Moderate Resolution Imaging Spectroradiometer Time Series Data Research Articles

Deriving drought indices from MODIS vegetation indices (NDVI/EVI) and Land Surface Temperature (LST): Is data reconstruction necessary?

Droughts pose significant economic and ecological concerns, and considering climate change projections, timely monitoring and early warning based on satellite observations must be realized at regional to global scales. Nevertheless, whether data reconstruction is necessary to produce high-quality satellite-based time series data for drought monitoring and the data reconstruction approaches to be applied, if necessary, remain unclear. We attempted to fill this knowledge gap by investigating three widely used data reconstruction approaches, i.e., the Savitzky–Golay filter, harmonic analysis of time series (HANTS) and Whittaker Smoother, across the Lancang–Mekong river basin through the Moderate Resolution Imaging Spectroradiometer (MODIS) MOD13Q1 and MOD11A2 products for 2001–2018 and Google Earth Engine cloud platform. Several remote sensing drought indices based on the unreconstructed and reconstructed vegetation indices (VIs) and Land Surface Temperature (LST) were retrieved for basin-wide drought detection. The performance of the examined reconstruction approaches was evaluated using three statistical indices (coefficient of determination (R2), standard deviation of bias (BIAS(std)) and mean squared error (MSE)), spatial consistency with the reference dataset and capability to characterize the drought events. Data reconstruction considerably enhanced the drought index performances for drought detection; however, reconstruction was not necessary in all situations. The reconstructed drought indices exhibited higher R2 values (by 10.6–10.8%), lower BIAS(std) values (by −1.7– −12.5%), and smaller MSE values (by −5.8– −13.4%) compared to those of unreconstructed indices. For most evaluation indicators, HANTS outperformed the other methods, and Vegetation Condition Index (VCI) and Vegetation Health Index (VHI) outperformed the other drought indices. The findings highlight the importance of data reconstruction to detect and characterize drought events and the dependency of the performance of reconstruction methods on drought indices and evaluation metrics when using MODIS time series data.

Assessment of spatio-temporal vegetation dynamics in tropical arid ecosystem of India using MODIS time-series vegetation indices

In the present study, we analyzed spatio-temporal vegetation dynamics to identify and delineate the vegetation stress zones in tropical arid ecosystem of Anantapuramu district, Andhra Pradesh, India, using Normalized Difference Vegetation Index (NDVI), Vegetation Condition Index (VCI), and Vegetation Anomaly Index (VAI) derived from time-series Moderate Resolution Imaging Spectroradiometer (MODIS) 16-day products (MOD13Q1) at 250 m spatial resolution for the growing season (June to September) of 19 years during 2000 to 2018. The 1-month Standardized Precipitation Index (SPI) was computed for 30 years (1989 to 2018) to quantify the precipitation deficit/surplus regions and assess its influence on vegetation dynamics. The growing season mean NDVI and VCI were correlated with growing season mean 1-month SPI of dry (2003) and wet (2007) years to analyze the spatio-temporal vegetation dynamics. The correlation analysis between SPI and NDVI for dry year (2003) showed strong positive correlation (r = 0.89). Analysis of VAI for dry year (2003) indicates that the central, western, and south-western parts of the district reported high vegetation stress with VAI of less than − 2.0. This might be due to the fact that central and south-western parts of the district are more prone to droughts than the other parts of the district. The correlation analysis of SPI, NDVI, and VCI distinctly shows the impact of rainfall on vegetation dynamics. The study clearly demonstrates the robustness of NDVI, VCI, and VAI derived from time-series MODIS data in monitoring the spatio-temporal vegetation dynamics and delineate vegetation stress zones in tropical arid ecosystem of India.

Drought Risk Assessment in Cultivated Areas of Central Asia Using MODIS Time-Series Data

Drought is one of the most damaging environmental hazards and a naturally occurring phenomenon in Central Asia that is accompanied by crucial consequences for the agriculture sector. This research aimed at understanding the nature and extent of drought over the cropland regions of Central Asia with the help of spatiotemporal information from the region. We assessed drought occurrence using the vegetation health index (VHI). An algorithm was developed to reduce the noise of heterogeneous land surfaces by adjusting the vegetation index and brightness temperature. The vegetation condition index (VCI) and temperature condition index (TCI) were calculated using Moderate Resolution Imaging Spectroradiometer (MODIS) products for the growing season (April–September) from 2000 to 2015. The intense drought years were identified and a drought map (drought probability occurrence) was generated. The findings of this research indicated regional heterogeneity in the cropland areas having experienced droughts, observed through spatiotemporal variations. Some of the rain-fed and irrigated croplands of Kazakhstan demonstrated a higher vulnerability to annual drought occurrences and climate change impacts, while other cropland regions were found to be more resistant to such changes. The development of policy tools is required to support informed decision-making and planning processes to adapt to the occurrence of droughts. This could be achieved by the timely assessment, monitoring, and evaluation of the spatiotemporal distribution trends and variabilities of drought occurrences in this region. The results from this study focus on the spatiotemporal variations in drought to reveal the bigger picture in order to better understand the regional capacity for sustainable land management and agricultural activities within a changing environment.

CART-RF Classification with Multifilter for Monitoring Land Use Changes Based on MODIS Time-Series Data: A Case Study from Jiangsu Province, China

The periodic determination of land use changes over large areas is crucial for improving our understanding of land system dynamics. Jiangsu lies at the center of China’s Yangtze Delta and has one of the fastest-developing economies in China. However, it is also a region where serious conflicts exist between the available land resources and the human demand for land. To address these conflicts, it is important to analyze the patterns of land use change in Jiangsu, as they can serve as a useful reference for other rapidly urbanizing regions in China as well as other developing countries. In this study, we propose a method of classification and regression tree-random forest (CART-RF) classification with a multifilter based on time-series Moderate Resolution Imaging Spectroradiometer (MODIS) imaging data. The proposed method integrates the CART decision tree and the random forest algorithms (CART-RF) to obtain accurate yearly land use data for large areas from multivariate time-series remote sensing data and employs a spatial-temporal-logical filter to exclude any abnormal changes in the multivariate time-series pixel data. The obtained results indicated that (1) the CART-RF classifier is effective for land use classification based on the multivariate time-series MODIS data, with the overall classification accuracy being greater than 90%; (2) the use of the proposed combinatorial spatial-temporal-logical filtering method effectively eliminates most anomalous changes and minimizes the effects of “salt-and-pepper” noise; and (3) from 2000 to 2015, land use in Jiangsu province underwent significant and spatiotemporally heterogeneous changes on a province-wide scale, owing to various factors, such as those related to the economy, location, and government policies. These changes were manifested as continuous expansions in the built-up land at the expense of farmland. While this expansion of built-up land has been very rapid in southern Jiangsu, especially in the region close to Yangtze River Delta, it has been relatively slower in northern Jiangsu.

Estimation of planting date in paddy fields by time-series MODIS data for basin-scale rice production modeling

Projection of future changes in agricultural water demand and supply through numerical model simulations can be one of the reference materials used for long-term rural planning under climate change. With a distributed hydrological model incorporating an agricultural sub-model, time-series rice growth and its yield can be simulated in correspondence with time-series soil moisture simulated by a hydrological model. Whereas such an agro-hydrological model is useful for assessing the climate change impact on agriculture in that it can simulate the potential rice production and the water-related damages on it against the given rainfall and meteorological condition, one of the challenges in simulating a realistic situation by a model is how to give a planting date in the model in regions where it is not fixed to a certain calendar date and differs in different fields and in different years. In this study, we examined an algorithm to estimate planting date in paddy fields using satellite data. The data we used are the 16-day composite Enhanced Vegetation Index (EVI) product by the moderate resolution imaging spectroradiometer (MODIS), which provides global coverage with 250-m spatial resolution. The time series of the EVI values at each pixel was analyzed first to detect heading dates as the local maxima in each year. Considering the number of days for growing rice, the existence of the second and the third rice cropping was judged from the time intervals of the local maxima. The planting dates were then estimated from the determined heading dates and local minima of the EVI time-series EVI. The estimated planting dates were validated against national statistical data in Japan and also with field-surveyed data in Cambodia. They showed reasonable estimations in the areal statistics, but there remained gaps in the comparison at individual fields.

Mapping croplands, cropping patterns, and crop types using MODIS time-series data

The importance of mapping regional and global cropland distribution in timely ways has been recognized, but separation of crop types and multiple cropping patterns is challenging due to their spectral similarity. This study developed a new approach to identify crop types (including soy, cotton and maize) and cropping patterns (Soy-Maize, Soy-Cotton, Soy-Pasture, Soy-Fallow, Fallow-Cotton and Single crop) in the state of Mato Grosso, Brazil. The Moderate Resolution Imaging Spectroradiometer (MODIS) normalized difference vegetation index (NDVI) time series data for 2015 and 2016 and field survey data were used in this research. The major steps of this proposed approach include: (1) reconstructing NDVI time series data by removing the cloud-contaminated pixels using the temporal interpolation algorithm, (2) identifying the best periods and developing temporal indices and phenological parameters to distinguish croplands from other land cover types, and (3) developing crop temporal indices to extract cropping patterns using NDVI time-series data and group cropping patterns into crop types. Decision tree classifier was used to map cropping patterns based on these temporal indices. Croplands from Landsat imagery in 2016, cropping pattern samples from field survey in 2016, and the planted area of crop types in 2015 were used for accuracy assessment. Overall accuracies of approximately 90%, 73% and 86%, respectively were obtained for croplands, cropping patterns, and crop types. The adjusted coefficients of determination of total crop, soy, maize, and cotton areas with corresponding statistical areas were 0.94, 0.94, 0.88 and 0.88, respectively. This research indicates that the proposed approach is promising for mapping large-scale croplands, their cropping patterns and crop types.

Wetlands classification and assessment of Ramsar sites in China based on time series Moderate Resolution Imaging Spectroradiometer (MODIS) imagery

Globally, wetland loss and degradation have become serious environmental and ecological issues. Wetland monitoring of Ramsar sites in China is important for developing reasonable strategies to protect wetlands. Satellite image time series may be used for the long-term monitoring of wetland ecosystems. The present study used moderate-resolution imaging spectroradiometer (MODIS) time series data collected in 2001 and 2013 for 20 Ramsar sites in China and assessed the environmental status of these reserves using landscape metrics. The results showed that specific seasonal wetland classes, such as flooded mud, permanent water and seasonal marshes, can be identified using MODIS time series data with acceptable accuracy. In addition to wetland area, we suggest using other landscape metrics, including landscape integrity and landscape disturbance or degradation indices, to assess wetland environmental quality. The slight wetland loss (0.8%) noted in the 20 reserves evaluated herein could indicate the effectiveness of efforts of the Chinese government and local government agencies to protect Ramsar sites. The existing unfavourable environmental conditions, which were manifested by low landscape integrity and high landscape disturbance or degradation for some reserves, were caused primarily by increasing water requirements outside the reserves and by agricultural development within reserves. Therefore, determining how to balance relationships between economic development and ecological protection of the reserves will be important in the future.

Automated cropland mapping of continental Africa using Google Earth Engine cloud computing

The automation of agricultural mapping using satellite-derived remotely sensed data remains a challenge in Africa because of the heterogeneous and fragmental landscape, complex crop cycles, and limited access to local knowledge. Currently, consistent, continent-wide routine cropland mapping of Africa does not exist, with most studies focused either on certain portions of the continent or at most a one-time effort at mapping the continent at coarse resolution remote sensing. In this research, we addressed these limitations by applying an automated cropland mapping algorithm (ACMA) that captures extensive knowledge on the croplands of Africa available through: (a) ground-based training samples, (b) very high (sub-meter to five-meter) resolution imagery (VHRI), and (c) local knowledge captured during field visits and/or sourced from country reports and literature. The study used 16-day time-series of Moderate Resolution Imaging Spectroradiometer (MODIS) normalized difference vegetation index (NDVI) composited data at 250-m resolution for the entire African continent. Based on these data, the study first produced accurate reference cropland layers or RCLs (cropland extent/areas, irrigation versus rainfed, cropping intensities, crop dominance, and croplands versus cropland fallows) for the year 2014 that provided an overall accuracy of around 90% for crop extent in different agro-ecological zones (AEZs). The RCLs for the year 2014 (RCL2014) were then used in the development of the ACMA algorithm to create ACMA-derived cropland layers for 2014 (ACL2014). ACL2014 when compared pixel-by-pixel with the RCL2014 had an overall similarity greater than 95%. Based on the ACL2014, the African continent had 296Mha of net cropland areas (260Mha cultivated plus 36Mha fallows) and 330Mha of gross cropland areas. Of the 260Mha of net cropland areas cultivated during 2014, 90.6% (236Mha) was rainfed and just 9.4% (24Mha) was irrigated. Africa has about 15% of the world’s population, but only about 6% of world’s irrigation. Net cropland area distribution was 95Mha during season 1, 117Mha during season 2, and 84Mha continuous. About 58% of the rainfed and 39% of the irrigated were single crops (net cropland area without cropland fallows) cropped during either season 1 (January-May) or season 2 (June-September). The ACMA algorithm was deployed on Google Earth Engine (GEE) cloud computing platform and applied on MODIS time-series data from 2003 through 2014 to obtain ACMA-derived cropland layers for these years (ACL2003 to ACL2014). The results indicated that over these twelve years, on average: (a) croplands increased by 1Mha/yr, and (b) cropland fallows decreased by 1Mha/year. Cropland areas computed from ACL2014 for the 55 African countries were largely underestimated when compared with an independent source of census-based cropland data, with a root-mean-square error (RMSE) of 3.5Mha. ACMA demonstrated the ability to hind-cast (past years), now-cast (present year), and forecast (future years) cropland products using MODIS 250-m time-series data rapidly, but currently, insufficient reference data exist to rigorously report trends from these results.

Mapping winter wheat using phenological feature of peak before winter on the North China Plain based on time-series MODIS data

By employing the unique phenological feature of winter wheat extracted from peak before winter (PBW) and the advantages of moderate resolution imaging spectroradiometer (MODIS) data with high temporal resolution and intermediate spatial resolution, a remote sensing-based model for mapping winter wheat on the North China Plain was built through integration with Landsat images and land-use data. First, a phenological window, PBW was drawn from time-series MODIS data. Next, feature extraction was performed for the PBW to reduce feature dimension and enhance its information. Finally, a regression model was built to model the relationship of the phenological feature and the sample data. The amount of information of the PBW was evaluated and compared with that of the main peak (MP). The relative precision of the mapping reached up to 92% in comparison to the Landsat sample data, and ranged between 87 and 96% in comparison to the statistical data. These results were sufficient to satisfy the accuracy requirements for winter wheat mapping at a large scale. Moreover, the proposed method has the ability to obtain the distribution information for winter wheat in an earlier period than previous studies. This study could throw light on the monitoring of winter wheat in China by using unique phenological feature of winter wheat.

Deforestation alters rainfall: a myth or reality

To cope with the issue of food safety and human shelter, natural landscape has gone through a number of alterations. In the coming future, the expansion of urban land and agricultural farms will likely disrupt the natural environment. Researchers have claimed that land use change may become the most serious issue of the current century. Thus, it is necessary to understand the consequences of land use change on the climatic variables, e.g., rainfall. This study investigated the impact of deforestation on local rainfall. An integrated methodology was adopted to achieve the objectives. Above ground biomass was considered as the indicator of forest areas. Time series data of a Moderate Resolution Imaging Spectroradiometer (MODIS) sensor were obtained for the year of 2000, 2005, and 2010. Rainfall data were collected from the Department of Irrigation and Drainage, Malaysia. The MODIS time series data were classified and four major classes were developed based on the Normalised Difference Vegetation Index (NDVI) ranges. The results of the classification showed that water, and urban and agricultural lands have increased in their area by 2, 3, and 6%, respectively. On the other hand, the area of forest has decreased 10% collectively from 2000 to 2010. The results of NDVI and rainfall data were analysed by using a linear regression analysis. The results showed a significant relationship at a 90% confidence interval between rainfall and deforestation (t = 1.92, p = 0.06). The results of this study may provide information about the consequences of land use on the climate on the local scale.

The application of a binary division procedure to the classification of forest subcategories using MODIS time-series data during 2000–2010 in China

ABSTRACTForests account for more than 23% of China’s total area. As the most important terrestrial ecosystem, forests have tremendous ecological value. However, it remains difficult to classify forest subcategories at the national scale. In this study, a newly developed binary division procedure was used to categorize forest areas, including their spatiotemporal dynamics, during the period 2000–2010. Time-series images acquired using the Moderate Resolution Imaging Spectroradiometer (MODIS), together with auxiliary data on land use, climate zoning, and topography, were utilized. Hierarchical classification and zoning were combined with remote-sensing auto-classification. Based on the forest extent mask, the state-level forest system was divided into four classes and 18 subcategories. The method achieved an acceptable overall accuracy of 73.1%, based on a comparison to the sample points of China’s fourth forest general survey data set. In 2010, the total forest area was 1.755 × 106 km2, and the total area of and shrubs was 4.885 × 105 km2. The total area of woodland increased by 2536.25 km2 during the decade 2000–2010. The shrub subcategories exhibited almost no change during this time period; however, significant changes in forest area occurred in the mountainous region of Northeast China as well as in the hilly regions of Southern China. The main transformations took place in cold-temperate and temperate mountainous deciduous coniferous forest, subtropical deciduous coniferous forest, subtropical evergreen coniferous forest, and temperate and subtropical deciduous broadleaved mixed forests. The binary division procedure proposed herein can be used not only to rapidly classify more forest subcategories and monitor their dynamic changes, but also to improve the classification accuracy compared with global and national land-cover maps.

Identifying Major Crop Types in Eastern Canada Using a Fuzzy Decision Tree Classifier and Phenological Indicators Derived from Time Series MODIS Data

. This article presents a methodology that uses a fuzzy decision tree classifier and phenological indicators derived from remote sensing data for identifying major crop types in southwestern Ontario in eastern Canada. Phenological indicators were derived from time series Normalized Difference Vegetation Index (NDVI) calculated from 250-m surface reflectance data of the Moderate Resolution Imaging Spectroradiometer (MODIS). Training and testing samples were derived from crop classification maps at 30-m resolution for 2011, 2012, and 2013. Training samples for 2013 were used for discrimination rule development, and the classifier was then applied to all 3 years. Results showed that the classifier was able to discriminate major crop types such as winter wheat, corn, soybean, and forage crops with an overall accuracy of 75.3 % for 2013 and comparable accuracy for 2011 and 2012. Confusion exists mainly between corn and soybean, and between winter wheat and forage crops. This indicates that phenological indicators derived from optical remote sensing data are intrinsic to a crop and might be more indicative than the commonly used remote sensing features that are susceptible to environmental and management impacts. This methodology provides an opportunity for discriminating general crop types without requiring a year-specific training sample set.

Reconstruction of Daily 30 m Data from HJ CCD, GF-1 WFV, Landsat, and MODIS Data for Crop Monitoring

With the recent launch of new satellites and the developments of spatiotemporal data fusion methods, we are entering an era of high spatiotemporal resolution remote-sensing analysis. This study proposed a method to reconstruct daily 30 m remote-sensing data for monitoring crop types and phenology in two study areas located in Xinjiang Province, China. First, the Spatial and Temporal Data Fusion Approach (STDFA) was used to reconstruct the time series high spatiotemporal resolution data from the Huanjing satellite charge coupled device (HJ CCD), Gaofen satellite no. 1 wide field-of-view camera (GF-1 WFV), Landsat, and Moderate Resolution Imaging Spectroradiometer (MODIS) data. Then, the reconstructed time series were applied to extract crop phenology using a Hybrid Piecewise Logistic Model (HPLM). In addition, the onset date of greenness increase (OGI) and greenness decrease (OGD) were also calculated using the simulated phenology. Finally, crop types were mapped using the phenology information. The results show that the reconstructed high spatiotemporal data had a high quality with a proportion of good observations (PGQ) higher than 0.95 and the HPLM approach can simulate time series Normalized Different Vegetation Index (NDVI) very well with R2 ranging from 0.635 to 0.952 in Luntai and 0.719 to 0.991 in Bole, respectively. The reconstructed high spatiotemporal data were able to extract crop phenology in single crop fields, which provided a very detailed pattern relative to that from time series MODIS data. Moreover, the crop types can be classified using the reconstructed time series high spatiotemporal data with overall accuracy equal to 0.91 in Luntai and 0.95 in Bole, which is 0.028 and 0.046 higher than those obtained by using multi-temporal Landsat NDVI data.

Adaptively weighted decision fusion in 30 m land-cover mapping with Landsat and MODIS data

Although the combined use of Landsat and Moderate Resolution Imaging Spectroradiometer (MODIS) time-series data in land-cover classification has been widely adopted, the majority of such use of Landsat and MODIS data is done at the pixel level or feature input level in land-cover classification. We propose in this research a new method to make integrated use of different satellite data by adaptively weighted decision-level fusion. Training and validation samples were collected independently. Training samples were obtained from 329 regions and validation samples from 439 randomly distributed single-point positions. A Support Vector Machine (SVM) classifier was applied to the Landsat 8 data for classification and probability estimation. A Random Forests (RF) classifier was applied to the MODIS time-series data for probability estimation. Weight values were computed based on decision credibility, and reliability values were computed based on data quality. Three decision fusion procedures were performed. In the first procedure, decisions obtained from a Landsat 8 pixel and its corresponding MODIS pixel were fused for improvements (FUSION1). In the second, decisions obtained from the spatial neighbours of the Landsat 8 pixel were added to FUSION1 (FUSION2). In the third, decision fusion only among the Landsat 8 pixel and its spatial neighbours was performed (FUSION3) for comparison. Overall accuracies for the results with Landsat data only, FUSION1, FUSION2, and FUSION3 are 74.0%, 79.3%, 80.6%, and 75.6%, respectively. As a comparison, we also experimented on the use of Landsat and MODIS data by concatenating their features directly. Two classifiers, SVM and RF, were trained and validated on the concatenated features. The overall accuracies were 72.9% and 75.4%, respectively. Results show that the proposed method can utilize information selectively, so that considerable improvements can be obtained and fewer errors introduced. Moreover, it can be easily extended to handle more than two types of data source.

Mapping Flooded Rice Paddies Using Time Series of MODIS Imagery in the Krishna River Basin, India

Rice is one of the major crops cultivated predominantly in flooded paddies, thus a large amount of water is consumed during its growing season. Accurate paddy rice maps are therefore important inputs for improved estimates of actual evapotranspiration in the agricultural landscape. The main objective of this study was to obtain flooded paddy rice maps using multi-temporal images of Moderate Resolution Imaging Spectroradiometer (MODIS) in the Krishna River Basin, India. First, ground-based spectral samples collected by a field spectroradiometer, CROPSCAN, were used to demonstrate unique contrasts between the Normalized Difference Vegetation Index (NDVI) and the Land Surface Water Index (LSWI) observed during the transplanting season of rice. The contrast between Enhanced Vegetation Index (EVI) and Land Surface Water Index (LSWI) from MODIS time series data was then used to generate classification decision rules to map flooded rice paddies, for the transplanting seasons of Kharif and Rabi rice crops in the Krishna River Basin. Consistent with ground spectral observations, the relationship of the MODIS EVI vs. LSWI of paddy rice fields showed distinct features from other crops during the transplanting seasons. The MODIS-derived maps were validated against extensive reference data collected from multiple land use field surveys. The accuracy of the paddy rice maps, when determined using field plot data, was approximately 78%. The MODIS-derived rice crop areas were also compared with the areas reported by Department of Agriculture (DOA), Government of India (Government Statistics). The estimated root mean square difference (RMSD) of rice area estimated using MODIS and those reported by the Department of Agriculture over 10 districts varied between 3.4% and 6.6% during 10 years of our study period. Some of the major factors responsible for this difference include high noise of the MODIS images during the prolonged monsoon seasons (typically June–October) and the coarse spatial resolution (500 m) of the MODIS images compared to the small crop fields in the basin. However, this study demonstrates, based on multi-year analysis, that MODIS images can still provide robust and consistent flooded paddy rice extent and areas over a highly heterogeneous large river basin.

Assessment of ecological disturbance in the mangrove forest of Sundarbans caused by cyclones using MODIS time-series data (2001–2011)

The mangrove ecosystem of Sundarbans region plays an important ecological and socioeconomical role in both India and Bangladesh. The satellite-based approach has been adopted for assessing the ecological disturbance caused by major cyclones. Moderate Resolution Imaging Spectroradiometer (MODIS) time-series (2001–2011) Enhanced Vegetation Index (EVI) and Land Surface Temperature (LST) products were used to compute the MODIS Global Disturbance Index (MGDI). The pre- and post-cyclonic changes in the EVI as well as LST were analyzed to study the impact of cyclones on the vegetation and surface temperature, respectively. In the present study, time-series MGDI was transformed to the percentage change in MGDI using temporal mean for each pixel, and its response toward cyclonic events was studied. The pre- and post-cyclonic Landsat data were analyzed to identify the affected areas discernible on the image and a threshold was decided to mark the “disturbed pixels.” The threshold for the instantaneous disturbance caused by cyclones was found to be more than 11 % of the temporal mean MGDI. The selected threshold was applied on the time-series MGDI images to delineate the disturbed pixels, and percent-disturbed area of each Island was computed. The classified disturbed area maps were able to characterize the impact of cyclones and effectively represent the distribution of the spatial intensity of the ecological disturbance in Sundarbans region.

A Framework for Consistent Estimation of Leaf Area Index, Fraction of Absorbed Photosynthetically Active Radiation, and Surface Albedo from MODIS Time-Series Data

Currently available land-surface parameter products are generated using parameter-specific algorithms from various satellite data and contain several inconsistencies. This paper developed a new data assimilation framework for consistent estimation of multiple land-surface parameters from time-series MODerate Resolution Imaging Spectroradiometer (MODIS) surface reflectance data. If the reflectance data showed snow-free areas, an ensemble Kalman filter (EnKF) technique was used to estimate leaf area index (LAI) for a two-layer canopy reflectance model (ACRM) by combining predictions from a phenology model and the MODIS surface reflectance data. The estimated LAI values were then input into the ACRM to calculate the surface albedo and the fraction of absorbed photosynthetically active radiation (FAPAR). For snow-covered areas, the surface albedo was calculated as the underlying vegetation canopy albedo plus the weighted distance between the underlying vegetation canopy albedo and the albedo over deep snow. The LAI/FAPAR and surface albedo values estimated using this framework were compared with MODIS collection 5 eight-day 1-km LAI/FAPAR products (MOD15A2) and 500-m surface albedo product (MCD43A3), and GEOV1 LAI/FAPAR products at 1/112° spatial resolution and a ten-day frequency, respectively, and validated by ground measurement data from several sites with different vegetation types. The results demonstrate that this new data assimilation framework can estimate temporally complete land-surface parameter profiles from MODIS time-series reflectance data even if some of the reflectance data are contaminated by residual cloud or are missing and that the retrieved LAI, FAPAR, and surface albedo values are physically consistent. The root mean square errors of the retrieved LAI, FAPAR, and surface albedo against ground measurements are 0.5791, 0.0453, and 0.0190, respectively.

Detecting soil salinity with MODIS time series VI data

Mapping of salinization using the satellite derived vegetation indices (VIs) remains difficult at broad regional scales due to the low classification accuracy. Satellite derived VIs from the Moderate Resolution Imaging Spectroradiometer (MODIS) have more potential because the MODIS balances the requirements of spatial detail, spectral and temporal density and tends to reflect vegetation responses through time. However, the relationship between MODIS data and salinity may be underestimated in previous studies because the MODIS time series data were not investigated thoroughly, especially regarding vegetation phenology. This study assessed the applicability of MODIS time series VI data for monitoring soil salinization with a series of MODIS pixels selected in the Yellow River Delta, China. The hidden information in vegetation phenology was investigated by improving the quality of VIs time series data with the Savitzky–Golay filter, extracting the phenological markers and differentiating VIs time series data based on vegetation types. The results showed that the quality of the enhanced vegetation index (EVI) time series data were improved by the Savitzky–Golay filter, which could provide more accurate thresholds of phenological stages than the empirical definition. The seasonal integral of EVI (EVI-SI) extracted from the smoothed EVI time series profile was verified as the best indicator of the degree of soil salinity. Additionally, the correlation of EVI-SI and soil salinity was highly dependent on land cover heterogeneity, and the ranges of correlation coefficients were as high as 0.59–0.92. EVI-SI was linearly correlated with ECe in cropland with a high model fit (R2=0.85). The relationship of EVI-SI and ECe fit best with a binomial line and EVI-SI was able to explain 70% of the variance of ECe. Despite the poor fit of the linear regression model in mixed sites limited by spatial resolution (R2=0.32), MODIS time series VI data, as well as the extracted seasonal parameters, still show great potential to assess large-scale soil salinization.

Monitoring paddy rice phenology using time series MODIS data over Jiangxi Province, China.

Abstract: Paddy rice is one of the most important crops in the world. Accurate estimation and monitoring of paddy rice phenology is necessary for management and yield prediction. Remotely sensed time-series data are essential for estimation of crop phenology stages across large areas. Here, the paddy rice phenological stages (i.e., transplanting, tillering, heading, and harvesting) were detected in Jiangxi Province, China. A comparison study was conducted using ground observation data from 10 agricultural meteorological stations, collected between 2006 and 2008. The phenological stages were detected using Moderate Resolution Imaging Spectroradiometer (MODIS) time-series enhanced vegetation index (EVI) data. Savitzky–Golay filter and wavelet transform were used to reduce the noise in the time-series EVI data and reconstruct the smoothed EVI time-series profile. Key phenological stages of double-cropping rice were detected using the characteristics of the smoothed EVI profile. The root mean square errors (RMSEs) for each stage were ±10 days around the ground observation data. The results suggest that Savitzky–Golay filter and wavelet transform are promising approaches for reconstructing high-quality EVI time-series data. Moreover, the phenological stages of double-cropping rice could be detected using time-series MODIS EVI data smoothed by Savitzky–Golay filter and wavelet transform. Keywords: remote sensing, phenology, paddy rice, time series MODIS EVI, growth monitoring, Savitzky-Golay filter, wavelet transform DOI: 10.3965/j.ijabe.20140706.005 Citation: Li S H, Xiao J T, Ni P, Zhang J, Wang H S, Wang J X. Monitoring paddy rice phenology using time series MODIS data over Jiangxi Province, China. Int J Agric & Biol Eng, 2014; 7(6): 28－36.

Near real-time prediction of U.S. corn yields based on time-series MODIS data

Annual variation of U.S. corn production is an important matter of world concern. To assure immediate response to large-scale harvest failure in crop exporting regions, as was the case during the severe U.S. drought in 2012, and to enhance global food security, a practical crop growth monitoring system based on satellite data is required. This study developed a practical method for near real-time prediction of U.S. corn yields using Moderate Resolution Imaging Spectroradiometer (MODIS)-derived Wide Dynamic Range Vegetation Index (WDRVI) taken 7days before the corn silking stage. We incorporated two algorithms into the MODIS-based corn yield prediction method; namely, (1) a MODIS-based crop classification algorithm in consideration of differences in emergence dates between corn and soybean, and (2) a simple bias correction algorithm for correcting region-dependent yield prediction errors. The method is able to predict the annual variation of national and state level corn grain yields with high accuracy in early August and detect corn yield reductions and poor-harvest regions due to drought damage, as in 2002 and 2012, on a near real-time basis in advance of those provided by the U.S. Department of Agriculture's National Agricultural Statistics Service. The method also predicted a national-level U.S. corn grain yield for 2013, which was 3.8% lower than the NASS-statistical data.