TECHNICAL SPECIFICATION FOR THE VALIDATION OF REMOTE SENSING PRODUCTS

Transcription

1 TECHNICAL SPECIFICATION FOR THE VALIDATION OF REMOTE SENSING PRODUCTS Y. Ge a, *, X. Li b, M. G. Hu a, J.H. Wag a, R. Ji b, J. F. Wag a, R.H. Zhag a a Istitute of Geographic Scieces ad Natural Resources Research, Chiese Academy of Scieces, 11A, Datu Road, Beijig, Chia b Cold ad Arid Regios Evirometal ad Egieerig Research Istitute, Chiese Academy of Scieces, 320 Doggag West Road, Lazhou, Chia KEY WORDS: Techical Specificatio, Validatio, Remote Sesig Product, Heterogeeity ABSTRACT: The validatio of remote sesig products (RSPs) is fudametal work before the proper use of RSPs. This paper maily itroduces the techical specificatio for the validatio of remote sesig product, which is used to guide user how to validate the available products. The validatio of remote sesig products (VRSP) maily there are two levels. The first level focuses o the validatio at pixel scale ad the other is the product level which cotais a umber of pixels that are distributed i differet lad use types which are selected to represet both the space-time characteristics of surface parameters ivestigated. At pixel scale, the validatio procedure maily cosists of three steps: samplig desig for groud observatio, data collectio ad estimatio the mea value at pixel scale. The data types of groud observatio iclude the wireless sesor etwork, which are used to capture multi-scale heterogeeities of surface parameters, ad the footprit which could be a flux observatio such as eddy covariace (EC) ad large aperture scitillometer (LAS). After fiishig the validatio at pixel scale, the secod-level validatio for the whole remote sesig product is followed. The assessmet with idexes is the used to evaluate the performace of the validatio. Last, a validatio report o the descriptio of the validatio will be icluded i the techical specificatio. 1. INTRODUCTION Curretly, remote sesig products (RSPs) have bee widely applied to several fields of geoscieces icludig atmospheric sciece, geology, geography, ecology, evirometal sciece ad soil sciece. With rich kids of remote sesig products, it is gradually recogized that the validatio of RSP should be implemeted prior to the use of RSPs as it has bee foud that some products might overestimate/uderestimate the accuracy of target variable (Li et al., 2008 ad 2009; Su et al., 2009; Zhag, 2009; Liu et al., 2010; Zhag et al., 2010; Jia et al., 2012). The validatio of remote sesig products (VRSP) is a process which idepedetly assesses the accuracy ad precisio of RSP with groud observatio data (Li et al, 2009 ad Li et al., 2012). This paper maily itroduces the techical specificatio for the validatio of remote sesig product, which is used to guide user how to validate the available products. I the VRSP, there are two levels of validatios. The first level focuses o the validatio at pixel scale which ivolves the samplig desig for groud observatio data ad the statistical iferece from samples to estimate the value at pixel scale. The other is the product level which cotais a umber of pixels that are distributed i differet lad use types which are selected to represet both the space-time characteristics of surface parameters ivestigated. The groud observatio data types of groud observatio iclude the wireless sesor etwork, which are used to capture multi-scale heterogeeities of surface parameters, ad the footprit which could be a flux observatio such as eddy covariace (EC) ad large aperture scitillometer (LAS). This techical specificatio ot oly icludes the geeral situatios for the VRSP such as samplig desig for sigle variable at static coditio but also icludes the more complex situatios such as samplig desig for multiple variables static/dyamic coditios (Ge et al., 2012). I particular, this specificatio provides validatio strategies for RSP i homogeeous ad heterogeeous surface coditios (Li 2006). Furthermore, some scalig techiques such as liear scalig coversio ad o-liear scalig from samples to pixel scale are also ivolved i this specificatio. 2. FRAMEWORK AND COMPONENTS OF TECHNICAL SPECIFICATION RS products validatio cotais a lot of techique ad method details due to differet characteristics of rich kids of products (Cihlar et al., 1997; Justice et al., 2000). Here a geeral framework ad its compoets are provided for RS products validatio. I the framework, four compoets are defied ad specified (Figure 1). The research scopes ad related coceptio, otatios, abbreviatios ad cosistecy are defied i the basic items part. Twety six kids of products are cotaied i the specificatio, ivolvig atmospheric products (such as dowward shortwave radiatio, PAR ad AOD), categorical lad surface products (such as lad cover type, surface freezig ad thawig), cotiuous lad surface products (such as NDVI, NPP ad soil moisture), polar regio categorical products (such as Atarctic coastlie, thickess of ice sheet ad groudig lie), ad high spatial resolutio RS images. The validatio is based o groud observatio. Pixel scale estimatio is first level validatio of RS products. The the whole spatial ad temporal products are validated based o the first level pixel scale validatio. Last, the validatio results, icludig validatio method ad quality idicator, are icluded ito a validatio report for users. Correspodig author. Tel.: ; fax: address: gey@lreis.ac.c (Y. Ge). 13

2 cotiuously. The method of aggregatio from groud samples distributed i a selected pixel is the spatial samplig, ad the aggregated value is a weighted sum of all samples where the sample weight is determied by the samplig method. (2) Categorical products of lad surface: the pixel value of this type product is a categorical variable. The value is usually limited i two or more categories. There is o order ad size betwee the values. The pixel ca also be validated by a groud based spatial samplig, while the aggregatio method is differet from the cotiuous products. The value whose area domiated the pixel regio ca be selected as the pixel value. (3) Atmospheric products: differet from the above lad surface RS products, atmospheric products are little affected by the groud surface coditio. A sample measuremet i the pixel ca usually represet the whole pixel. Figure 1. Geeral framework of RSP techical specificatio 2.1 Groud observatio sites selectio ad istrumet observatioal stadard Groud observatio sites (GOS) could be a place which has a relatively low stadard deviatio of the reflectace for vicarious remote sesig product validatio. For a differet purpose of validatio, GOS ca be divided ito two categories: specializatio GOS ad itegrated GOS. Specializatio GOS is desiged to validate the pixel which has a homogeeity lad cover. For each type of lad cover, a GOS is eeded i which to observe the parameter values ad validate the correspodig remote sesig product. Itegrated GOS is desiged to validate several types of remote sesig products. It is ecessary to select ad characterize a umber of cadidate GOS i terms of the criteria of spatial homogeeity, temporal stability at all scales, ad cloud cover. The size of the selected site should be compatible with the size of pixels of product. Ad 2 2 to 5 5 pixels size are recommeded, for the cosideratio of pixel shift. Furthermore, a umber of calibrated istrumets will be istalled for VRSP. As the result of validatio of RSP chages over time, the groud-based observatio should be cosistet with the satellite passed time. 2.3 Whole RS product Validatio Sigle pixel-scale validatio of RS product evaluates the accuracy of selected pixels. But it is ot eough to evaluate the accuracy of whole RS product, which might chage across the space ad time. To validate the whole RS product, the spacetime characteristics of groud parameter should be covered i the validatio process (Figure 2). Pixel-scale validatio is the base of the whole RS product validatio. To make sure the product s accuracy is rather stable alog with the time, it is ecessary to observe ad validate the selected pixels cotiuously. For spatial variatio of RS products i a heterogeeous regio, typical groud surfaces should be covered by spatial samples, where observatio data are obtaied simultaeously. The whole accuracy is the estimated by sampled pixels. Validatio reports should be supplied to users to adjust whether the products meet their requiremets. 2.2 Pixel-scale validatio of RS products Pixel is the basic compositio of RS product. It is the first stage to validate whether the pixel value is i accord with the actual parameters. A commo direct validatio method for the pixelscale product is to compare the product with a idepedetly observed data, which observes the same groud parameter at the same time ad locatio (Li et al, 2009 ad Li et al., 2012). The observed data ca be obtaied through a groud etwork desiged by spatial samplig or foot pritig. Apart from the direct validatio, idirect validatio is ca also be doe by compariso with the output from models that simulatig the geographical process well. Both the theory of the model ad the data etered ito the model should be with high accuracy to reflect the actual groud parameter. Aother method to validate the pixel products is cross validatio with other same type RS products, whose accuracy is kow ad high. Amog the three validatio methods, direct validatio should be adopted durig all kids of RS products. Cosiderig to sigificat differece i spatial samplig ad aggregatio method for direct validatio, RS products ca be classified ito followig three categories: (1) Cotiuous products of lad surface: the pixel value ca be treated as a cotiuous variable whose value chages Figure 2. RS products validatio There is aother particular type of RS products for Polar Regios, such as Atarctic coastlie, thickess of ice sheet ad groudig lie. Validatio of these products ivolves ot oly attributes but also geometrical features. The cotets to be validated iclude data itegrity, spatial/temporal resolutios, 14

3 horizotal/vertical accuracy, ad so o. Meawhile, it is hard to be directly checked at the i-situ regio. Thus it is ofte validated by idirect method, such as compariso with same type higher accurate products. 2.4 High resolutio satellite imagery quality validatio High resolutio satellite imagery quality validatio is differet from the quatitative remote sesig products. The followig four idexes eed to be evaluated i practice. (1) Modulated trasferrig fuctio (MTF). MTF is determied by a so-called sample image method ad is commoly evaluated o remote sesig level 1 product. (2) Radiatio quality. Validatio of radiatio quality icludes: image sigal to oise ratio; dyamic rage of radiatio; relative accuracy of radiometric calibratio; absolute accuracy of radiometric calibratio. (3) Geometric quality. Validatio of geometric quality icludes: calculatio of bias matrix; cofirmatio of attitude data; validatio of sigle image positioig accuracy, regioal image positioig accuracy, iteral distortio ad registratio accuracy. (4) Groud pixel resolutio. Groud pixel resolutio is acquired by decomposig the differece betwee groud cotrol poits ad the actual coordiates ito course ad lateral directios, the divide the decompositio by the umber of pixels i course ad lateral directios respectively. 2.5 Assessmet for the performace of VRSP The assessmet system icludes the followig parts: (1) Assessmet at pixel scale. It meas that oly oe or a umber of pixels i remote sesig product are tested for a particular time period. I this circumstace, we ca directly compare the remote sesig iversio value with the pixel true value to get the accuracy of product with the idex of absolute error or relative error. (2) Assessmet i time domai. This assessmet is to test the stability of the remote sesig product accuracy as time varies. I this coditio, we ca directly prove the accuracy of remote sesig product by testig the time series variatio betwee the remote sesig product ad pixel true value. (3) Assessmet i space domai. For the multiple pixels validatio, several validatio statios eed to be established simultaeously to obtai the true value of multiple pixels for oe remote sesig product. Meawhile, the above multiple pixels true value eeds to be compared with the remote sesig product, thus gaiig the accuracy of whole product. (4) The validatio for a category remote sesig product. For this kid of remote sesig product, a umber of calibratio samples eed to be established i each category to evaluate the accuracy of products by usig the commoly used error matrix assessmet method. Table 1. Required iformatio for differet samplig models Classificatio Validatio idex Formula Pixel scale Time series Spatial domai Category products absolute error, = X L relative error, = / L100% time series correlatio, r xy mea absolute 1 = i differece, i1 mea relative 1 = i error, i1 1 RMSE RMSE= error matrix r xy ( xi x)( yi y) i1 2 2 ( xi x) ( yi y) i1 i1 i1 2 i producer accuracy, user accuracy; overall accuracy, ad kappa coefficiet Notatio: X ad L are the measuremet value ad pixel true value. is the umber of measure, x, y are the two time series measuremet, ad x, y are the mea values of two time series. 2.6 VRST Report A validatio report will be issued after fiishig the remote sesig product validatio. This report icludes the four followig parts. (1) The mai descriptio of the target remote sesig product. It icludes the ame of validatio product, data source ad metadata, temporal ad spatial resolutio, spatial coverage, ad retrieval algorithms et al. (2) The summary of validatio method. (3) Ucertaity aalysis durig the process of validatio. It is maily about the aalysis of error sources form the observatio equipmet error, model error. (4) The coclusio of the validatio ad the accuracy of the product. (5) People, uits, time, locatio, ad sigature of the validatio. 3. METHODS FOR VRSP I the pixel-scale validatio stage, samplig is the basic method to obtai the true value of the selected pixel. For a homogeeous pixel regio, such as atmospheric products, a small umber of samples are eough to capture most iformatio i the regio. While for a heterogeeous pixel regio, more samples are usually eeded to reach a high estimated accuracy. Moreover, the samples should be carefully desiged to obtai a ubiased estimatio. Thus, it is a importat issue i the pixel-scale validatio that how to select appropriate samplig method. 3.1 Samplig method i pixel-scale RS product validatio Samplig is a ofte used to uderstad objects i may disciplies. Compared to geeral survey, samplig has some particular advatages (Cochra, 1977; Haiig, 2003). First, it is rapid ad efficiet. A samplig survey ca be doe i a short time, ad it eables cotiuous observatio possible to validate spatial/temporal variatio of RS products. Secod, it is fud savig. Oly a small umber of samples are surveyed. Third, a well desiged samplig scheme ca decrease systematic errors durig the ivestigatio process ad icrease the estimatio accuracy. 15

4 A suitable samplig method should be determied accordig to the particular characteristics i the selected pixel regio. Spatial autocorrelatio ad homogeeity are two of the most importat characteristics i samplig desig. Whe there is o spatial autocorrelatio of object groud attribute i the pixel regio, classical samplig methods (such as simple radom samplig, systematic samplig ad stratified samplig) ca be used i the product validatio. Otherwise, spatial samplig methods are recommeded (Table 2). Stratified samplig ad MSN model take ito cosideratio of stratificatio where it is ecessary if the regio is heterogeeous (Wag et al., 2009; Stei ad Ettema, 2003). I Krigig ad MSN based samplig, there is a object fuctio to miimize variace of the estimated populatio (Hu ad Wag, 2011; Ge et al., 2012). So the result is a best liear ubiased estimator of the real populatio. Whe cosiderig the autocorrelatio iformatio i Krigig ad MSN, semivariogram models should be built before the samplig process, which usually requires some prior iformatio about the pixel regio. Samplig Autocorrelatio fuctio Object Stratum Model Prior ifo. Simple radom Stratified Krigig MSN ote: required; ot required; partial required Table 2. Required iformatio for differet samplig models 3.2 Statistical iferece from samples i pixel-scale RS product validatio Statistical iferece is the ext stage after samples is obtaied uder the determied samplig scheme. It estimates the pixel value from the samples. The estimatio model ca be the same as that used i samplig stage, icludig both classical samplig models ad spatial samplig models. However, they eed ot to be the same whe ew iformatio is foud that aother model is more suitable for the ew situatio. For example, i samplig stage, samples are collected by radom samplig model while strog spatial autocorrelatio is foud from the collected samples. The, Krigig model or MSN model rather tha the simple radom model ca be selected to estimate the pixel value. Bias sample is aother problem i cotiuous observatio. There would be systematic error betwee the results from bias sample ad true value if the bias is ot processed properly. Although the samples are well desiged at the begiig of the research, attritio bias is ofte occurred due to samples missig or uavailable. Whe correlatio betwee samples is ot chage ad ca be calculated from historical data, the bias could be corrected by posterior aalysis, for example, the B-SHADE model (Wag et al., 2011). Above statistical iferece method is based o the assumptio that the surface parameter ca be estimated with a liear system. However, i practice, surface pheomea maybe disorgaized ad caot be estimated with a liear method, but they still have a certai mathematical laws whe aalyzed by fractal theory, ad it ca be estimated with a fractal upscalig method (Kim ad Berg 2000). Fractal dimesio is used to quatitative describe this quality. Through the self-similarity dimesio, fractal theory aalyzes fractal dimesio chages of differet levels i oliear system, cotacts patter characteristics o differet scales ad prompts the similarities ad differeces of the multi-scale system characteristics, so as to provide the basis for upscalig. 4. RESULTS AND DISCUSSION Validatio is a fudametal work for the applicatio of RS products. I this techical specificatio, a geeral framework ad priciples of VRSP are defied. Some specific method ad techiques are particularly itroduced for pixel scale ad whole product validatio, icludig the groud observatio field selectio criteria, istrumet observatio stadards ad methods, validatig flow for three types of pixel scale RS productio (cotiuous products of lad surface, categorical products of lad surface ad atmospheric products), evaluatio idicators, ad samplig ad iferece methods. However, there are some problems eed to be further ivestigated. Oe is samplig ad iferece for dyamic groud objects. Whe the object chages greatly due to some exteral factors, it is difficult to desig a sole sample scheme to capture all dyamic iformatio. A optioal method is to desig samples by simple radom samplig or systematic samplig, ad the iferece model is data adaptive accordig to characteristic of the collected samples. Pixel validatio by footprit is aother difficult problem sice the footprit source is ofte easily chaged by exteral factors. More efficiet statistical methods, such as area to area iterpolatio, might be helpful to solve the problem. REFERENCES Cihlar, J., Che, J., Li, Z., O the validatio of satellitederived products for lad applicatios. Caadia Joural of Remote Sesig, 23(4), pp Cochra, W.G., Samplig Techiques, 3d ed. Joh Wiley & Sos, USA. 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