Corn Area Extraction by the Integration of MODIS-EVI Time Series Data and China’s Environment Satellite (HJ-1) Data

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.

Detection and estimation of mixed paddy rice cropping patterns with MODIS data

On intra-annual EVI variability in the dry season of tropical forest: A case study with MODIS and hyperspectral data

Validation of water vapor retrieval from Moderate Resolution Imaging Spectro-radiometer (MODIS) in near infrared channels using GPS data over IAO-Hanle, in the trans-Himalayan region

Remotely sensed vegetation indices such as the normalized difference vegetation index (NDVI) or enhanced vegetation index (EVI) have been used to scale up flux-based gross primary production (GPP) measurements. Recently, the use of visible-band (VIS) indices for estimation of GPP has been proposed, and VIS_indices derived from digital cameras have been used for detecting phenological changes. To confirm the utility of remotely sensed VIS_indices for the evaluation of GPP in a Japanese larch forest, we investigated the relationships between flux-based GPP measurements and indices derived from both moderate resolution imaging spectroradiometer (MODIS) data and tower-mounted digital camera images. We evaluated the suitability of both traditional (NDVI and EVI) and VIS_indices (the greenred vegetation index (GRVI) and green ratio (GR)) at both satellite and near-surface scales for GPP estimation. We also used the MODIS data to evaluate the sensitivity of the indices to the effects of a severe forest disturbance. The results showed that VIS_indices had several advantages over the traditional indices: (1) seasonal variations in VIS_indices were more strongly correlated with GPP variations; (2) the vegetation growing season could be easily discriminated from the winter dormant period, because ground surface conditions affect VIS_indices less than they affect traditional indices; (3) the seasonal dynamics of vegetation could be determined at a satellite scale from MODIS data, and possibly even at a canopy scale from digital camera images; and (4) inter-annual variations of VIS_indices were likely to be more sensitive to vegetation changes after a disturbance. These results demonstrate the utility of VIS_indices for estimating GPP at satellite scales and possibly at the canopy scale. We suggest that multi-scale visible-band remote sensing could help our understanding of the ecosystem by improving the temporal and spatial resolutions of satellite data.

The moderate resolution imaging spectroradiometer (MODIS) is one of the widely used sensors to address environmental and climate change subjects with a daily global coverage. MODIS Collection 6 aerosol products at 10-km resolution are used in this study to monitor aerosol variability and assess its uncertainty using ground-based measurements. The aerosol optical depth (AOD) is retrieved by different algorithms based on the pixel surface, determining between land and ocean. Using data collected from Sidi Salem Aerosol Robotic Network (AERONET) station, we computed the accuracy for aerosol optical depth (AOD) retrieved from MODIS aboard the AQUA satellite using two validation methods. The results show a good agreement between MODIS and AERONET data for the study period using both the algorithms. We obtained high values of the correlation coefficient. These findings indicate that MODIS data perform well over Ben Salem AERONET station and are recommended for air quality monitoring over Tunisia. The conducted validation throughout the AERONET leads to a degree of confidence that allows a deep investigation of the AOD spatial variability over Tunisia. Then, MODIS data shows high performance with good certainty to identify the principal dust sources and typical transport paths occurring on the study region.

<strong class="journal-contentHeaderColor">Abstract.</strong> The AnisoVeg product consists of monthly 1 km composites of anisotropy (ANI) and nadir-normalized (NAD) surface reflectance layers obtained from the Moderate Resolution Imaging Spectroradiometer (MODIS) sensor over the entire South American continent. The satellite data were preprocessed using the multi-angle implementation atmospheric correction (MAIAC). The AnisoVeg product spans 22 years of observations (2000 to 2021) and includes the reflectance of MODIS bands 1 to 8 and two vegetation indices (VIs), namely the normalized difference vegetation index (NDVI) and enhanced vegetation index (EVI). While the NAD layers reduce the data variability added by bidirectional effects on the reflectance and VI time series, the unique ANI layers allow the use of this multi-angular data variability as a source of information for vegetation studies. The AnisoVeg product has been generated using daily MODIS MAIAC data from both Terra and Aqua satellites, normalized for a fixed solar zenith angle (SZA <span class="inline-formula">=</span> 45<span class="inline-formula">^∘</span>), modeled for three sensor view directions (nadir, forward, and backward scattering), and aggregated to monthly composites. The anisotropy was calculated by the subtraction of modeled backward and forward scattering surface reflectance. The release of the ANI data for open usage is novel, and the NAD data are at an advanced processing level. We demonstrate the use of such data for vegetation studies using three types of forests in the eastern Amazon with distinct gradients of vegetation structure and aboveground biomass (AGB). The gradient of AGB was positively associated with ANI, while NAD values were related to different canopy structural characteristics. This was further illustrated by the strong and significant relationship between EVI<span class="inline-formula">_ANI</span> and forest height observations from the Global Ecosystem Dynamics Investigation (GEDI) lidar sensor considering a simple linear model (<span class="inline-formula"><i>R</i>²=0.55</span>). Overall, the time series of the AnisoVeg product (NAD and ANI) provide distinct information for various applications aiming at understanding vegetation structure, dynamics, and disturbance patterns. All data, processing codes, and results are made publicly available to enable research and the extension of AnisoVeg products for other regions outside of South America. The code can be found at <a href="https://doi.org/10.5281/zenodo.6561351">https://doi.org/10.5281/zenodo.6561351</a> (Dalagnol and Wagner, 2022), EVI<span class="inline-formula">_ANI</span> and EVI<span class="inline-formula">_NAD</span> can be found as assets in the Google Earth Engine (GEE; described in the data availability section), and the full dataset is available from the open repository <a href="https://doi.org/10.5281/zenodo.3878879">https://doi.org/10.5281/zenodo.3878879</a> (Dalagnol et al., 2022).

Remote sensing has been widely used in vegetation-dynamics monitoring. Many studies have used data acquired by multispectral sensors, such as the Landsat TM sensor, due to their high spatial resolution (30 m). However, during the growing season, the temporal resolution (16 day) cannot capture rapid changes of vegetation. Meanwhile, coarse-spectral-resolution sensors, such as Moderate Resolution Imaging Spectroradiometer (MODIS), have high-frequency temporal information that can catch the details of landscape changes. In this research, we proposed a data-fusion approach that lead us to merge the MODIS and Landsat TM data to create a dataset of vegetation dynamics with both a high spatial resolution and a fine temporal resolution. The Comanche and Faith Ranches, located in west Texas, were chosen for this study. The MODIS product was used as a regionally consistent reference dataset to correct the Landsat imagery. Based on this new dataset, NDVI time-series curves from 2004 to 2011 were calculated with the MODIS 13 Vegetation Dataset. One random sample of red-band images was tested and compared with MODIS data. A high correlation coefficient 0.907 and RMSE 0.0245 was found.

The objective of this research is into detecting sea ice by using PALSAR (Phased-Array type L-band SAR) polarimetric data. It is generally difficult to detect thin sea ice area by using the methods based on the backscattering coefficient. We propose a new method utilizing scattering entropy to detect sea ice. This paper shows the results of extracting sea ice area from PALSAR fully polarimetric data acquired from 2009 to 2010. The MODIS (Moderate Resolution Imaging Spectroradiometer) data are used as sea ice reference. The threshold of PALSAR scattering entropy to discriminate sea ice from open water is determined from the distribution of scattering entropy for both sea ice and open water. We compared PALSAR derived sea ice area with MODIS derived area. Since most of PALSAR detected sea ice area is also detected by MODIS data, we can conclude that our proposed method is reliable to detect thin sea ice area in the Sea of Okhotsk. The high resolution backscattering and scattering entropy images give us an idea that there are some difficulties in detecting thin sea ice only by backscattering coefficient.

Mapping abandoned agriculture with multi-temporal MODIS satellite data

Estimation of surface upward longwave radiation from MODIS and VIIRS clear-sky data in the Tibetan Plateau

A quantitative method is developed for deriving water fraction from coarse- to medium-resolution satellite data with visible to short-wave infrared (SWIR) channels based on the linear mixture theory. The method uses a SWIR channel (1.64 μm) by assuming that the water-surface-leaving radiance in this channel is insignificant and is thus less affected by water types and water depth than near-infrared (NIR) channels for inland water bodies. For a mixed water pixel, a dynamic nearest neighbor searching (DNNS) method is used to find the nearby land pixels to determine the average land reflectance. The nearby pure water pixels with a similar water type to the subpixel water portion of the mixed water pixel are found dynamically to derive the average water reflectance. The average reflectance in the SWIR channel from both pure land pixels and water pixels is used to calculate the water fraction from a linear mixture model. The developed method is applied to Moderate Resolution Imaging Spectroradiometer (MODIS) data and shows promising results. High-resolution satellite data from the Thematic Mapper (TM) are used to evaluate the water fraction derived from MODIS. During pixel-to-pixel water fraction evaluation, TM data are spatially aggregated to MODIS resolution. When evaluated against the high-resolution TM observations, water fractions derived from MODIS using the DNNS method with the SWIR channel show a bias of -0.021 with a standard deviation of 0.0338. Comparing lake areas between TM and MODIS data also shows consistent results with the pixel-to-pixel water fraction comparison. The DNNS method is also compared to the traditional histogram method both with SWIR channel and NIR channel. The results show that the DNNS method is more accurate than the histogram method and that the SWIR channel is better than the NIR channel to derive highly accurate water fraction from coarse- to medium-resolution satellite data.

Monitoring systems benefit from high temporal frequency image data collected from the Moderate Resolution Imaging Spectroradiometer (MODIS) system. Because of near-daily global coverage, MODIS data are beneficial to applications that require timely information about vegetation condition related to drought, flooding, or fire danger. Rapid satellite data streams in operational applications have clear benefits for monitoring vegetation, especially when information can be delivered as fast as changing surface conditions. An “expedited” processing system called “eMODIS” operated by the U.S. Geological Survey provides rapid MODIS surface reflectance data to operational applications in less than 24 h offering tailored, consistently-processed information products that complement standard MODIS products. We assessed eMODIS quality and consistency by comparing to standard MODIS data. Only land data with known high quality were analyzed in a central U.S. study area. When compared to standard MODIS (MOD/MYD09Q1), the eMODIS Normalized Difference Vegetation Index (NDVI) maintained a strong, significant relationship to standard MODIS NDVI, whether from morning (Terra) or afternoon (Aqua) orbits. The Aqua eMODIS data were more prone to noise than the Terra data, likely due to differences in the internal cloud mask used in MOD/MYD09Q1 or compositing rules. Post-processing temporal smoothing decreased noise in eMODIS data.

Dinocysts as proxy of primary productivity in mid–high latitudes of the Northern Hemisphere

Bi-scale analysis of multitemporal land cover fractions for wetland vegetation mapping