Monthly Moderate Resolution Imaging Spectroradiometer Research Articles

Modelling Floodplain Vegetation Response to Groundwater Variability Using the ArcSWAT Hydrological Model, MODIS NDVI Data, and Machine Learning

This study modelled the relationships between vegetation response and available water below the soil surface using Terra’s moderate resolution imaging spectroradiometer (MODIS), Normalised Difference Vegetation Index (NDVI), and soil water content (SWC). The Soil & Water Assessment Tool (SWAT) interface known as ArcSWAT was used in ArcGIS for the groundwater analysis. The SWAT model was calibrated and validated in SWAT-CUP software using 10 years (2001–2010) of monthly streamflow data. The average Nash-Sutcliffe efficiency during the calibration and validation was 0.54 and 0.51, respectively, indicating that the model performances were good. Nineteen years (2002–2020) of monthly MODIS NDVI data for three different types of vegetation (forest, shrub, and grass) and soil water content for 43 sub-basins were analysed using the WEKA, machine learning tool with a selection of two supervised machine learning algorithms, i.e., support vector machine (SVM) and random forest (RF). The modelling results show that different types of vegetation response and soil water content vary in the dry and wet seasons. For example, the model generated high positive relationships (r = 0.76, 0.73, and 0.81) between the measured and predicted NDVI values of all vegetation in the sub-basin against the groundwater flow (GW), soil water content (SWC), and combination of these two variables, respectively, during the dry season. However, these relationships were reduced by 36.8% (r = 0.48) and 13.6% (r = 0.63) against GW and SWC, respectively, in the wet season. Our models also predicted that vegetation in the top location (upper part) of the sub-basin is highly responsive to GW and SWC (r = 0.78, and 0.70) during the dry season. Although the rainfall pattern is highly variable in the study area, the summer rainfall is very effective for the growth of the grass vegetation type. The results predicted that the growth of vegetation in the top-point location is highly dependent on groundwater flow in both the dry and wet seasons, and any instability or long-term drought can negatively affect these floodplain vegetation communities. This study has enriched our knowledge of vegetation responses to groundwater in each season, which will facilitate better floodplain vegetation management.

Remote sensing-aided rainfall–runoff modeling in the tropics of Costa Rica

Abstract. Streamflow simulation across the tropics is limited by the lack of data to calibrate and validate large-scale hydrological models. Here, we applied the process-based, conceptual HYPE (Hydrological Predictions for the Environment) model to quantitatively assess Costa Rica's water resources at a national scale. Data scarcity was compensated for by using adjusted global topography and remotely sensed climate products to force, calibrate, and independently evaluate the model. We used a global temperature product and bias-corrected precipitation from Climate Hazards Group InfraRed Precipitation with Station data (CHIRPS) as model forcings. Daily streamflow from 13 gauges for the period 1990–2003 and monthly Moderate Resolution Imaging Spectroradiometer (MODIS) potential evapotranspiration (PET) and actual evapotranspiration (AET) for the period 2000–2014 were used to calibrate and evaluate the model applying four different model configurations (M1, M2, M3, M4). The calibration consisted of step-wise parameter constraints preserving the best parameter sets from previous simulations in an attempt to balance the variable data availability and time periods. The model configurations were independently evaluated using hydrological signatures such as the baseflow index, runoff coefficient, and aridity index, among others. Results suggested that a two-step calibration using monthly and daily streamflow (M2) was a better option than calibrating only with daily streamflow (M1), with similar mean Kling–Gupta efficiency (KGE ∼ 0.53) for daily streamflow time series, but with improvements to reproduce the flow duration curves, with a median root mean squared error (RMSE) of 0.42 for M2 and a median RMSE of 1.15 for M1. Additionally, including AET (M3 and M4) in the calibration statistically improved the simulated water balance and better matched hydrological signatures, with a mean KGE of 0.49 for KGE in M3–M4, in comparison to M1–M2 with mean KGE < 0.3. Furthermore, Kruskal–Wallis and Mann–Whitney statistical tests support a similar model performance for M3 and M4, suggesting that monthly PET-AET and daily streamflow (M3) represents an appropriate calibration sequence for regional modeling. Such a large-scale hydrological model has the potential to be used operationally across the humid tropics informing decision-making at relatively high spatial and temporal resolution.

Filling Gaps of Monthly Terra/MODIS Daytime Land Surface Temperature Using Discrete Cosine Transform Method

Land surface temperature (LST) is a key parameter in geophysical fields. The Moderate Resolution Imaging Spectroradiometer (MODIS) onboard Terra provides an accurate LST dataset with global coverage and monthly series, but the monthly MODIS LST data are often obscured by clouds and other atmospheric disturbances and consequently exhibit significant data gaps at a global scale, resulting in a difficult interpretation of LST trends and climatological characteristics. In this paper, an effective and fast LST reconstruction method to fill data gaps in monthly MODIS LST is presented. The proposal combines the Discrete Cosine Transform (DCT) and the Penalized Least Square approach (PLS) together with the Generalized Cross-Validation (GCV) criterion. It depends only on the spatial high-frequency information from original LST estimates and allows a fast and automatic filling process without the help of any other ancillary data. To analyze its performance, the method is applied to fill data gaps on three continents with synthetic random missing values introduced as validation sets. The statistical evaluation shows that this method is capable of filling a large number of missing values in MODIS LST datasets with very high accuracy. In addition, the trend differences between the original LST and reconstructed LST have assessed the significance by computing 95% confidence intervals for a time series of trend differences is examined. Simulated experiments show that data gaps with large missing counts lead to significant differences in trend patterns and the patterns on validation sets are well estimated by this method, which confirms that the filling process of MODIS LST is necessary and favorable results can be produced for substantial data gaps by the DCT-PLS method.

Intercomparison of MODIS AQUA and VIIRS I-Band Fires and Emissions in an Agricultural Landscape-Implications for Air Pollution Research.

Quantifying emissions from crop residue burning is crucial as it is a significant source of air pollution. In this study, we first compared the fire products from two different sensors, the Visible Infrared Imaging Radiometer Suite (VIIRS) 375 m active fire product (VNP14IMG) and Moderate Resolution Imaging Spectroradiometer (MODIS) 1 km fire product (MCD14ML) in an agricultural landscape, Punjab, India. We then performed an intercomparison of three different approaches for estimating total particulate matter (TPM) emissions which includes the fire radiative power (FRP) based approach using VIIRS and MODIS data, the Global Fire Emissions Database (GFED) burnt area emissions and a bottom-up emissions approach involving agricultural census data. Results revealed that VIIRS detected fires were higher by a factor of 4.8 compared to MODIS Aqua and Terra sensors. Further, VIIRS detected fires were higher by a factor of 6.5 than Aqua. The mean monthly MODIS Aqua FRP was found to be higher than the VIIRS FRP; however, the sum of FRP from VIIRS was higher than MODIS data due to the large number of fires detected by the VIIRS. Besides, the VIIRS sum of FRP was 2.5 times more than the MODIS sum of FRP. MODIS and VIIRS monthly FRP data were found to be strongly correlated (r2 = 0.98). The bottom-up approach suggested TPM emissions in the range of 88.19–91.19 Gg compared to 42.0–61.71 Gg, 42.59–58.75 Gg and 93.98–111.72 Gg using the GFED, MODIS FRP, and VIIRS FRP based approaches, respectively. Of the different approaches, VIIRS FRP TPM emissions were highest. Since VIIRS data are only available since 2012 compared to MODIS Aqua data which have been available since May 2002, a prediction model combining MODIS and VIIRS FRP was derived to obtain potential TPM emissions from 2003–2016. The results suggested a range of 2.56–63.66 (Gg) TPM emissions per month, with the highest crop residue emissions during November of each year. Our results on TPM emissions for seasonality matched the ground-based data from the literature. As a mitigation option, stringent policy measures are recommended to curtail agricultural residue burning in the study area.

Comparison between TVDI and CWSI for drought monitoring in the Guanzhong Plain, China

Temperature vegetation dryness index (TVDI) and crop water stress index (CWSI) are two commonly used remote sensing-based agricultural drought indicators. This study explored the applicability of monthly moderate resolution imaging spectroradiometer (MODIS) normalized difference vegetation index (NDVI) and land surface temperature (LST) data for agricultural drought monitoring in the Guanzhong Plain, China in 2003. The data were processed using TVDI, calculated by parameterizing the relationship between the MODIS NDVI and LST data. We compared the effectiveness of TVDI against CWSI, derived from the MOD16 products, for drought monitoring. In addition, the surface soil moisture and monthly precipitation were collected and used for verification of the results. Results from the study showed that: (1) drought conditions measured by TVDI and CWSI had a number of similarities, which indicated that both CWSI and TVDI can be used for drought monitoring, although they had some discrepancies in the spatiotemporal characteristics of drought intensity of this region; and (2) both standardized precipitation index (SPI) and SM contents at the depth of 10 and 20 cm had better correlations to CWSI than to TVDI, indicating that there were more statistically significant relationships between CWSI and SPI/SM, and that CWSI is a more reliable indicator for assessing and monitoring droughts in this region.

Detecting Long-Term Trends in Precipitable Water over the Tibetan Plateau by Synthesis of Station and MODIS Observations*

Abstract Long-term trends in precipitable water (PW) are an important component of climate change assessments for the Tibetan Plateau (TP). PW products from Moderate Resolution Imaging Spectroradiometer (MODIS) are able to provide good spatial coverage of PW over the TP but limited in time coverage, while the meteorological stations in the TP can estimate long-term PW but unevenly distributed. To detect the decadal trend in PW over the TP, Bayesian inference theory is used to construct long-term and spatially continuous PW data for the TP based on the station and MODIS observations. The prior information on the monthly-mean PW from MODIS and the 63 stations over the TP for 2000–06 is used to get the posterior probability knowledge that is utilized to build a Bayesian estimation model. This model is then operated to estimate continuous monthly-mean PW for 1970–2011 and its performance is evaluated using the monthly MODIS PW anomalies (2007–11) and annual GPS PW anomalies (1995–2011), with RMSEs below 0.65 mm, to demonstrate that the model estimation can reproduce the PW variability over the TP in both space and time. Annual PW series show a significant increasing trend of 0.19 mm decade−1 for the TP during the 42 years. The most significant PW increase of 0.47 mm decade−1 occurs for 1986–99 and an insignificant decrease occurs for 2000–11. From the comparison of the PW data from JRA-55, ERA-40, ERA-Interim, MERRA, NCEP-2, and ISCCP, it is found that none of them are able to show the actual long-term trends and variability in PW for the TP as the Bayesian estimation.

Temperature corrections in the Priestley–Taylor equation of evaporation

Summary The Priestley–Taylor equation (PTE) is frequently applied in actual areal evapotranspiration (ET) estimation methods for obtaining the maximum daily rate of evaporation with data from sub-humid conditions. Since PTE was parameterized under humid conditions, a temperature correction is necessary to avoid overestimation of the maximum rate of ET. Wet-environment surface temperature (Tws), a proxy of the wet-environment air temperature (Twa), is estimated by the Szilagyi–Jozsa (SJ) approach as well as by a re-parameterized version of Monteith. The latter yields higher values but typically within 1 °C of the former. Tested by daily FLUXNET data, the estimates are only mildly sensitive to the mean daily wind velocity which thus can be replaced by a region-representative monthly average. From long-term simplified water-balances – plus monthly Moderate Resolution Imaging Spectroradiometer (MODIS) and ERA-Interim re-analysis data – the re-parameterized Monteith method appears to yield more accurate Tws estimates, while the PTE performs better with the SJ provided Tws values since they are closer to Twa, the PTE expects. Both methods require net radiation, air temperature, humidity and monthly mean wind velocity values plus ground heat fluxes when employed on a daily basis.

Evaluation of MODIS NDVI and LST for indicating soil moisture of forest areas based on SWAT modeling

This study examined the capability of remotely sensed information gained using the terra moderate resolution imaging spectroradiometer (MODIS) normalized difference vegetation index (NDVI) and land surface temperature (LST) to explain forest soil moisture. The soil and water assessment tool (SWAT) was used for the analysis. Nine years (2000–2008) of monthly MODIS NDVI and LST data from a 2,694.4 km2 watershed consisting of forest-dominant areas in South Korea were compared with SWAT simulated soil moisture. Before the analysis, the SWAT model was calibrated and verified using 9 years of daily streamflow at three gauging stations and 6 years (2003–2008) of daily measured soil moisture at three locations within the watershed. The average Nash–Sutcliffe model efficiency during the streamflow calibration and validation was 0.72 and 0.70, respectively. The SWAT soil moisture showed a higher correlation with MODIS LST during the forest leaf growing period (March–June) and with MODIS NDVI during the leaf falling period (September–December). Low correlation was observed in the year of frequent rains, regardless of the leaf periods.

Alaska ecosystem carbon fluxes estimated from MODIS satellite data inputs from 2000 to 2010

BackgroundTrends in Alaska ecosystem carbon fluxes were predicted from inputs of monthly MODerate resolution Imaging Spectroradiometer (MODIS) vegetation index time-series combined with the NASA-CASA (Carnegie Ames Stanford Approach) carbon cycle simulation model over the past decade. CASA simulates monthly net ecosystem production (NEP) as the difference in carbon fluxes between net primary production (NPP) and soil microbial respiration (Rh).ResultsModel results showed that NEP on a unit area basis was estimated to be highest (> +10 g C m-2 yr-1) on average over the period 2000 to 2010 within the Major Land Resource Areas (MRLAs) of the Interior Brooks Range Mountains, the Arctic Foothills, and the Western Brooks Range Mountains. The lowest (as negative land C source fluxes) mean NEP fluxes were predicted for the MLRAs of the Cook Inlet Lowlands, the Ahklun Mountains, and Bristol Bay-Northern Alaska Peninsula Lowlands. High levels of interannual variation in NEP were predicted for most MLRAs of Alaska.ConclusionsThe relatively warm and wet years of 2004 and 2007 resulted in the highest positive NEP flux totals across MLRAs in the northern and western coastal locations in the state (i.e., the Brooks Range Mountains and Arctic Foothills). The relatively cold and dry years of 2001 and 2006 were predicted with the lowest (negative) NEP flux totals for these MLRAs, and likewise across the Ahklun Mountains and the Yukon-Kuskokwim Highlands.

MODIS-driven estimation of terrestrial latent heat flux in China based on a modified Priestley–Taylor algorithm

Because of China's large size, satellite observations are necessary for estimation of the land surface latent heat flux (LE). We describe here a satellite-driven Priestley–Taylor (PT)-based algorithm constrained by the Normalized Difference Vegetation Index (NDVI) and Apparent Thermal Inertia (ATI) derived from temperature change over time. We compare to the satellite-driven PT-based approach, PT-JPL, and validate both models using data collected from 16 eddy covariance flux towers in China. Like PT-JPL, our proposed algorithm avoids the computational complexities of aerodynamic resistance parameters. We run the algorithms with monthly Moderate Resolution Imaging Spectroradiometer (MODIS) products (0.05° resolution), including albedo, Land Surface Temperature (LST), surface emissivity, and NDVI; and, Insolation from the Japan Aerospace Exploration Agency (JAXA). We find good agreement between our estimates of monthly LE and field-measured LE, with respective Root Mean Square Error (RMSE) and bias differences of 12.5Wm−2 and −6.4Wm−2. As compared with PT-JPL, our proposed algorithm has higher correlations with ground-measurements. Between 2001 and 2010, LE shows generally negative trends in most regions of China, though positive LE trends occur over 39% of the region, primarily in Northeast, North and South China. Our results indicate that the variations of terrestrial LE are responding to large-scale droughts and afforestation caused by human activity with direct links to terrestrial energy exchange, both spatially and temporally.

Changes in Alaskan Tundra Ecosystems Estimated from MODIS Greenness Trends, 2000 to 2010

Trends in the monthly moderate resolution Imaging Spectroradiometer (MODIS) Enhanced Vegetation Index (EVI) time-series were analyzed for tundra ecosystems of Alaska over the past decade. Results showed that 10% of all tundra-dominated areas in Alaska were detected with significant (p<0.05) positive or negative MODIS growing season EVI trends from 2000 to 2010. Nearly three-quarters of these pixel areas were detected with significant positive growing season EVI trends. This 3:1 ratio of positive to negative EVI trends was consistent across both wetland and nonwetland tundra cover categories. Ecoregions of Alaska that revealed the highest density of positive areas for tundra growing season EVI slope were along the Pacific coast, namely the western Arctic Foothills, the Seward Peninsula, and the southern coastal plain. Associations between annual temperature and moisture patterns and tundra EVI trends across these regions revealed that change patterns in both the climate moisture index (CMI) and growing degree days (GDD) were related to increasing tundra EVI growing season trends. Results showed a notable association between the largest positive trends in MODIS greenness and annual warming trends of greater than 2 GDD per year.

Vegetation regrowth trends in post forest fire ecosystems across North America from 2000 to 2010

The goal of this study was to determine whether climate has affected vegetation regrowth over the past decade (2000 to 2010) in post-fire forest ecosystems of the United States and Canada. Our methodology detected trends in the monthly MODerate resolution Imaging Spectroradiometer (MODIS) Enhanced Vegetation Index (EVI) timeseries within forest areas that burned between 1984 and 1999. The trends in summed growing season EVI (composited to 8 km spatial resolution) within all burned area perimeters showed that nearly 1.6% post-fire forest area declined in vegetation greenness cover significantly (p < 0.05) over the past decade. Nearly 62% of all post-fire forest area showed a non significant EVI regrowth trend from 2000 to 2010. Regression results detected numerous significantly negative trend pixels in post-fire areas from 1994-1999 to indicate that forest regrowth has not yet occurred to any measurable level in many recent wildfire areas across the continent. We found several noteworthy relationships between annual temperature and precipitation patterns and negative post-fire forest EVI trends across North America. Change patterns in the climate moisture index (CMI), growing degree days (GDD), and the standardized precipitation index (SPI) were associated with post-fire forest EVI trends. We conclude that temperature warming-induced change and variability of precipitation at local and regional scales may have altered the trends of large post-fire forest regrowth and could be impacting the resilience of post-fire forest ecosystems in North America.

Declining Vegetation Growth Rates in the Eastern United States from 2000 to 2010

Negative trends in the monthly MODerate resolution Imaging Spectroradiometer (MODIS) Enhanced Vegetation Index (EVI) time-series were found to be widespread in natural (non-cropland) ecosystems of the eastern United States from 2000 to 2010. Four sub-regions were detected with significant declines in summed growing season (May-September) EVI, namely the Upper Great Lakes, the Southern Appalachian, the Mid-Atlantic, and the southeastern Coastal Plain forests ecosystems. More than 20% of the undeveloped ecosystem areas in the four sub-regions with significant negative EVI growing season trends were classified as forested land cover over the entire study period. We detected relationships between annual temperature and precipitation patterns and negative forest EVI trends across these regions. Change patterns in both the climate moisture index (CMI) and growing degree days (GDD) were associated with declining forest EVI growing season trends. We conclude that temperature warming-induced change and variability of precipitation at local and regional scales may have altered the growth trends of large forested areas of the eastern United States over the past decade.

Comparing MODIS and AERONET aerosol optical depth over China

The newest daily and monthly Moderate Resolution Imaging Spectroradiometer (MODIS) aerosol optical depths (AOD or τ) dataset over land, C005, retrieved using the second-generation operational algorithm, were evaluated using a ground-based Aerosol Robotic Network (AERONET) dataset from 13 sites over China. The dataset covers the period 2003–2006. Daily MODIS C005 AODs over China were found to have a positive bias with a relationship of τMODIS = 0.135 + 1.022τAERONET, for which the offset is larger than reported global validation results. However, the relationship τMODIS = 0.021 + 0.929τAERONET showed that monthly MODIS C005 AODs were an overestimation for small AOD and underestimation for high AOD. Both daily and monthly MODIS AOD retrievals showed poor performance in extreme aerosol conditions, e.g. under dust events or heavy urban/industrial haze. Nevertheless, both daily and monthly MODIS C005 AOD datasets can be used for investigation of aerosol spatial distribution and temporal variation over China.

Biophysical Evaluation of Land-Cover Products for Land–Climate Modeling

Abstract The need for accurate characterization of the land surface as boundary conditions in climate models has been recognized widely in the climate modeling community. A large number of land-cover datasets are currently used in climate models either to better represent surface conditions or to study the impacts of surface changes. Deciding upon land-cover datasets can be challenging because the datasets are made with different sensors, ranging methodologies, and varying classification objectives. A new statistical measure Q was developed to evaluate land-cover datasets in land–climate interaction research. This measure calculates biophysical precision of land-cover datasets using 1-km monthly Moderate Resolution Imaging Spectroradiometer (MODIS) leaf area index (LAI) product. This method aggregates within-class biophysical consistency, calculated as LAI variation, across a study domain and over multiple years into a single statistic. A smaller mean Q value for a land-cover product indicates more precise biophysical characterization within the classes. As an illustration, four land-cover products were assessed in the East Africa region: Global Land Cover 2000 (GLC2000), MODIS land cover, Olson Global Ecosystems (OGE), and Land Ecosystem–Atmosphere Feedback (LEAF) model. The evaluation was conducted at three different spatial scales corresponding to 30 × 30, 50 × 50, and 100 × 100 km quadrates. The Q measure found that GLC2000 ranked higher compared to the other three land-cover products for every quadrate size. For the 30 × 30 km quadrate size GLC2000 was significantly better than LEAF, which is currently used in the Regional Atmospheric Modeling System. The statistic ranks MODIS land cover above OGE, which is above LEAF. As quadrate size increases, differences between Q decrease indicating greater uncertainty at coarser resolution. The utility of the measure is that it can be applied to any continuous parameter over any scale (space or time) to evaluate the biophysical precision of any land-cover dataset.