Abstract
Crop residue left in the field after harvest helps to protect against water and wind erosion, increase soil organic matter, and improve soil quality, so a proper estimate of the quantity of crop residue is crucial to optimize tillage and for research into environmental effects. Although remote-sensing-based techniques to estimate crop residue cover (CRC) have proven to be good tools for determining CRC, their application is limited by variations in the moisture of crop residue and soil. In this study, we propose a crop residue angle index (CRAI) to estimate the CRC for four distinct soils with varying soil moisture (SM) content and crop residue moisture (CRM). The current study uses laboratory-based tests ((i) a dry dataset (air-dried soils and crop residues, n = 392); (ii) a wet dataset (wet soils and crop residues, n = 822); (iii) a saturated dataset (saturated soils and crop residues, n = 402); and (iv) all datasets (n = 1616)), which allows us to analysis the soil and crop residue hyperspectral response to varying SM/CRM. The CRAI combines two features that reflect the moisture content in soil and crop residue. The first is the different reflectance of soil and crop residue as a function of moisture in the near-infrared band (833 nm) and short-wave near-infrared band (1670 nm), and the second is different reflectance of soils and crop residues to lignin, cellulose, and moisture in the bands at 2101, 2031, and 2201 nm. The effects of moisture and soil type on the proposed CRAI and selected traditional spectral indices ((i) hyperspectral cellulose absorption index; (ii) hyperspectral shortwave infrared normalized difference residue index; and (iii) selected broad-band spectral indices) were compared by using a laboratory-based dataset. The results show that the SM/CRM significantly affects the broad-band spectral indices and all other spectral indices investigated are less correlated with CRC when using all datasets than when using only the dry, wet, or saturated dataset. Laboratory study suggests that the CRAI is promising for estimating CRC with the four soils and with varying SM/CRM. However, because the CRAI was only validated by a laboratory-based dataset, additional field testing is thus required to verify the use of satellite hyperspectral remote-sensing images for different crops and ecological areas.
Highlights
Crop residue left in the field after harvest helps protect against water and wind erosion, increase soil organic matter, and improve soil quality [1,2,3]
The results show that all the measured indices are correlated to varying degrees with the crop residue cover (CRC)
Based on the large quantities of laboratory-based hyperspectral dataset, this study proposes a crop residue angle index (CRAI) for estimating CRC based on five bands (833, 1670, 2031, 2101, and 2201 nm)
Summary
Crop residue left in the field after harvest helps protect against water and wind erosion, increase soil organic matter, and improve soil quality [1,2,3]. A series of methods to estimate CRC based on remote-sensing data has been proposed and applied on local and regional scales to monitor crop residue. These methods may be divided into three types: (i) the linear spectral unmixing technique, (ii) the spectral index (SI) technique, and (iii) the triangle space technique. The use of remote-sensing-based techniques to estimate CRC has been limited by the variations in the field of moisture in the crop residue and soil [18,20,23]. Of remote-sensing-based techniques to estimate CRC are limited by variations in the field of moisture in crop residue and soil. Where Ref 833, Ref 1670, Ref 2031, Ref 2101, and Ref 2201 are the reflectance at 833, 1670, 2031, 2101, and 2201 nm, respectively, and wav (1670–833), wav (2101–2031), and wav (2201–2101) are the normalized distance from (i) 1670 to 833 nm, (ii) 2101 to 2301 nm, and (iii) 2201 to 2101 nm (calculated by using Equation (4))
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