Remote sensing of crop residue cover and soil tillage intensity

Daughtry, Craig S. T.; Doraiswamy, P. C.; Hunt, E. Raymond; Stern, Alan J.; McMurtrey, J. E.; Prueger, John H.

doi:10.1016/j.still.2005.11.013

Cited by 154 publications

(49 citation statements)

References 19 publications

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“…Results suggested that tillage indices developed by van Deventeer et al (1997) poorly explained variability in cover amount (r 2 = 0.02 to 0.56). Daughtry et al (2006) reported similar results in Iowa, showing tillage indices were only weakly correlated with crop residue cover. However, Thoma et al (2004) showed classification improvement using the Crop Residue Index Multiband Model (Biard and Baret 1997), having coefficients of determination ranging from 30% to 64%.…”

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confidence: 52%

“…Within the last decade, researchers have investigated both multispectral and hyperspectral satellite remote sensing platforms, such as Landsat TM and EO-1 Hyperion, as tools to derive maps of conservation tillage adoption (van Deventeer et al 1997;Gowda et al 2001Gowda et al , 2003Thoma et al 2004;Daughtry et al 2005Daughtry et al , 2006. Early efforts by van Deventeer et al (1997) used a combination of Landsat TM bands to differentiate between conventional tillage and conservation tillage at 27 farm sites in Ohio.…”

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confidence: 99%

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Satellite mapping of conservation tillage adoption in the Little River experimental watershed, Georgia

Sullivan¹,

Strickland²,

Masters³

2008

Journal of Soil and Water Conservation

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Conservation tillage is a commonly adopted best management practice for improving soil quality and reducing erosion. However, there are currently no methods in place to monitor conservation tillage adoption at the watershed scale. The primary objective of this study was to evaluate the usefulness of Landsat TM data as a tool to depict conservation tillage in a small Coastal Plain watershed. Satellite imagery was used to calculate four commonly used indices: Normalized Difference Vegetation Index, Crop Residue Cover Index, Normalized Difference Tillage Index, and the Simple Tillage Index. Ground truth data consisted of a windshield survey, assigning each site a tillage regime (conventional or conservation tillage) at 138 locations throughout the watershed and surrounding areas. A logistical regression approach was used on two subsets of the data set (n = 20 or n = 44) to determine the influence of the number of ground control points on the success of modeling the occurrence of conservation tillage. The most accurate model was re-applied to the satellite image and evaluated using an independent sample of 94 survey sites. Results indicate that the normalized difference tillage and simple tillage indices performed best, with an overall accuracy of 71% and 78% for models developed using n = 20 and n = 44 sample locations, respectively. Errors were typically in the form of commission. Results are encouraging and suggest that currently available satellite imagery can be used for rapid assessment of conservation tillage adoption using minimal a priori information.

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confidence: 52%

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confidence: 99%

Satellite mapping of conservation tillage adoption in the Little River experimental watershed, Georgia

Sullivan¹,

Strickland²,

Masters³

2008

Journal of Soil and Water Conservation

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“…residue management) can impact upon albedo (Luyssaert et al, 2014), to the extent that remote sensing can be used to detect residues on fields (Daughtry et al, 2006;Pacheco & McNairn, 2010). Although soil organic carbon content is unlikely to impact directly upon albedo to a significant extent, some practices that are used for SCS (e.g.…”

Section: Soil Carbon Sequestration (Scs) Biochar Addition To Soilmentioning

confidence: 99%

Soil carbon sequestration and biochar as negative emission technologies

Smith

2016

Global Change Biology

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419

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Despite 20 years of effort to curb emissions, greenhouse gas (GHG) emissions grew faster during the 2000s than in the 1990s, which presents a major challenge for meeting the international goal of limiting warming to <2 °C relative to the preindustrial era. Most recent scenarios from integrated assessment models require large-scale deployment of negative emissions technologies (NETs) to reach the 2 °C target. A recent analysis of NETs, including direct air capture, enhanced weathering, bioenergy with carbon capture and storage and afforestation/deforestation, showed that all NETs have significant limits to implementation, including economic cost, energy requirements, land use, and water use. In this paper, I assess the potential for negative emissions from soil carbon sequestration and biochar addition to land, and also the potential global impacts on land use, water, nutrients, albedo, energy and cost. Results indicate that soil carbon sequestration and biochar have useful negative emission potential (each 0.7 GtCeq. yr(-1) ) and that they potentially have lower impact on land, water use, nutrients, albedo, energy requirement and cost, so have fewer disadvantages than many NETs. Limitations of soil carbon sequestration as a NET centre around issues of sink saturation and reversibility. Biochar could be implemented in combination with bioenergy with carbon capture and storage. Current integrated assessment models do not represent soil carbon sequestration or biochar. Given the negative emission potential of SCS and biochar and their potential advantages compared to other NETs, efforts should be made to include these options within IAMs, so that their potential can be explored further in comparison with other NETs for climate stabilization.

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“…He et al [2006] therefore proposed the litter-corrected ATSAVI (L-ATSAVI) to minimize litter influence by incorporating cellulose absorption index (CAI) in the ATSAVI. The CAI was developed by Daughtry et al [1996] to estimate crop residue coverage based on lignocellulose absorption feature near 2100 nm [Daughtry et al, 2004[Daughtry et al, , 2005[Daughtry et al, , 2006, and was also found effective for estimating litter coverage and litter mass in grassland [Cao et al 2010;Ren and Zhou, 2012]. Results of He et al [2006] showed that, the L-ATSAVI improved leaf area index estimation accuracy in a mixed grassland ecosystem by about 10%.…”

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confidence: 99%

Determination of green aboveground biomass in desert steppe using litter-soil-adjusted vegetation index

Ren

Zhou

2014

European Journal of Remote Sensing

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Accurate estimation of green aboveground biomass in arid and semiarid grassland is essential for a variety of studies, such as sustainable grassland management, fire risk assessment, climate change, and environmental degradation. A great need exists for the establishment of robust method for estimating green aboveground biomass in arid and semiarid grassland due to the influences of soil background and litter. In the study, a new index (litter-soil-adjusted vegetation index, L-SAVI) was proposed to estimate green aboveground biomass in arid and semiarid grassland. The L-SAVI was also evaluated based on biomass and spectra in situ measurements in the desert steppe of Inner Mongolia. Results showed that, the performance of the new index was better than that of NDVI (normalized difference vegetation index), SAVI (soil-adjusted vegetation index), MSAVI (modified soil-adjusted vegetation index), OSAVI (optimised soil-adjusted vegetation index), TSAVI (transformed soil-adjusted vegetation index), ATSAVI (adjusted transformed soil-adjusted vegetation index), PVI (perpendicular vegetation index), GSAVI (green-adjusted vegetation index), and L-ATSAVI (litter-corrected ATSAVI) in our study site. The logic behind the L-SAVI was to enable the SAVI to be less sensitive to litter by incorporating the CAI (cellulose absorption index) in the SAVI. In conclusion, the L-SAVI is a suitable predictor for complementing existing vegetation indices on green aboveground biomass estimation in arid and semiarid grassland.

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Remote sensing of crop residue cover and soil tillage intensity

Cited by 154 publications

References 19 publications

Satellite mapping of conservation tillage adoption in the Little River experimental watershed, Georgia

Satellite mapping of conservation tillage adoption in the Little River experimental watershed, Georgia

Soil carbon sequestration and biochar as negative emission technologies

Determination of green aboveground biomass in desert steppe using litter-soil-adjusted vegetation index

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