Remote Sensing Analysis of Crop Residue Levels and Cover Crop Emergence

Soil loss on agricultural fields from wind and water erosion reduces soil productivity. Delivery of eroded sediment to nearby water resources causes turbidity and phosphorus pollution that contribute to increased eutrophication of surface waters. Minnesota rivers, streams, and lakes can be better protected from water quality degradation when agricultural practices protect against soil erosion. One practice that is effective at reducing soil loss is conservation tillage, defined as leaving at least 30% of the soil covered by crop residues at the time of planting. Another beneficial practice is the planting of cover crops that protect the soils after harvest in the fall until the next crop is planted in the spring. <br/><br/>Remote sensing methods have been developed to assess soil residue cover over large areas accurately and effectively (Gowda et al., 2001; Daughtry et al., 2006) with imagery from the Landsat satellite and hyperspectral satellite imagery. Gowda et al. (2001) showed that regression models based on Landsat ratios for band 5 (1550-1750 nm) and band 7 (2080-2350 nm) could measure soil residue cover in the lower Minnesota River watershed with an accuracy of between 42-77%.<br/><br/>Assessing soil residue cover at the time of planting has traditionally been accomplished using windshield surveys. This method is time-consuming and only assesses a small fraction of agricultural fields within a given county. Traditionally surveys have been conducted by local Soil and Water Conservation District (SWCD) staff. There is a large variability in crop residue measurements across county boundaries due to subjectivity of the methods used. There is a pressing need to develop accurate, more objective methods of assessing soil residue cover at the time of planting over wide areas of the state. <br/><br/>Cover crops are increasingly being planted after harvest of the main crop in fall to reduce soil erosion, take up nitrogen and sequester carbon in soils that are often relatively exposed and unprotected from rainfall and snowmelt runoff. Remote sensing has been investigated as a tool for estimating the adoption of cover crop planting in the Midwestern region (Seifert et al., 2018) and Chesapeake Bay region (Hively et al., 2015). Both studies relied on fall Landsat imagery to estimate the Normalized Difference Vegetative Index (NDVI), which is the ratio of (NIR-R)/(NIR+R), where NIR is near-infrared and R is red reflectance. Areas with higher NDVI were associated with cover crops. Neither study attempted to account for interference from perennial crops other than cover crops which are green in the late fall after the harvest of grain crops.<br/><br/>Spring crop residue levels and fall cover crop emergence are dependent on many factors, such as, but not limited to seasonal precipitation and weather patterns, spring planting dates, growing degree days, fall harvest dates, and localized impacts such as crop disasters from droughts or floods. It is important to analyze annual crop conditions for the area of interest. The USDA National Agricultural Statistics Service (NASS) Crop Progress Reports ( ) provide a monthly report of the field conditions and progress on planting and harvest and are the best available data for background information on background conditions.

Additional Info

Field Value
dsAccessConst The remote sensing modeling efforts underlying these data products are in continual development and past years of data will be updated periodically. The user accepts data as is and assumes all risks with using the data.
dsCurrentRef Dataset references spring crop residue percentages and fall cover crop emergence percentages from May 2016 to June 2021.
dsModifiedDate 2022-07-12 23:51:22
dsOriginator Minnesota Board of Water and Soil Resources (BWSR) and the University of Minnesota
dsPurpose The purpose of this work is to develop a long-term program to systematically collect data concerning crop residue cover in spring and cover crop emergence in fall to better estimate trends in the adoption of soil conserving practices and to use these estimates with the Daily Erosion Project (DEP) to better estimate soil erosion on agricultural landscapes in Minnesota.
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Dataset extent

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