Moderate Resolution Imaging Spectroradiometer Vegetation Research Articles

Analyzing Effects of Crops on SMAP Satellite-Based Soil Moisture Using a Rainfall–Runoff Model in the U.S. Corn Belt

<italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">L</i> -band microwave satellite missions provide soil moisture information potentially useful for streamflow and, hence, flood predictions. However, these observations are also sensitive to the presence of vegetation that makes satellite soil moisture estimations prone to errors. In this study, the authors evaluate satellite soil moisture estimations from Soil Moisture Active Passive (SMAP) and Soil Moisture Ocean Salinity and two distributed hydrologic models with measurements from <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">in situ</i> sensors in the Corn Belt state of Iowa, a region dominated by annual row crops of corn and soybean. First, the authors compare model and satellite soil moisture products across Iowa using <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">in situ</i> data for more than 30 stations. Then, they compare satellite soil moisture products with state-wide model-based fields to identify regions of low and high agreement. Finally, the authors analyze and explain the resulting spatial patterns with Moderate Resolution Imaging Spectroradiometer vegetation indices and SMAP vegetation optical depth. The results indicate that satellite soil moisture estimations are drier than those provided by the hydrologic model, and the spatial bias depends on the intensity of row-crop agriculture. The work highlights the importance of developing a revised SMAP algorithm for regions of intensive row-crop agriculture to increase SMAP utility in the real-time streamflowpredictions.

Impacts of vegetation properties and temperature characteristics on species richness patterns in drylands: Case study from Xinjiang

Energy availability at trophic and hydrologic level dominates species richness gradients by constraining food resources, and regulating population sizes and extinction rates. Remote sensing datasets have mapped vegetation productivities as a proxy for energy availability, for example, using Dynamic Habitat Indices (DHIs). Considering the sparse vegetation across drylands, we developed indices of Land surface temperature (ILST) based on daytime land surface temperature from Terra and Aqua Moderate Resolution Imaging Spectroradiometer (MODIS), which has three components: (1) annual mean temperature (LSTmean), (2) annual maximum temperature (LSTmax), and (3) standard deviation of temperature (LSTstd). We hypothesized that the temperature variables, such as ILST, would predict species richness better than productivity proxies across drylands. Thus, our objective was to determine how well they would predict the richness of plants, mammals and birds across the Xinjiang Uygur Autonomous Region. We calculated the DHIs and ILST from the MODIS vegetation and temperature products from 2001 to 2015. We found that: (1) ILST could capture more additive information compared with DHIs in terms of the relatively high variance explanation of species richness and high variable importance, and the combination of ILST and DHIs gave better predictions than single metrics for species richness patterns. (2) Plants and birds were more sensitive to temperature than vegetation productivity, probably due to physiological tolerance and evolutionary processes. (3) LSTstd was the most important variable affecting species richness, except on mammals. High LSTstd was related to more food resources and habitats, and low LSTstd represented extreme environment and environmental stress. Combined vegetation properties and temperature variabilities are good determinants of species richness, and should be carefully considered in future research.

Evolution of the representation of global vegetation by vegetation continuous fields

Two decades of global annual fractional vegetation cover products have been derived using daily surface reflectance and land surface temperature data from the Moderate Resolution Imaging Spectroradiometer (MODIS) instrument aboard NASA's Terra satellite. These MODIS Vegetation Continuous Fields (VCF) products are unique and a distinct advance over discrete (ordinal) land cover characterizations. VCF consists of three continuous fractional cover layers at 250 m resolution summing to 100%: tree cover, non-tree vegetation and non-vegetated surface plus surface water. This paper describes the fully-automated machine learning algorithm used to create MODIS VCF and details recent algorithm improvements. Comparisons of the latest VCF products in the MOD44B collection 6, with field-based fractional tree cover and other remote sensing fractional cover products demonstrate improved agreement: the root mean square (RMS) deviations range from 9 to 23%. A comparison of MODIS canopy cover values with those from the Global Ecosystem Dynamics Investigation (GEDI) gives an R2 value of 0.53. Automation of the algorithm provides flexibility for readily porting the method to derive fractional cover products from additional instruments with similar radiometric characteristics such as the Visible Infrared Imaging Radiometer (VIIRS) payload aboard Suomi NPP and JPSS platforms, and successors.

Assessment of oil palm yield and biophysical suitability in Indonesia and Malaysia

ABSTRACT The most crucial technical challenge facing the Malaysian and Indonesian oil palm industry is that the actual yield in the form of Fresh Fruit Bunch (FFB; unit in tonne per hectare (t ha−1)) are well below of potential levels and have stagnated over last two decades. Closing this wide yield gap would have a positive impact on the revenue as it increases productivity per hectare and it eventually leads to less pressure on opening new land and mitigates environmental costs of production. With respect to the indispensable need for closing this gap for future prosperity of this industry and sustainable production of palm oil, this study assessed oil palm yield, considering the potential growth of oil palm dependent on the site qualities and actual yield. Firstly, we mapped oil palm plantations combining yearly Advanced Land Observation Satellite (ALOS) Phased Array type L-band Synthetic Aperture Radar (PALSAR) and ALOS-2 mosaics of L-band backscatter, Moderate Resolution Imaging Spectroradiometer (MODIS) reflectance (MOD13Q1), and the MODIS Vegetation Continuous Field canopy cover product (MOD44B); where 10.3 and 6.68 million ha (Mha) of oil palm plantations were mapped, respectively, in Indonesia and Malaysia in 2017. Secondly, the age after planting was estimated at detected plantations using time series of MODIS canopy cover with correlation coefficient (r) of 0.68 and Root Mean Square Error (RMSE) of 4.7 years. Thirdly, the biophysical suitability of detected plantations was evaluated considering the spatial-temporal variation of different biophysical criteria. Combining information from second and third steps, we estimated the potential yield at 250 m spatial resolution. The average potential yield in Malaysia ranges between 13.8 t ha−1 and 19.3 t ha−1 in 2017, where in Indonesia it ranges between 17.8 t ha−1 and 21.7 t ha−1 in the same year. The actual yield in next step, has been quantified by HH-HV attribute of ALOS PALSAR and ALOS-2 mosaics, where the average actual yield in Malaysia ranges between 14.48 t ha−1 and 20.63 t ha−1 and in Indonesia it ranges between 8.49 t ha−1 and15.40 t ha−1 in 2017. Finally, comparing estimated potential and actual yields, we evaluated oil palm industries’ performances where distinct differences were found between two countries. In most of the Malaysian states quantified actual yields were above or at the level of estimated potential yields, whereas in all Indonesian provinces quantified actual yields were well below the potential level. Considering the favourability of environment, among all provinces/states, Sabah, and Sarawak states in Malaysia and Aceh and North Kalimantan provinces in Indonesia distinctly differ due to their poor performances from rest of provinces/states. The information on different yields provided in this study are indispensable needs for efficient and accountable policies as it enables governors to directly target specific objectives such as subsidies on fertilizers, productive cultivars, and new technologies for the plantations suffering from low yield. Also, this study provides benchmarks for each province/state for scopes of actual yield improvements for long-term planning.

No Proportional Increase of Terrestrial Gross Carbon Sequestration From the Greening Earth

AbstractTerrestrial vegetation, as the key component of the biosphere, has a greening trend since the beginning of this century. However, how this substantial greening translated to global gross carbon sequestration or gross primary production (GPP) is not clear. Here we investigated terrestrial GPP dynamics and the respective contributions of climate change and vegetation cover change (VCC) from 2000 to 2015. We adopted a remote sensing based data‐driven model, which was calibrated based on the global eddy flux data set (FLUXNET2015) and Moderate Resolution Imaging Spectroradiometer vegetation index data (Collection 6). A series of simulation experiments were conducted to disaggregate the effects of climate and VCC. We found a much weaker increase in global GPP (0.08%/year; P = 0.07) when compared with the global greening rate (0.23%/year; P &lt; 0.001). The positive effect of VCC on GPP was reduced by 53% due to climate stress. Enhanced global GPP were largely contributed by nonforests, especially croplands. However, tropical forests, once a major driver of the global GPP increase, negatively contributed to global GPP trend due to warming‐induced moisture stress and deforestation. Given the limited potential of cropland carbon storage due to harvest and consumption, the contrasting GPP changes (i.e., cropland GPP increase vs. forest GPP reduction) may have shifted the distribution of the land carbon sink. Our study highlights the potential vulnerability of terrestrial gross carbon sequestration under climate and land use changes and has important implications in the global carbon cycle and climate warming mitigation.

Assessing the relationship between shire winter crop yield and seasonal variability of the MODIS NDVI and EVI images

Australian researchers have been developing robust yield estimation models, based mainly on the crop growth response to water availability during the crop season. However, knowledge of spatial distribution of yields within and across the production regions can be improved by the use of remote sensing techniques. Images of Moderate Resolution Imaging Spectroradiometer (MODIS) vegetation indices, available since 1999, have the potential to contribute to crop yield estimation. The objective of this study was to analyse the relationship between winter crop yields and the spectral information available in MODIS vegetation index images at the shire level. The study was carried out in the Jondaryan and Pittsworth shires, Queensland , Australia . Five years (2000 to 2004) of 250m resolution, 16-day composite of MODIS Normalized Difference Vegetation Index (NDVI) and Enhanced Vegetation Index (EVI) images were used during the winter crop season (April to November). Seasonal variability of the profiles of the vegetation index images for each crop season using different regions of interest (cropping mask) were displayed and analysed. Correlation analysis between wheat and barley yield data and MODIS image values were also conducted. The results showed high seasonal variability in the NDVI and EVI profiles, and the EVI values were consistently lower than those of the NDVI. The highest image values were observed in 2003 (in contrast to 2004), and were associated with rainfall amount and distribution. The seasonal variability of the profiles was similar in both shires, with minimum values in June and maximum values at the end of August. NDVI and EVI images showed sensitivity to seasonal variability of the vegetation and exhibited good association (e.g. r = 0.84, r = 0.77) with winter crop yields.

Improved meteorology from an updated WRF/CMAQ modeling system with MODIS vegetation and albedo

AbstractRealistic vegetation characteristics and phenology from the Moderate Resolution Imaging Spectroradiometer (MODIS) products improve the simulation for the meteorology and air quality modeling system WRF/CMAQ (Weather Research and Forecasting model and Community Multiscale Air Quality model) that employs the Pleim‐Xiu land surface model (PX LSM). Recently, PX LSM WRF/CMAQ has been updated in vegetation, soil, and boundary layer processes resulting in improved 2 m temperature (T) and mixing ratio (Q), 10 m wind speed, and surface ozone simulations across the domain compared to the previous version for a period around August 2006. Yearlong meteorology simulations with the updated system demonstrate that MODIS input helps reduce bias of the 2 m Q estimation during the growing season from April to September. Improvements follow the green‐up in the southeast from April and move toward the west and north through August. From October to March, MODIS input does not have much influence on the system because vegetation is not as active. The greatest effects of MODIS input include more accurate phenology, better representation of leaf area index (LAI) for various forest ecosystems and agricultural areas, and realistically sparse vegetation coverage in the western drylands. Despite the improved meteorology, MODIS input causes higher bias for the surface O3 simulation in April, August, and October in areas where MODIS LAI is much less than the base LAI. Thus, improvements may be needed in the CMAQ dry deposition model for low LAI areas where deposition on the soil surface becomes important.

A Generalized Deforestation and Land-Use Change Scenario Generator for Use in Climate Modelling Studies.

A new deforestation and land-use change scenario generator model (FOREST-SAGE) is presented that is designed to interface directly with dynamic vegetation models used in latest generation earth system models. The model requires a regional-scale scenario for aggregate land-use change that may be time-dependent, provided by observational studies or by regional land-use change/economic models for future projections. These land-use categories of the observations/economic model are first translated into equivalent plant function types used by the particular vegetation model, and then FOREST-SAGE disaggregates the regional-scale scenario to the local grid-scale of the earth system model using a set of risk-rules based on factors such as proximity to transport networks, distance weighted population density, forest fragmentation and presence of protected areas and logging concessions. These rules presently focus on the conversion of forest to agriculture and pasture use, but could be generalized to other land use change conversions. After introducing the model, an evaluation of its performance is shown for the land-cover changes that have occurred in the Central African Basin from 2001–2010 using retrievals from MODerate Resolution Imaging Spectroradiometer Vegetation Continuous Field data. The model is able to broadly reproduce the spatial patterns of forest cover change observed by MODIS, and the use of the local-scale risk factors enables FOREST-SAGE to improve land use change patterns considerably relative to benchmark scenarios used in the latest Coupled Model Intercomparison Project integrations. The uncertainty to the various risk factors is investigated using an ensemble of investigations, and it is shown that the model is sensitive to the population density, forest fragmentation and reforestation factors specified.

Annual Carbon Emissions from Deforestation in the Amazon Basin between 2000 and 2010.

Reducing emissions from deforestation and forest degradation (REDD+) is considered one of the most cost-effective strategies for mitigating climate change. However, historical deforestation and emission rates―critical inputs for setting reference emission levels for REDD+―are poorly understood. Here we use multi-source, time-series satellite data to quantify carbon emissions from deforestation in the Amazon basin on a year-to-year basis between 2000 and 2010. We first derive annual deforestation indicators by using the Moderate Resolution Imaging Spectroradiometer Vegetation Continuous Fields (MODIS VCF) product. MODIS indicators are calibrated by using a large sample of Landsat data to generate accurate deforestation rates, which are subsequently combined with a spatially explicit biomass dataset to calculate committed annual carbon emissions. Across the study area, the average deforestation and associated carbon emissions were estimated to be 1.59 ± 0.25 M ha•yr−1 and 0.18 ± 0.07 Pg C•yr−1 respectively, with substantially different trends and inter-annual variability in different regions. Deforestation in the Brazilian Amazon increased between 2001 and 2004 and declined substantially afterwards, whereas deforestation in the Bolivian Amazon, the Colombian Amazon, and the Peruvian Amazon increased over the study period. The average carbon density of lost forests after 2005 was 130 Mg C•ha−1, ~11% lower than the average carbon density of remaining forests in year 2010 (144 Mg C•ha−1). Moreover, the average carbon density of cleared forests increased at a rate of 7 Mg C•ha−1•yr−1 from 2005 to 2010, suggesting that deforestation has been progressively encroaching into high-biomass lands in the Amazon basin. Spatially explicit, annual deforestation and emission estimates like the ones derived in this study are useful for setting baselines for REDD+ and other emission mitigation programs, and for evaluating the performance of such efforts.

Application of MODIS Land Products to Assessment of Land Degradation of Alpine Rangeland in Northern India with Limited Ground-Based Information

Land degradation of alpine rangeland in Dachigam National Park, Northern India, was evaluated in this study using MODerate resolution Imaging Spectroradiometer (MODIS) land products. The park has been used by a variety of livestock holders. With increasing numbers of livestock, the managers and users of the park are apprehensive about degradation of the grazing land. However, owing to weak infrastructure for scientific and statistical data collection and sociopolitical restrictions in the region, a lack of quality ground-based weather, vegetation, and livestock statistical data had prevented scientific assessment. Under these circumstances, the present study aimed to assess the rangeland environment and its degradation using MODIS vegetation, snow, and evapotranspiration products as primary input data for assessment. The result of the analysis indicated that soil water content and the timing of snowmelt play an important role in grass production in the area. Additionally, the possibility of land degradation in heavily-grazed rangeland was indicated via a multiple regression analysis at a decadal timescale, whereas weather conditions, such as rainfall and snow cover, primarily explained year-by-year differences in grass production. Although statistical uncertainties remain in the results derived in this study, the satellite-based data and the analyses will promote understanding of the rangeland environment and suggest the potential for unsustainable land management based on statistical probability. This study provides an important initial evaluation of alpine rangeland, for which ground-based information is limited.

Analysis of agricultural intensification in a basin with remote sensing data

The last few decades have been marked by important changes in the Brazilian agriculture, especially with respect to crop-management practices. This study aimed to analyze the dynamics of cropping systems in a typical agricultural river basin in the state of Mato Grosso. Landsat satellite images and time series of Moderate Resolution Imaging Spectroradiometer vegetation index profiles were analyzed from 2000 to 2010. First, we assessed the horizontal expansion that occurred in the agricultural areas. Subsequently, using the product MOD13Q1, some metrics were established to identify the vertical intensification of soil use (single- or double-cropping systems). Results showed stagnation in the expansion of new deforested areas for agriculture in the 2003/2004 growing season, with simultaneous vertical intensification of agriculture. The adoption of the double-cropping system (e.g., soybean/corn and soybean/cotton) expanded by 266% during the studied period and reached 56% of the croplands in the 2009/2010 growing season.

Drought and elevation effects on MODIS vegetation indices in northern Arizona ecosystems

Northern Arizona ecosystems are particularly sensitive to plant-available moisture and have experienced a severe drought with considerable impacts on ecosystems from desert shrub and grasslands to pinyon-juniper and conifer forests. Long-term time-series from the Moderate Resolution Imaging Spectroradiometer (MODIS) normalized difference vegetation index (NDVI) and enhanced vegetation index (EVI) are used to monitor recent regional vegetation activity and temporal patterns across various ecosystems. Surface air temperature, solar radiation and precipitation are used to represent meteorological anomalies and to investigate associated impacts on vegetation greenness. Vegetation index anomalies in the northern Arizona ecosystem have a decreasing trend with increasing surface air temperature and decreasing precipitation. MODIS NDVI and EVI anomalies are likely sensitive to the amount of rainfall for northern Arizona ecosystem conditions, whereas inter-annual variability of surface air temperature accounts for MODIS NDVI anomaly variation. The higher elevation area shows the slow vegetation recovery through trend analysis from MODIS vegetation indices for 2000–2011 within the study domain and along elevation.

Winter wheat mapping using temporal signatures of MODIS vegetation index data

Because most land-cover types have distinct seasonal changes and corresponding reflectance characteristics in remotely sensed images, the signatures in time-series data are useful for discriminating different land covers. Although temporal signatures have been used to classify different land-cover types, they have not been fully exploited to classify specific crops, and the influence of low resolution should be evaluated. The aims of this study were to seek an effective method to classify specific crops using the temporal signatures in coarse time-series data and to examine the applicability of the data for crop classification as well. A winter wheat-producing region in China was selected for this case study. Moderate-Resolution Imaging Spectroradiometer (MODIS) 8-day composite land surface reflectance product (MOD09Q1) data with a 250 m spatial resolution were used to calculate the vegetation index data, which was applied to detect the properties of live green plants. The noise in the time series was filtered to minimize the classification uncertainties. The curve shape in the time-series vegetation index profile was used as the major metric to classify winter wheat, and other phenological metrics extracted from the data were used conjunctly as auxiliary functions to improve the separability. The metrics for winter wheat classification were quantified in the large fields with relatively pure pixels. Winter wheat was successfully extracted from the MODIS vegetation index data, and the MODIS-derived result was validated with a fine-resolution (19.5 m) thematic map derived from images collected by the charge-coupled device sensor on board the China–Brazil Earth Resources Satellite (CBERS). It showed that the MODIS-derived result had inevitable low-resolution bias, and the errors of commission and omission were 32.3 and 33.8%, respectively. The overall classification effect of the MODIS-derived result relied upon the distribution of pixel purity in the study area.

Seasonal variations in litter production and its relation with MODIS vegetation indices in a semi-deciduous forest of Mato Grosso

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

Assessing viewing and illumination geometry effects on the MODIS vegetation index (MOD13Q1) time series: implications for monitoring phenology and disturbances in forest communities in Queensland, Australia

Time series analysis of satellite data can be used to monitor temporal dynamics of forested environments, thus providing spatial data for a range of forest science, monitoring and management issues. The moderate resolution imaging spectroradiometer (MODIS) vegetation index (MOD13Q1) product has potential for monitoring forest dynamics and disturbances. However, the suitability of the product to accurately measure temporal changes due to phenology and disturbances is questionable as the effects of variable solar and viewing geometry have not been removed from these data. This study aimed to examine the impact that viewing and illumination geometry differences had on MOD13Q1 vegetation index values, and their subsequent ability to map changes arising from phenology and disturbances in a number of forest communities in Queensland, Australia. MOD13Q1 normalized difference vegetation index (NDVI) and enhanced vegetation index (EVI) were compared to normalized NDVI and EVI (NDVInormalized and EVInormalized), which were derived from the reflectance modelled from a bidirectional reflectance distribution function (BRDF)/albedo parameters product (MCD43A1) using fixed viewing and illumination geometry. Time series plots of the vegetation index values from a number of pixels representing different forest types and known disturbances showed that the NDVInormalized time series was more effective at capturing the changes in vegetation than the NDVI. MOD13Q1 NDVI showed higher seasonal amplitude, but was less accurate at capturing phenology and disturbances compared to the NDVInormalized. The EVI was less affected by variable viewing and illumination geometry in terms of amplitude, but was affected in terms of time shift in periodicities providing erroneous information on phenology. More studies in different environments with appropriate vegetation phenology reference data will be needed to confirm these observations.

Land Surface Water Index (LSWI) response to rainfall and NDVI using the MODIS Vegetation Index product

For more than 20 years the Normalized Difference Vegetation Index (NDVI) has been widely used to monitor vegetation stress. It takes advantage of the differential reflection of green vegetation in the visible and near-infrared (NIR) portions of the spectrum and provides information on the vegetation condition. The Land Surface Water Index (LSWI) uses the shortwave infrared (SWIR) and the NIR regions of the electromagnetic spectrum. There is strong light absorption by liquid water in the SWIR, and the LSWI is known to be sensitive to the total amount of liquid water in vegetation and its soil background. In this study we investigated the LSWI characteristics relative to conventional NDVI-based drought assessment, particularly in the early crop season. The area chosen for the study was the state of Andhra Pradesh located in the Indian peninsular. The Moderate Resolution Imaging Spectroradiometer (MODIS) Vegetation Index (VI) product from the Aqua satellite was used in the study. The analysis was carried out for the years 2002 (deficit year) and 2005 (normal year) using the NDVI from the MODIS VI product and deriving the LSWI using the NIR and SWIR reflectance available with the MODIS VI product. The response of LSWI to rainfall, observed in the rate of increase in LSWI in the subsequent fortnights, shows that this index could be used to monitor the increase in soil and vegetation liquid water content, especially during the early part of the season. The relationship between the cumulative rainfall and the current fortnight LSWI is stronger in the low rainfall region (<500 mm), while the one-fortnight lagged LSWI had a stronger relationship in the high rainfall region (>500 mm). The relationship between LSWI and the cumulative rainfall for the entire state was mixed in 2002 and 2005. The strength of the relationship was weak in the high rainfall region. When LSWI was regressed directly with NDVI for three LSWI ranges, it was observed that the NDVI with the one-fortnight lag had a strong relationship with the LSWI in most of the categories.

MODIS vegetation metrics as indicators of hydrological response in watersheds of California Mediterranean-type climate zones

Vegetation characteristics of a watershed can be important in estimating hydrological response variables (HRVs) such as streamflow (Q), evapotranspiration (ET), and river yield (Q/P). Quantifying the relationship between satellite-derived vegetation metrics and hydrological response to precipitation (P) has the potential to facilitate prediction of HRVs for ungauged watersheds, and/or aid in the assessment of watershed similarity as an initial phase of hydrological regionalization. The utility of Moderate Resolution Imaging Spectroradiometer (MODIS) satellite data to estimate HRVs of watersheds at the regional scale (southern and central California) is tested in this study. An exhaustive statistical regression analysis was conducted to quantify the relationships between MODIS vegetation metrics and HRVs using both ordinary least squares and spatially varying parameter models. Additionally a confirmatory analysis was conducted to test the effect precipitation and potential evaporation have on the exploratory regression results. Results from both the exploratory and confirmatory analyses suggest that (1) while there are limitations in the water balance approach to estimating ET (errors associated with changes in storage and meteorological data are unknown), moderate statistical relationships exist between MODIS vegetation metrics and HRVs; (2) these relationships are heavily influenced by vegetation–precipitation relationships and general precipitation magnitudes; (3) relationships between MODIS metrics and precipitation/HRVs are strongest when drought conditions prevail; and (4) LAI has the strongest relationship with precipitation and HRVs compared to other MODIS vegetation metrics.

Assessing the response of the MODIS vegetation indices to landscape disturbance in the forested areas of the legal Brazilian Amazon

In this study we assessed the impacts of forest fragmentation on the Amazon landscape using remote sensing techniques. Landscape disturbance, obtained for an area of approximately 3.5 × 106 km2 through simple spatial metrics (i.e. number of fragments, mean fragment area and border size) and principal component transformation were then compared to the MODIS (Moderate Resolution Imaging Spectroradiometer) NDVI (Normalized Difference Vegetation Index) and EVI (Enhanced Vegetation Index) seasonal responses. As expected, higher disturbance values prevail in the southern border of the Amazon, near the intensively converted deforestation arc, and close to the major roads. NDVI seasonal responses more closely follow human-induced patterns, i.e. forest remnants from areas more intensively converted were associated with higher NDVI seasonal values. The significant correlation between NDVI seasonal responses and landscape disturbances were corroborated through analysis of geographically weighted regression (GWR) parameters and predictions. On the other hand, EVI seasonal responses were more complex with significant variations found over intact, less fragmented forest patches, thus restricting its utility to assess landscape disturbance. Although further research is needed, our results suggest that the degree of fragmentation of the forest remnants can be remotely sensed with MODIS vegetation indices. Thus, it may become possible to upscale field-based data on overall canopy condition and fragmentation status for basin-wide extrapolations.

Analysis of seven-year moderate resolution imaging spectroradiometer vegetation water indices for drought and fire activity assessment over Georgia of the United States

Using a seven-year history of the satellite measurements and meteorology data over Georgia, USA, this study investigated the capability of a series of satellite-derived vegetation water indices for drought monitoring, as well as the connection between fire occurrence and drought conditions. The vegetation water indices are normalized calculations between MODIS near infrared band 2 and shortwave infrared bands 5, 6, 7, and the difference between bands 6 and 7, denoted by NDWI2,5, NDWI2,6, NDWI2,7 and NMDI, respectively. Results show that the drought conditions indicated by NMDI, NDWI2,6 and NDWI2,7 agree well with what have been identified by meteorology data. NMDI, however, has demonstrated more dependable results regarding seasonal moisture variations, as well as stronger responses to drought conditions than NDWI2,6, and NDWI2,7. The annual fire number exhibits high correspondence to drought conditions. Drought intensity also influenced fire extent and the most widespread fires occurred during the driest years. Analysis reveals that NMDI can be used as an effective indicator for fire risk monitoring. It is expected that this research will greatly benefit many applications, such as monitoring drought and detecting fire danger potential.

MODIS tasselled cap: land cover characteristics expressed through transformed MODIS data

The tasselled cap concept is extended to Moderate Resolution Imaging Spectroradiometer (MODIS) Nadir BRDF‐Adjusted Reflectance (NBAR, MOD43) data. The transformation is based on a rigid rotation of principal component axes (PCAs) derived from a global sample spanning one full year of NBAR 16‐day composites. To provide a standard for MODIS tasselled cap axes, we recommend an orientation in MODIS spectral band space as similar as possible to the orientation of the Landsat Thematic Mapper (TM) tasselled cap axes. To achieve this we first transformed our global sample of MODIS NBAR reflectance values to TM tasselled cap values using the existing TM transformation, then used an existing algorithm (Procrustes) to compute the transformation that minimizes the mean square difference between the TM transformed NBAR values and NBAR PCA values. This transformation can then be used as a standard to rotate the MODIS NBAR PCA axes into a new MODIS Kauth–Thomas (KT) orientation. Global land cover patterns in tasselled cap space are demonstrated graphically by linking the global sample with several other products, including the MODIS Land Cover product (MOD12) and the MODIS Vegetation Continuous Fields product (MOD44). Patterns seen at this global scale agree with previous explorations of TM tasselled cap space, but are shown here in greater detail with a globally representative sample. Temporal trends of eight smaller‐scale BigFoot Project (www.fsl.orst.edu/larse/bigfoot) sites were also examined, confirming the spectral shifts in tasselled cap space related to phenology.