Estimating leaf area index from satellite data in deciduous forests of southern Sweden

Leaf area index, LAI, is an important biophysical parameter in ecological modeling. It is the ratio of leaf area per unit ground area. To obtain LAI over large areas in a fast and convenient way the use of satellite data is important. Aim of the project was to determine if there is a relationship between LAI and red-, NIR reflectance and a couple of vegetation indices: Global Environment Monitoring Index (GEMI), Normalized Difference Vegetation Index (NDVI), Vegetation Phenology Index (VPI) and the two-band Enhanced Vegetation Index (EVI2). A related measure of LAI called effective LAI, Le, which assumes a random foliage distribution, was estimated in the field with an optical instrument. The vegetation indices/reflectance was obtained from SPOT satellite data. Results showed that there is a linear relationship and good correlations, about 0.8, between Le and the vegetation indices and NIR reflectance data. The red reflectance showed a weak relation to Le. The results indicated that it is the NIR reflectance that forms the relations. The relationships are empirical and thus time and site specific. Some caution should be taken when using the relations obtained in this study when these might change under different conditions. The linear relationships could be used to get an estimate of LAI, in deciduous forests, from the relations with the vegetation indices within the range of Le values of this study.Att uppskatta lövyteindex med satellitdata Vegetation har unika spektrala egenskaper som kan användas inom fjärranalys för att få information om viktiga ekologiska parametrar, som t.ex löyteindex (eng. leaf area index, LAI, lövyta per markenhet). LAI styr många ekologiska processer som fotosyntes och transpiration. Med hjälp av satellitbilder kan man uppskatta LAI i stor skala. Vegetation absorberar mycket ljus i det röda våglängdsområdet p.g.a. att den tar energi från det röda ljuset till sin fotosyntes, samtidigt som mycket ljus i det nära infraröda, NIR, våglängdsområdet blir reflekterat av lövens interna struktur. På satelliter finns sensorer som registrerar det reflekterade ljuset från vegetationen i olika våglängder, bland annat i ett rött och NIR våglängdsband. Dessa band kan sedan användas för att studera vegetationen och aritmetiskt kombineras till olika så kallade vegetationsindex. I denna studie försökte jag hitta samband mellan olika vegetationsindex och LAI för att se om man kan använda dessa samband till att uppskatta LAI i stor skala från satellitbilder. LAI uppskattades i fält med ett optiskt instrument, LAI-2000, i skånska lövskogar och vegetationsindex beräknades utifrån satellitdata från SPOT (franska: Satellite Pour l’Observation de la Terre). Fyra olika vegetationsindex som alla bygger på de röda och NIR banden testades för att utröna om de hade samband med det uppmätta LAI i fält. Dessa var följande: NDVI (eng. Normalized Difference Vegetation Index), VPI (eng. Vegetation Phenology Index), GEMI (eng. Global Environment Monitoring Index) och EVI2 (eng. Enhanced Vegetation Index baserat på två band). Resultaten visade att samtliga vegetationsindex hade starka linjära samband med LAI. Försiktighet bör dock tas eftersom sambanden är utformade efter de speciella omständigheter (t. ex. under en viss tid på året) och miljöförhållanden som rådde under mätningarna och att dessa kan ändras. De starka sambanden visar dock stor potential till att användas för att beräkna LAI i lövskogar utifrån satellitdata i stor skala

Implications of whole-disc DSCOVR EPIC spectral observations for estimating Earth's spectral reflectivity based on low-earth-orbiting and geostationary observations

Earth’s reflectivity is among the key parameters of climate research. National Aeronautics and Space Administration (NASA)’s Earth Polychromatic Imaging Camera (EPIC) onboard National Oceanic and Atmospheric Administration (NOAA)’s Deep Space Climate Observatory (DSCOVR) spacecraft provides spectral reflectance of the entire sunlit Earth in the near backscattering direction every 65 to 110 min. Unlike EPIC, sensors onboard the Earth Orbiting Satellites (EOS) sample reflectance over swaths at a specific local solar time (LST) or over a fixed area. Such intrinsic sampling limits result in an apparent Earth’s reflectivity. We generated spectral reflectance over sampling areas using EPIC data. The difference between the EPIC and EOS estimates is an uncertainty in Earth’s reflectivity. We developed an Earth Reflector Type Index (ERTI) to discriminate between major Earth atmosphere components: clouds, cloud-free ocean, bare and vegetated land. Temporal variations in Earth’s reflectivity are mostly determined by clouds. The sampling area of EOS sensors may not be sufficient to represent cloud variability, resulting in biased estimates. Taking EPIC reflectivity as a reference, low-earth-orbiting-measurements at the sensor-specific LST tend to overestimate EPIC values by 0.8% to 8%. Biases in geostationary orbiting approximations due to a limited sampling area are between −0.7% and 12%. Analyses of ERTI-based Earth component reflectivity indicate that the disagreement between EPIC and EOS estimates depends on the sampling area, observation time and vary between −10% and 23%.The NASA/GSFC DSCOVR project is funded by NASA Earth Science Division. W. Song, G. Yan, and X. Mu were also supported by the key program of National Natural Science Foundation of China (NSFC; Grant No. 41331171). This research was conducted and completed during a 13-month research stay of the lead author in the Department of Earth and Environment, Boston University as a joint Ph.D. student, which was supported by the Chinese Scholarship Council (201606040098). DSCOVR EPIC L1B data were obtained from the NASA Langley Research Center Atmospheric Science Data Center. The authors would like to thank the editor who handled this paper and the two anonymous reviewers for providing helpful and constructive comments and suggestions that significantly helped us improve the quality of this paper. (NASA Earth Science Division; 41331171 - key program of National Natural Science Foundation of China (NSFC); 201606040098 - Chinese Scholarship Council)Accepted manuscrip

Evapotranspiration in a catchment dominated by eucalypt forest and woodland

There is on-going need for reliable estimates of evapotranspiration (ET) at catchment scales to support objective decision-making for managing water supplies, and enhancing understanding of processes and modelling. Without reliable estimates of ET, water supply and catchment management agencies are exposed to significant economic, social and even environmental risks. This thesis focuses on identifying possible methodologies for estimating ET in a catchment dominated by eucalypt forest and woodland. More specifically, this thesis tests the hypothesis that different methods of deriving daily, catchment ET for a headwater in Australia meet underlying assumptions and yield similar results. The hypothesis was tested by using three approaches to estimate catchment ET: soil moisture changes (point scale), satellite imagery of leaf area index (MODIS, hillslope scale), and discharge (streamflow) and the storage-discharge relationship (catchment scale). Data from Corin Catchment, an unregulated catchment vital to the Australian Capital Territory and the surrounding region, is the basis of this study. After the General Introduction (Chapter 1), methods for estimating ET in eucalypt communities throughout Australia at various temporal and spatial scales are systematically reviewed (Chapter 2). Of the 1614 original research papers investigated, 52 were included for further investigation. A clear outcome is that transpiration by the overstorey, measured using sap flow, is the most frequently measured component of ET, and that physiological studies dominate estimates of ET. Very few studies were conducted at the catchment scale. Further, scaling ET from tree to catchment scales was rarely attempted, and the effect of scaling for water resource management is mostly unquantified and requires attention. The first method used to calculate catchment ET is based on up-scaling of soil moisture changes on the basis of a digital soil map (Chapter 4). The data presented here rejects the hypothesis that ET (derived from soil moisture) and overstorey transpiration (derived from sap flow) are well correlated. Instead, the data suggest that soil moisture-derived ET and overstorey transpiration obtained water from different sources. The key findings of this chapter are that this approach is not suitable for estimating ET at catchment scales because it is restricted to drier periods, and because trees did not solely rely on the defined root-zone for water supply. The second method to calculate catchment scale ET (Chapter 5) tests if hillslope-scale satellite imagery (MODIS leaf area index) can be up-scaled to estimate catchment ET. An outcome of this work is that caution is needed when using MODIS leaf area index for water resource planning in evergreen forests across the globe, particularly for forests with significant understorey and a relatively open overstorey canopy at some periods of the year. This method is deemed not suitable for estimating ET over the study area. The third method to calculate catchment scale ET (Chapter 6) is based on integrating discharge using a single non-linear equation to characterise the study area. This method yielded catchment ET far greater (18 times larger) than the largest observed measure of potential ET. As with the method based on soil moisture changes, it was restricted to drier periods. This method was clearly unsuitable for estimating ET over the study area due to relatively quick recession, large range in hourly discharge and significant scattered recession at low discharge. Overall, this thesis rejects the hypothesis that different methods of deriving daily, catchment ET for a headwater in Australia meet underlying assumptions and yield similar results. An important limitation identified through this research is the ability to determine a ‘correct’ estimate of catchment ET. Further research should focus on enhancing understanding of scaling ET within and beyond Australia, generating more daily catchment ET from up-scaled soil moisture changes, further evaluating ET from up-scaled satellite imagery, and identifying catchment characteristics to allow ET to be derived from discharge. Water resource managers must be diligent when selecting and applying a method to estimate catchment ET

First assessment of the plant phenology index (PPI) for estimating gross primary productivity in African semi-arid ecosystems

The importance of semi-arid ecosystems in the global carbon cycle as sinks for CO2 emissions has recently been highlighted. Africa is a carbon sink and nearly half its area comprises arid and semi-arid ecosystems. However, there are uncertainties regarding CO2 fluxes for semi-arid ecosystems in Africa, particularly savannas and dry tropical woodlands. In order to improve on existing remote-sensing based methods for estimating carbon uptake across semi-arid Africa we applied and tested the recently developed plant phenology index (PPI). We developed a PPI-based model estimating gross primary productivity (GPP) that accounts for canopy water stress, and compared it against three other Earth observation-based GPP models: the temperature and greenness model, the greenness and radiation model and a light use efficiency model. The models were evaluated against in situ data from four semi-arid sites in Africa with varying tree canopy cover (3 to 65 percent). Evaluation results from the four GPP models showed reasonable agreement with in situ GPP measured from eddy covariance flux towers (EC GPP) based on coefficient of variation, root-mean-square error, and Bayesian information criterion. The PPI-based GPP model was able to capture the magnitude of EC GPP better than the other tested models. The results of this study show that a PPI-based GPP model is a promising tool for the estimation of GPP in the semi-arid ecosystems of Africa.Comment: Accepted manuscript; 12 pages, 4 tables, 9 figure

Generating global products of LAI and FPAR from SNPP-VIIRS data: theoretical background and implementation

Leaf area index (LAI) and fraction of photosynthetically active radiation (FPAR) absorbed by vegetation have been successfully generated from the Moderate Resolution Imaging Spectroradiometer (MODIS) data since early 2000. As the Visible Infrared Imaging Radiometer Suite (VIIRS) instrument onboard, the Suomi National Polar-orbiting Partnership (SNPP) has inherited the scientific role of MODIS, and the development of a continuous, consistent, and well-characterized VIIRS LAI/FPAR data set is critical to continue the MODIS time series. In this paper, we build the radiative transfer-based VIIRS-specific lookup tables by achieving minimal difference with the MODIS data set and maximal spatial coverage of retrievals from the main algorithm. The theory of spectral invariants provides the configurable physical parameters, i.e., single scattering albedos (SSAs) that are optimized for VIIRS-specific characteristics. The effort finds a set of smaller red-band SSA and larger near-infraredband SSA for VIIRS compared with the MODIS heritage. The VIIRS LAI/FPAR is evaluated through comparisons with one year of MODIS product in terms of both spatial and temporal patterns. Further validation efforts are still necessary to ensure the product quality. Current results, however, imbue confidence in the VIIRS data set and suggest that the efforts described here meet the goal of achieving the operationally consistent multisensor LAI/FPAR data sets. Moreover, the strategies of parametric adjustment and LAI/FPAR evaluation applied to SNPP-VIIRS can also be employed to the subsequent Joint Polar Satellite System VIIRS or other instruments.Accepted manuscrip

Inconsistencies of interannual variability and trends in long-term satellite leaf area index products

Understanding the long-term performance of global satellite leaf area index (LAI) products is important for global change research. However, few effort has been devoted to evaluating the long-term time-series consistencies of LAI products. This study compared four long-term LAI products (GLASS, GLOBMAP, LAI3g, and TCDR) in terms of trends, interannual variabilities, and uncertainty variations from 1982 through 2011. This study also used four ancillary LAI products (GEOV1, MERIS, MODIS C5, and MODIS C6) from 2003 through 2011 to help clarify the performances of the four long-term LAI products. In general, there were marked discrepancies between the four long-term LAI products. During the pre-MODIS period (1982-1999), both linear trends and interannual variabilities of global mean LAI followed the order GLASS>LAI3g>TCDR>GLOBMAP. The GLASS linear trend and interannual variability were almost 4.5 times those of GLOBMAP. During the overlap period (2003-2011), GLASS and GLOBMAP exhibited a decreasing trend, TCDR no trend, and LAI3g an increasing trend. GEOV1, MERIS, and MODIS C6 also exhibited an increasing trend, but to a much smaller extent than that from LAI3g. During both periods, the R2 of detrended anomalies between the four long-term LAI products was smaller than 0.4 for most regions. Interannual variabilities of the four long-term LAI products were considerably different over the two periods, and the differences followed the order GLASS>LAI3g>TCDR>GLOBMAP. Uncertainty variations quantified by a collocation error model followed the same order. Our results indicate that the four long-term LAI products were neither intraconsistent over time nor interconsistent with each other. These inconsistencies may be due to NOAA satellite orbit changes and MODIS sensor degradation. Caution should be used in the interpretation of global changes derived from the four long-term LAI products

A Mixed Data-Based Deep Neural Network to Estimate Leaf Area Index in Wheat Breeding Trials

Remote and non-destructive estimation of leaf area index (LAI) has been a challenge in the last few decades as the direct and indirect methods available are laborious and time-consuming. The recent emergence of high-throughput plant phenotyping platforms has increased the need to develop new phenotyping tools for better decision-making by breeders. In this paper, a novel model based on artificial intelligence algorithms and nadir-view red green blue (RGB) images taken from a terrestrial high throughput phenotyping platform is presented. The model mixes numerical data collected in a wheat breeding field and visual features extracted from the images to make rapid and accurate LAI estimations. Model-based LAI estimations were validated against LAI measurements determined non-destructively using an allometric relationship obtained in this study. The model performance was also compared with LAI estimates obtained by other classical indirect methods based on bottom-up hemispherical images and gaps fraction theory. Model-based LAI estimations were highly correlated with ground-truth LAI. The model performance was slightly better than that of the hemispherical image-based method, which tended to underestimate LAI. These results show the great potential of the developed model for near real-time LAI estimation, which can be further improved in the future by increasing the dataset used to train the model

Estimation of photosynthetic capacity using MODIS polarization: 1988 proposal to NASA Headquarters

The remote sensing community has clearly identified the utility of NDVI (normalized difference vegetation index) and SR (simple ratio) and other vegetation indices for estimating such metrics of landscape ecology as green foliar biomass, photosynthetic capacity, and net primary production. Both theoretical and empirical investigations have established cause and effect relationships between the photosynthetic process in plant canopies and these combinations of remotely sensed data. Yet it has also been established that the relationships exhibit considerable variability that appears to be ecosystem-dependent and may represent a source of ecologically important information. The overall hypothesis of this proposal is that the ecosystem-dependent variability in the various vegetation indices is in part attributable to the effects of specular reflection. The polarization channels on MODIS provide the potential to estimate this specularly reflected light and allow the modification of the vegetation indices to better measure the photosynthetic process in plant canopies. In addition, these polarization channels potentially provide additional ecologically important information about the plant canopy