images unifying DEM (digital elevation model). By analyzing the relationships between the volume and the area, apparent friction coefficients, and the height, two models were established, one for the adaptation of a magnitude scale landslide events in a wide range of region, another for the adaptation in a small scope. The correlation coefficients (R2) are 0.7977 and 0.8913, respectively. The results estimated by the two models agree well with the measurement data.

Study on soil erosion dynamics in typical regionof southern China based on remote sensing, GISand gray forecast model

This paper StUdies soil erosion dynamics in the typical region of southem China based onremote sensing, GIS tecndques and gray forecast model. The resultS of survey on Xingguo countyshown the soil eroded area and annual soil erosion amount decreased by 19.09% and 43.05%reSPectively from 1958 to 1988. The results of gray forecast model presented that soil eroded areaincreased from 818.04 km2 in 1988 to 1276.69 km2 in 1995. in the meanthne the total soil erosiollamount decreased from 607.21×104 ba in 1988 to 472. 12 ×104 t/a in 1995. By comparing differentlanduse types, the soil loss modulus of the forest was the lowest with 177. 16~187.75t/km2. a, on thecontraly the bare land was the highest with 10626.76~11265.48 t/km2. a. so the high vegetationcoverage can decrease soil and water loss effectively.

THE DYNAMIC MONITORING OF HORQIN SAND LAND USING REMOTE SENSING

Horqin Sand Land is regarded as the t ypical region for studying the probl em of desertification.The integra-tion of 3S(GIS,GPS and RS)techniques offer a most helpful meth od to study and monitor the dynamics o f desertification.Based on the data derived from3perio ds’ multitemporal Landsat TMimagery of the 1990s,the regional land use a nd dynam-ics of desertification in Horqin San d Land were studied.The main results revealed that:1)as long as the general change tendency was concerned,the deserti fication of Horqin Sand Land would co ntinue to spread;2)there was a gradual de-crease in the area of both moving sand dunes and semi-stabilized ones,whi ch meant that fruitful progress had b een made to control the desertification during the 1990s;3)as a result of unreasonable cultivation,the total area of stabilized san d dunes and grassland in the middle and western region decreased obviously.It suggested that the increasing damage caused by human was leading to the hazard of further desertification.So i n the future,it is necessary to take more effec-tive measures to control the spread o f desertification and restore the degraded ecosystems for the purpose of optimizing the global eco-environment in Horqin Sand Land.

RESEARCH ON THE LOCAL CORRECTION MODEL OF ATMOSPHERIC DRY DELAY IN GPS REMOTE SENSING WATER VAPOR

The precision of atmospheric dry delay model is closely correlated with the accuracy of GPS water vapor in the process of GPS (Global Position System) remote sensing. Radiosonde data (from 1996 to 2001) at Qingyuan are used to calculate the exact values of the atmospheric dry delay. Base on these calculations and the surface meteorological parameters, the local year and month correction models of dry delay at the zenith angle of 0° are established by statistical methods. The analysis result shows that the local model works better and is slight more sensitive to altitude angle than universal models and that it is not necessary to build models for each month due to the slight difference between year model and month model. Furthermore, when the altitude angle is less than 75°, the difference between curve path and straight path increases rapidly with altitude angle’s decrease.

MONITORING THE DIFFUSION OF SUSPENDED SEDIMENTS AND STABILITY OF TIDAL RADIAL SAND RIDGES AREA USING MULTI-SATELLITE REMOTE SENSING DATA

Nine Landsat TM tapes and images and MSS images, 10 NOAA tapes and images and 1 SAR image from 1973 to 1997 were used to analyze the diffusion of suspended sediments and the change of tidal radial sand ridges in the northern part of the Changjiang River delta, the South Yellow Sea. The results showed that the diffusion of suspended sediments was controlled by the tide, net current, and submarine topography in this area. The distribution of suspended sediments had close relationship with the submarine topography. The old Huanghe River delta and the Changjiang River comprise the main sediment supply for the formation of radial sand ridges, whose evolution can be divided into three stages since the Huanghe River changed its course and flowed northward into the Bohai Sea.

Extraction of coastline in high-resolution remote sensing images based on the active contour model

While executing tasks such as ocean pollution monitoring,maritime rescue,geographic mapping,and automatic navigation utilizing remote sensing images,the coastline feature should be determined.Traditional methods are not satisfactory to extract coastline in high-resolution panchromatic remote sensing image.Active contour model,also called snakes,have proven useful for interactive specification of image contours,so it is used as an effective coastlines extraction technique.Firstly,coastlines are detected by water segmentation and boundary tracking,which are considered initial contours to be optimized through active contour model.As better energy functions are developed,the power assist of snakes becomes effective.New internal energy has been done to reduce problems caused by convergence to local minima,and new external energy can greatly enlarge the capture region around features of interest.After normalization processing,energies are iterated using greedy algorithm to accelerate convergence rate.The experimental results encompassed examples in images and demonstrated the capabilities and efficiencies of the improvement.