Composite Moderate Resolution Imaging Spectroradiometer Research Articles

Sustainable Development Through Green-Themed Environmental Impact Assessment: Lessons for Developing Countries

In this study, we used Geographic Information System and remote sensing to explore the stimulation of sustainable development through “Green-Conscious” Environmental Impact Assessment of industrial projects. Despite the global clamour for sustainable development as panacea for recent climatic concerns; sustainable development initiatives like Nano and green technologies are not highlighted in key Sections of the Nigerian EIA Act. The study adopted time series of 16-day composite Moderate Resolution Imaging Spectroradiometer 250m resolution NDVI data (MOD13Q1) covering South West, Nigeria between 2002–2017. We conclude that Nigeria cannot attain sustainable development if relevant laws are not amended to include sustainability initiatives. Keywords: Sustainable Development; Going-Green; Environmental Impact Assessment; EIA; Industrial Projects, Nanotechnology DOI: 10.7176/JEES/10-5-06 Publication date: May 31 st 2020

A Data-Intensive Approach to Address Food Sustainability: Integrating Optic and Microwave Satellite Imagery for Developing Long-Term Global Cropping Intensity and Sowing Month from 2001 to 2015

It is necessary to develop a sustainable food production system to ensure future food security around the globe. Cropping intensity and sowing month are two essential parameters for analyzing the food–water–climate tradeoff as food sustainability indicators. This study presents a global-scale analysis of cropping intensity and sowing month from 2000 to 2015, divided into three groups of years. The study methodology integrates the satellite-derived normalized vegetation index (NDVI) of 16-day composite Moderate Resolution Imaging Spectroradiometer (MODIS) and daily land-surface-water coverage (LSWC) data obtained from The Advanced Microwave Scanning Radiometer (AMSR-E/2) in 1-km aggregate pixel resolution. A fast Fourier transform was applied to normalize the MODIS NDVI time-series data. By using advanced methods with intensive optic and microwave time-series data, this study set out to anticipate potential dynamic changes in global cropland activity over 15 years representing the Millennium Development Goal period. These products are the first global datasets that provide information on crop activities in 15-year data derived from optic and microwave satellite data. The results show that in 2000–2005, the total global double-crop intensity was 7.1 million km2, which increased to 8.3 million km2 in 2006–2010, and then to approximately 8.6 million km2 in 2011–2015. In the same periods, global triple-crop agriculture showed a rapid positive growth from 0.73 to 1.12 and then 1.28 million km2, respectively. The results show that Asia dominated double- and triple-crop growth, while showcasing the expansion of single-cropping area in Africa. The finer spatial resolution, combined with a long-term global analysis, means that this methodology has the potential to be applied in several sustainability studies, from global- to local-level perspectives.

Response of Seasonal Vegetation Dynamics to Climatic Constraints in Northeastern Burundi

The climate crises in East Africa (EA), particularly in Burundi, have affected vegetation which, in turn, plays a key role in the climate system by modifying the terrestrial water and energy balance. Consequently, it is vital to understand vegetation dynamics and its response to current and projected climate conditions to support the design of climate resilient land management strategies. The objective of this study was to study the dynamics of vegetation cover over the Northeastern Burundi (NEB) in response to climatic constraints. The methodology used consisted of the interpretation of satellite images along with the analysis of data collected through rain-gauge stations. The data sets used include time series composite moderate resolution imaging spectroradiometer (MODIS) normalized difference vegetation index (NDVI) data collected between February 2000 and December 2017; long term (1986-2017) rainfall data acquired from two meteorological stations throughout the Northeastern provinces in Burundi and precipitation and mean temperature data (1986-2017) from Climate Hazards Group InfraRed Precipitation with Station data (CHIRPS) and ERA5 Daily aggregates for the study area. The study provides an assessment of the vegetation trends in NEB using the NDVI time series. In addition, regression analysis is applied to assess the relations between NDVI and precipitation, air temperature, potential evapotranspiration, as well as with growing season characteristics (onset, cessation, and length). Results show that the rate in vegetation productivity is persistently gradual between 2000 and 2011 despite fluctuations from the mean position, followed by a lower growth rate over the period 2011-2017. There has been trend variation in precipitation, neither the temperature was constant. The temperature over the region has increased while the precipitation has decreased. The onset of the growing season and air temperature also show a significant influence on seasonal vegetation dynamics in the region. Drought-induced plant stress observed at the onset of the rainy season was the most important contributor to the subsequent less greening of vegetation especially over the area near the northern lakes.

Mapping spatiotemporal dynamics of maize in China from 2005 to 2017 through designing leaf moisture based indicator from Normalized Multi-band Drought Index

Maize agriculture is experiencing substantial changes in the spatiotemporal pattern of planting areas in the most populous country-China. However, there is no spatially explicit and continuous information at national scale. Mapping maize at national scale is challenging due to intra-class variability of Vegetation Indices (VIs) temporal profile. This study coped with this challenge through combined utilizations of the EVI with two bands (EVI2) and Normalized Multi-band Drought Index (NMDI) time series datasets. A novel Maize mapping algorithm was proposed through Exploring Leaf moisture variation during flowering Stage (MELS). An indicator, the Ratio of Cumulative Positive slope to Negative slope (RCPN) during flowering stage, was developed based on NMDI and utilized as the unique metric for maize mapping. The capability of the MELS method was verified using the 8-day composite MODerate resolution Imaging Spectroradiometer (MODIS) datasets in China from 2005 to 2017. The derived maize map was consistent with the agricultural census data (r2 = 0.8875 in 2015) and 2020 ground truth observations (overall accuracy = 91.49%). Validation with Landsat-interpreted images in the test regions further confirmed its fairly good accuracy, with overall accuracy of 87.91% and kappa coefficient of 0.8577. We first generated annual maize maps from 2005 to 2017 in China. Maize planting areas increased continuously 100,130 km2 (by 33.20%) during the period 2005–2015 and decreased 10,424 km2 (by 2.60%) from 2015 to 2017. The increase of cropping intensity, replacement of paddy rice and other non-maize dryland crops areas accounted for 36.48%, 34.23% and 29.29% of the dramatic increased maize areas from 2005 to 2015, respectively.

Mapping of Shorea robusta Forest Using Time Series MODIS Data

Mapping forest types in a natural heterogeneous forest environment using remote sensing data is a long-standing challenge due to similar spectral reflectance from different tree species and significant time and resources are required for acquiring and processing the remote sensing data. The purpose of this research was to determine the optimum number of remote sensing images and map the Sal forest through the analysis of Vegetation Index (VI) signatures. We analyzed the eight days’ composite moderate resolution imaging spectroradiometer (MODIS) time series normalized differential vegetation index (NDVI), and enhanced vegetation index (EVI) for the whole year of 2015. Jeffries-Matusita (J-M) distance was used for the separability index. Performance of EVI and NDVI was tested using random forest (RF) and support vector machine (SVM) classifiers. Boruta algorithm and statistical analysis were performed to identify the optimum set of imageries. We also performed data level five-fold cross validation of the model and field level accuracy assessment of the classification map. The finding confirmed that EVI with SVM (F-score of Sal 0.88) performed better than NDVI with either SVM or RF. The optimum 12 images during growing and post monsoon season significantly decreased processing time (to one-fourth) without much deteriorating accuracy. Accordingly, we were able to map the Sal forest whose area is accounted for about 36% of the 82% forest cover in the study area. The proposed methodology can be extended to produce a temporal forest type classification map in any other location.

Temporal Interpolation of Satellite-Derived Leaf Area Index Time Series by Introducing Spatial-Temporal Constraints for Heterogeneous Grasslands

Continuous satellite-derived leaf area index (LAI) time series are critical for modeling land surface process. In this study, we present an interpolation algorithm to predict the missing data in LAI time series for ecosystems with high within-ecosystem heterogeneity, particularly heterogeneous grasslands. The algorithm is based on spatial-temporal constraints, i.e., the missing data in the LAI time series of a pixel are predicted by the phenological links with other pixels. To address the uncertainties in the construction and selection of reference curves in a heterogeneous landscape, the algorithm constructs a reference dataset for each missing data in the LAI time series from all pixels showing very strong linear phenological links with the target pixel within a region. We also use an iterative process to update the available spatial-temporal constraints. We tested the algorithm with an eight-day composite Moderate Resolution Imaging Spectroradiometer (MODIS) LAI product in the Songnen grasslands, Northeast China in 2010 and 2011. The validation dataset was generated based on high quality time series by artificially adding data gaps. The algorithm achieved high overall interpolation accuracies with high coefficient of determination R2 (>0.9) and low root mean square error (RMSE) (<0.2) in both dry (2010) and wet (2011) years. The algorithm showed advantages in predicting missing data for different seasons and proportions of missing data versus the algorithm that uses regional average LAI curve as a reference. These results suggest that the proposed algorithm could more effectively characterize spatial-temporal constraint information in heterogeneous grasslands for temporal interpolation.

Developing soil indices based on brightness, darkness, and greenness to improve land surface mapping accuracy

Soil, as one of the three basic biophysical components, has been understudied using remote sensing techniques compared to vegetation and impervious surface areas (ISA). This study characterized land surfaces based on the brightness–darkness–greenness model. These three dimensions, brightness, darkness, and greenness, were represented by the first Tasseled Cap Transformation (TC1), Normalize Difference Snow Index (NDSI), and Normalized Difference Vegetation Index (NDVI), respectively. The Ratio Index for Bright Soil (RIBS) was developed based on TC1 and NDSI, and the Product Index for Dark Soil (PIDS) was established by TC1 and NDVI. Their applications to the Landsat 8 Operational Land Imager images and 500 m 8-day composite Moderate Resolution Imaging Spectroradiometer (MODIS) in China revealed the efficiency. The two soil indices proficiently highlighted soil covers with consistently the smallest values, due to larger TC1 and smaller NDSI values in bright soil, and smaller NDVI and TC1 values in dark soil. The RIBS is capable of distinguishing bright soil from ISA without masking vegetation and water body. The spectral separability bright soil and ISA were perfect, with a Jeffries–Matusita distance of 1.916. And the PIDS was the only soil index that could discriminate dark soil from other land covers including ISA. The soil areas in China were classified using a simple threshold method based on MODIS images. An overall accuracy of 94.00% was obtained, with the kappa index of 0.8789. This study provided valuable insights into developing indices for characterizing land surfaces from different perspectives.

Fallow-land Algorithm based on Neighborhood and Temporal Anomalies (FANTA) to map planted versus fallowed croplands using MODIS data to assist in drought studies leading to water and food security assessments

An important metric to monitor for optimizing water use in agricultural areas is the amount of cropland left fallowed, or unplanted. Fallowed croplands are difficult to model because they have many expressions; for example, they can be managed and remain free of vegetation or be abandoned and become weedy if the climate for that season permits. We used 250 m, 8-day composite Moderate Resolution Imaging Spectroradiometer normalized difference vegetation index data to develop an algorithm that can routinely map cropland status (planted or fallowed) with over 75% user’s and producer’s accuracies. The Fallow-land Algorithm based on Neighborhood and Temporal Anomalies (FANTA) compares the current greenness of a cultivated pixel to its historical greenness and to the greenness of all cultivated pixels within a defined spatial neighborhood, and is therefore transportable across space and through time. This article introduces FANTA and applies it to California from 2001 to 2015 as a case study for use in data-poor places and for use in historical modeling. Timely and accurate knowledge of the extent of fallowing can provide decision makers with insights and knowledge to mitigate the impacts of drought and provide a scientific basis for effective management response. This study is part of the WaterSMART (Sustain and Manage America’s Resources for Tomorrow) project, an interdisciplinary and collaborative research effort focused on improving water conservation and optimizing water use.

Winter wheat mapping combining variations before and after estimated heading dates

Accurate and updated information on winter wheat distribution is vital for food security. The intra-class variability of the temporal profiles of vegetation indices presents substantial challenges to current time series-based approaches. This study developed a new method to identify winter wheat over large regions through a transformation and metric-based approach. First, the trend surfaces were established to identify key phenological parameters of winter wheat based on altitude and latitude with references to crop calendar data from the agro-meteorological stations. Second, two phenology-based indicators were developed based on the EVI2 differences between estimated heading and seedling/harvesting dates and the change amplitudes. These two phenology-based indicators revealed variations during the estimated early and late growth stages. Finally, winter wheat data were extracted based on these two metrics. The winter wheat mapping method was applied to China based on the 250m 8-day composite Moderate Resolution Imaging Spectroradiometer (MODIS) 2-band Enhanced Vegetation Index (EVI2) time series datasets. Accuracy was validated with field survey data, agricultural census data, and Landsat-interpreted results in test regions. When evaluated with 653 field survey sites and Landsat image interpreted data, the overall accuracy of MODIS-derived images in 2012–2013 was 92.19% and 88.86%, respectively. The MODIS-derived winter wheat areas accounted for over 82% of the variability at the municipal level when compared with agricultural census data. The winter wheat mapping method developed in this study demonstrates great adaptability to intra-class variability of the vegetation temporal profiles and has great potential for further applications to broader regions and other types of agricultural crop mapping.

A global study of NDVI difference among moderate-resolution satellite sensors

Moderate-resolution sensors, including AVHRR (Advanced Very High Resolution Radiometer), MODIS (MODerate-resolution Imaging Spectroradiometer) and VIIRS (Visible-Infrared Imager-Radiometer Suite), have provided over forty years of global scientific data. In the form of NDVI (Normalized Difference Vegetation Index), these data greatly benefit environmental studies. However, their usefulness is compromised by sensor differences. This study investigates the global NDVI difference and its spatio-temporal patterns among typical moderate-resolution sensors, as supported by state-of-the-art remote sensing derived products. Our study demonstrates that the atmosphere plays a secondary role to LULC (Land Use/Land Cover) in inter-sensor NDVI differences. With reference to AVHRR/3, AVHRR/1 and 2 exhibit negative NDVI biases for vegetated land cover types. In summer (July), the area of negative bias shifts northward, and the magnitude increases in the Northern Hemisphere. For most LULC types, the bias generally shifts in the negative direction from winter (January) to summer. A linear regression of the NDVI difference versus NDVI shows a close correlation between the slope value and vegetation phenology. Overall, NDVI differences are controlled by LULC type and vegetation phenology. Our study can be used to generate a long-term, consistent NDVI data set from composite MODIS and AVHRR NDVI data. LULC-dependent and temporally variable correction equations are recommended to reduce inter-sensor NDVI differences.

Automated cropping intensity extraction from isolines of wavelet spectra

Timely and accurate monitoring of cropping intensity (CI) is essential to help us understand changes in food production. This paper aims to develop an automatic Cropping Intensity extraction method based on the Isolines of Wavelet Spectra (CIIWS) with consideration of intra-class variability. The CIIWS method involves the following procedures: (1) characterizing vegetation dynamics from time–frequency dimensions through a continuous wavelet transform performed on vegetation index temporal profiles; (2) deriving three main features, the skeleton width, maximum number of strong brightness centers and the intersection of their scale intervals, through computing a series of wavelet isolines from the wavelet spectra; and (3) developing an automatic cropping intensity classifier based on these three features. The proposed CIIWS method improves the understanding in the spectral–temporal properties of vegetation dynamic processes. To test its efficiency, the CIIWS method is applied to China’s Henan province using 250m 8days composite Moderate Resolution Imaging Spectroradiometer (MODIS) Enhanced Vegetation Index (EVI) time series datasets. An overall accuracy of 88.9% is achieved when compared with in-situ observation data. The mapping result is also evaluated with 30m Chinese Environmental Disaster Reduction Satellite (HJ-1)-derived data and an overall accuracy of 86.7% is obtained. At county level, the MODIS-derived sown areas and agricultural statistical data are well correlated (r2=0.85). The merit and uniqueness of the CIIWS method is the ability to cope with the complex intra-class variability through continuous wavelet transform and efficient feature extraction based on wavelet isolines. As an objective and meaningful algorithm, it guarantees easy applications and greatly contributes to satellite observations of vegetation dynamics and food security efforts.

Developing indices of temporal dispersion and continuity to map natural vegetation

An accurate and updated natural vegetation map is imperative for sustainable environmental management. This paper proposed a novel natural vegetation mapping algorithm based on time series images. Several indices of temporal dispersion and continuity were established for this purpose: low density (LD), medium density (MD), high density (HD) and medium continuity (MC). These indices were developed based on the particular percentiles-determined section of the EVI2 temporal profiles obtained through continuous wavelet transform. The natural vegetation was generally characterized as with lower temporal dispersion and greater temporal continuity compared with agricultural crops. The proposed methodology incorporated the indices of temporal dispersion and continuity and was applied to 13 provinces in central East China based on 500m 8-day composite Moderate Resolution Imaging Spectroradiometer (MODIS) Enhanced Vegetation Index with two bands (EVI2) in 2013. An overall accuracy of 92.97% was obtained when compared with 2715 ground truth sites. There was also a good agreement (kappa index=0.8049) on the distribution and areas of different vegetation types between the MODIS-estimated image and the Landsat 8 OLI interpreted data on two test regions. This study demonstrated the efficiency of the transform and metric integrated time series classification approaches in the fields of land and vegetation cover mapping.

A simple smoother based on continuous wavelet transform: Comparative evaluation based on the fidelity, smoothness and efficiency in phenological estimation

This study proposed a simple Smoother without any local adjustments based on Continuous Wavelet Transform (SCWT). And then it evaluated its performance together with other commonly applied techniques in phenological estimation. These noise reduction methods included Savitzky–Golay filter (SG), Double Logistic function (DL), Asymmetric Gaussian function (AG), Whittaker Smoother (WS) and Harmonic Analysis of Time-Series (HANTS). They were evaluated based on fidelity and smoothness, and their efficiencies in deriving phenological parameters through the inflexion point-based method with the 8-day composite Moderate Resolution Imaging Spectroradiometer (MODIS) 2-band Enhanced Vegetation Index (EVI2) in 2013 in China. The following conclusions were drawn: (1) The SG method exhibited strong fidelity, but weak smoothness and spatial continuity. (2) The HANTS method had very robust smoothness but weak fidelity. (3) The AG and DL methods performed weakly for vegetation with more than one growth cycle (i.e., multiple crops). (4) The WS and SCWT smoothers outperformed others with combined considerations of fidelity and smoothness, and consistent phenological patterns (correlation coefficients greater than 0.8 except evergreen broadleaf forests (0.68)). (5) Compared with WS methods, the SCWT smoother was capable in preservation of real local minima and maxima with fewer inflexions. (6) Large discrepancy was examined from the estimated phenological dates with SG and HANTS methods, particularly in evergreen forests and multiple cropping regions (the absolute mean deviation rates were 6.2–17.5 days and correlation coefficients less than 0.34 for estimated start dates).

Rice cropping density and intensity lessened in southeast China during the twenty-first century.

Accurate and updated time series maps of paddy rice distribution and planting intensity will greatly improve our knowledge. Unfortunately, spatiotemporal explicit information on rice fields is relatively limited, and considerable uncertainties still exist as regards to its inter-annual variations in China. In this study, an improved rice mapping methodology was proposed through combined consideration of vegetation phenology and surface moisture variations from different seasonal rice. This method was applied to southeast China based on 500 m 8 day composite Moderate Resolution Imaging Spectroradiometer (MODIS) Enhance Vegetation Indices with two bands (EVI2) during the period 2001-2013. Its efficiency was validated with 763 ground survey sites, with an overall accuracy of 95.02 % and the kappa index of 0.9217. Spatiotemporal analysis indicated that rice cropping density and intensity lessened in southeast China during the period 2001-2013. Particularly, the paddy rice-planted areas reduced by 30.09 %, changing from 231,005 to 161,484 km(2). Among them, the planted areas of double rice decreased by 49.34 %, changing from 34,215 to 17,335 km(2). Therefore, averaged rice cropping intensity in southeast China decreased from 1.148 to 1.107. The primary dynamic patterns were from single rice or a rotation of rice plus other crops to non-rice (93,386 km(2)) and double rice to non-double rice (24,132 km(2)). When analyzed at provincial and altitudinal gradient levels, it was obvious that areas with greater rice cropping density or intensity were associated with more remarkable reductions. Graphical abstract The left graph shows that the rice cropping density lessened in Hubei, Hunan, Guangdong, Jiangxi, Anhui, Jiangsu, Henan provinces and other three provincial-level administrative units (Zhejiang, Fujian and Shanghai) from 2001 to 2013. The middle graph indicates the movement of gravity center as well as the variations in the total planted areas of single rice, rice plus others and double rice. The right graph denotes that the rice cropping intensity decreased in each provincial-level administrative unit from 2001 to 2013.

Mapping paddy rice areas based on vegetation phenology and surface moisture conditions

Accurate and timely rice mapping is important for food security and environmental sustainability. We developed a novel approach for rice mapping through Combined Consideration of Vegetation phenology and Surface water variations (CCVS). Variation of the Land Surface Water Index (LSWI) in rice fields was relatively smaller than that in other crops fields during the period from tillering to heading dates. Therefore, the ratios of change amplitude of LSWI to 2-band Enhanced Vegetation Index 2 (EVI2) during that period were utilized as the primary metric for paddy rice mapping. This algorithm was applied to map paddy rice fields in southern China using an 8-day composite Moderate Resolution Imaging Spectroradiometer (MODIS) in 2013. The resultant rice cropping map was consistent with the agricultural census data (r2=0.8258) and ground truth observations (overall accuracy=93.4%). Validation with Landsat Thematic Mapper images in test regions also revealed its high accuracy (with overall accuracy of 94.3% and kappa coefficient of 0.86). The proposed CCVS method was more robust to intra-class variability and other related uncertainties compared with other related methods in rice mapping. Its successful application in southern China revealed its efficiency and great potential for further utilization.

Analysis of vegetation change associated with human disturbance using MODIS data on the rangelands of the Qinghai-Tibet Plateau

This paper evaluated changes in vegetation from 2000 to 2012, based on 1-km resolution 16-day composite Moderate Resolution Imaging Spectroradiometer (MODIS) Normalised Difference Vegetation Index (NDVI), and related them to changes in estimates of human disturbance on the rangelands of the Qinghai-Tibet Plateau. The main rangeland types studied were desert, steppe and meadow with the latter mainly found in the southern and eastern parts of the study area. The results indicated that human disturbance was distributed mainly in the southern and eastern parts of the study area and corresponded with high NDVI values. The NDVI values showed an upward trend over the study period, with 28.5% of the study area exhibiting a significant increase. The proportion of rangelands that experienced a downward trend in NDVI increased as the level of human disturbance increased. Of the different rangeland types, meadow had the highest NDVI values, the greatest human disturbance, and the highest proportion of rangelands that exhibited a significant decrease in NDVI. Compared with areas with no human disturbance, meadow and steppe rangelands that experienced an increase in human disturbance had lower rates of increase in their NDVI values but, conversely, desert rangelands showed the opposite trend. In addition, it was found that precipitation had the dominant influence on NDVI values and that higher precipitation and slighter lower temperatures over the period of the study were related to an increase in NDVI values.

Crop Condition Assessment with Adjusted NDVI Using the Uncropped Arable Land Ratio

Crop condition assessment in the early growing stage is essential for crop monitoring and crop yield prediction. A normalized difference vegetation index (NDVI)-based method is employed to evaluate crop condition by inter-annual comparisons of both spatial variability (using NDVI images) and seasonal dynamics (based on crop condition profiles). Since this type of method will generate false information if there are changes in crop rotation, cropping area or crop phenology, information on cropped/uncropped arable land is integrated to improve the accuracy of crop condition monitoring. The study proposes a new method to retrieve adjusted NDVI for cropped arable land during the growing season of winter crops by integrating 16-day composite Moderate Resolution Imaging Spectroradiometer (MODIS) reflectance data at 250-m resolution with a cropped and uncropped arable land map derived from the multi-temporal China Environmental Satellite (Huan Jing Satellite) charge-coupled device (HJ-1 CCD) images at 30-m resolution. Using the land map’s data on cropped and uncropped arable land, a pixel-based uncropped arable land ratio (UALR) at 250-m resolution was generated. Next, the UALR-adjusted NDVI was produced by assuming that the MODIS reflectance value for each pixel is a linear mixed signal composed of the proportional reflectance of cropped and uncropped arable land. When UALR-adjusted NDVI data are used for crop condition assessment, results are expected to be more accurate, because: (i) pixels with only uncropped arable land are not included in the assessment; and (ii) the adjusted NDVI corrects for interannual variation in cropping area. On the provincial level, crop growing profiles based on the two kinds of NDVI data illustrate the difference between the regular and the adjusted NDVI, with the difference depending on the total area of uncropped arable land in the region. The results suggested that the proposed method can be used to improve the assessment of early crop condition, but additional evaluation in other major crop producing regions is needed to better assess the method’s application in other regions and agricultural systems.

Reconstruction of Time-Series MODIS LST in Central Qinghai-Tibet Plateau Using Geostatistical Approach

We present an approach for reconstructing land surface temperature (LST) time series over mountainous areas based on Regression Kriging (RK) technique and a data processing scheme for filtering out LST noise and artifacts. A total of 1462 eight-day composite Moderate Resolution Imaging Spectroradiometer LST images over central Qinghai-Tibet Plateau over 2003-2010 are reconstructed. The regression model includes four auxiliary predictors-latitude, longitude, elevation, and NDVI-which are proven to be a good estimator for the 8-day LST. Comparison of ground surface temperature (GST) measurements at eight meteorological stations with the raw and reconstructed LST series shows that the reconstruction strategy can effectively recover complete high-quality over-land LST maps and significantly improve the consistency between LST and GST.

Exploration of sensor comparability: a case study of composite MODIS Aqua and Terra data

Comparability across sensors is a common goal of remotely sensed data products; however, sensor calibration and atmospheric conditions often limit implementation. Recently, a slight degradation in the blue band sensor on the Moderate Resolution Imaging Spectroradiometer (MODIS) Terra satellite was reported (Wang et al. 2012), and it was suggested at the 2011 fall NASA Carbon Cycle and Ecosystems/Biodiversity and Ecological Forecasting Team meeting that users switch to using MODIS Aqua data until the corrected collection 6 Terra data are released. Following this recommendation, a switch from Terra to Aqua data inputs used in the Tsetse Ecological Distribution (TED) model was implemented; however, significant variability between model outputs for Kenya was found. This letter considers the possible sources of incongruity between the MODIS Aqua and Terra sensors that might result in the observed differences between model outputs, including sensor degradation, temporal overlap, daily acquisition and data quality. While for data of Kenya it appears that differences in daily acquisition times and data quality result in differences between composite products, ultimately the selection of what data product and sensor to use should be based on reported data quality and biophysical conditions of the region under study.

Characterizing spatiotemporal non-stationarity in vegetation dynamics in China using MODIS EVI dataset

This paper evaluated the spatiotemporal non-stationarity in the vegetation dynamic based on 1-km resolution 16-day composite Moderate Resolution Imaging Spectroradiometer (MODIS) Enhanced Vegetation Index (EVI) datasets in China during 2001-2011 through a wavelet transform method. First, it revealed from selected pixels that agricultural crops, natural forests, and meadows were characterized by their distinct intra-annual temporal variation patterns in different climate regions. The amplitude of intra-annual variability generally increased with latitude. Second, parameters calculated using a per-pixel strategy indicated that the natural forests had the strongest variation pattern from seasonal to semiannual scales, and the multiple-cropping croplands typically showed almost equal variances distributed at monthly, seasonal, and semiannual scales. Third, spatiotemporal non-stationarity induced from cloud cover was also evaluated. It revealed that the EVI temporal profiles were significantly distorted with regular summer cloud cover in tropical and subtropical regions. Nevertheless, no significant differences were observed from those statistical parameters related to the interannual and interannual components between the de-clouded and the original MODIS EVI datasets across the whole country. Finally, 12 vegetation zones were proposed based on spatiotemporal variability, as indicated by the magnitude of interannual and intra-annual dynamic components, normalized wavelet variances of detailed components from monthly to semiannual scale, and proportion of cloud cover in summer. This paper provides insightful solutions for addressing spatiotemporal non-stationarity by evaluating the magnitude and frequency of vegetation variability using monthly, seasonal, semiannual to interannual scales across the whole study area.