Hyperspectral versus multispectral crop-productivity modeling and type discrimination for the HyspIRI mission

Mariotto, Isabella; Thenkabail, Prasad S.; Huete, Alfredo; Slonecker, E. Terrence; Platonov, Alexander E.

doi:10.1016/j.rse.2013.08.002

Cited by 155 publications

(99 citation statements)

References 29 publications

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“…Another study suggested that the first-derivative reflectance at 740 nm has high sensitivity to the difference in LAI of paddy rice at the panicle-formation stage [6]. The results of further studies investigating the spectral response of DSIR760-R739 for LAI assessment of multiple crops or vegetation types would be interesting and significant to confirm the robustness of the DSIR760-R739 predictive model because of the differing spectral responses among crops [32,42,43]. For example, an analysis comparing wheat and paddy rice, which are the two major gramineous crops that have relatively similar canopy structures, would be very interesting and required to determine the suitability of DSIR760-R739 for remotely assessing LAI.…”

Section: Discussionmentioning

confidence: 95%

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Spectral Index for Quantifying Leaf Area Index of Winter Wheat by Field Hyperspectral Measurements: A Case Study in Gifu Prefecture, Central Japan

et al. 2015

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Timely and nondestructive monitoring of leaf area index (LAI) using remote sensing techniques is crucial for precise and efficient management of crops. In this paper, a new spectral index (SI) for estimating LAI of winter wheat (Triticum aestivum L.) is proposed on the basis of field hyperspectral measurements. A simple index based on the empirical relationships between LAIs and SIs of all available two-waveband combinations from hyperspectral data is developed by considering the difference between reflectance values at 760 and 739 nm (DSIR760-R739 = R760 -R739). Among published and newly developed SIs, DSIR760-R739 exhibited a significant and strong linear relationship with LAI OPEN ACCESSRemote Sens. 2015, 7 5330 and showed outstanding performance in LAI assessments. The permissible bandwidths for broad-band DSIR760-R739 investigated using simulated reflectance were 5 nm for both 760 and 739 nm center wavelengths. The results indicate that the linear regression model based on the narrow-band and broad-band DSIR760-R739 is a simple but accurate method for timely and nondestructive monitoring of LAI.

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Section: Discussionmentioning

confidence: 95%

“…Previous research demonstrated the suitability of hyperspectral remote sensing for monitoring crop growth [31][32][33]. Particularly for LAI predictions, the saturation problem of NDVI under moderate to high LAI conditions has been extensively investigated by developing new SIs using hyperspectral data [34,35].…”

Section: Introductionmentioning

confidence: 99%

Spectral Index for Quantifying Leaf Area Index of Winter Wheat by Field Hyperspectral Measurements: A Case Study in Gifu Prefecture, Central Japan

et al. 2015

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“…When comparing literature findings, where similar correlation matrices are presented, it is remarkable that identical band combinations are rarely reported for studies based on the use of narrowband (hyperspectral) imaging [31,60,61]. This leads us to suggest that established SI models are often case specific and thus are likely to perform sub-optimal for local hyperspectral mapping applications.…”

Section: Towards a New Generation Of Spectral Indicesmentioning

confidence: 91%

“…In an attempt to generate more generic relationships, RTM-based dataset simulations have been used to assess for the most sensitive SIs [18,[25][26][27][28][29]. A spectral index can quite often be used as an estimator for a biophysical parameter using a fitting function through the data; usually by simple linear regression, but also exponential, power, logarithmic and polynomial fitting functions, among others, are commonly applied [22,30,31]. In statistics, this approach can be categorized as a parametric regression modeling approach.…”

Section: Introductionmentioning

confidence: 99%

On the Semi-Automatic Retrieval of Biophysical Parameters Based on Spectral Index Optimization

et al. 2014

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Regression models based on spectral indices are typically empirical formulae enabling the mapping of biophysical parameters derived from Earth Observation (EO) data. Due to its empirical nature, it remains nevertheless uncertain to what extent a selected regression model is the most appropriate one, until all band combinations and curve fitting functions are assessed. This paper describes the application of a Spectral Index (SI) assessment toolbox in the Automated Radiative Transfer Models Operator (ARTMO) package. ARTMO enables semi-automatic retrieval and mapping of biophysical parameters from optical remote sensing observations. The SI toolbox facilitates the assessment of biophysical parameter retrieval accuracy of established as well as new and generic SIs. For instance, based on the SI formulation used, all possible band combinations of formulations with up to ten bands can be defined and evaluated. Several options are available in the SI assessment: calibration/validation data partitioning, the addition of noise and the definition of curve fitting models. To illustrate its functioning, all two-band combinations according to simple ratio (SR) and normalized difference (ND) formulations as well as various fitting functions (linear, exponential, power, logarithmic, polynomial) functions led to similar best correlations, though spatial differences emerged when applying the functions to HyMap imagery. We suggest that a systematic SI assessment is a strong requirement in the quality assurance approach for accurate biophysical parameter retrieval.

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“…Crop types are separated in some other studies (e.g. Kumar et al 2016;Kontgis, Schneider, and Ozdogan 2015;Mariotto et al 2013;Marshall and Thenkabail 2015;Qin et al 2015). However, these studies used, one or more of the following: (a) high spatial resolution (e.g.…”

Section: Discussionmentioning

confidence: 99%

Spectral matching techniques (SMTs) and automated cropland classification algorithms (ACCAs) for mapping croplands of Australia using MODIS 250-m time-series (2000–2015) data

Teluguntla

Thenkabail

Xiong

et al. 2017

International Journal of Digital Earth

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Mapping croplands, including fallow areas, are an important measure to determine the quantity of food that is produced, where they are produced, and when they are produced (e.g. seasonality). Furthermore, croplands are known as water guzzlers by consuming anywhere between 70% and 90% of all human water use globally. Given these facts and the increase in global population to nearly 10 billion by the year 2050, the need for routine, rapid, and automated cropland mapping year-after-year and/or season-after-season is of great importance. The overarching goal of this study was to generate standard and routine cropland products, year-after-year, over very large areas through the use of two novel methods: (a) quantitative spectral matching techniques (QSMTs) applied at continental level and (b) rulebased Automated Cropland Classification Algorithm (ACCA) with the ability to hind-cast, now-cast, and future-cast. Australia was chosen for the study given its extensive croplands, rich history of agriculture, and yet nonexistent routine yearly generated cropland products using multitemporal remote sensing. This research produced three distinct cropland products using Moderate Resolution Imaging Spectroradiometer (MODIS) 250-m normalized difference vegetation index 16-day composite time-series data for 16 years: 2000 through 2015. The products consisted of: (1) cropland extent/areas versus cropland fallow areas, (2) irrigated versus rainfed croplands, and (3) cropping intensities: single, double, and continuous cropping. An accurate reference cropland product (RCP) for the year 2014 (RCP2014) produced using QSMT was used as a knowledge base to train and develop the ACCA algorithm that was then applied to the MODIS time-series data for the years 2000-2015. A comparison between the ACCA-derived cropland products

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Hyperspectral versus multispectral crop-productivity modeling and type discrimination for the HyspIRI mission

Cited by 155 publications

References 29 publications

Spectral Index for Quantifying Leaf Area Index of Winter Wheat by Field Hyperspectral Measurements: A Case Study in Gifu Prefecture, Central Japan

Spectral Index for Quantifying Leaf Area Index of Winter Wheat by Field Hyperspectral Measurements: A Case Study in Gifu Prefecture, Central Japan

On the Semi-Automatic Retrieval of Biophysical Parameters Based on Spectral Index Optimization

Spectral matching techniques (SMTs) and automated cropland classification algorithms (ACCAs) for mapping croplands of Australia using MODIS 250-m time-series (2000–2015) data

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