User needs for future Landsat missions

Wu, Zhuoting; Snyder, Gregory I.; Vadnais, Carolyn M.; Arora, Rohit; Babcock, Michael; Stensaas, Gregory L.; Doucette, Peter; Newman, T. J.

doi:10.1016/j.rse.2019.111214

Cited by 48 publications

(34 citation statements)

References 74 publications

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“…GreenSeeker®, WeedSeeker®, OptRx®, WEEDit®) have a limited radiometer resolution, recording only a few bands or VIs relative to the FieldSpec, individual wavelengths (recorded by FieldSpec) were grouped in order to form spectral bands (Table ). The definition of the spectral bands was based on the spectral bands from WorldView‐2, which agrees with the critical spectral bands identified for future spectral capabilities to satellites …”

Section: Methodsmentioning

confidence: 76%

“…The definition of the spectral bands was based on the spectral bands from WorldView-2, which agrees with the critical spectral bands identified for future spectral capabilities to satellites. 43 All spectral bands (only using training data) were subjected to conditional inference tree analysis within the partykit package in R in order to select spectral bands that allow a more evident separation between weeds and other targets. 44 These analysis are based on hierarchically ordered and recursively repeated binary splits, where the strength of each association is measured by a P value.…”

Section: Data Analysesmentioning

confidence: 99%

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Pre‐planting weed detection based on ground field spectral data

Pott

Amado

Schwalbert

et al. 2019

Pest Management Science

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BACKGROUND Site‐specific weed management (SSWM) demands higher resolution data for mapping weeds in fields, but the success of this tool relies on the efficiency of optical sensors to discriminate weeds relative to other targets (soils and residues) before cash crop establishment. The objectives of this study were to (i) evaluate the accuracy of spectral bands to differentiate weeds (target) and other non‐targets, (ii) access vegetation indices (VIs) to assist in the discrimination process, and (iii) evaluate the accuracy of the thresholds to distinguish weeds relative to non‐targets for each VI using training and validation data sets. RESULTS The main outcomes of this study for effectively distinguishing weeds from other non‐targets are (i) training and validation data exhibited similar spectral curves, (ii) red and near‐infrared spectral bands presented greater accuracy relative to the other bands, and (iii) the tested VIs increased the discrimination accuracy related to single bands, with an overall accuracy above 95% and a kappa above 0.93. CONCLUSION This study provided a novel approach to distinguish weeds from other non‐targets utilizing a ground‐level sensor before cash crop planting based on field spectral data. However, the limitations of this study are related to the spatial resolution to distinguish weeds that might be closer to the one this study presented, and also related to the soil and crop residues conditions at the time of collecting the readings. Overall the results presented contribute to an improved understanding of spectral signatures from different targets (weeds, soils, and residues) before planting time supporting SSWM. © 2019 Society of Chemical Industry

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Section: Methodsmentioning

confidence: 76%

Section: Data Analysesmentioning

confidence: 99%

Pre‐planting weed detection based on ground field spectral data

Pott

Amado

Schwalbert

et al. 2019

Pest Management Science

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“…This study contributes uniquely to previous literature, however, in that it uses very high spatial resolution (pixel size < 10 m) satellite imagery for this temporal sampling analysis. Because image heterogeneity, which is affected by pixel size, is an important consideration in agricultural monitoring [9], and because recent agricultural monitoring efforts have been shifting towards the use of higher spatial resolution data [14,16,23], it is important to understand how these effects play out in high spatial resolution systems. This work builds on previous studies defining temporal sampling requirements for cloud-free imaging [7] and for "reasonably clear" (70-95% cloud-free) imaging of agriculture [8] by considering the specific effect of temporal revisit intervals on corn phenology monitoring via shape model fitting.…”

Section: Temporal Resamplingmentioning

confidence: 99%

“…These efforts include fusion of Landsat and MODIS data [10][11][12], creation of a harmonized Landsat 8 and Sentinel-2 image time-series [13,14], and comparison of imagery across multiple scales (for example, using PhenoCam, Landsat, MODIS, and the Visible Infrared Imaging Radiometer Suite (VIIRS) to monitor savanna and grassland [15]). Although sensor combination and data fusion are good ways to work with the imagery that is currently available, plans are being made so that future Landsat systems will meet user requirements for temporal sampling frequency [16]. In order to understand the effect of imaging frequency on our ability to monitor crop growth and predict yield, one way is to start with a very high frequency image time-series and remove data points as needed.…”

Section: Introductionmentioning

confidence: 99%

“…Estimation of phenological transition dates based on shape model fitting has been shown to be affected by the temporal and spatial resolutions of the imagery [3,6,28], but there is more work to be done in this area. First, to the best of the authors' knowledge, temporal-specific studies have not been conducted at the finer spatial resolutions (10 m or less [16]) being considered for Landsat 10. Second, the quality and utility of the image data provided by low-cost CubeSat constellations are still under investigation.…”

Section: Introductionmentioning

confidence: 99%

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Assessing the Impact of Satellite Revisit Rate on Estimation of Corn Phenological Transition Timing through Shape Model Fitting

et al. 2019

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Agricultural monitoring is an important application of earth-observing satellite systems. In particular, image time-series data are often fit to functions called shape models that are used to derive phenological transition dates or predict yield. This paper aimed to investigate the impact of imaging frequency on model fitting and estimation of corn phenological transition timing. Images (PlanetScope 4-band surface reflectance) and in situ measurements (Soil Plant Analysis Development (SPAD) and leaf area index (LAI)) were collected over a corn field in the mid-Atlantic during the 2018 growing season. Correlation was performed between candidate vegetation indices and SPAD and LAI measurements. The Normalized Difference Vegetation Index (NDVI) was chosen for shape model fitting based on the ground truth correlation and initial fitting results. Plot-average NDVI time-series were cleaned and fit to an asymmetric double sigmoid function, from which the day of year (DOY) of six different function parameters were extracted. These points were related to ground-measured phenological stages. New time-series were then created by removing images from the original time-series, so that average temporal spacing between images ranged from 3 to 24 days. Fitting was performed on the resampled time-series, and phenological transition dates were recalculated. Average range of estimated dates increased by 1 day and average absolute deviation between dates estimated from original and resampled time-series data increased by 1/3 of a day for every day of increase in average revisit interval. In the context of this study, higher imaging frequency led to greater precision in estimates of shape model fitting parameters used to estimate corn phenological transition timing.

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