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21
Time-series validation of MODIS land biophysical products in a Kalahari
- International Journal of Remote Sensing
, 2005
"... Monthly measurements of leaf area index (LAI) and the fraction of absorbed photosynthetically active radiation (fAPAR) taken at approximately monthly intervals were collected along three 750 m transects in a Kalahari woodland near Mongu in western Zambia. These data were compared with MODIS NDVI (MO ..."
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Monthly measurements of leaf area index (LAI) and the fraction of absorbed photosynthetically active radiation (fAPAR) taken at approximately monthly intervals were collected along three 750 m transects in a Kalahari woodland near Mongu in western Zambia. These data were compared with MODIS NDVI (MOD13, Collection 3) and MODIS LAI and fAPAR products (MOD15, Collection 3) over a 2 year period (2000–2002). MODIS and ground-measured LAI values corresponded well, while there was a significant bias between MODIS and ground-measured fAPAR even though both MODIS variables are produced from the same algorithm. Solar zenith angle effects, differences between intercepted and absorbed photosynthetically active radiation, and differences in measurement of fAPAR (photon counts versus energy) were examined and rejected as explanations for the discrepancies between MODIS and groundmeasured fAPAR. Canopy reflectance model simulations produced different values of fAPAR with the same LAI when canopy cover was varied, indicating that errors in the estimation of canopy cover in the MODIS algorithm due to the land cover classification used are a possible cause of the fAPAR discrepancy. This is one of the first studies of MODIS land product performance in a time-series context. Despite a bias in fAPAR, our results demonstrate that the woodland canopy phonology is captured in the MODIS product.
Remote Sensing for crop management
- Photogramm. Eng. Remote Sens. 2003
"... Scientists with the Agricultural Research Service (ARS) and various government agencies and private institutions have provided a great deal of fundamental information relating spectral reflectance and thermal emittance properties of soils and crops to their agronomic and biophysical characteristics. ..."
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Cited by 20 (1 self)
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Scientists with the Agricultural Research Service (ARS) and various government agencies and private institutions have provided a great deal of fundamental information relating spectral reflectance and thermal emittance properties of soils and crops to their agronomic and biophysical characteristics. This knowledge has facilitated the development and use of various remote sensing methods for non-destructive monitor-ing of plant growth and development and for the detection of many environmental stresses which limit plant productivity. Coupled with rapid advances in computing and position-locating technologies, remote sensing from ground-, air-, and space-based platforms is now capable of providing detailed spatial and temporal information on plant response to their local environment that is needed for site specific agricultural management approaches. This manuscript, which empha-sizes contributions by ARS researchers, reviews the biophysi-cal basis of remote sensing; examines approaches that have been developed, refined, and tested for management of water, nutrients, and pests in agricultural crops; and as-sesses the role of remote sensing in yield prediction. It con-cludes with a discussion of challenges facing remote sens-ing in the future.
Application of spectral remote sensing for agronomic decisions
- Agron. J
, 2008
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Value of using different vegetative indices to quantify agricultural crop characteristics at different growth stages under varying management practices. Remote Sens
"... Abstract: The paper investigates the value of using distinct vegetation indices to quantify and characterize agricultural crop characteristics at different growth stages. Research was conducted on four crops (corn, soybean, wheat, and canola) over eight years grown under different tillage practices ..."
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Cited by 10 (0 self)
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Abstract: The paper investigates the value of using distinct vegetation indices to quantify and characterize agricultural crop characteristics at different growth stages. Research was conducted on four crops (corn, soybean, wheat, and canola) over eight years grown under different tillage practices and nitrogen management practices that varied rate and timing. Six different vegetation indices were found most useful, depending on crop phenology and management practices: (a) simple ratio for biomass, (b) NDVI for intercepted PAR, (c) SAVI for early stages of LAI, (d) EVI for later stages of LAI, (e) CIgreen for leaf chlorophyll, (f) NPCI for chlorophyll during later stages, and (g) PSRI to quantify plant senescence. There were differences among varieties of corn and soybean for the vegetation indices during the growing season and these differences were a function of growth stage and vegetative index. These results clearly imply the need to use multiple vegetation indices to best capture agricultural crop characteristics.
The Normalized Difference Vegetation Index
"... The usage of vegetation indices such as the Normalized Difference Vegetation Index (NDVI) calculated by means of remote sensing data is widely spread for describing vegetation status on large space scale. However, a big limi-tation of these indices is their inadequate time resolution for agricultura ..."
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The usage of vegetation indices such as the Normalized Difference Vegetation Index (NDVI) calculated by means of remote sensing data is widely spread for describing vegetation status on large space scale. However, a big limi-tation of these indices is their inadequate time resolution for agricultural purposes. This limitation could be over-come by the ground-based vegetation indices that could provide an interesting tool for integrating satellite-based value. In this work, three techniques to calculate the ground-NDVI have been evaluated for sugar beet cultivated in South Italy in all its phenological phases: the NDVI1 based on hand made reflectance measurements, the NDVI2 calculated on automatically reflectance measurements and the broadband NDVIb based on Photosynthetically Ac-tive Radiation (PAR) and global radiation measurements. The best performance was obtained by the NDVIb. More-over, crop-microclimate-NDVI relations were investigated. In particular, the relationship between NDVI and the Leaf Area Index (LAI) was found logarithmic with a saturation of NDVI at LAI around 1.5 m2 m-2. A clear rela-tion was found between NDVI and crop coefficient Kc experimentally determined by the ratio between actual and reference measured or modelled evapotranspirations, while the relation between NDVI and crop actual evapotran-spiration was very weak and not usable for practical purposes. Lastly, no relationship between the microclimate and the NDVI was found. Key-words: actual evapotranspiration, broadband NDVI, eddy covariance, sugar beet.
1 Characterizing and Estimating Fungal Disease Severity in Wheat
"... Many studies have shown the usefulness of hyperspectral crop reflectance data for detecting plant pathological stress. However, there is still a need to identify unique signatures for specific stresses amidst the constantly changing background associated with normal crop growth and development. Comp ..."
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Many studies have shown the usefulness of hyperspectral crop reflectance data for detecting plant pathological stress. However, there is still a need to identify unique signatures for specific stresses amidst the constantly changing background associated with normal crop growth and development. Comparing spatial and temporal patterns in crop spectra can provide such signatures. This work is concerned with characterizing and estimating fungal disease severity in a spring wheat crop. This goal can be accomplished by using a reference data set consisting of hyperspectral crop reflectance data vectors and the corresponding disease severity field assessments. The hyperspectral vectors are first normalised into zero-mean and unit-variance vectors by performing various combinations of spectral- and band-wise normalisations. Then, after applying the same normalisation procedures to the new hyperspectral data, a nearest neighbour classifier is used to classify the new data against the reference data. Finally, the corresponding stress signatures are computed using a linear transformation model. High correlation is obtained between the classification results and the corresponding field assessments of fungal disease severity, confirming the usefulness and efficiency of this approach. The effects of increased disease severity can be characterised by analysing the resulting disease signatures obtained when applying the different normalisation procedures. The low computational load of this approach makes it suitable for real-time on-vehicle applications. 1
Retrieval of biophysical vegetation parameters using simultaneous inversion of high resolution remote sensing imagery constrained by a vegetation index Separate inversions for
, 2013
"... Abstract This study proposes a new method for inverting radiative transfer models to retrieve canopy biophysical parameters using remote sensing imagery. The inversion procedure is improved with respect to standard inversion, and achieves simultaneous inversion of leaf area index (LAI), soil reflec ..."
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Abstract This study proposes a new method for inverting radiative transfer models to retrieve canopy biophysical parameters using remote sensing imagery. The inversion procedure is improved with respect to standard inversion, and achieves simultaneous inversion of leaf area index (LAI), soil reflectance (q soil ), chlorophyll content (C a?b ) and average leaf angle (ALA). In this approach, LAI is used to constrain modelling conditions during the inversion process, providing information about the phenological state of each plot under study. Due to the small area of the vegetation plots used for the inversion procedure and in order to avoid redundant information and improve computation efficiency, existing plot segmentation was used. All retrieved biophysical parameters, except LAI, were assumed to be invariant within each plot. The proposed methodology, based on the combination of PROSPECT and SAILH models, was tested over 16 cereal fields and 51 plots, on two dates, which were chosen to ensure crop assessment at different phenological stages. Plots were selected to provide a wide range of LAI between 0 and 6. Field measurements of LAI, ALA and C a?b were conducted and used as ground truth for validation of the proposed model-inversion methodology. The approach was applied to very high spatial resolution remote sensing data from the QuickBird 2 satellite. The inversion procedure was successfully applied to the imagery and retrieved LAI with R 2 = 0.83 and RMSE = 0.63 when compared to LAI2000 ground measurements. Separate inversions for
www.hydrol-earth-syst-sci.net/11/1609/2007/ © Author(s) 2007. This work is licensed under a Creative Commons License.
, 2007
"... Unsupervised classification of saturated areas using a time series of remotely sensed images ..."
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Unsupervised classification of saturated areas using a time series of remotely sensed images
System for Outdoor Plant Detection and Agricultural Embedded Systems
, 2013
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