Earticle

원격 관측 자료인 위성 자료는 한계점이 있으며, 특히 광학 관측기를 활용하면 구름이나 기타 요인에 의해 손실 자료가 발생한다. 본 연구에서는 MODerate resolution Imaging Spectrometer(MODIS)의 관측 자료 중, 지표면 온도 자료를 대상으로 손실 자료를 복원하기 위한 방법인 평균 편차 방법, 회귀 분석 방법, 지역 변동 방법의 세 가지 복원 방법을 개발하였다. 검증을 위해 2014년과 2015년의 위성 자료에서 관측 비율을 근거로 사례를 선택하였다. 검증 자료에서 확인된 지역 변동 방법의 평균 제곱근 편차(RMSE) 는 일부 사례에서 약 2 K 이상으로 다른 복원 방법에 비해 낮은 정확도를 보였으며, 회귀 분석 방법의 RMSE는 평균 약 1.13 K으로 대부분의 사례에서 가장 좋은 결과를 보였다. 평균 편차 방법 사용 시, RMSE는 회귀 분석 방법 시와 유사하게 약 1.32 K으로 나타났다.

Satellite data for remote sensing technology has limitations, especially with visible range sensor, cloud and/or other environmental factors cause missing data. In this study, using land surface temperature data from the MODerate resolution Imaging Spectro-radiometer(MODIS), we developed retrieving methods for satellite missing data and developed three methods; mean bias, regression analysis and local variation method. These methods used the previous day data as reference data. In order to validate these methods, we selected a specific measurement ratio using artificial missing data from 2014 to 2015. The local variation method showed low accuracy with root mean square error(RMSE) more than 2 K in some cases, and the regression analysis method showed reliable results in most cases with small RMSE values, 1.13 K, approximately. RMSE with the mean bias method was similar to RMSE with the regression analysis method, 1.32 K, approximately.

본 연구는 용담댐 유역(904.4 km²)을 대상으로 준분포형 장기유출 모델인 SWAT (Soil and Water Assessment Tool)과 Terra MODIS (Moderate Resolution Imaging Spectroradiometer)의 엽면적지수 위성자료를 활용하여 임상별로 수문에 미치는 영향을 비교 및 분석하였다. 수문 평가 기간은 2010년부터 2019년까지 10년으로 설정하였으며, 8일 간격의 MOD15A2 LAI (Leaf Area Index)자료, 토양수분 TDR (Time Domain Reflectometry) 관측소 3개소(GB, JC, CC), 증발산량 Flux Tower 1개소(DU)와 용담댐(YDD) 유입량 자료를 SWAT 모의결과와 비교하여 적용성을 검토하였다. 검·보정 결과, 침엽수, 활엽수, 혼효림 LAI의 R²는 각각 0.95, 0.89, 0.90이며, 토양수분 및 증발산량 관측소 R²는 각각 0.50 ~ 0.55, 0.51로 분석되었으며, 용담댐 유입량의 경우 R²의 경우 0.74, RMSE 2.75 mm/day, NSE 0.70, PBIAS 14.3 %로 분석되었다. 검·보정된 유역을 기반으로 하여 HRU에서 침엽수, 활엽수, 혼효림 수문분석 결과 총 연평균 증발산량은 침엽수 469.7 mm 이며, 활엽수는 501.0 mm, 혼효림의 경우 511.5 mm로 산정되었으며, 유출량은 침엽수 909.8 mm, 활엽수 860.6 mm, 혼효림 864.2 mm로 산정되었다. 연중 패턴이 비슷한 다른 수문과 다르게 여름과 가을에 엽면적지수가 높은 활엽수의 증발산량이 침엽수에 비해 높아 연평균 증발산량이 약 7% 높게 산정되었다. 또한, 유출량의 경우 지표유출 및 중간유출의 경우 활엽수가 각각 9%, 6% 높았으나, 침엽수의 기저유출이 77% 더 높은 것으로 산정됐다. 따라서, 총유출량이 침엽수 혼효림 활엽수 순으로 많은 것을 확인할 수 있었다.

This study compares and analyzes the Soil and Water Assessment Tool (SWAT) and Terra MODIS (Moderate Resolution Imaging Spectroradiometer) as coniferous, deciduous and mixed forest with Yongdam Dam upstream (904.4 km²). The hydrologic evaluation period was set to 10 years from 2010 to 2019, and the applicability of the 8-day MOD15A2 Leaf Area Index (LAI) data, 3 TDR (Time Domain Reflectometry) (GB, JC, CC), and 1 Flux Tower (DU) evaporation volume (YDD) data was simulated. As a result, the R² of coniferous forest, deciduous forest and mixed forest are 0.95, 0.89, 0.90, soil moisture and evaportranspiration stations R² were analyzed at 0.50 to 0.55 and 0.51, respectively, with R² at 0.74, RMSE 2.75 mm/day, NSE 0.70 and PBIAS 14.3% for Yongdam inflow. Based on the calibrated and validated watersheds, the annual average evaportranspiration was calculated as coniferous 469.7 mm, deciduous 501. mm and 511.5 mm mixed forest, total runoff were estimated at coniferous 909.8 mm, deciduous 860.6 mm and 864.2 mm mixed forest. In the case of annual average evaportranspiration, it was evaluated that deciduous were high, but in the case of streamflow, it was evaluated that coniferous were high. Unlike other hydrologic with similar patterns throughout the year, the average annual evapotranspiration was about 7% higher than coniferous due to the higher evapotranspiration of deciduous with high leaf area index in summer and fall. In addition, deciduous were 9% and 6% higher for surface runoff and lateral flow, but the groundwater of coniferous was 77% higher. Therefore, it was confirmed that the total runoff was in order of coniferous, mixed forest, and deciduous.

가뭄은 기후 변화에 따라 심각도가 증대되고 있는 광범위한 재해로, 이에 대한 정밀하고 연속적인 현황 진단이 필수적이다. 본 연구는 수명 만료로 자료 중단이 예정된 MODerate resolution Imaging Spectroradiometer (MODIS)의 후속 센서인 Suomi National Polar-orbiting Partnership (SNPP)와 Visible Infrared Imaging Radiometer Suite (VIIRS)를 활용하여 장기 시계열 자료의 연속성을 검증하고, Normalized Difference Vegetation Index (NDVI) 기반으로 산정되는 Dry Condition Index (DCI)를 활용하여 국내 논 지역에 대한 농업가뭄 평가 능력을 분석하는 것을 목표로 하였다. 연구를 위해 2015년부터 2024년까지 Google Earth Engine (GEE)을 활용하여 MODIS 및 VIIRS NDVI를 구축하고 분석을 수행하였다. 그 결과 VIIRS NDVI 자료는 MODIS 자료와 높은 상관성을 보였으며, 특히 농업가뭄 주요시기인 2월 ~ 5월에 두 센서 간 편차가 0.008로 높은 자료 호환성을 입증하였다. DCI 시계열 분석을 통해 VIIRS는 MODIS 기반 가뭄 인자를 활용한 연속적인 농업가뭄 탐지 능력을 성공적으로 검증하였으며, 가뭄 발생 시기에 가뭄의 천이 양상을 효과적으로 포착하였다. 본 연구의 결과는 MODIS를 대체하여 VIIRS 자료를 가뭄 연구에 연속적으로 활용할 수 있는 객관적인 근거를 제시하였으며, 가뭄 대응 차원에서 실효적인 정책 자료로 기여할 것으로 기대된다.

Drought is a widespread hazard whose severity is increasing under climate change, making precise and continuous assessment indispensable. This study aims to verify the continuity of long-term records by employing the Suomi National Polar-orbiting Partnership (SNPP) Visible Infrared Imaging Radiometer Suite (VIIRS) the successor to the MODerate resolution Imaging Spectroradiometer (MODIS), whose data production is expected to cease as it reaches end-of-life and to evaluate the capability of the Normalized Difference Vegetation Index (NDVI) based Dry Condition Index (DCI) for assessing agricultural drought in Korean paddy areas. Using Google Earth Engine, we compiled and analyzed MODIS and VIIRS NDVI datasets for 2015-2024. VIIRS NDVI showed high consistency with MODIS, demonstrating strong each sensor compatibility; in particular, during the key agricultural drought season (February-May), the each sensor deviation was 0.008. Time series analysis of DCI confirmed that VIIRS can sustain continuous agricultural drought detection based on MODIS derived drought metrics and effectively capture the timing and progression of drought events. These findings provide an objective basis for the continued use of VIIRS as a replacement for MODIS in drought research and are expected to contribute policy-relevant information for effective drought response.

최근 기후변화에 따라 전 세계적으로 큰 규모의 산불 발생이 증가하고 있으며, 우리나라도 산불발생이 전반적으로 증가하는 경향을 보인다. 최근 우리나라와 인접한 북한에서 발생한 산불이 비무장 지대 등의 국내 영토로 번지는 사건이 많이 발생하고 있다. 우리나라에서는 오픈 API(application programming interface)를 통하여 과거 산불발생 기록을 검색할 수 있으나 행정기관에서 수집한 자료여서 국내 지역으로 정보가 국한되어 있다. 이에 본 연구에서는 접근불능지역인 북한을 포함한 한반도의 산불발생에 대한 장기 시계열 정보를 생산하기 위하여 2000년부터 2015년까지의 MODIS(Moderate Resolution Imaging Spectroradiometer) 위성자료를 수집 및 재가공하여 일자별 산불발생 현황의 GIS 데이터베이스를 구축하고자 한다. 데이터베이스 구축을 위한 입력자료로서 Terra 위성의 MOD14A1 산출물과 Aqua 위성의 MYD14A1 산출물을 사용하였으며, 16년간의 일자별 영상을 일괄작업으로 재처리하여 산불의 발생일자, 발생 위치, 탐지 신뢰도, 방사열 에너지(fire radiative power: FRP) 등의 정보를 추출하고 이들을 결합하여 Shapefile 형태로 생성하였다. 본 연구에서 구축한 장기시계열 GIS 데이터베이스는 다른 연구자들에 의해 한반도 산불정보의 시공간 특성 분석에 활용될 수 있으며, 이를 위하여 인터넷에 결과 파일을 탑재하여 자유롭게 다운로드 가능하도록 하였다.

In response to recent climate change, the occurrences of large-scale wildfire are increasing around the world, and the wildfires in South Korea also tends to increase as well. Recently, many wildfires which occurred in North Korea have spread to the territory of South Korea including the demilitarized zone. The records of past wildfire occurrences can be retrieved by using an open API (application programming interface), but only domestic wildfire cases are provided because they are collected by administrative agencies. For this reason, we built a GIS database of daily wildfire occurrences by reprocessing MODIS (Moderate Resolution Imaging Spectroradiometer) data for the period of 2000-2015 in order to create long-term time-series wildfire dataset of the Korean Peninsula including North Korea. We used MOD14A1/MYD14A1 products of Terra/Aqua satellites for extracting wildfire information such as occurrence location, reliability of detection, and fire radiative power, which were written in the Shapefile format. Our GIS database can be utilized by other researchers to analyze the spatio-temporal characteristics of wildfire in the Korean Peninsula, so the data files are provided on the web for free download.

In this paper, we evaluate a red tide detection possibility of GOCI(Geostationary Ocean Color Imager) which will be launched in 2008. To detect red tide, we use a similar wavelength range of MODIS normalized water-leaving radiance data instead of GOCI data. Supposed to GOCI, red tide detection algorithm is based on MRI(MODIS Red tide Index) and use 667nm band to filter turbid water. The algorithm's effectiveness is verified by detecting large Cochlodinium polykrikoides red tide event that was appeared in Korean coastal waters. The evaluation was done by comparing the result with the update data provided by the NFRDI.

One of the major obstacles to classify and validate Land Cover maps is the high cost of acquiring reference data. In case of inaccessible areas such as North Korea, the high resolution satellite imagery may be used for reference data. The objective of this paper is to investigate the possibility of utilizing QuickBird high resolution imagery of North Korea that can be obtained from Google Earth data via internet for reference data of land cover classification. Monthly MODIS NDVI data of nine months from the summer of 2004 were classified into L=54 cluster using ISODATA algorithm, and these L clusters were assigned to 7 classes - coniferous forest, deciduous forest, mixed forest, paddy field, dry field, water, and built-up areas - by careful use of reference data obtained through visual interpretation of the high resolution imagery. The overall accuracy and Kappa index were 85.98% and 0.82, respectively, which represents about 10% point increase of classification accuracy than our previous study based on GCP point data around North Korea. Thus we can conclude that Google Earth may be used to substitute the traditional reference data collection on the site where the accessibility is severely limited.

Since longer wavelength microwave radiation can penetrate clouds, satellite passive microwave sensors can observe sea ice of the entire polar region on a daily basis. Thus, it is becoming popular to derive sea ice motion vectors from a pair of satellite passive microwave sensor images observed at one or few day interval. Usually, the accuracies of derived vectors are validated by comparing with the position data of drifting buoys. However, the number of buoys for validation is always quite limited compared to a large number of vectors derived from satellite images. In this study, the sea ice motion vectors automatically derived from pairs of AMSR-E 89GHz images (IFOV = 3.5 ${\times}$ 5.9km) by an image-to-image cross correlation were validated by comparing with sea ice motion vectors manually derived from pairs of cloudless MODIS images (IFOV=250 ${\times}$ 250m). Since AMSR-E and MODIS are both on the same Aqua satellite of NASA, the observation time of both sensors are the same. The relative errors of AMSR-E vectors against MODIS vectors were calculated. The accuracy validation has been conducted for 5 scenes. If we accept relative error of less than 30% as correct vectors, 75% to 92% of AMSR-E vectors derived from one scene were correct. On the other hand, the percentage of correct sea ice vectors derived from a pair of SSM/I 85GHz images (IFOV = 15 ${\times}$ 13km) observed nearly simultaneously with one of the AMSR-E images was 46%. The difference of the accuracy between AMSR-E and SSM/I is reflecting the difference of IFOV. The accuracies of H and V polarization were different from scene to scene, which may reflect the difference of sea ice distributions and their snow cover of each scene.

PM (particulate matter) is of interest to everyone because it can have adverse effects on human health by the infiltration from respiratory to internal organs. To date, many studies have made efforts for the prediction of PM10 and PM2.5 concentrations. Unlike previous studies, we conducted the prediction of tomorrow's PM10 concentration for the Air Korea stations using Chinese PM10 data in addition to the satellite AOD and weather variables. We constructed 230,639 matchups from the raw data over 3 million and built an RF (random forest) model from the matchups to cope with the complexity and nonlinearity. The validation statistics from the blind test showed excellent accuracy with the RMSE (root mean square error) of 9.905 ㎍/㎥ and the CC (correlation coefficient) of 0.918. Moreover, our prediction model showed a stable performance without the dependency on seasons or the degree of PM10 concentration. However, part of coastal areas had a relatively low accuracy, which implies that a dedicated model for coastal areas will be necessary. Additional input variables such as wind direction, precipitation, and air stability should also be incorporated into the prediction model as future work.

Traditionally, it is measured by using basin or empirical formula with meteorology data, while it does not represent the evaportransporation over a regional area. With the advent of improved remote sensing technology, it becomes feasible to assess the ET over a regional scale. Firstly, the IMAGINE ATCOR atmospheric module is used to preprocess for the MODIS imagery. Then MODIS satellite images which have been corrected by radiation and geometry in conjunction with the in-situ surface meteorological measurement are used to estimate the surface heat fluxes such as soil heat flux, sensible heat flux, and latent heat flux. In addition, the correlation coefficient between the derived latent heat and the in-situ measurement is found to be over 0.76. In the future, we will continue to monitor the surface heat fluxes of paddy rice field in Chiayi area.

The Moderate Resolution Imaging Spectroradiometer (MODIS) on the Earth Observing System (EOS) Terra and Aqua satellites, launched in 1999 and 2002, is directly received by Korea Aerospace Research Institute (KARI) ground station facility. BURI engineers develop a system to receive direct broadcast downlink from MODIS to provide near-realtime, remotely-sensed, spaceborne data to the user community in Korea. MODIS scans a swath width of 2330 km that is sufficiently wide to cover Korean peninsular, Yellow and East Sea at once. The MODIS has 36 spectral bands between 0.415 fm and 14.235 $\mu$m, i.e. through the visible into the thermal infrared. MODIS has been observed active fires, floods, smoke transport, dust storms, severe storms since February of 2000. The KARI is preparing for distribution of direct broadcasted MODIS data to users in Korea. The MODIS database system will be designed and developed by KARI engineer for data service from year of 2003. MODIS data user group will be organized from $\.{O}$ctober to December 2002.

The role of remote sensing in phenological studies is increasingly regarded as a key in understanding large area seasonal phenomena. This paper describes the application of Moderate Resolution Imaging Spectroradiometer (MODIS) time series data for vegetation classification using seasonal variation patterns. The vegetation seasonal variation phase of Seoul and provinces in Korea was inferred using 8 day composite MODIS NDVI (Normalized Difference Vegetation Index) dataset of 2006. The seasonal vegetation classification approach is performed with reclassification of 4 categories as urban, crop land, broad-leaf and needle-leaf forest area. The BISE (Best Index Slope Extraction) filtering algorithm was applied for a smoothing processing of MODIS NDVI time series data and fuzzy classification method was used for vegetation classification. The overall accuracy of classification was 77.5% and the kappa coefficient was 0.61%, thus suggesting overall high classification accuracy.

Large plumes of yellow send or yellow dust blow out over the Sea of Japan and the Japanese archipelago from mainland of China. In this study, the methodology to capture the perspective on the large Yellow dust storm by using MODIS data is discussed. As the typical image of yellow send, MODIS data obtained of April 8, 2002 were used in this study.

Forest biomass is the basis of forest ecosystem. With the rapid development of remote sensing and computer technology, forest biomass estimation using remote sensing data is paid great attention and has acquired great achievements. This article focuses on discussion of methods of forest biomass estimation methods using Terra/MODIS data in Northeast China. The research include: combining the MODIS time series parameters with seasonal characteristics of forest species to identify major forest species; establishing a model to estimate forest biomass based on forest species; analyzing the effects of the existent forest biomass and increasing biomass on terrestrial carbon cycle. This research can help to make clear the mechanism of carbon cycle.

The crop growing conditions make accurate predictions of yield ahead of harvest time difficult. Such predictions are needed by the government to estimate, ahead of time, the amount of crop required to be imported to meet the expected domestic shortfall. Corn and soybean especially are widely cultivated throughout the world and a staple food in many regions of the world. On the other hand, the CASA (Carnegie-Ames-Stanford Approach) model is a process-based model to estimate the land plant NPP (Net Primary Productivity) based on the plant growing mechanism. In this paper, therefore, a methodology for the estimation of corn/soybean yield ahead of harvest time is developed specifically for the growing conditions particular to Iowa and Illinois. The method is based on CASA model using MODIS data, and uses Net Primary Productivity (NPP) to predict corn/soybean yield. As a result, NPP at DOY 217 (in Illinois) and DOY 241 (in Iowa) tend to have high correlation with corn/soybean yields. The corn/soybean yields of Iowa in 2013 was estimated to be 11.24/3.55 ton/ha and Illinois was estimated to be 10.09/3.06 ton/ha. Errors were 6.06/17.58% and -10.64/-7.07%, respectively, compared with the yield forecast of the USDA. Crop yield distributions in 2013 were presented to show spatial variability in the state. This leads to the conclusion that NPP changes in the crop field were well reflected crop yield in this study.

Many researches conducted to investigate the relationship between surface emissivity and surface temperature in the past two decades and pointed out that the emissivity play a key role in applying remote sensing data to retrieve surface temperature. The task of surface temperature estimation is so important in many research fields, such as earth energy budgets, evapotranspiration, drought, global change and heat island effect. Therefore, it is indispensable to develop an effective and accurate technique to estimate the emissivity for accurate surface temperature estimations. This study developed an improved emissivity estimation technique for the use of surface temperature retrievals with MODIS data. The result of applying this improved technique using Band 31 of MODIS shows that the accuracy of estimated surface temperatures will be improved. This study also uses MODIS data observed in 2005 to establish the relationship between the surface emissivity correction factor and NDVI. Through the use of these correction factors, the land surface temperature can be retrieved more accurate with MODIS data.

This study compares the relative accuracy and consistency of four split-window land surface temperature (LST) algorithms (Becker and Li, Kerr et ai., Price, Ulivieri et al.) using 24 sets of Terra (Aqua)/Moderate Resolution Imaging Spectroradiometer (MODIS) data, observed ground grass temperature and air temperature over South Korea. The effective spectral emissivities of two thermal infrared bands have been retrieved by vegetation coverage method using the normalized difference vegetation index. The intercomparison results among the four LST algorithms show that the three algorithms (Becker-Li, Price, and Ulivieri et al.) show very similar performances. The LST estimated by the Becker and Li's algorithm is the highest, whereas that by the Kerr et al.'s algorithm is the lowest without regard to the geographic locations and seasons. The performance of four LST algorithms is significantly better during cold season (night) than warm season (day). And the LST derived from Terra/MODIS is closer to the observed LST than that of Aqua/MODIS. In general, the performances of Becker-Li and Ulivieri et al algorithms are systematically better than the others without regard to the day/night, seasons, and satellites. And the root mean square error and bias of Ulivieri et al. algorithm are consistently less than that of Becker-Li for the four seasons.

We have estimated the vertical column density (VCD) of formaldehyde (HCHO) on a global scale using a multiple linear regression method (MRM) with Ozone Monitoring Instrument (OMI) and Moderate-Resolution Imaging Spectroradiometer (MODIS) data. HCHO VCDs were estimated in regions of biogenic, pyrogenic, and anthropogenic emissions using independent variables, including $NO_2$ VCD, land surface temperature (LST), an enhanced vegetation index (EVI), and the mean fire radiative power (MFRP), which are strongly correlated with HCHO. To evaluate the HCHO estimates obtained using the MRM, we compared estimates of HCHO VCD data measured by OMI ($HCHO_{OMI}$) with those estimated by multiple linear regression equations (MRE) ($HCHO_{MRE}$). Good MRM performances were found, having the average statistical values (R = 0.91, slope = 1.03, mean bias = $-0.12{\times}10^{15}molecules\;cm^{-2}$, percent difference = 11.27%) between $HCHO_{MRE}$ and $HCHO_{OMI}$ in our study regions where high HCHO levels are present. Our results demonstrate that the MRM can be a useful tool for estimating atmospheric HCHO levels.

A multi-temporal approach using remotely sensed time series data obtained over multiple years is a very useful method for monitoring land covers and land-cover changes. While spectral-based methods at any particular time limits the application utility due to instability of the quality of data obtained at that time, the approach based on the temporal profile can produce more accurate results since data is analyzed from a long-term perspective rather than on one point in time. In this study, a multi-temporal approach applying a multi-periodic harmonic model is proposed for classification of remotely sensed data. A harmonic model characterizes the seasonal variation of a time series by four parameters: average level, frequency, phase, and amplitude. The availability of high-quality data is very important for multi-temporal analysis.An satellite image usually have many unobserved data and bad-quality data due to the influence of observation environment and sensing system, which impede the analysis and might possibly produce inaccurate results. Harmonic analysis is also very useful for real-time data reconstruction. Multi-periodic harmonic model is applied to the reconstructed data to classify land covers and monitor land-cover change by tracking the temporal profiles. The proposed method is tested with the MODIS and GOCI NDVI time series over the Korean Peninsula for 5 years from 2012 to 2016. The results show that the multi-periodic harmonic model has a great potential for classification of land-cover types and monitoring of land-cover changes through characterizing annual temporal dynamics.

As a standard water clarity variable, the vertical underwater visibility, called Secchi depth, is estimated with ocean color satellite data. In the present study, Moderate Resolvtion Imaging Spectradiometer (MODIS) data are used to measure the Secchi depth which is a useful indicator of ocean transparency for estimating the water quality and productivity. To estimate the Secchi depth $Z_v$, the empirical regression model is developed based on the satellite optical data and in-situ data. In the previous study, a semi-analytical algorithm for estimating $Z_v$ was developed and validated for Case 1 and 2 waters in both coastal and oceanic waters using extensive sets of satellite and in-situ data. The algorithm uses the vertical diffuse attenuation coefficient, $K_d$($m^{-1}$) and the beam attenuation coefficient, c($m^{-1}$) obtained from satellite ocean color data to estimate $Z_v$. In this study, the semi-analytical algorithm is validated using temporal MODIS data and in-situ data over the Yellow, Southern and East Seas including Case 1 and 2 waters. Using total 156 matching data, MODIS $Z_v$ data showed about 3.6m RMSE value and 1.7m bias value. The $Z_v$ values of the East Sea and Southern Sea showed higher RMSE than the Yellow Sea. Although the semi-analytical algorithm used the fixed coupling constant (= 6.0) transformed from Inherent Optical Properties (IOP) and Apparent Optical Properties (AOP) to Secchi depth, various coupling constants are needed for different sea types and water depth for the optimum estimation of $Z_v$.

Remotely sensed data can be used to estimate biomass production using methodologies relating vegetation indices to light absorption or to leaf photosynthetic capacity. The considerations of both light absorption and photosynthetic capacity in remote sensing-based modeling to estimate biomass production or NPP was introduced based upon Monteith model NPP is one of a evaluation of land degradation. NPP was estimated from annual maximum NDVI by MODIS data. It was known that NPP of the grassland that except the forest and the farming ground was distributed between 50-200g /m2.