Mapping China using MODIS data : Method, software and data products

Institute of Geographical Scienceand Natural Resources Reseach, Chinese Academy of Sciences2005 International Symposium on Environmental Mornitoring in East Asia -Remote Sensing and Forests-,Hosted The EMEA Project, Kanazawa University 21st=Century COE Program -Environmental Monitoring and Predicition of Long- and Short- Term Dynamics of Pan-Japan Sea Area- ,予稿集, EMEA 2005 in Kanazawa, 国際学術研究公開シンポジウム『東アジアの環境モニタリング』－リモートセンシングと森林－,年月日：200511月28日～29日, 場所：KKRホテル金沢, 金沢大学自然科学研究科, 主催：金沢大学EMEAプロジェクト, 共催：金沢大学21世紀COEプログラム「環日本海域の環境変動と長期・短期変動予測

Estimation of Systematic Errors of MODIS Thermal Infrared Bands

This letter reports a statistical method to estimate detector-dependent systematic error in Moderate Resolution Imaging Spectroradiometer (MODIS) thermal infrared (TIR) Bands 20–25 and 27–36. There exist scan-to-scan overlapped pixels in MODIS data. By analyzing a sufficiently large amount of those most overlapped pixels, the systematic error of each detector in the TIR bands can be estimated. The results show that the Aqua MODIS data are generally better than the Terra MODIS data in 160 MODIS TIR detectors. There are no detector-dependent systematic errors in Bands 31 and 32 for both Terra and Aqua MODIS data. The maximum detector errors are 3.00 K in Band 21 of Terra and −8.15 K in that of Aqua for brightness temperatures of more than 250 K

6.EMEA International Symposium in Kanazawa, Japan

Chinese Academy of SciencesProject Number 14404021, Peport of Research Project ; Grant-in-Aid for Scientific Research(B)(2), from April 2002 to March 2006, Edited by Muramoto,Ken-ichiroKamata, NaotoKawanishi, TakuyaKubo, MamoruLiu, JiyuanLee, Kyu-Sung , 人工衛星データ活用のための東アジアの植生調査、課題番号14404021, 平成14年度～平成17年度科学研究費補助金, 基盤研究（B)(2)研究成果報告書, 研究代表者：村本, 健一郎, 金沢大学自然科学研究科教

4.EAFES International Congress in Mokpo, Korea

Institute of Geograpical Sciences and Natural Resources Research, Chinese Academy of SciencesUniversity of Toronto,CanadaProject Number 14404021, Peport of Research Project ; Grant-in-Aid for Scientific Research(B)(2), from April 2002 to March 2006, Edited by Muramoto,Ken-ichiroKamata, NaotoKawanishi, TakuyaKubo, MamoruLiu, JiyuanLee, Kyu-Sung , 人工衛星データ活用のための東アジアの植生調査、課題番号14404021, 平成14年度～平成17年度科学研究費補助金, 基盤研究（B)(2)研究成果報告書, 研究代表者：村本, 健一郎, 金沢大学自然科学研究科教

5.IEEE-IGARSS in Seoul, Korea

Chinese Academy of SciencesForest Fire Monitoring CenterThe burned area is an important parameters for modelling the carbon cycles. The remote sensing tenology is a only way to monitor it at large scale region. In general, two temporal vegetation index difference was used to detect the burned area. But this technology is difficult to be applied to large-scale region owing to the BRDF effect, atmospheric contamination, geolocation errors, phenological changes, vegetation regrowth and others. In this paper, a new approach was proposed to detect the burned area using MODIS data, which is based on the vector-change technology, and combines the MODIS 500 and 250 meter resolution bands data to find 250 meter resolution burned area. The method adequately uses the spectral and multi-spatial resolution character of MODIS data that can resist the noise pollution and improve the detection accuracy. The detection results are very corresponding with the visual interpretation under different background. Since the method needs no prior knowledge, it could also be applied in large region scale. Based on this algorithm, the burned area dataset covering all China from 2000 to 2004 were produced. © 2005 IEEE.Project Number 14404021, Peport of Research Project ; Grant-in-Aid for Scientific Research(B)(2), from April 2002 to March 2006, Edited by Muramoto,Ken-ichiroKamata, NaotoKawanishi, TakuyaKubo, MamoruLiu, JiyuanLee, Kyu-Sung , 人工衛星データ活用のための東アジアの植生調査、課題番号14404021, 平成14年度～平成17年度科学研究費補助金, 基盤研究（B)(2)研究成果報告書, 研究代表者：村本, 健一郎, 金沢大学自然科学研究科教

Estimation of Incident Photosynthetically Active Radiation From Moderate Resolution Imaging Spectrometer Data

Incident photosynthetically active radiation (PAR) is a key variable needed by almost all terrestrial ecosystem models. Unfortunately, the current incident PAR products estimated from remotely sensed data at spatial and temporal resolutions are not sufficient for carbon cycle modeling and various applications. In this study, the authors develop a new method based on the look-up table approach for estimating instantaneous incident PAR from the polar-orbiting Moderate Resolution Imaging Spectrometer (MODIS) data. Since the top-of-atmosphere (TOA) radiance depends on both surface reflectance and atmospheric properties that largely determine the incident PAR, our first step is to estimate surface reflectance. The approach assumes known aerosol properties for the observations with minimum blue reflectance from a temporal window of each pixel. Their inverted surface reflectance is then interpolated to determine the surface reflectance of other observations. The second step is to calculate PAR by matching the computed TOA reflectance from the look-up table with the TOA values of the satellite observations. Both the direct and diffuse PAR components, as well as the total shortwave radiation, are determined in exactly the same fashion. The calculation of a daily average PAR value from one or two instantaneous PAR values is also explored. Ground measurements from seven FLUXNET sites are used for validating the algorithm. The results indicate that this approach can produce reasonable PAR product at 1 km resolution and is suitable for global applications, although more quantitative validation activities are still needed

Comparison of Sampling Designs for Estimating Deforestation from Landsat TM and MODIS Imagery: A Case Study in Mato Grosso, Brazil

Sampling designs are commonly used to estimate deforestation over large areas, but comparisons between different sampling strategies are required. Using PRODES deforestation data as a reference, deforestation in the state of Mato Grosso in Brazil from 2005 to 2006 is evaluated using Landsat imagery and a nearly synchronous MODIS dataset. The MODIS-derived deforestation is used to assist in sampling and extrapolation. Three sampling designs are compared according to the estimated deforestation of the entire study area based on simple extrapolation and linear regression models. The results show that stratified sampling for strata construction and sample allocation using the MODIS-derived deforestation hotspots provided more precise estimations than simple random and systematic sampling. Moreover, the relationship between the MODIS-derived and TM-derived deforestation provides a precise estimate of the total deforestation area as well as the distribution of deforestation in each block