Gross primary productivity (GPP) through photosynthesis is a crucial ecosystem function that significantly influences food security, carbon cycle, and climate change. Current remote sensing estimates of GPP rely on look-up tables containing biome-specific parameters to model light-use efficiency (ε) using coarse resolution interpolated meteorology data, resulting in significant uncertainties in global GPP estimates. To address this challenge, we propose a simple yet effective ecosystem light-use efficiency (eLUE) model to GPP from FLUXNET tower sites to a global scale. Defined as GPP/PAR, eLUE differs from the traditional LUE (GPP/APAR, or ε) in that eLUE essentially integrates canopy light absorption (fAPAR) and the physiological efficiency of photosynthesis (ε), thus eliminating the need for a separate estimate of ε. eLUE was calibrated as a function of MODIS Enhanced Vegetation Index (EVI), and then GPP can be modelled directly as eLUE × PAR. To quantify the carbon cycle error budget, we analytically derived GPP uncertainty based on the law of error propagation. Cross-validation against 120 global FLUXNET sites, encompassing 11 plant functional types (PFTs), demonstrated satisfactory performance of the eLUE model (R2= 0.74, RMSE = 2.05 g C m-2d-1, NSE = 0.74), outperforming or performing comparably to more sophisticated models. Our estimate of global total terrestrial GPP, averaged between 2001 and 2024, is 135.12±11.02 Pg C yr-1. Meanwhile, we found a significant increasing trend in global total GPP at a rate of 0.26±0.06 Pg C yr-1(p2sequestration in terrestrial ecosystems across the Northern Hemisphere. We suggest that our eLUE model, with its robust performance and clear error representation, will help constrain the global carbon budget and improve the diagnostic analysis of carbon cycle dynamics and climate change feedback. The eLUE-GPP product, available at both global scale and FLUXNET sites, can be accessed for free at: https://doi.org/10.5061/dryad.v9s4mw74h.

Presents corrections to the paper, (“Global Upscaling of Gross Primary Productivity Using a Simple and Robust Modeling Scheme”).

A mangrove mapping (MM) algorithm is an essential classification tool for environmental monitoring. The recent literature shows that compared with other index-based MM methods that treat pixels as spatially independent, convolutional neural networks (CNNs) are crucial for leveraging spatial continuity information, leading to improved classification performance. In this work, we go a step further to show that quantum features provide radically new information for CNN to further upgrade the classification results. Simply speaking, CNN computes affine-mapping features, while quantum neural network (QNN) offers unitary-computing features, thereby offering a fresh perspective in the final decision-making (classification). To address the challenging MM problem, we design an entangled spatial-spectral quantum feature extraction module. Notably, to ensure that the quantum features contribute genuinely novel information (unaffected by traditional CNN features), we design a separate network track consisting solely of quantum neurons with built-in interpretability. The extracted pure quantum information is then fused with traditional feature information to jointly make the final decision. The proposed quantum-empowered deep network (QEDNet) is very lightweight, so the improvement does come from the cooperation between CNN and QNN (rather than parameter augmentation). Extensive experiments will be conducted to demonstrate the superiority of QEDNet.

Due to the intensifying impacts of extreme climate changes, drought forecasting (DF), which aims to predict droughts from historical meteorological data, has become increasingly critical for monitoring and managing water resources. Despite the spatial coherence of drought conditions, benchmark deep learning-based DF models predict each region independently while ignoring the neighboring spatial information. Using the Standardized Precipitation Evapotranspiration Index (SPEI), we designed and trained a novel and transformative spatially-aware DF neural network, which effectively captures local interactions among neighboring regions, resulting in enhanced spatial coherence and prediction accuracy. As DF also requires sophisticated temporal analysis, the Mamba network, recognized as the most accurate and efficient existing time-sequence modeling, was adopted to extract temporal features from short-term time frames. We also adopted quantum neural networks (QNN) to entangle the spatial features of different time instances, leading to refined spatiotemporal features of seven different meteorological variables for effectively identifying short-term climate fluctuations. In the last stage of our proposed SPEI-driven quantum spatially-aware Mamba network (SQUARE-Mamba), the extracted spatiotemporal features of seven different meteorological variables were fused to achieve more accurate DF. Validation experiments across El Niño, La Niña, and normal years demonstrated the superiority of the proposed SQUARE-Mamba, remarkably achieving an average improvement of more than 9.8% in the coefficient of determination index (R2) compared to baseline methods, thereby illustrating the promising roles of the temporal quantum entanglement and Mamba temporal analysis to achieve more accurate DF. Notably, the integration of QNN further upgrades the naive Mamba baseline by over 2.7% in R2on average, highlighting the model’s sensitivity to transient climate variations.

Vegetation phenology represents a key attribute of an ecosystem and plays an important role in regulating terrestrial carbon and water cycles. Here we used observations from the Advanced Himawari Imager (AHI) onboard the new generation Japanese geostationary (GEO) satellite Himawari-8. The objective was to assess the potentials of retrieving savanna phenology from H8/AHI vegetation index time series along a 1100 km ecological rainfall gradient, known as the North Australian Tropical Transect (NATT). Key phenology transition dates (start, peak, end, and length of season) were extracted from H8/AHI Enhanced Vegetation Index (EVI) time series and then compared to those extracted from MODIS EVI. Results showed that H8/AHI with its higher temporal resolution offers several advantages in monitoring savanna vegetation dynamics than MODIS. The denser EVI time series from H8/AHI not only avoids the artefacts caused by data interpolation but also enabled a more certain characterization of seasonal vegetation growth patterns than MODIS. The short lived, rainfall pulse-driven vegetation cycles in dry savannas were also better detected using H8/AHI.

Anthropogenic disturbances in tropical forests cause short-and long-term alterations in forest structure, species composition, and successional processes. However, improved understanding of the impacts of disturbance on forest functioning is needed to support forest management and conservation. In this study, we investigated the phenological responses of two sites in the Brazilian Amazon to forest degradation (selective logging and forest fires). We used MODIS-derived time-series to assess pre- and post-disturbance trajectories of two vegetation indices: Normalized Burn Ratio (NBR) and Enhanced Vegetation Index (EVI). We found that our study sites present different VI seasonality, and that selective logging did not cause phenological shifts. Fires, on the other hand, caused explicit EVI shifts in the transition from wet to dry season in the driest site and barely no shifts in the wettest site, and yearlong NBR shifts compared to intact forests at both sites. Changes in the magnitude and timing of phenological events highlight human-induced changes in tropical forests functioning. If widespread, these shifts may have large-scale implications for carbon sink stability in tropical regions.

The availability of global high-resolution land cover maps provides promising a priori knowledge for characterizing subpixel heterogeneity and improving predictions of directional reflectance of coarse-resolution pixels. Due to mutual shadowing and sheltering effects between the adjacent forest and cropland patches, the spectral nonlinear mixing of patchy ecotones is significant, especially when the sun illuminates the ecotone from the forest side with high solar zenith angle. The spectral linear mixture (SLM) approach leads to overestimation of the bidirectional reflectance factor (BRF) in the red band in the principal plane (PP), with a maximum absolute error (MAE) of 0.0063 and a maximum relative error (MRE) of 52.5%, and to underestimation in the near-infrared band in PP with an MAE of 0.0940 and an MRE of 14.5%. In a scenario with randomly distributed boundary orientations, the overestimation of SLM increases with the degree of fragmentation and the view zenith angle. We propose a Radiative Transfer model for patchy ECotones (RTEC). which improves R2from 0.61 to 0.94 in the red band of Landsat-8 directional reflectance at the validation site. The RTEC model provides an efficient and analytical approach for directional reflectance predictions over heterogeneous patchy landscapes at coarse resolution and will be used for biophysical parameter retrievals [e.g., the leaf area index (LAI)] in future applications.

Grass pollens are a major source of aeroallergens globally, inducing allergic asthma and hay fever in up to 500 million people worldwide. Pollen forecasting research and methods are site-dependent and tend to be empirically derived composites of expert knowledge and weather data. In this study we utilize satellite-based information of landscape conditions and phenology to better discern and predict grass pollen evolution. We employed machine learning approaches to formulate and better understand relationships between landscape phenology and seasonal flowering-induced pollen concentrations. We show that machine learning approaches significantly improved pollen prediction capabilities and provided key information to better attribute changes in pollen counts driven by shifting ecological landscapes from climate change drivers.

Rangelands comprise ∼81% of Australia's landmass, extend over a broad range of climates and vegetation types, and provide important social-economical functions (Fig. 1). The climate of Australia's rangelands is extremely variable. This variability was reflected in recent events of extreme flooding immediately following one of the most intense droughts in history over the early 21stcentury [1,2]. These extreme climatic events provide an opportunity to assess how Australian rangelands respond to hydroclimatic variations, and further generalise knowledge regarding resilience of these ecosystems to contrasting drought and wet extremes.

Discerning photosynthetic seasonality in tropical forests is fundamental to both basic ecology (plant strategies for resource acquisition when resources are limiting) and the need to understand vegetation responses and feedbacks to a changing climate. The seasonality question provides an important threshold test to advance predictions of tropical forest response to future climate changes. Despite its importance, spatial and temporal photosynthesis patterns in tropical forests are highly uncertain and remain controversial as ecosystem models yield divergent results while satellite-based observations are subject to various artifacts associated with cloud leakage, aerosols, and sensor-sun observation geometries. In this research, in situ seasonal tower carbon flux and leaf-scale phenology measures from cameras were combined with satellite data to investigate the roles of climate drivers and biologic processes (leaf phenology and demography traits) on vegetation canopy photosynthesis. Our results show the importance of phenology traits on controls on seasonal photosynthesis.

Wheat is the most important grain crop in Australia, which plays a significant role in world grain-trading market. However, climate warming, water shortage, as well as more frequent extreme weather events (e.g., heatwaves, droughts and floods), under pressure of food demand, would pose great risks to all aspects of wheat production worldwide, especially in Australia with high climate variability. This study aggregated multi-source observational data by using meteorological statistics, in-situ investigation data and the MODIS Enhanced Vegetation Index (EVI) product to explore and examine the correlation between climate variability and spatial-temporal patterns of wheat phenology metrics and productivity. The results from tests over 370 wheat trial sites showed: 1) narrower and earlier sowing and harvesting windows occurred in a drought year (2006) compared with a normal year (2005). Differences in sowing and harvesting window lengths were 9 and 5 days, respectively; 2) different weather patterns in each agro-climatic zone were followed by different remotely sensed crop EVI seasonality profiles. Crop growth was least affected by climate variability in agro-climate region E2, which is located in the south part of study area. This study reveals new information on cropland-climate relationships across the wheat belt in NSW in a changing climate.

Spectral data have been widely used to estimate the disease severity (DS) levels of different plants. However, such data have not been evaluated to estimate the disease stages of the plant. This study aimed at developing a spectral disease index (SDI) that is able to identify the stages of wheat leaf rust disease at various DS levels. To meet the aim of the study, the reflectance spectra (350-2500 nm) of infected leaves with different symptom fractions and DS levels were measured with a spectroradiometer. Then, pure spectra of the different disease symptoms at the leaf scale were analyzed, and a new function was developed to find the wavelengths most sensitive to disease symptom fraction. The reflectance spectra with highest sensitivity were found at 675 and 775 nm. Finally, the normalized difference of DS and the ratio ρ675/ρ775was used as a new SDI to discriminate three different levels of the disease stage at the canopy level. The suggested SDI showed a promising performance to improve the detection disease stages in precision plant protection.

Current bidirectional reflectance distribution function (BRDF) inversions using ordinary least squares (OLS) criterion can be easily contaminated by observations with residual cloud and undetected high aerosols, which leads to abrupt fluctuations in the normalized difference vegetation index (NDVI) time series. The OLS criterion assumes the noise has Gaussian distribution, which is often violated due to positive noise biases caused by clouds and high aerosols. A changing-weight iterative BRDF/NDVI inversion algorithm (CWI) based on a posteriori variance estimation of observation errors is presented to explicitly consider the asymmetrically distributed noise and observations with unequal accuracy in the BRDF retrieval. CWI employs a posteriori variance estimation and an NDVI-based indicator to iteratively adjust the weight of each observation according to its noise level. The validation results suggest CWI performs better than the Li-Gao and OLS approaches. The rmse was reduced from 0.074 to 0.028, and the relative error decreased from 13.4% to 3.8% at the U.S. Department of Agriculture Beltsville Agricultural Research Center site. Similarly, at the Harvard Forest site, the rmse was reduced from 0.086 to 0.031, and the relative error decreased from 9.5% to 2.7%. The average noise and relative noise of the CWI NDVI time series over ten EOS Land Validation Core Sites from 2003-2009 was smaller (0.028, 3.7%) than those of MOD13A2 (0.041, 5.2%), MYD13A2 (0.039, 4.9%) and MCD43B4 (0.030, 4.4%). The results demonstrate the robustness of the CWI approach in suppressing the influence of contaminated observations in BRDF retrievals by producing results that are less affected by undetected clouds and high aerosols.

Landscape heterogeneity is a common natural phenomenon but is seldom considered in current radiative transfer (RT) models for predicting the surface reflectance. This paper developed an analytical RT model for heterogeneous Agro-Forestry scenarios (RTAF) by dividing the scenario into nonboundary regions (NRs) and boundary regions (BRs). The scattering contribution of the NRs can be estimated from the scattering-by-arbitrarily-inclined-leaves-with-the-hot-spot-effect model as homogeneous canopies, whereas that of the BRs is calculated based on the bidirectional gap probability by considering the interactions and mutual shadowing effects among different patches. The multiangular airborne observations and discrete-anisotropic-RT model simulations were used to validate and evaluate the RTAF model over an agro-forestry scenario in the Heihe River Basin, China. The results suggest that the RTAF model can accurately simulate the hemispherical-directional reflectance factors (HDRFs) of the heterogeneous scenarios in the red and near-infrared (NIR) bands. The boundary effect can significantly influence the angular distribution of the HDRFs and consequently enlarge the HDRF variations between the backward and forward directions. Compared with the widely used dominant cover type (DCT) and spectral linear mixture (SLM) models, the RTAF model reduced the maximum relative error from 25.7% (SLM) and 23.0% (DCT) to 9.8% in the red band and from 19.6% (DCT) and 13.7% (SLM) to 8.7% in the NIR band. The RTAF model provides a promising way to improve the retrieval of biophysical parameters (e.g., leaf area index) from remote sensing data over heterogeneous agro-forestry scenarios.

Australia recently experienced a long-term continental drought (“big dry”, 2001-2009) followed by an anomalous wet two-year period (“big wet”, 2010-2011). Despite the significance of the two extreme events, continental-wide information regarding the effects of the high and low precipitation conditions on the hydrological components, stress and recovery is not available. In this paper, we use terrestrial total water storage changes (ATWS) from the Gravity Recovery and Climate Experiment (GRACE) and precipitation data from the Tropical Rainfall Measuring Mission (TRMM) spanning from 2002 to 2013, where ATWS represents the main source of water available for human consumption, agriculture and natural ecosystems. We rely on a combination of temporal trend analysis and spatial statistics methods in order to evaluate the terrestrial total water storage (TWS) dynamics and the relationship between TWS and rainfall during the “big dry” and “big wet” events. Here we report the occurrence of hydrological cycle intensification during the study period in Australia which exhibited strong spatial variations: the wet areas (the northern and northeast regions) got wetter while the dry areas (the west and interior of the continent) became drier. By contrast, in southeastern Australia TWS changes over time showed sudden extreme responses to both events. Our results constitute a step beyond quantifying droughts/anomalous wet years that rely solely on precipitation data. This work demonstrates the ability of TWS observations as a significant indicator of hydrological system performance during hydroclimatic events and also an important tool for understanding continental-wide and regional spatial and temporal patterns of water availability.

There is an urgent need within the international remote sensing community to establish a metadata standard for field spectroscopy that ensures high quality, interoperable metadata sets that can be archived and shared efficiently within Earth observation data sharing systems. Careful examination of all stages of metadata collection and analysis can inform a robust standard that is applicable to a range of field campaigns. This paper presents approaches towards a standard that encompasses in situ metadata collection and initiatives towards sharing metadata within intelligent archiving systems.

Temperate grasslands in Australia show dynamic responses to climate, which renders them difficult to study using conventional remote sensing tools. However, the need to adequately describe native grassland variables is critical in maintaining ecological and agricultural values. We used a spectroradiometer to measure leaf-and canopy-level spectra from grassland plots in a controlled environment and compared results to fractional cover and species type. We found that the target species, Themeda australis and Poa labillardierei were separable at canopy and leaf level for both healthy and senescent foliage. In particular, we found differences in the 470-510 nm and 660-700 nm spectral regions. Comparison of narrow band vegetation indices for different combinations of photosynthetic and non-photosynthetic material showed strong relationships across a range of fractional cover values which was co-linear for both species. This method demonstrates the potential for remote sensing to identify Australian grasslands of different quality and composition.

We evaluated the cross-sensor compatibilities of the normalized difference vegetation index (NDVI) across the Visible/Infrared Imager/Radiometer Suite (VIIRS), Moderate Resolution Imaging Spectroradiometer (MODIS), and the National Oceanic and Atmospheric Administration (NOAA)-14 and NOAA-19 Advanced Very High Resolution Radiometer (AVHRR) (AVHRR/2 and AVHRR/3) bandpasses using a global set of Earth Observing One Hyperion hyperspectral data. Five levels of atmospheric correction were simulated to examine the impact of the atmosphere on intersensor NDVI compatibility. These were the uncorrected “top-of-atmosphere”; Rayleigh (RAY); Rayleigh and ozone (RO); Rayleigh, ozone, and water vapor (ROW); and total atmosphere-corrected “top-of-canopy (TOC)” reflectances. Among all possible sensor pairs examined, the highest compatibility was observed for VIIRS versus MODIS. Cross-sensor NDVI relationships between the two sensor bandpasses remained nearly the same throughout all levels of atmospheric correction. AVHRR/3-versus-AVHRR/2 NDVI relationships changed very little and also showed an equivalent level of compatibility to VIIRS versus MODIS across all levels of atmospheric correction although they were subject to systematic differences. Intersensor NDVI compatibilities of VIIRS and MODIS to AVHRR/2 and to AVHRR/3 were lower due primarily to the differential sensitivities of these sensors' near-infrared bands to the atmospheric water vapor effects. Comparisons of cross-sensor NDVI compatibilities where operational atmospheric correction schemes were assumed for each of the sensors suggest the need of VIIRS TOC NDVI for long-term continuity with MODIS and AVHRR, which is not currently produced as part of the standard VIIRS Vegetation Index Environmental Data Record.

In this study, we present an approach for phenology analysis of Amazon green-up using Linear Spectral Mixing Model applied to Hyperion multitemporal data. The study area was selected in the Tapajo¿s National Forest located in Para¿ State, Brazilian Amazonia. The region has well-defined dry and wet seasons with yearly rain about 2, 100 mm a dry season occurring from June to October. The study area is primarily covered by dense tropical rain forest (¿Floresta Ombro¿fila Densa¿) with a high number of emergent tree species. The EO-1 Hyperion data were acquired in July, August and September 2001, corresponding to the dry season in this region. The Linear Spectral Mixing Model was applied on each calibrated surface reflectance data, generating vegetation, soil, and shade fraction images. Then fundamental statistical analyses were carried out to evaluate the differences within the vegetation and shade fraction images derived from medium spatial resolution Hyperion images for rainforest phenology analysis.

MODIS 16-day composite EVI, NDVI, and VI Quality Analysis values for 2000-2005 were extracted for 21 varzea forest sites along the Solimoes-Amazon floodplain west of Manaus, Brazil. VI values were filtered to exclude dates with VI-QA values greater than 3, and time series of median values of the remaining pixels were examined in conjunction with river stage levels recorded at the Manacapuru gauge. All sites showed a regular seasonal variation in EVI, ranging from a mean low for all sites of 0.41 to a mean high of 0.61. The amplitude of variability in NDVI was about 50% that of EVI. Minimum EVI, corresponding to minimum leaf area, occurred in late May, about 40 days preceding maximum river stage; EVI peaked in mid-October, about 30 days before lowest river levels. These temporal patterns are in general agreement with field observations of leaf phenology at va¿rzea stands near Manaus.

The metabolism and phenology of Amazon rainforests significantly influence global dynamics of climate, carbon and water, but remain poorly understood. In this study we utilized Moderate Resolution Imaging Spectroradiometer (MODIS) terrestrial ecosystem variables to analyze Amazon rainforest dynamics utilizing the satellite products; vegetation indices (VI), leaf area index (LAI), fraction of absorbed photosynthetically- active radiation (FPAR), and gross primary production (GPP). We found the MODIS products to greatly facilitate analyses and monitoring of ecosystem metabolism in both intact and disturbed rainforests.

The Amazon region contains one of the world's richest biodiversity and plays a major role in global dynamics of climate and global water and biogeochemical cycles. A better understanding of vegetation phenology and associated seasonal variations in carbon dynamics is necessary to develop reliable biosphere-climate models in the region. In this study, we investigated the interaction of climate and vegetation physiognomy on Amazon vegetation phenology through various eco-climatic transects traversing the Amazon transition ecotone. Our objective was to develop a better understanding of transitional ecotone vegetation dynamics and assess their relationship to rainforest and cerrado phenology patterns. The Moderate Resolution Imaging Spectroradiometer (MODIS) Enhanced Vegetation Index (EVI) time-series data were utilized for measuring seasonal variations in "greenness" across a series of eco-climatic transects. Overall, MODIS EVI derived phenology, showed pronounced, moisture induced dry- wet seasonal contrasts in the cerrado and intermediate seasonal contrast level in the light-limited rainforests. The transitional forests, representing a mixed response of light, water, and other factors, showed the least seasonality, however, the ecotone areas of converted forests depicted strong, moisture-limited, dry-wet seasonal contrasts due to the dominance of shallow rooted plants that cannot exploit water from deeper soil moisture layers in the dry season. The EVI seasonal profiles of tropical rainforests, transition ecotone forests, converted forests, and cerrado were unique. This yielded important phenology information useful in land cover characterization and for parameterization for biosphere-climate models.

Satellite data such as the vegetation indices are a crucial tool for studying vegetation phenology patterns from regional to global scales. In this study, we investigated the relationship of the fraction images, derived from the linear spectral mixture model, with the NDVI and EVI, the most used indices to evaluate the phenological response using remote sensing data from the MODIS sensor. Our objectives were to understand how the vegetation indices are related with the vegetation fraction and to evaluate if the information provided by the shade and soil fraction images can be used to explain the vegetation indices behavior. We used a temporal series data of the MOD13A1 product for the 2002 year, the precipitation data from 125 meteorological stations, and a land cover map generated based on the 2002 images. We studied two different vegetation physiognomies to analyse if the fraction images were landscape dependent. Our results showed that for the open tropical forest, the vegetation fraction image presented a significant correlation with the EVI (r2=0.84) but not with the NDVI. For the Cerrado grassland landscape, the vegetation fraction image presented high correlation with the NDVI (r2=0.93) and EVI (r2=0.98). Significant correlations were also found for the shade and soil fraction images for the land cover studied, showing that these additional information are a useful source of data to understand the vegetation canopy structural changes and to analyze the responses provided by the vegetation indices correctly.

Remotely sensed, angle-based vegetation indices that measure vegetation amounts by the angle between an approximated soil line and a simulated vegetation isoline in the red–near-infrared reflectance space were developed and evaluated in this paper. Ünsalan and Boyer previously proposed an angle-based vegetation index,$theta$(denoted as$theta_ NDVI$in this paper), based on the normalized difference vegetation index (NDVI) with the objective of overcoming the saturation problem in the NDVI. However,$theta_ NDVI$did not consider strong soil background influences present in the NDVI. To reduce soil background noise, an angle-based vegetation index,$theta_ SAVI$, based on the soil-adjusted vegetation index (SAVI), was derived using trigonometric analysis. The performance of$theta_ NDVI$and$theta_ SAVI$was evaluated and compared with their corresponding vegetation indices, NDVI and SAVI. The soil background influence on$theta_ NDVI$was found to be as significant as that on the NDVI.$theta_ NDVI$was found to be more sensitive to vegetation amount than the NDVI at low vegetation density levels, but less sensitive to vegetation fraction at high vegetation density levels. Thus, the saturation effect at high vegetation density levels encountered in the NDVI was not mitigated by$theta_ NDVI$. By contrast,$theta_ SAVI$exhibited insignificant soil background effects and weaker saturation, as in SAVI, but also improved upon the dynamic range of SAVI. Analyses and evaluation suggest that$theta_ SAVI$is an optimal vegetation index to assess and monitor vegetation cover across the entire range of vegetation fraction density levels and over a wide variety of soil backgrounds.

Broadleaf forest is a major type of Earth's land cover with the highest observable vegetation density. Retrievals of biophysical parameters, such as leaf area index (LAI), of broadleaf forests at global scale constitute a major challenge to modern remote sensing techniques in view of low sensitivity (saturation) of surface reflectances to such parameters over dense vegetation. The goal of the performed research is to demonstrate physical principles of LAI retrievals over broadleaf forests with the Moderate Resolution Imaging Spectroradiometer (MODIS) LAI algorithm and to establish a basis for algorithm refinement. To sample natural variability in biophysical parameters of broadleaf forests, we selected MODIS data subsets covering deciduous broadleaf forests of the eastern part of North America and evergreen broadleaf forests of Amazonia. The analysis of an annual course of the Terra MODIS Collection 4 LAI product over broadleaf forests indicated a low portion of best quality main radiative transfer-based algorithm retrievals and dominance of low-reliable backup algorithm retrievals during the growing season. We found that this retrieval anomaly was due to an inconsistency between simulated and MODIS surface reflectances. LAI retrievals over dense vegetation are mostly performed over a compact location in the spectral space of saturated surface reflectances, which need to be accurately modeled. New simulations were performed with the stochastic radiative transfer model, which poses high numerical accuracy at the condition of saturation. Separate sets of parameters of the LAI algorithm were generated for deciduous and evergreen broadleaf forests to account for the differences in the corresponding surface reflectance properties. The optimized algorithm closely captures physics of seasonal variations in surface reflectances and delivers a majority of LAI retrievals during a phenological cycle, consistent with field measurements. The analysis of the optimized retrievals indicates that the precision of MODIS surface reflectances, the natural variability, and mixture of species set a limit to improvements of the accuracy of LAI retrievals over broadleaf forests.

Climate change has important implications on the global distribution and dynamics of vegetation that, in turn, impacts the global carbon cycle. Irrespective of the forcings driving these changes, a characterization of global vegetation dynamics and the establishment of accurate metrics that can be linked to forcings are paramount to addressing questions related to climate change and its bearings on the terrestrial biosphere. Terrestrial ecosystems affect the climate through a complex system of interactions among water, carbon and energy. An increase, or decrease in these fluxes forces new equilibrium states and feedbacks to the climate system, which in turn impacts the ecosystems. NDVI-based time series analysis of satellite imagery from the NOAA-AVHRR sensor, collected during the last two decades narrates an enhanced vegetation activity over key areas of the Earth (high and mid latitudes). Most of this increase in activities has been indirectly linked to an increase in the Earth's temperature and CO2 concentration. In this study, we assessed the relationships between vegetation dynamic metrics and climate-ecosystem parameters. We analyzed 3 years of MODIS Vegetation Index (VI) data augmented by a global land cover map derived from the same sensor, and the GTOPO DEM data. Using a stratified spatial analysis, we assessed the role of the following characteristics on vegetation: 1) Latitude: to isolate temperature regimes and seasonality, 2) Elevation: to isolate land cover and precipitation distribution, 3) Land cover: to isolate phenological characteristics. A combinatorial analysis using the above stratification was applied in successive orders to generate compound results. The results yielded coherent time series profiles depicting vegetation dynamics as it relates to elevation, latitude and land cover

We investigated the use of the MODIS vegetation indices and the effect of distinct change thresholds for monitoring land cover change in the Cerrado biome, the largest region of neotropical savanna vegetation in the world and the most threatened biome in Brazil. On a preliminary basis, our results suggest the use of change thresholds between 35 and 42% and the us pe of the enhanced vegetation index (EVI), which, in comparison to the normalized difference vegetation index (NDVI), showed a more stable and predictable behaviour.

High temporal frequency observations with the Moderate Resolution Imaging Spectroradiometer (MODIS) onboard the Terra Earth Observing System platform offer unique opportunities to study climate- and anthropogenic-induced land transformations in the temporal domain. Shifts in vegetation type and physiognomies alter biologic activity and responses to climate patterns in unpredictable ways. Increases in insect populations (e.g. bark beetle) and fire associated with a multi-year drought in the Southwest U.S.A have greatly impacted the health of its native ecosystems from pinyon-juniper, Ponderosa pine, and mixed conifer forests to the savanna, grassland, and desert shrub ecosystems. In this study, MODIS time series data combined with AVIRIS overflights were analyzed for detection and evaluation of the causes, severity, and extent of changes in ecosystem health. We used the 16-day MODIS enhanced vegetation index (EVI) product and a normalized difference water index (NDWI) to analyze the seasonal, inter-annual, and spatial patterns of vegetation activity over a wide range of land cover types across eco-climatic and elevational gradients and through the winter and variable monsoon rainfall `pulses'. The temporal dynamics of vegetation were found to be highly sensitive to both anthropogenic and climatic forcings found in the semiarid and arid Southwest with seasonal and inter-annual profiles varying markedly with land cover type and land surface disturbance (e.g., drought, insects). All land cover types and eco-climatic gradients from desert shrub to montane forest were significantly affected by the drought, with grasslands most impacted. Tree mortality variability could also be assessed and we found that combined MODIS-AVIRIS data offer the potential of ecosystem health and risk assessment

The Cerrado or Brazilian savanna represents 23% of the land surface of the country. This important bid me, however, has been subjected to rapid rates of land conversion to agriculture and pasture. This has important environmental consequences to local and regional climate change and carbon fluxes. Therefore, a study of seasonal cerrado dynamics, including forest and converted areas, was conducted with four years of data (February 2000 to December 2003) from the Moderate Resolution Imaging Spectroradiometer (MODIS). The 16-day composite vegetation index (VI) data were used to analyze the seasonal patterns of photosynthetic vegetation activity and examine the separability of cerrado formations of varying physiognomies in Brasilia National Park and surrounding areas. The results showed that the cerrado formations exhibited a high seasonality contrast with a pronounced dry season from June through August and wet season from November to March. Discrimination within cerrado formations was difficult due to similarities in their seasonal dynamic behavior. Maximum contrast among all the cerrado formations occurred during dry season, suggesting this as the best time for cerrado physiognomy discrimination. The converted agricultural areas had a higher contrast than the native cerrado, and the forest formation had the lowest seasonal contrast. This enabled an operational method to discriminate the cerrado formation from the converted areas and adjoining forests. Thus, MODIS offers a useful tool to monitor the threatened cerrado biome

Vegetation indices (VIs) are one of the standard science products available from the Moderate Resolution Imaging Spectroradiometer (MODIS) instrument on the Earth Observing System (EOS) Terra platform, launched in December 1999. An important requirement of MODIS science products is that they be rigorously validated. In this study, we conducted a site-intensive MODIS VI product validation at the semiarid Jornada Experimental Range, New Mexico, an EOS Land Validation Core Site. Our validation approach involved scaling up independent fine-grained datasets, including ground and airborne radiometry, and high spatial resolution imagery [Enhanced Thematic Mapper Plus (ETM+)], to the coarser MODIS spatial resolutions. The MODIS VIs were evaluated with respect to their radiometric performances, the uncertainties of the compositing methodology, and their capabilities to depict seasonal variations in vegetation. The MODIS Quick Airborne Looks (MQUALS) radiometric package was found useful in up-scaling field in situ measurements to coarser spatial resolutions. Both single-day nadir-view and 16-day composited MODIS reflectances and VIs matched well with the nadir-based atmosphere-free MQUALS observations for all the land cover types found at Jornada, with the root mean squared deviations less than 0.03. The MODIS 16-day composited products also performed well with the single-day nadir-view MODIS data, despite some off-nadir view angles and uncertainties with the cloud mask algorithm. The quality assurance (QA)-based constrained view angle-maximum value composite (CV-MVC) algorithm successfully filtered out much of the cloud and aerosol contaminated observations and helped to minimize view angle-related problems. The MODIS seasonal VI profiles also matched quite well with the other multiple sensor datasets obtained at the finer spatial resolutions. The QA information was found to be crucial in achieving consistent spatial and temporal comparisons of global vegetation conditions and for deriving accurate depictions of important phenological features in multitemporal MODIS data. The results of this validation study over the Jornada Experimental Range demonstrated the accuracy, reliability, and science utility of the MODIS VI products in arid and semiarid areas.

Land degradation in semiarid areas results from various factors, including climate variations and human activity, and can lead to desertification. The process of degradation results in simultaneous and complex variations of many interrelated soil and vegetation biophysical parameters, rendering it difficult to develop simple and robust remote sensing mapping and monitoring approaches. In this study, we tested the use of Earth Observing 1 (EO-1) Hyperion hyperspectral data to analyze land degradation patterns within the protected Nacunan Biosphere Reserve and surrounding areas in the Monte Desert region of Argentina. The floristically diverse vegetation communities included mesquite forest (algarrobal), creosotebush (jarillal), sand-dune (medanal), and severely degraded (peladal) sites. Various optical measures of land degradation were employed, including vegetation indexes, spectral derivatives, albedo, and spectral mixture analysis. Spectral mixture analysis provided the best characterization of the unstable and spatially variable landscape encountered at the Nacunan Biosphere Reserve. Spectral unmixing provided simultaneous measures of green vegetation, nonphotosynthetic vegetation, and soil, all of which were deemed essential in characterizing land degradation. In conjunction with multitemporal data from the more commonly employed broadband sensors, hyperspectral data can provide a powerful methodology toward understanding environmental degradation.

The availability of similar satellite data products from multiple sensors has focused much attention on the issue of continuity across satellite data products from past, current, and future sensors. Hyperspectral datasets acquired over a variety of land cover types are extremely useful in attempting to resolve spectral differences in the global datasets from different sensors. The datasets from the Earth Observing 1 (EO-1) Hyperion sensor are very suitable for this purpose, as is airborne hyperspectral data. In this paper, we examine the possibility of translating vegetation index (VI) data between two sensors by using imagery from the Hyperion sensor and utilizing the vegetation isoline concept. The objectives of this paper are to introduce and test a VI translation technique, focused on the spectral differences associated with sensor spectral bandpass filters. The translation of global VI datasets from one sensor to another requires a methodology applicable over various land cover types and throughout the wide ranges in VI values. To meet these requirements, a technique is proposed that utilizes adjustable translation coefficients, based on an estimation of the leaf area index value relative to a numerical canopy model. The theoretical basis of the proposed translation algorithm is explained in terms of the vegetation isoline concept. Its performance was tested through a numerical experiment with a Hyperion image, focusing on the normalized difference vegetation index (NDVI) as a representative vegetation index. The results indicate the potential of the isoline-based translation technique for stable translation throughout wide ranges of NDVI values.

Conversion and degradation in arid, semi-arid, and dry sub-humid areas result from various factors, including climate variations and human activity, and can lead to desertification. The process of degradation results in simultaneous and complex variations of many interrelated and coupled soil-vegetation parameters. General information and data regarding the degree and extent of land degradation and the resulting impacts remain poorly understood and remote sensing can play a major role by providing a quantifiable and replicable technique for monitoring and assessing the extent and severity of soil degradation. We investigated various 'optical' indicators and early warning signals of land degradation and desertification with the use of hyperspectral remote sensing data derived from EO-1 Hyperion and AVIRIS sensors. Utilizing spectral mixture analysis, we analyzed simultaneous spatial variations in vegetation cover, species, physiognomy, albedo, and soil properties at various sites representing different stages of land degradation in the Mendoza region of Argentina and at sites in the Southwest U.S.A. We found that both soil and vegetation parameters were required to characterize the unstable and spatially variable landscape dynamics found in actively degrading environments.

In order to use satellite data from multiple sensors for monitoring of the Earth's vegetation, satellite data product continuity and compatibility need to be investigated. In this study, we assessed inter-sensor compatibility of the normalized difference vegetation index (NDVI) by simulating bandpasses of the NOAA-14 AVHRR, Terra-MODIS, and Landsat-7 ETM+ from airborne hyperspectral and EO-1 Hyperion data acquired over a savanna-forest transitional zone in Brazil. Our results showed that the simulated reflectances among the sensors examined here were near-linearly related, but with relationships that were land cover-dependent, e.g., reflectances over green vegetation targets formed separate relationships. The deviations were larger for greener targets. On the other hand, the NDVI crossplots among the sensors formed single, land-cover independent relationships. They were, however, curve-linearly related with the degree of curve-linearity increasing with atmosphere contamination. Also, land cover dependencies appeared in the NDVI cross-plots when contaminated by atmosphere. These results suggest that inter-sensor calibration and continuity of the NDVI are achievable, but require a careful examination of surface and atmospheric conditions and the development of a theoretical basis.

Utilization of satellite date from multiple platforms increases our chances of more frequent and accurate observations of the Earth's surface in both global and regional scale For the purpose of vegetation monitoring, this will be particularly true by combining the data from sensors of various spatial, spectral, and temporal resolutions, e.g. the combinations of data from AVHRR (broad band), MODIS (narrow band) and ETM+ (higher spatial resolution). Even though the same spectral vegetation index can be obtained from these sensors, the two main issues need to be considered, one is the systematic differences caused by the spectral response functions, and the other is the differences in spatial resolutions. This paper investigates the spectral issue and its role in the spectral calibration of NDVI among sensors. Hyperspectral data from Hyperion onboard the EO-1 platform were used to simulate outputs from various sensors by band convolution. The data were initially corrected for Rayleigh scattering and Ozone absorption to produce the top-of-the-canopy reflectance as a starting point. The technique first designs a sensor-specific vegetation index (VI) and background brightness index (BI) by accounting for the differences in band-pass filters. These VIs and BIs are then used to estimate the common parameters (sensor independent parameters) attributed to vegetation amount and background brightness. Finally, these parameters are used for the translation of VI among sensors.

The impact of reflectance calibration uncertainties on the accuracies of several vegetation indices (VIs) was evaluated for the Moderate Resolution Imaging Spectroradiometer (MODIS) sensor onboard the TERRA platform. A set of uncertainty propagation equations were designed to model the propagation of calibration uncertainties from top-of-atmosphere (TOA) reflectances to atmospherically-corrected VIs. The soil-adjusted vegetation index (SAVI), the atmospherically-resistant vegetation index (ARVI), and the enhanced vegetation index (EVI) were evaluated along with the normalized difference vegetation index (NDVI). The resultant VI uncertainties associated with calibration u/sub cal/(VI) varied with both surface reflectances and atmospheric conditions. Uncertainties in the NDVI and ARVI were highly dependent on pixel brightness, with the largest uncertainties occurring over dark targets with little or no vegetation. The SAVI uncertainties were nearly constant throughout a range of target brightness and vegetation abundance. The EVI uncertainties linearly increased with increasing EVI values. Atmosphere turbidities increased calibration uncertainties in all the VIs through their effect on TOA reflectances. The VI uncertainties were also found to decrease when the calibration errors were positively correlated between bands. Using field observational canopy reflectance data, the mean VI uncertainties were estimated to be /spl plusmn/0.01 VI units for the NDVI and SAVI, and /spl plusmn/0.02 VI units for the ARVI and EVI under normal atmosphere conditions (/spl ges/20 km visibility) and for a 2% reflectance calibration uncertainty.

A technique to derive vegetation isoline equations in red-NIR reflectance space for homogeneous canopies is proposed and demonstrated. A canopy radiative transfer model, known as the Cooper-Smith-Pitts model, is utilized with truncation of the higher order interaction term between the canopy and soil layers. The technique consists of two model simulations, one with a perfect absorber as canopy background and the other with an arbitrary background to estimate the canopy optical properties necessary for the determination of the isoline parameters. These cases are independent of the soil optical properties of any specific site. Hence, the results can be used for any type or series of soils to construct the vegetation isoline equation. A set of simulations was also conducted using the SAIL model to demonstrate the vegetation isoline derivation by the proposed technique. Reflectances and vegetation indices (VI) estimated from the vegetation isoline generally showed good agreement with those simulated by the SAIL model, especially for relatively darker soil. The isoline equation and derivation were found to be useful for further study of two-band VIs and their variation with canopy background.

The first Moderate Resolution Imaging Spectroradiometer (MODIS) instrument is planned for launch by NASA in 1998. This instrument will provide a new and improved capability for terrestrial satellite remote sensing aimed at meeting the needs of global change research. The MODIS standard products will provide new and improved tools for moderate resolution land surface monitoring. These higher order data products have been designed to remove the burden of certain common types of data processing from the user community and meet the more general needs of global-to-regional monitoring, modeling, and assessment. The near-daily coverage of moderate resolution data from MODIS, coupled with the planned increase in high-resolution sampling from Landsat 7, will provide a powerful combination of observations. The full potential of MODIS will be realized once a stable and well-calibrated time-series of multispectral data has been established. In this paper the proposed MODIS standard products for land applications are described along with the current plans for data quality assessment and product validation.

The Normalized Difference Vegetation Index (NDVI) equation has a simple, open loop structure (no feedback), which renders it susceptible to large sources of error and uncertainty over variable atmospheric and canopy background conditions. In this study, a systems analysis approach is used to examine noise sources in existing vegetation indices (VI'S) and to develop a stable, modified NDVI (MNDVI) equation. The MNDVI, a closedloop version of the NDVI, was constructed by adding 1) a soil and atmospheric noise feedback loop, and 2) an atmospheric noise compensation forward loop. The coefficients developed for the MNDVI are physically-based and are empirically related to the expected range of atmospheric and background “boundary” conditions. The MNDVI can be used with data uncorrected for atmosphere, as well as with Rayleigh corrected and atmospherically corrected data. In the field observational and simulated data sets tested here, the MNDVI was found to considerably reduce noise for any complex soil and atmospheric situation. The resulting uncertainty, expressed as vegetation equivalent noise, was +0.11 leaf area index (LAI) units, which was 7 times less than encountered with the NDVI (+0.8 LAI). These results indicate that the MNDVI may be satisfactory in meeting the need for accurate, long term vegetation measurements for the Earth Observing System (EOS) program.

Several soil- and atmospheric-correcting variants of the normalized difference vegetation index (NDVI) have been proposed to improve the accuracy in estimating biophysical plant parameters. In this study, a sensitivity analysis, utilizing simulated model data, was conducted on the NDVI and variants by analyzing the atmospheric- and soil-perturbed responses as a continuous function of leaf area index. Percent relative error and vegetation equivalent "noise" (VEN) were calculated for soil and atmospheric influences, separately and combined. The NDVI variants included the soil-adjusted vegetation index (SAVI), the atmospherically resistant vegetation index (ARVI), the soil-adjusted and atmospherically resistant vegetation index (SARVI), the modified SAVI (MSAVI), and modified SARVI (MSARVI). Soil and atmospheric error were of similar magnitudes, but varied with the vegetation index. All new variants outperformed the NDVI. The atmospherically resistant versions minimized atmospheric noise, but enhanced soil noise, while the soil adjusted variants minimized soil noise, but remained sensitive to the atmosphere. The SARVI, which had both a soil and atmosphere calibration term, performed the best with a relative error of 10 percent and VEN of /spl plusmn/0.33 LAI. By contrast, the NDM had a relative error of 20 percent and VEN of /spl plusmn/0.97 LAI.>