Temperature Vegetation Drought Index Research Articles

Assessing Drought Conditions in Cloudy Regions Using Reconstructed Land Surface Temperature

Temperature vegetation dryness index (TVDI) in a triangular or trapezoidal feature space can be calculated from the land surface temperature (LST) and normalized difference vegetation index (NDVI), and has been widely applied to regional drought monitoring. However, thermal infrared sensors cannot penetrate clouds to detect surface information of sub-cloud pixels. In cloudy areas, LST data include a large number of cloudy pixels, seriously degrading the spatial and temporal continuity of drought monitoring. In this paper, the Remotely Sensed Daily Land Surface Temperature Reconstruction model (RSDAST) is combined with the LST reconstructed (RLST) by the RSDAST and applied to drought monitoring in a cloudy area. The drought monitoring capability of the reconstructed temperature vegetation drought index (RTVDI) under cloudy conditions is evaluated by comparing the correlation between land surface observations for soil moisture and the TVDI before and after surface temperature reconstruction. Results show that the effective duration and area of the RTVDI in the study area were larger than those of the original TVDI (OTVDI) in 2011. In addition, RLST/NDVI scatter plots cover a wide range of values, with the fitted dry–wet boundaries more representative of real soil moisture conditions. Under continuously cloudy conditions, the OTVDI inverted from the original LST (OLST) loses its drought monitoring capability, whereas RTVDI can completely and accurately reconstruct surface moisture conditions across the entire study area. The correlation between TVDI and soil moisture is stronger for RTVDI (R = −0.45) than that for OTVDI (R = −0.33). In terms of the spatial and temporal distributions, the R value for correlation between RTVDI and soil moisture was higher than that for OTVDI. Hence, in continuously cloudy areas, RTVDI not only expands drought monitoring capability in time and space, but also improves the accuracy of surface soil moisture monitoring and enhances the applicability and reliability of thermal infrared data under extreme conditions.

REMOTE SENSING RETRIEVAL OF SOIL MOISTURE IN GUANGXI BASED ON ATI AND TVDI MODELS

Abstract. In order to further to improve the monitoring accuracy of soil moisture spatial and temporal changes in Guangxi, this paper uses MODIS, Landsat8, ASTER GDEM data and measured relative soil moisture data as data sources. According to the design idea of complementary advantages, the EVI value of Landsat8 image is partitioned, and the relative soil moisture is inverted by ATI model method in the area of EVI≤0.33, and the relative soil moisture is inverted by TVDI model in the area of EVI>0.33. The apparent thermal inertia model (ATI) and the temperature vegetation drought index model (TVDI) was used to invert the relative soil moisture in Guangxi. The results show that the temporal variation of relative soil moisture in the study area in 2017 is the change cycle from rising to falling: the rising period is from January to July, and the falling period is from August to December, in which the relative soil moisture reaches the peak in July. The minimum value are December; the relative soil moisture in the north of Guangxi are generally higher than that in southern Guangxi. The correlation between the relative soil moisture value of the ATI model and the TVDI model partition retrieval and the measured relative soil moisture data is higher, and the relative soil moisture retrieval effects is better.

Characterization of drought monitoring events through MODIS- and TRMM-based DSI and TVDI over South Asia during 2001-2017.

South Asia is susceptible to drought due to high variation in monthly precipitation. The drought indices deriving from remote sensing data have been used to monitor drought events. To secure agricultural land in South Asia, timely and effective drought monitoring is very important. In this study, TRMM data was utilized along with remote sensing techniques for reliable drought monitoring. The Drought Severity Index (DSI), Temperature Vegetation Drought Index (TVDI), NDVI, and Normalized Vegetation Supply Water Index (NVSWI) are more helpful in describing the drought events in South Asia due to the dryness and low vegetation. To categorize drought-affected areas, the spatial maps of TRMM were used to confirm MODIS-derived TVDI, DSI, and NVSWI. The DSI, TVDI, NVSWI, and Normalized Monthly Precipitation Anomaly Percentage (NAP) indices with an integrated use of MODIS-derived ET/PET and NDVI were selected as a tool for monitoring drought in South Asia. The seasonal DSI, TVDI, NVSWI, NAP, and NDVI values confirmed that South Asia suffered an extreme drought in 2001, which continued up to 2003. The correlation was generated among DSI, NAP, NVWSI, NDVI, TVDI, and TCI on a seasonal basis. The significantly positive correlation values of DSI, TVDI, and NVSWI were in DJF, MAM, and SON seasons, which were described as good drought monitoring indices during these seasons. During summer, the distribution values of drought indicated that more droughts occurred in the southwest regions as compared to the northeast region of South Asia. From 2001 to 2017, the change trend of drought was characterized; the difference of drought trend was obviously indicated among different regions. In South Asia, generally, the frequency of drought showed declining trends from 2001 to 2017. It was verified that these drought indices are a comprehensive drought monitoring indicator and would reduce drought risk in South Asia.

Does Climate Change or Human Activity Lead to the Degradation in the Grassland Ecosystem in a Mountain-Basin System in an Arid Region of China?

In mountain-basin systems in the arid region, grasslands are sensitive to the impacts of climate change and human activities. In this study, we aimed to resolve two key scientific issues: (1) distinguish and explain the laws of grassland ecosystem deterioration in a mountain-basin system and identify the key factors related; and (2) evaluate whether damaged grasslands ecosystem have the potential for natural revegetation. Hence, by combining spatial analysis with statistical methods, we studied the trends of the deterioration of the grassland ecosystem and its spatial characteristics in Kulusitai, a mountain-basin system in the arid region of Northwest China. According to our results, vegetation coverage and productivity exhibited significant decreasing trends, while the temperature vegetation drought index (TVDI) exhibited a significant increasing trend. Drainage of groundwater, because of increase in irrigation for the expanded irrigated area around Kulusitai, and climate warming were the critical triggers that leaded to the soil drought. Soil drought and overgrazing, resulting from the impact of human activities, were the main factors responsible for the deterioration of the grassland ecosystems. However, limiting the number of livestock to a reasonable scale and reducing the irrigated area may help to increase the soil moisture, thus promoting the germination of soil seed banks and facilitating the normal growth of grassland vegetation. Furthermore, based on analysis of the phenology of the grassland vegetation, the reasonable period for harvesting and storage is from July 29 to August 5. The results of this study provide a scientific basis and practical guide for restoring mountain-basin grassland systems in arid regions.

Estimates of shifts in ecosystem service values due to changes in key factors in the Manas River basin, northwest China

The ecosystem service value (ESV) can be quantitatively assessed based on changes in ecosystem services. However, few studies have considered two scientific problems: (1) how to quantify the contributions of factors that drive ecosystem services, and (2) how to improve the spatial variability of ESV distributions. In this study, a geographic information system was used to investigate spatial and temporal changes in three factors, i.e., vegetation cover (VC), human disturbance index (HDI), and temperature vegetation drought index (TVDI), and their effects on the ESV in the Manas River basin. We found that high VC areas were expanding and the HDI was significantly enhanced. The contributions of VC, HDI, and TVDI to the total ESV were 38.0%, 31.6%, and 30.4%, respectively, and the total ESV increased by 3.7%. The cold spots were mainly distributed in the desert area, mountains, and the southern parts of the oasis area, whereas the hot spots occurred mainly to the north of the oasis area and southwest of the desert. VC, HDI, and TVDI reflected the spatial and temporal differences in the ESV determined in this river basin ecosystem. Furtherly, it may be possible to formulate appropriate ecological compensations by analyzing the changes in ESV cold/hot spots. The results of this study provide a scientific basis to facilitate the selection of the key factors that drive changes in the ESV in arid land areas, as well as for achieving a sustainable basin management in similar regions around the world.

A comparison of two methods for estimating surface soil moisture based on the triangle model using optical/thermal infrared remote sensing over the source area of the Yellow River

ABSTRACTAs an important surface parameter, surface soil moisture (SSM) plays a significant role in water resources management, crop growth, land degradation, and vegetation coverage as well as global climate change studies. In particular, the triangle model based on the spatial relationship between the land surface temperature (LST) and vegetation index from optical/thermal infrared remote sensing data has been widely used for the estimation of SSM. In the present study, Moderate Resolution Imaging Spectroradiometer (MODIS) remote sensing data, including the MOD11A2 LST and MOD13A2 normalised differential vegetation index (NDVI) from July to October, 2008 to 2010, over the source area of the Yellow River (SAYR) in the alpine vegetation region of the Tibetan Plateau, are selected to construct the LST/NDVI triangle space. Two methods based on the triangle space, namely, the temperature vegetation drought index (TVDI) method and second order polynomial method, is used to estimate the SSM at the regional scale. Finally, an analysis between the estimated SSM and ground-measured SSM is carried out to explore not only the quality of these two methods in triangle space but also the applicability of these two methods in the alpine vegetation region of the Tibetan Plateau. In addition, the spatial distribution of SSM in the study area is investigated. The results of the validation of the estimated SSM results using ground-measured data show that the accuracy of the second order polynomial method is significantly higher than that of the TVDI method. The TVDI method root mean square error (RMSE) is 0.072 m3 m−3, coefficient of determination R2 is 0.461, and bias is −0.022 m3 m−3, while the second order polynomial method RMSE is 0.075 m3 m−3, R2 is 0.519, and bias is 0.007 m3 m−3. Moreover, the SSM decreases from west to the east; this distribution can be obtained with the estimated results by using either of the two methods. In addition, the SSM estimations by using the two methods are in good correlation with the Climate Change Initiative (CCI) soil moisture product. The present study indicates that the second order polynomial method can simulate the change in SSM in the triangle space more realistically and effectively and that the second order polynomial method is more suitable for the estimation of SSM over the alpine vegetation region of the Tibetan Plateau.

Improving Spatial Soil Moisture Representation through the Integration of SMAP and PROBA-V Products

To increase the spatial resolution of Soil Moisture Active Passive (SMAP), this study modifies the downscaling factor model based on the Temperature Vegetation Drought Index (TVDI) using data from the Project for On-Board Autonomy (PROBA-V). In the modified model, TVDI parameters were derived from the temperature-vegetation space and the Enhanced Vegetation Index (EVI). This study was conducted in the north China region using SMAP, PROBA-V, and Moderate Resolution Imaging Spectroradiometer satellite images. The 9-km spatial resolution SMAP data was downscaled to 0.3-km spatial resolution soil moisture using a modified downscaling method. Downscaling accuracies from the original and modified downscaling factor models were compared based on field observations. The results show that both methods generated similar spatial distributions in which soil moisture estimates increased as vegetation coverage increased from built-up areas to forest. However, based on the root mean square error between observations and estimations, the modified model demonstrated an increased estimation accuracy of 4.2% for soil moisture compared to the original method. This study also implies that downscaled soil moisture shows promise as a data source for subsequent watershed scale studies.

A soil moisture estimation framework based on the CART algorithm and its application in China

Soil moisture is an important parameter associated with the land-atmosphere interface and is highly influenced by multiple factors. Previous studies have provided an effective mechanism for accurately estimating soil moisture by building a global estimation model that comprehensively integrates multiple factors at a local scale. However, a global model is inefficient for accurately estimating soil moisture at a large or even global scale because of the complex surface features that make it difficult to fit data globally. Furthermore, inconsistencies in the spatial integrity between multisource data and the mismatch between the training space and application space decrease the generalizability of the model, which may lead to unreasonable soil moisture values in certain areas. This study proposes a “pyramid” framework that integrates multiple factors from different sources using the classification and regression tree (CART) algorithm, a machine learning method, to estimate soil moisture at a high spatial resolution (1 km). The framework considers soil moisture as a response variable and several factors, such as precipitation, soil properties, and temperature, as explanatory variables. The framework uses piecewise fitting instead of global fitting and avoids the generation of unreasonable values. A k-fold cross-validation approach using “hold-out” years was used to assess the performance of the soil moisture estimation framework for the summer period. The results show that the performance of the framework was relatively stable during the study period with low variabilities in the r values (1 STD < 0.06) and error measures (1 STD < 0.05). The results predicted based on the framework are more accurate than the temperature vegetation drought index (TVDI) results. The correlation coefficients between the TVDI and soil moisture observations in June, July and August were 0.49, 0.29 and 0.49, respectively, whereas those between the predictions and observations were 0.70, 0.68 and 0.69, respectively, which reflected increases of 0.21, 0.39 and 0.20, respectively. The spatiotemporal analysis of summer soil moisture from 2000 to 2014 exhibited a significant wetting trend; the spatial patterns were characterized by wetting trends over arid and humid regions and drying trends over semi-arid regions. The results indicate that the “pyramid” framework can provide a soil moisture dataset with reasonable accuracy and high spatial resolution.

Spatial-Temporal Variation of Drought in China from 1982 to 2010 Based on a modified Temperature Vegetation Drought Index (mTVDI)

Droughts cause huge losses of society and environment, therefore it is important to study the spatial-temporal pattern of drought. The traditional remote sensing drought indices (AVI, VCI and TCI) only consider the single factor representing the soil moisture (surface temperature or NDVI). The comprehensive remote sensing drought indices (VSWI and TVDI) can estimate the soil moisture more accurately, but they are not suitable for large scale region especially with great elevation variation. In this study, a modified Temperature Vegetation Drought Index (mTVDI) was constructed based on the correction of elevation and dry edge. Compared with the traditional drought indices, mTVDI had a better relationship with soil moisture in all selected months (R = −0.376, −0.406, −0.459, and −0.265, p < 0.05). mTVDI was used to analyze the spatial-temporal patterns of drought in China from 1982 to 2010. The results showed that droughts appeared more frequently in Northwest China and the southwest of Tibet while drought centers of North and Southwest China appeared in Huanghuaihai Plain and Yunnan-Guizhou Plateau respectively. The frequency of drought was increasing as a whole while the frequency of severe drought increased significantly by 4.86% and slight drought increased slowly during 1982 to 2010. The results are useful for the understanding of drought and policy making of climate change.

The Microwave Temperature Vegetation Drought Index (MTVDI) based on AMSR-E brightness temperatures for long-term drought assessment across China (2003–2010)

Satellite-based drought indices have been proved to be effective and convenient in detecting drought conditions at regional and global scales. However, most current drought indices are based on the visible/near infrared/thermal remote sensing, which might be influenced greatly by cloud, atmospheric water content and rain-fall. Microwave sensors can overcome the shortages of visible/near infrared/thermal remote sensing and show to be another important approach for drought monitoring due to its all-weather working advantages. But to date, the application of microwave vegetation drought indices in drought monitoring has not been thoroughly investigated. Here, for the first time we constructed a microwave derived Temperature Vegetation Drought Index (TVDI) - MTVDI based on the theory of optical TVDI using the brightness temperatures (Tb) from the Advanced Microwave Scanning Radiometer (AMSR‐E) onboard Aqua satellite. Firstly, we built a new land surface temperature (Ts) inversion model based on the AMSR‐E 18.7GHz horizontal, 23.8GHz and 89.0GHz vertical polarized Tb, and then developed the Microwave Normalized Difference Vegetation Index (MNDVI) from the AMSR‐E 23.8GHz Microwave Polarization Difference Index (MPDI). After that, we constructed three versions of MTVDI: original MTVDI using Ts and MNDVI; Imp‐MTVDI (Improved MTVDI) using the Ts‐Tair (the difference between land surface temperature and air temperature) to replace the Ts; and NonL‐MTVDI (Nonlinear MTVDI) using nonlinear equation to fit the dry and wet edges, respectively. Finally, we used precipitation, soil moisture (SM) and P/PET (the ratio of precipitation to potential evapotranspiration) to validate the performances of MTVDI, Imp‐MTVDI, NonL‐MTVDI, MODIS derived TVDI and iTVDI (improved TVDI). The time-series drought assessments across China from 2003 to 2010 indicated that the trends of the proposed MTVDI showed the most negative correlations with the variations of precipitation, P/PET and SM, and showed best performances of significance test in most regions of China. Moreover, the MTVDI could better separate the drought levels in different degrees than MODIS-derived TVDI. However, the proposed MTVDI still has some uncertainties in regions widely covered by desert, Gobi and large water surfaces. In addition, this paper mainly focuses on large spatial scale and long term drought monitoring and only uses satellite data for model validation. Further studies are needed to develop a higher spatial- and temporal-resolution MTVDI for short-term and small spatial-scale drought monitoring.

A modified vegetation water supply index (MVWSI) and its application in drought monitoring over Sichuan and Chongqing, China

Based on the principle of vegetation water supply index (VWSI), relative normalized difference vegetation index (RNDVI) and relative land surface temperature (RLST) are proposed, with which a modified vegetation water supply index (MVWSI) is constructed, i.e., MVWSI=RNDVIRLST2 Compared with VWSI, MVWSI eliminates the difference caused by the spatial and temporal distribution of VWSI. MVWSI was applied to monitor drought over Sichuan and Chongqing, China in 2006, suggesting that MVWSI can demonstrate the whole process of drought from its emergence and development to its weakening and disappearance. The relative precipitation index (RPI) is used to examine the effectiveness of MVWSI. The examination result shows that MVWSI and RPI have high consistency in spatial distribution and in-site time series analysis. Compared with vegetation condition index (VCI), temperature condition index (TCI), vegetation temperature condition index (VTCI) and temperature vegetation drought index (TVDI), MVWSI is simpler in calculation and more stable hence more easily realized in application.

Improving spatial representation of soil moisture by integration of microwave observations and the temperature–vegetation–drought index derived from MODIS products

The microwave observations of land surface soil moisture have been widely used for studying environmental change at large spatial scales. However, the coarse spatial resolution of the products limits their local-scale applications. In this paper, we developed a new method, which integrates the coarse spatial resolution soil moisture derived from microwave sensors and the temperature–vegetation–drought-index (TVDI) derived from the Moderate-resolution Imaging Spectroradiometer (MODIS) products, to spatially downscale soil moisture data from 25-km resolution to 1-km resolution. First, we assessed the quality of the remotely sensed soil moisture by comparing their values with field measured soil moisture at three temporal scales and two spatial scales. Second, we analyzed the robustness of the developed approach namely the PKU method by comparing its performance with the results of three published methods (i.e., the triangle-based method, the Merlin method, and the UCLA method) at the Magqu soil moisture monitoring network located in the northeastern Tibetan grasslands. The modeling results showed that by integrating the contextual information from the relatively fine spatial resolution MODIS products, spatial soil moisture representations were significantly improved. The PKU method produced the most accurate spatially disaggregated soil moisture among the four methods. In conclusion, the PKU method developed in this study is a practical and efficient approach for improving spatial representations of the coarse spatial resolution soil moisture data derived from microwave remote sensors. Within the PKU method, our refined method for estimating the parameters of the dry-edge outperforms the traditional method.

Drought variability in Inner Mongolia of northern China during 1960–2013 based on standardized precipitation evapotranspiration index

The standardized precipitation evapotranspiration index (SPEI) was widely used in climatology and hydrology studies, because its combination the sensitivity of the PDSI to the changes in ET (caused by air temperature fluctuations and trends) with the simplicity of calculation, but also has the robustness of the multitemporal nature of the SPI. In this article, the temporal and spatial pattern of drought based on SPEI was explored in the Inner Mongolia during 1960–2013. The results showed that SPEI can effectively reveal the multiscalar feature of drought, and the effect of air temperature rising on drought severity and cumulative effect of drought itself. SPEI is suitable for those longer time scales such as 6-month or longer, and better in semi-humid and semi-arid areas than in arid area, due to much zero values or extreme maximum values at shorter time scale or in arid area. Generally, there is a drying trend in the whole Inner Mongolia, and a very severe drought was revealed at multi-time scales during the 2000s. Annual SPEI change at 6-month scale from APR to SEP indicated that drought in plant-growth season is very frequent and increasingly serious, and it should be given more attention. The severities of drought vary in different sub-regions. A significant abrupt change point of drought change trend in the whole Inner Mongolia at 12-month time scale was diagnosed in late 1990s, while this abrupt point in the middle-west subarea is a little earlier than those in the northeast subarea and the southeast subarea. This lasting severe drought should be the result of joint action of increasing air temperature and obviously decreasing precipitation since 2000s. At the same time, the most severe and frequent drought mainly occurred along the Sino-Mongolia border and the Horqin Sandy Land, while the continuous belt along the Da Hinggan Ling Mountains, the Yinshan Mountains, the Hetao Plain and the Mu Us Sandy Land is the area with relative slight drying trend during the last decades. Drought spatial pattern indicated by SPEI was supported by spatial distribution of temperature vegetation drought index based on remote sensing.

Desertification assessments in the Hexi corridor of northern China’s Gansu Province by remote sensing

The Hexi corridor region lies in the arid and semiarid regions of Gansu Province, China. The sensitive ecological environment has undergone severe desertification caused by natural environmental change and unsustainable exploitation of the region. Remote sensing and GIS are being used to study desertification in this region. In this paper, we used Landsat TM and HJ data between June and August in 2000, 2005, and 2010 to analyze the spatial and temporal patterns of the desertification using five indices: the modified soil-adjusted vegetation index, fractional vegetation cover, the surface albedo, land surface temperature, and the temperature–vegetation drought index. We normalized the indicators, determined their weights, and defined five grades of desertification: none, light, moderate, severe, and extremely severe. We found that desertification in the Hexi corridor covered more than 76 % of the total land area; the total area of desertified land was 22.02 × 104, 21.22 × 104, and 21.06 × 104 km2 in 2000, 2005, and 2010, respectively, which amounted to 79.8, 76.9, and 76.3 % of the total area. In both periods, the most spatial changes in desertification showed a stability or reversal of desertification from 2000 to 2005 and from 2005 to 2010. From 2000 to 2010, the area in which desertification reversed (1.00 × 104 km2) was a little more than the area of newly desertified land (0.96 × 104 km2). The reversal of desertification mainly involved a change from severely and moderately desertified land to non-desertified land. Natural factor and human activity controlled the desertification process, but the reversal of desertification mainly resulted from human activity.

Drought Change Trend Using MODIS TVDI and Its Relationship with Climate Factors in China from 2001 to 2010

Changes in drought trends and its relationship with climate change in China were examined in this study. The temperature vegetation drought index (TVDI) datasets recorded during 2001 to 2010 in China were constructed by using the moderate-resolution imaging spectroradiometer (MODIS) data, and the temporal and spatial variations in drought were analyzed. In addition, the mean temperature, mean precipitation, mean relative humidity, and mean sunshine duration data collected from 557 local weather stations in China were analyzed. The relationships between drought and these climate factors were also analyzed by using correlation analysis and partial correlation analysis. Changes in drought tendency were shown to differ among four regions throughout the country. The fluctuations in climate in the regions of northeastern China, Qinling-Huaihe, and central Qinghai were caused by the increase in soil moisture, and that in southern Tibet was caused by the intensification of drought. Meteorological factors exhibited varied effects on drought among the regions. In southern China, the main influential factor was temperature; other factors only showed minimal effects. That in the northern and northwestern regions was sunshine duration, and those in the Qinghai-Tibet Plateau were relative humidity and temperature.

Using Temperature Vegetation Drought Index for Monitoring Drought Based on Remote Sensing Data

Drought is one of the major natural disasters in China, it has extremely affected national food security. In this study, Normalized Difference Vegetation Index (NDVI) and surface temperature (Ts) were calculated by using 8-day composite Moderate-Resolution Imaging Spectroradiometer (MODIS) reflectance product data MOD09A1 and MOD11A2, then NDVI-Ts feature space was obtained and dry edge and wet edge equation was fit. According to coefficients of dry edge and wet edge equation, Temperature Vegetation Drought Index (TVDI) will be calculated and refer it as a drought monitoring indicator. In addition, drought monitoring and classification of Shandong province (China) was completed by TVDI from February to May,2011. Furthermore, the drought classification diagram was made and the drought area in each period was counted. The results showed revealed that: NDVI-Ts feature was roughly a triangular shape in the two-dimensional plane, and drought conditions could be better monitored through TVDI. Finally, the desktop demonstration system of drought monitoring was designed and some general functions were realized based on ArcGis Engine.

Zonal Calculation of Drought Inspection Using Remote Sensing in Large-scale

Remote sensing technique has being widely used in drought inspection.Aiming at temperature/vegetation drought index(TVDI)method, using NOAA/AVHRR data,the spatial phenophase difference caused by the latitude,topography of underlying surface in the large-scale drought inspection was reported,which showed the necessity of zonal calculation of drought index.By the calculation in difference latitude,the area of surface temperature-vegetation index space is reduced,which improved the accuracy of equations of drought line and wet line.Comparison of the relationship between temperature/vegetation drought index(TVDI) obtained from the zonal calculation and whole calculation respectively and the measured soil relative humidity of 10cm,20 cm,50 cm showed that,in the zonal calculation,the relativity between temperature/vegetation drought index(TVDI) and the soil relative humidity in different depth was evidently improved,which increased the veracity of drought inspection in large-scale using remote sensing.

Evaluation of Data Quality and Drought Monitoring Capability of FY-3A MERSI Data

FY-3A is the second Chinese Polar Orbital Meteorological Satellite with global, three-dimensional, quantitative, and multispectral capabilities. Its missions include monitoring global disasters and environment changes. This study describes some basic parameters and major technical indicators of the FY-3A and evaluates data quality and drought monitoring capability of the Medium-Resolution Imager (MERSI) onboard the FY-3A. Data obtained with the MERSI was compared with that of the MODerate-resolution Imaging Spectroradiometer (MODIS), imaged at the same time period and geographic zone. In addition, the Temperature/Vegetation Drought Index (TVDI), a highly accurate and stable monitoring model, was used to monitor drought condition with MERSI and MODIS sensors. It is found in the study that the relative accuracy of data, obtained with these two devices, was consistent with the acceptable overall accuracy of 93.8. Furthermore, spatial resolution of MERSI is superior as compared to that of MODIS. Therefore, FY-3A MERSI can serve a reliable and new data source for drought monitoring.