Corn yield estimation in Serbia using MODIS 13Q1 product

Vegetation indices (VIs) such as the Normalized Difference Vegetation Index (NDVI) are widely used for assessing vegetation cover and condition. One of the NDVI's significant disadvantages is its sensitivity to aerosols in the atmosphere, hence several atmospherically resistant VIs were formulated using the difference in the radiance between the blue and the red spectral bands. The state‐of‐the‐art atmospherically resistant VI, which is a standard Moderate Resolution Imaging Spectroradiometer (MODIS) product, together with the NDVI, is the Enhanced Vegetation Index (EVI). A different approach introduced the Aerosol‐free Vegetation Index (AFRI) that is based on the correlation between the shortwave infrared (SWIR) and the visible red bands. The AFRI main advantage is in penetrating an opaque atmosphere influenced by biomass burning smoke, without the need for explicit correction for the aerosol effect. The objective of this research was to compare the performance of these three VIs under smoke conditions. The AFRI was applied to the 2.1 µm SWIR channel of the MODIS sensor onboard the Earth Observing System (EOS) Terra and Aqua satellites in order to assess its functionality on these imaging platforms. The AFRI performance was compared with those of NDVI and EVI. All VIs were calculated on images with and without present smoke, using the surface‐reflectance MODIS product, for three case studies of fires in Arizona, California, and Zambia. The MODIS Fire Product was embedded on the images in order to identify the exact location of the active fires. Although good correlations were observed between all VIs in the absence of smoke (in the Arizona case R 2 = 0.86, 0.77, 0.88 for the NDVI–EVI, AFRI–EVI, and AFRI–NDVI, respectively) under smoke conditions a high correlation was maintained between the NDVI and the EVI, while low correlations were found for the AFRI–EVI and AFRI–NDVI (0.21 and 0.16, for the Arizona case, respectively). A time series of MODIS images recorded over Zambia during the summer of 2000 was tested and showed high NDVI fluctuations during the study period due to oscillations in aerosol optical thickness values despite application of aerosol corrections on the images. In contrast, the AFRI showed smoother variations and managed to better assess the vegetation condition. It is concluded that, beneath the biomass burning smoke, the AFRI is more effective than the EVI in observing the vegetation conditions.

The suitability of using Moderate Resolution Imaging Spectroradiometer (MODIS) images for surface soil moisture estimation to investigate the importance of soil moisture in different applications, such as agriculture, hydrology, meteorology and natural disaster management, is evaluated in this study. Soil moisture field measurements and MODIS images of relevant dates have been acquired. Normalized Difference Vegetation Index (NDVI), Enhanced Vegetation Index (EVI) and Normalized Difference Water Index (NDWI) are calculated from MODIS images. In addition, MODIS Land Surface Temperature (LST) data (MOD11A1) are used in this analysis. Four different soil moisture estimation models, which are based on NDVI–LST, EVI–LST, NDVI–LST–NDWI and EVI–LST–NDWI, are developed and their accuracies are assessed. Statistical analysis shows that replacing EVI with NDVI in the model that is based on LST and NDVI increases the accuracy of soil moisture estimation. Accuracy evaluation of soil moisture estimation using check points shows that the model based on LST, EVI and NDWI values gives a higher accuracy than that based on LST and EVI values. It is concluded that the model based on the three indices is a suitable model to estimate soil moisture through MODIS imagery.

Litter production is related to canopy processes including the timing and amount of leaf development, reproduction and net primary production. However, quantifying spatial and temporal patterns in litter production is complicated in Amazonian semi-deciduous forests because of high spatial heterogeneity and seasonal variation in rainfall. Here, we use monthly measurements of litter production and Moderate Resolution Imaging Spectroradiometer (MODIS) Normalized Difference Vegetation Index (NDVI) and Enhanced Vegetation Index (EVI) composites over a 6-year period to assess whether MODIS can be used to quantify litter production of tropical semi-deciduous forests. Original MODIS NDVI and EVI values were poorly related to the seasonal periodicity in litter production, but after using singular spectrum analysis (SSA) signal extraction techniques, clear relationships between litter production and the NDVI and EVI emerged. These results indicate that MODIS NDVI and EVI data are useful for detecting temporal patterns in litter production for Amazonian semi-deciduous forests if signal extraction analyses such as SSA are conducted.

Estimation of crop gross primary production (GPP): I. impact of MODIS observation footprint and impact of vegetation BRDF characteristics

Monitoring impacts of soil bund on spatial variation of teff and finger millet yield with Sentinel-2 and spectroradiometric data in Ethiopia

Moderate Resolution Imaging Spectroradiometer (MODIS) 16-day 1-km vegetation index products, daily temperature, photosynthetically active radiation (PAR), and precipitation from 2001 to 2004 were utilized to analyze the temporal variations of the MODIS normalized difference vegetation index (NDVI) and enhanced vegetation index (EVI), as well as their correlations with climate over the evergreen forested sites in Zhejiang-a humid subtropical region in the southeast of China. The results showed that both NDVI and EVI could discern the seasonal variation of the evergreen forests. Attributed to the sufficient precipitation in the study area, the growth of vegetation is mainly controlled by energy; as a result, NDVI, and especially EVI, is more correlated with temperature and PAR than precipitation. Compared with NDVI, EVI is more sensitive to climate condition and is a better indicator to study vegetation variations in the study region

Estimating root zone soil moisture at distant sites using MODIS NDVI and EVI in a semi-arid region of southwestern USA

Crop phenology is a critical component of implementing agricultural activities and providing important indicators for climate related research. The needs for crop phenology estimation not only exist in large scale but also in medium or small scale. In this work, Jianhu County was chosen to test the ability of crop phenology estimation by Moderate Resolution Imaging Spectroradiometer (MODIS) data. 794 cloudy-free MODIS 250-m resolution images of Jianhu County from 2000 to 2013 were finally chosen. Two vegetation indices were compared to evaluate their effectiveness in crop phenology estimation. These were the Normalized Difference Vegetation Index (NDVI) and the Enhanced Vegetation Index (EVI). EVI was less sensitive than NDVI to aerosol effects. Therefore, EVI was chosen to estimate crop phenology of Jianhu County. Then, the crop spatial distribution data were generated from RapidEye 5-m resolution images by visual interpretation to exclude those mixed pixels. In the long-term monitoring, ground survey and correlation analyses, we found that the MODIS-EVI time series data were suitable to estimate the turning green, the heading and the maturation of winter wheat and were suitable to estimate the transplanting, the heading and the maturation of rice. Because of the free availability and frequent coverage of MODIS data, the cost-effective assessment here by using MODIS 250-m resolution images may serve as a template applicable to other counties of Jiangsu province. Moreover, crop phenology estimation in county scale will be particularly helpful in providing some baseline information to yield estimation and food security, and will be useful to promote crop phenology estimation methods.

Accurate vegetation detection is important for many applications, such as crop yield estimation, land cover land use monitoring, urban growth monitoring, drought monitoring, etc. Popular conventional approaches to vegetation detection incorporate the normalized difference vegetation index (NDVI), which uses the red and near infrared (NIR) bands, and enhanced vegetation index (EVI), which uses red, NIR, and the blue bands. Although NDVI and EVI are efficient, their accuracies still have room for further improvement. In this paper, we propose a new approach to vegetation detection based on land cover classification. That is, we first perform an accurate classification of 15 or more land cover types. The land covers such as grass, shrub, and trees are then grouped into vegetation and other land cover types such as roads, buildings, etc. are grouped into non-vegetation. Similar to NDVI and EVI, only RGB and NIR bands are needed in our proposed approach. If Laser imaging, Detection, and Ranging (LiDAR) data are available, our approach can also incorporate LiDAR in the detection process. Results using a well-known dataset demonstrated that the proposed approach is feasible and achieves more accurate vegetation detection than both NDVI and EVI. In particular, a Support Vector Machine (SVM) approach performed 6% better than NDVI and 50% better than EVI in terms of overall accuracy (OA).

Vegetation indices have been widely used as indicators of seasonal and inter‐annual variations in vegetation caused by either human activities or climate, with the overall goal of observing and documenting changes in the Earth system. While existing satellite remote sensing systems, such as NASA's Multi‐angle Imaging SpectroRadiometer (MISR) and Moderate Resolution Imaging Spectroradiometer (MODIS), are providing improved vegetation index data products through correcting for the distortions in surface reflectance caused by atmospheric particles as well as ground covers below vegetation canopy, the impact of land‐cover mixing on vegetation indices has not been fully addressed. In this study, based on real image spectral samples for two‐component mixtures of forest and common nonforest land‐cover types directly extracted from a 1.1 km MISR image by referencing a 30 m land‐cover classification, the effect of land‐cover mixing on the Normalized Difference Vegetation Index (NDVI) and the Enhanced Vegetation Index (EVI) has been quantitatively evaluated. When the areal fraction of forest was lower than 80%, both NDVI and EVI varied greatly with mixed land‐cover types, although EVI varied less than NDVI. Such a phenomenon can cause errors in applications based on use of these vegetation indices. This study suggests that methods that reduce land‐cover mixing effects should be introduced when developing new spectral vegetation indices.

The enhanced vegetation index (EVI) has been found useful in improving linearity with biophysical vegetation properties and in reducing saturation effects found in densely vegetated surfaces, commonly encountered in the normalized difference vegetation index (NDVI). However, EVI requires a blue band and is sensitive to variations in blue band reflectance, which limits consistency of EVI across different sensors. The objectives of this study are to develop a 2-band EVI (EVI2) without a blue band that has the best similarity with the 3-band EVI, and to investigate the crosssensor continuity of the EVI2 from the Moderate Resolution Imaging Spectroradiometer (MODIS) and Advanced Very High Resolution Radiometer (AVHRR). A linearity-adjustment factor (β) was introduced and coupled with the soil adjustment factor (L) used in the soil-adjusted vegetation index (SAVI) in the development of the EVI2 equation. The similarity between EVI and EVI2 was validated at the global scale. After a linear adjustment, the AVHRR EVI2 was found to be comparable with the MODIS EVI2. The good agreement between the AVHRR and MODIS EVI2 suggests the possibility of extending the current MODIS EVI time series to the historical AVHRR data, providing another longterm vegetation record different from the NDVI counterpart.

It is very important to develop a rapid algorithm for rodent pest damage estimation,especially for an outbreak year. The Yangtze vole( Microtus fortis) has been identified as a primary rodent pest in the Dongting Lake region. In July2007,there was a sudden population explosion in that region,representing the most serious Yangtze vole disaster on record.More than two billion voles crossed the lake levee,causing massive crop damage. The study area of Beizhouzi,to the west of East Dongting Lake,was selected because it had the highest population density during the outbreak year. A difference method of vegetation index was used in the assessment,in which the outbreak year 2007 was contrasted with 2004,which was considered a normal year. All vegetation-index imagery was from the Moderate Resolution Imaging Spectroradiometer( MODIS),which was masked by a land-use map of the study area to remove water bodies and urban and built-up lands.The Normalized Difference Vegetation Index( NDVI) in the outbreak year declined during the vegetation growth period,in contrast with the normal year. This NDVI change was a result of plant destruction by the Yangtze vole population. Regarding the local crop system,Julian days 193 and 209 were chosen for observations representative of before and after the Yangtzevole problem. Performances of the NDVI and enhanced vegetation index( EVI) were analyzed. ΔVI was defined as the ratio of a seasonally detrended vegetation index( VI) reduction before and after the Yangtze vole problem to seasonally detrended VI before the problem. Given the mean ΔVI,shift amplitude( the difference of ΔVI between 2004 and 2007),statistical dispersion( inter quartile range( IQR) of ΔVI in 2007),and percentage of detected damaged pixels,the damage threshold was defined as the sum of the mean ΔVI and mean absolute deviation. These were calculated to evaluate sensitivity of the vegetation index. The EVI was selected as the optimal vegetation index for detecting damaged area after sensitivity analysis.An ΔEVI of 0.07 was considered the damage threshold in the study area. The damage indicator was then rescaled to a range of 0 to 1,for constructing damage severity. The upper and lower limits for rescaling were determined by statistical analysis of the damage indicator,whose values were the 95% point of the ΔEVI histogram for the study area and the damage threshold.Only damage pixels greater than 0 after rescaling,and the 25% and 75% quantiles,were treated as thresholds for three damage levels( serious,moderate and light). As a result,the study area was separated into four groups,in which 80.30%of pixels were damaged. The damage pixels were aggregated according to the action of the Yangtze vole population during the outbreak year,and were dominated by moderate-damage pixels. There was a certain distance between the damage area and levee,which could been explained by the population migration. Pixels with serious damage were dispersed over the entire study area. We constructed a disaster region map for the Dongting Lake region in 2007,from investigation and the spatial distribution of damage pixels with the threshold 0.07. This map was used to validate the method of assessing outbreak-year rodent pest damage based on MODIS imagery. The new approach to evaluating rodent pest damage proved to be rapid and promising.

Satellite-based remote sensed phenology has been widely used to assess global climate change. However, it is constrained by uncertain linkages with photosynthesis activity. Two dynamic threshold methods were employed to retrieve spring phenology metrics from four Moderate Resolution Imaging Spectroradiometer (MODIS) products, including fraction of Absorbed Photosynthetically Active Radiation (fAPAR), Leaf Area Index (LAI), Normalized Difference Vegetation Index (NDVI), and Enhanced Vegetation Index (EVI) for three temperate deciduous broadleaf forests in North America between 2001 and 2009. These MODIS-based spring phenology metrics were subsequently linked to the photosynthetic curves (daily gross primary productivity, GPP) measured by an eddy covariance flux tower. The 20% dynamic threshold spring onset metrics from MODIS products were closer to the photosynthesis onset metrics at the date of 2% GPP increase for NDVI and fAPAR, and closer to the date of 5% and 10% increase of GPP for EVI and LAI, respectively. The 50% dynamic threshold onset metrics were closer to the photosynthesis onset metrics at the date of 10% GPP increase for NDVI, and closer to the date of 20% GPP increase for fAPAR, LAI and EVI, respectively. These results can improve our knowledge on the photosynthesis activity status of remotely sensed spring phenology metrics.

The Normalized Difference Vegetation Index (NDVI) and Enhanced Vegetation Index (EVI) are vegetation indices widely used in remote sensing of above-ground biomass. Because both indexes are based on spectral features of plant canopy, NDVI and EVI may suffer reduced accuracy in estimating above-ground biomass when flower signals are mixed in the plant canopy. This paper addresses how flowers influence the estimation of above-ground biomass using NDVI and EVI for an alpine meadow with mixed yellow flowers of Halerpestes tricuspis (Ranunculaceae). Field spectral measurements were used in combination with simulated reflectance spectra with precisely controlled flower coverage by applying a linear spectral mixture model. Using the reflectance spectrum for the in-situ canopy with H. tricuspis flowers, we found no significant correlation between above-ground biomass and EVI (p = 0.17) or between above-ground biomass and NDVI (p = 0.78). However, both NDVI and EVI showed very good prediction of above-ground biomass with low root mean square errors (RMSE = 43 g m−2 for NDVI and RMSE = 43 g m−2 for EVI, both p < 0.01) when all the flowers were removed from the canopies. Simulation analysis based on the in-situ measurements further indicated that high variation in flower coverage among different quadrats could produce more noise in the relationship between above-ground biomass and NDVI, or EVI, which results in an evident decline in the accuracy of above-ground biomass estimation. Therefore, the study suggests that attention should be paid both to the flower fraction and the heterogeneity of flower distribution in the above-ground biomass estimation via NDVI and EVI.

Development of a two-band enhanced vegetation index without a blue band