Estimating Crop Residue Cover Research Articles

Estimation of Crop Residue Cover Utilizing Multiple Ground Truth Survey Techniques and Multi-Satellite Regression Models

Soil erosion within agricultural landscapes has significant environmental and economic impacts and is strongly driven by reduced residue cover in agricultural fields. Large-area soil erosion models such as the Daily Erosion Project are important tools for understanding the patterns of soil erosion, but they rely on the accurate estimation of crop residue cover over large regions to infer the tillage practices, an erosion model input. Remote sensing analyses are becoming accepted as a reliable way to estimate crop residue cover, but most use localized training datasets that may not scale well outside small study areas. An alternative source of training data may be commonly conducted tillage surveys that capture information via rapid “windshield” surveys. In this study, we utilized the Google Earth Engine to assess the utility of three crop residue survey types (windshield tillage surveys, windshield binned residue surveys, and photo analysis surveys) and one synthetic survey (retroactively binned photo analysis data) as sources of training data for crop residue cover regressions. We found that neither windshield-based survey method was able to produce reliable regressions but that they can produce reasonable distinctions between low-residue and high-residue fields. On the other hand, both photo analysis and retroactively binned photo analysis survey data were able to produce reliable regressions with r2 values of 0.57 and 0.56, respectively. Overall, this study demonstrates that photo analysis surveys are the most reliable dataset to use when creating crop residue cover models, but we also acknowledge that these surveys are expensive to conduct and suggest some ways these surveys could be made more efficient in the future.

Cross-scale sensing of field-level crop residue cover: Integrating field photos, airborne hyperspectral imaging, and satellite data

Conservation tillage practices can bring benefits to agricultural sustainability. Accurate spatial and temporal resolved information of field-scale crop residue cover, which reflects tillage intensity, is highly valuable for evaluating the outcomes of government conservation programs and voluntary ecosystem service markets, as well as facilitating agroecosystem modeling to quantify cropland biogeochemical processes. Remote sensing has the potential to cost-effectively detect crop residue cover, however, existing regional-scale studies were limited by insufficient ground truth data, scale mismatch between coarse satellite pixels and ground data, and the lack of key spectral data for detecting crop residues. Therefore, this study developed an innovative cross-sensing framework to integrate proximal sensing, airborne hyperspectral imaging, and satellite Earth Observation through deep learning to quantify field-level crop residue cover fractions at the regional scale. Specifically, we have collected intensive ground orthographic photos and conducted airborne hyperspectral surveys at corn and soybean fields of Champaign and nearby counties in Illinois, the heartland of the U.S. Corn Belt. Through semi-automatic labeling aided by ResNet-50 and superpixel image segmentation, we obtained 6719 records of ground residue fractions. With these ground data, we developed the 1-dimensional convolution neural network (CNN) model using airborne hyperspectral reflectance, which has 0.5 m spatial resolution and 3–5 nm spectral resolution from 400 to 2400 nm, to predict residue fractions. By applying the CNN model to airborne pixels, we augmented “ground truth” data of crop residues and further combined them with Harmonized Landsat and Sentinel-2 (HLS) satellite data to quantify regional residue fractions at 30 m resolution. Results show that airborne hyperspectral imagery with CNN can accurately detect residue fractions (R2 = 0.82, relative RMSE = 11.73%) to effectively generate quasi “ground truth” data to support satellite upscaling to all fields. With independent ground data for testing, we found that the ground-airborne-satellite integrative framework achieved better predictions in estimating crop residue cover (R2 = 0.67, relative RMSE = 17.53%) than the conventional ground-satellite upscaling (R2 = 0.22, relative RMSE = 32.09%). We also found that the shortwave infrared wavelengths, particularly 2100–2300 nm, are vital for predicting crop residue cover. Sentinel-2 and Landsat-8 data have a comparable capability to track residue fractions due to similar shortwave infrared wavelengths. This study highlights the high accuracy of hyperspectral imaging to detect agroecosystem tillage management practices and the advantages of cross-scale sensing to cost-effectively integrate multi-source data to quantify field-level agroecosystem variables across scales.

A Comparison of Estimating Crop Residue Cover from Sentinel-2 Data Using Empirical Regressions and Machine Learning Methods

Quantifying crop residue cover (CRC) on field surfaces is important for monitoring the tillage intensity and promoting sustainable management. Remote-sensing-based techniques have proven practical for determining CRC, however, the methods used are primarily limited to empirical regression based on crop residue indices (CRIs). This study provides a systematic evaluation of empirical regressions and machine learning (ML) algorithms based on their ability to estimate CRC using Sentinel-2 Multispectral Instrument (MSI) data. Unmanned aerial vehicle orthomosaics were used to extracted ground CRC for training Sentinel-2 data-based CRC models. For empirical regression, nine MSI bands, 10 published CRIs, three proposed CRIs, and four mean textural features were evaluated using univariate linear regression. The best performance was obtained by a three-band index calculated using (B2 − B4)/(B2 − B12), with an R2cv of 0.63 and RMSEcv of 6.509%, using a 10-fold cross-validation. The methodologies of partial least squares regression (PLSR), artificial neural network (ANN), Gaussian process regression (GPR), support vector regression (SVR), and random forest (RF) were compared with four groups of predictors, including nine MSI bands, 13 CRIs, a combination of MSI bands and mean textural features, and a combination of CRIs and textural features. In general, ML approaches achieved high accuracy. A PLSR model with 13 CRIs and textural features resulted in an accuracy of R2cv = 0.66 and RMSEcv = 6.427%. An RF model with predictors of MSI bands and textural features estimated CRC with an R2cv = 0.61 and RMSEcv = 6.415%. The estimation was improved by an SVR model with the same input predictors (R2cv = 0.67, RMSEcv = 6.343%), followed by a GPR model based on CRIs and textural features. The performance of GPR models was further improved by optimal input variables. A GPR model with six input variables, three MSI bands and three textural features, performed the best, with R2cv = 0.69 and RMSEcv = 6.149%. This study provides a reference for estimating CRC from Sentinel-2 imagery using ML approaches. The GPR approach is recommended. A combination of spectral information and textural features leads to an improvement in the retrieval of CRC.

A Sensitive Band to Optimize Winter Wheat Crop Residue Cover Estimation by Eliminating Moisture Effect

Crop residues can retain soil moisture and increase soil organic matter. Crop residue cover is also a hot issue in agricultural remote sensing. Crop residue cover can be estimated linearly with cellulose absorption index (CAI), while moisture of crop residues and soil would reduce the accuracy of crop residue cover estimation. Crop residue and soil were used as materials to carry out the laboratory experiment to reveal the impact of moisture on crop residue cover estimation and eliminate said impact. This paper discovered a sensitive band, R2005, which can invert water content of materials to eliminate moisture effect and improve estimation accuracy of crop residue cover. In terms of inverting water content, compared with two ratio water indices proposed in 2016 (R1.6/R1.5, R1.6/R2.0), using R2005 can increase R2 from 0.828 to 0.935 and decline root-mean-square error (RMSE) from 0.12 to 0.07. At the point of results validation, R2 is 0.958 and RMSE is 0.06, indicating R2005 has a high accuracy. Another advantage of R2005 is that it is more suitable to promote to actual production because of simple and efficient band calculation.

Using Hyperspectral Crop Residue Angle Index to Estimate Maize and Winter-Wheat Residue Cover: A Laboratory Study

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.

Using a Mobile Device “App” and Proximal Remote Sensing Technologies to Assess Soil Cover Fractions on Agricultural Fields

Quantifying the amount of crop residue left in the field after harvest is a key issue for sustainability. Conventional assessment approaches (e.g., line-transect) are labor intensive, time-consuming and costly. Many proximal remote sensing devices and systems have been developed for agricultural applications such as cover crop and residue mapping. For instance, current mobile devices (smartphones & tablets) are usually equipped with digital cameras and global positioning systems and use applications (apps) for in-field data collection and analysis. In this study, we assess the feasibility and strength of a mobile device app developed to estimate crop residue cover. The performance of this novel technique (from here on referred to as “app” method) was compared against two point counting approaches: an established digital photograph-grid method and a new automated residue counting script developed in MATLAB at the University of Guelph. Both photograph-grid and script methods were used to count residue under 100 grid points. Residue percent cover was estimated using the app, script and photograph-grid methods on 54 vertical digital photographs (images of the ground taken from above at a height of 1.5 m) collected from eighteen fields (9 corn and 9 soybean, 3 samples each) located in southern Ontario. Results showed that residue estimates from the app method were in good agreement with those obtained from both photograph–grid and script methods (R2 = 0.86 and 0.84, respectively). This study has found that the app underestimates the residue coverage by −6.3% and −10.8% when compared to the photograph-grid and script methods, respectively. With regards to residue type, soybean has a slightly lower bias than corn (i.e., −5.3% vs. −7.4%). For photos with residue <30%, the app derived residue measurements are within ±5% difference (bias) of both photograph-grid- and script-derived residue measurements. These methods could therefore be used to track the recommended minimum soil residue cover of 30%, implemented to reduce farmland topsoil and nutrient losses that impact water quality. Overall, the app method was found to be a good alternative to the point counting methods, which are more time-consuming.

Determining the number of measurements required to estimate crop residue cover by different methods

Crop residue left after harvest plays an important role in controlling against soil erosion and in increasing soil organic matter content of agricultural soils. Crop residue management is a practice of great importance in southwestern Ontario, where soil management practices have an effect on Great Lakes water quality. The use of remote sensing data to measure and monitor crop residue can be fast and efficient. However, remote sensing–based studies need calibration and validation using field observations. The objective of this study was to determine the optimal number of ground-truthing field measurements (i.e., digital photographs) required to estimate residue levels. To do so, we compared the residue estimates derived from digital photographs with those derived from the standard line-transect method. Residue was measured from 18 fields located in southern Ontario, and data collected included percentage of crop residue using line-transect and photographic grid methods. Results were analyzed using linear regression, correlation tests, ANOVA, and means tests. Analyses were also conducted to retrospectively determine the minimum number of line transects or digital photos required to estimate crop residue cover at specified levels of power. Results showed that (1) percentage of crop residue estimates derived from using digital photographs were strongly correlated (<i>r</i> = 0.91, <i>p</i> &lt; 0.001, <i>R</i>² = 0.83, and <i>n</i> = 90) to those derived from using line transects; (2) counting 50 to 100 points per digital photograph was sufficient to accurately estimate the percentage of residue cover; and (3) there was greater variability in the results for soybean (<i>Glycine max</i> [L.] Merr.) than for corn (<i>Zea mays</i> L.), with the highest variability for medium-level soybean residue. Overall, the digital photograph method to estimate percentage of residue was found to be a suitable alternative to the line-transect method, which is more time consuming and labor intensive. Determining the optimal numbers of measurements to estimate crop residue cover is important to those wishing to use digital photo capture methods to record, archive, and measure residue for remote sensing calibration and validation or for handheld mobile device applications.

Spectral Indices to Improve Crop Residue Cover Estimation under Varying Moisture Conditions

Crop residues on the soil surface protect the soil against erosion, increase water infiltration and reduce agrochemicals in runoff water. Crop residues and soils are spectrally different in the absorption features associated with cellulose and lignin. Our objectives were to: (1) assess the impact of water on the spectral indices for estimating crop residue cover (fR); (2) evaluate spectral water indices for estimating the relative water content (RWC) of crop residues and soils; and (3) propose methods that mitigate the uncertainty caused by variable moisture conditions on estimates of fR. Reflectance spectra of diverse crops and soils were acquired in the laboratory over the 400–2400-nm wavelength region. Using the laboratory data, a linear mixture model simulated the reflectance of scenes with various fR and levels of RWC. Additional reflectance spectra were acquired over agricultural fields with a wide range of crop residue covers and scene moisture conditions. Spectral indices for estimating crop residue cover that were evaluated in this study included the Normalized Difference Tillage Index (NDTI), the Shortwave Infrared Normalized Difference Residue Index (SINDRI) and the Cellulose Absorption Index (CAI). Multivariate linear models that used pairs of spectral indices—one for RWC and one for fR—significantly improved estimates of fR using CAI and SINDRI. For NDTI to reliably assess fR, scene RWC should be relatively dry (RWC &lt; 0.25). These techniques provide the tools needed to monitor the spatial and temporal changes in crop residue cover and help determine where additional conservation practices may be required.

Spatial Variability Mapping of Crop Residue Using Hyperion (EO-1) Hyperspectral Data

Soil management practices that maintain crop residue cover and reduce tillage improve soil structure, increase organic matter content in the soil, positively influence water infiltration, evaporation and soil temperature, and play an important role in fixing CO2 in the soil. Consequently, good residue management practices on agricultural land have many positive impacts on soil quality, crop production quality and decrease the rate of soil erosion. Several studies have been undertaken to develop and test methods to derive information on crop residue cover and soil tillage using empirical and semi-empirical methods in combination with remote sensing data. However, these methods are generally not sufficiently rigorous and accurate for characterizing the spatial variability of crop residue cover in agricultural fields. The goal of this research is to investigate the potential of hyperspectral Hyperion (Earth Observing-1, EO-1) data and constrained linear spectral mixture analysis (CLSMA) for percent crop residue cover estimation and mapping. Hyperion data were acquired together with ground-reference measurements for validation purposes at the beginning of the agricultural season (prior to spring crop planting) in Saskatchewan (Canada). At this time, only bare soil and crop residue were present with no crop cover development. In order to extract the crop residue fraction, the images were preprocessed, and then unmixed considering the entire spectral range (427 nm–2355 nm) and the pure spectra (endmember). The results showed that the correlation between ground-reference measurements and extracted fractions from the Hyperion data using CLMSA showed that the model was overall a very good predictor for crop residue percent cover (index of agreement (D) of 0.94, coefficient of determination (R2) of 0.73 and root mean square error (RMSE) of 8.7%) and soil percent cover (D of 0.91, R2 of 0.68 and RMSE of 10.3%). This performance of Hyperion is mainly due to the spectral band characteristics, especially the availability of contiguous narrow bands in the short-wave infrared (SWIR) region, which is sensitive to the residue (lignin and cellulose absorption features).

Neural network and crop residue index multiband models for estimating crop residue cover from Landsat TM and ETM+ images

Crop residues on the soil surface provide not only a barrier against water and wind erosion, but they also contribute to improving soil organic matter content, infiltration, evaporation, temperature, and soil structure, among others. In Argentina, soybean (Glycine max (L.) Merill) and corn (Zea mays L.) are the most important crops. The objective of this work was to develop and evaluate two different types of model for estimating soybean and corn residue cover: neural networks (NN) and crop residue index multiband (CRIM) index, from Landsat images. Data of crop residue were acquired throughout the summer growing season in the central plains of Córdoba (Argentina) and used for training and validating the models. The CRIM, a linear mixing model of composite soil and residue, and the NN design, included reflectance and digital numbers from a combination of different TM bands to estimate the fractional residue cover. The results show that both methodologies are appropriate for estimating the residue cover from Landsat data. The best developed NN model yielded R2 = 0.95 when estimating soybean and corn residue cover fraction, whereas the best fit using CRIM yielded R2 = 0.87; in addition, this index is dependent on the soil and residue lines considered.

Quantifying winter wheat residue biomass with a spectral angle index derived from China Environmental Satellite data

Quantification of crop residue biomass on cultivated lands is essential for studies of carbon cycling of agroecosystems, soil-atmospheric carbon exchange and Earth systems modeling. Previous studies focus on estimating crop residue cover (CRC) while limited research exists on quantifying crop residue biomass. This study takes advantage of the high temporal resolution of the China Environmental Satellite (HJ-1) data and utilizes the band configuration features of HJ-1B data to establish spectral angle indices to estimate crop residue biomass. Angles formed at the NIRIRS vertex by the three vertices at R, NIRIRS, and SWIR (ANIRIRS) of HJ-1B can effectively indicate winter wheat residue biomass. A coefficient of determination (R2) of 0.811 was obtained between measured winter wheat residue biomass and ANIRIRS derived from simulated HJ-1B reflectance data. The ability of ANIRIRS for quantifying winter wheat residue biomass using HJ-1B satellite data was also validated and evaluated. Results indicate that ANIRIRS performed well in estimating winter wheat residue biomass with different residue treatments; the root mean square error (RMSE) between measured and estimated residue biomass was 0.038kg/m2. ANIRIRS is a potential method for quantifying winter wheat residue biomass at a large scale due to wide swath width (350km) and four-day revisit rate of the HJ-1 satellite. While ANIRIRS can adequately estimate winter wheat residue biomass at different residue moisture conditions, the feasibility of ANIRIRS for winter wheat residue biomass estimation at different fractional coverage of green vegetation and different environmental conditions (soil type, soil moisture content, and crop residue type) needs to be further explored.

Alleviating Moisture Effects on Remote Sensing Estimation of Crop Residue Cover

A robust model for estimating crop residue cover by remote sensing is required to better quantify management of crop residue in agricultural fields. There is a notable lack of robustness in the model, however, when the moisture contents of soil and crop residue vary. To minimize the influence of soil and crop residue moisture on spectral reflectance, we applied a preprocessing method, external parameter orthogonalization (EPO), which estimates the influence of moisture variation on reflectance by performing a principal component analysis on a small set of spectra that represent the variation in moisture. In this study, reflectance spectra (400–2400 nm) for four soils and four crop residues with different moisture conditions were acquired in the laboratory. Reflectance values of mixed scenes with varying proportions of crop residue and soil were simulated using a linear mixture model. The results indicated that moisture significantly affected the cellulose absorption index, the lignin–cellulose absorption index, and the relationships between crop residue cover and these two indices. For applying EPO to remove moisture effects, we proposed two strategies of EPO application that corresponded to two scenarios: a change in moisture in either soil or crop residue, and a change in moisture in both soil and crop residue. The results demonstrated that EPO was able to effectively remove the moisture effects and that the calibration model was more robust after EPO. This study helps to quantify and remove the effects of moisture on the estimation of crop residue cover.

Performance assessment of the cellulose absorption index method for estimating crop residue cover

Accurate and quick field estimation of crop residues is important for carbon sequestration and biofuel production programs. Landscape-scale assessment of this vital information has resorted to the use of remote sensing technology. The cellulose absorption index (CAI) technique has outperformed other indices for discriminating bare soil and crop residue in the Midwest, but has not been tested in the Pacific Northwest and the Central and Northern Great Plains regions of the United States. The objective of this study is to assess the performance of CAI in these regions. Malting barley (Hordeum vulgare L.), spring wheat (Triticum aestivum L.), durum (Triticum turgidum L.), field pea (Pisum sativum L.), and fallow following spring wheat and proso millet (Panicum miliaceum L.) were used in the assessment. Using a portable ground-based spectroradiometer, as well as transect line and photo-grid methods, crop residue cover was measured after the 2009 harvest season. Samples were collected for laboratory analyses. Linear regression analysis showed CAI explained 41% to 55% of the variation in the residue cover. Field pea and fallow residue cover had better correlation with CAI than did small grain crop residues. Field and laboratory measurement of CAI showed that varying pea residue cover responded significantly different than other crop residues. The performance of CAI was affected by the type of crop rather than by location and soil type in the regions. Among other measured parameters, percentage crop residue cover, hemicellulose, and residue amount were most correlated with CAI. Results document that CAI can quickly and effectively estimate percentage of residue cover in the regions.

Spectral Estimates of Crop Residue Cover and Density for Standing and Flat Wheat Stubble

Crop residue is important for erosion control, soil water storage, filling gaps in various agroecosystem‐based modeling, and sink for atmospheric carbon. The use of remote sensing technology provides a fast, objective, and efficient tool for measuring and managing this resource. The challenge is to distinguish the crop residue from the soil and effectively estimate the residue cover across a variety of landscapes. The objective of this study is to assess a select Landsat Thematic Mapper (TM) and hyperspectral‐based indices in estimating crop residue cover and amount for both standing and laid flat, and between two winter wheat (Triticum aestivum L.) harvest managements (i.e., stripper‐header and conventional header) and fallow following proso‐millet (Panicum miliaceum L.) plots. The primary plots were located in Colorado with additional plots in eastern Montana, Oregon, and Washington states. Data collected include hyperspectral scans, crop residue amount (by weight) and residue cover (by photo‐grid). Mean analyses, correlation tests, and spectral signature comparison show that the relative position of the crop residues affected the values of some remote sensing indices more than harvest management. Geographical location did not seem to influence the results. There was not enough evidence to support the use of these indices to accurately estimate the amount of residue. Hyperspectral data may deliver better estimates, but in its absence, the use of two or more of these datasets might improve the estimation of residue cover. This information will be useful in guiding analysis of remotely sensed data and in planning data acquisition programs for crop residue, which are essentially nonexistent at present.

Spectral Reflectance of Wheat Residue during Decomposition and Remotely Sensed Estimates of Residue Cover

Remotely sensed estimates of crop residue cover (fR) are required to assess the extent of conservation tillage over large areas; the impact of decay processes on estimates of residue cover is unknown. Changes in wheat straw composition and spectral reflectance were measured during the decay process and their impact on estimates of fR were assessed. Proportions of cellulose and hemicellulose declined, while lignin increased. Spectral features associated with cellulose diminished during decomposition. Narrow-band spectral residue indices robustly estimated fR, while broad-band indices were inconsistent. Advanced multi-spectral sensors or hyperspectral sensors are required to assess fR reliably over diverse agricultural landscapes.

Effect of Soil Spectral Properties on Remote Sensing of Crop Residue Cover

Conservation tillage practices often leave appreciable amounts of crop residues on soil surfaces after harvesting and generally improve soil structure, enhance soil organic C (SOC) content, and reduce soil erosion. Remote sensing methods have shown great promise in efficiently estimating crop residue cover, and thus inferring soil tillage intensity. Furthermore, these tillage intensity estimates can be used in soil C models. Reflectance spectra of more than 4200 soils and 80 crop residues were measured in the laboratory across the 350‐ to 2500‐nm wavelength region. Six remote sensing spectral indices were used to estimate crop residue cover: the Cellulose Absorption Index (CAI), the Lignin–Cellulose Absorption Index (LCA), the Normalized Difference Tillage Index (NDTI), the Normalized Difference Senescent Vegetation Index (NDSVI), and the Normalized Difference Indices 5 and 7 (NDI5 and NDI7, respectively). Soil mineralogy and SOC affected these spectral indices for crop residue cover more than soil taxonomic order, which generally had little effect on spectral reflectance. The values of the spectral indices for soils were similar within Land Resource Regions and, specifically, for Major Land Resource Areas. The CAI showed the best separation between soils and residues, followed by LCA and NDTI. Although NDSVI, NDI5, and NDI7 had significant overlaps between soil and residue index values, assessments of crop residue cover classes may be possible with local calibrations. Future satellite sensors should include appropriate bands for assessing crop residue and nonphotosynthetic vegetation.

Effects of soil composition and mineralogy on remote sensing of crop residue cover

The management of crop residues (non-photosynthetic vegetation) in agricultural fields influences soil erosion and soil carbon sequestration. Remote sensing methods can efficiently assess crop residue cover and related tillage intensity over many fields in a region. Although the reflectance spectra of soils and crop residues are often similar in the visible, near infrared, and the lower part of the shortwave infrared (400–1900 nm) wavelength region, specific diagnostic chemical absorption features are evident in the upper shortwave infrared (1900–2500 nm) region. Two reflectance band height indices used for estimating residue cover are the Cellulose Absorption Index (CAI) and the Lignin-Cellulose Absorption (LCA) index, both of which use reflectances in the upper shortwave infrared (SWIR). Soil mineralogy and composition will affect soil spectral properties and may limit the usefulness of these spectral indices in certain areas. Our objectives were to (1) identify minerals and soil components with absorption features in the 2000 nm to 2400 nm wavelength region that would affect CAI and LCA and (2) assess their potential impact on remote sensing estimates of crop residue cover. Most common soil minerals had CAI values ≤ 0.5, whereas crop residues were always > 0.5, allowing for good contrast between soils and residues. However, a number of common soil minerals had LCA values > 0.5, and, in some cases, the mineral LCA values were greater than those of the crop residues, which could limit the effectiveness of LCA for residue cover estimation. The LCA of some dry residues and live corn canopies were similar in value, unlike CAI. Thus, the Normalized Difference Vegetation Index (NDVI) or similar method should be used to separate out green vegetation pixels. Mineral groups, such as garnets and chlorites, often have wide ranges of CAI and LCA values, and thus, mineralogical analyses often do not identify individual mineral species required for precise CAI estimation. However, these methods are still useful for identifying mineral soils requiring additional scrutiny. Future advanced multi- and hyperspectral remote sensing platforms should include CAI bands to allow for crop residue cover estimation.

Mitigating the effects of soil and residue water contents on remotely sensed estimates of crop residue cover

Crop residues on the soil surface decrease soil erosion and increase soil organic carbon and the management of crop residues is an integral part of many conservation tillage systems. Current methods of measuring residue cover are inadequate for characterizing the spatial variability of residue cover over large fields. The objectives of this research were to determine the effects of water content on the remotely sensed estimates of crop residue cover and to propose a method to mitigate the effects of water content on remotely sensed estimates of crop residue cover. Reflectance spectra of crop residues and soils were measured in the lab over the 400–2400 nm wavelength region. Reflectance of scenes with various residue cover fractions and water contents was simulated using a linear mixture model. Additional spectra of scenes with mixtures of crop residues and soil were also acquired in corn, soybean, and wheat fields with different tillage treatments and different water content conditions. Crop residue cover was linearly related to the cellulose absorption index (CAI), which was defined as the relative intensity of an absorption feature near 2100 nm. Water in the crop residue significantly attenuated CAI and changed the slope of the residue cover vs. CAI relationship. Without an appropriate correction, crop residue covers were underestimated as scene water content increased. Spectral vegetation water indices were poorly related to changes in the water contents of crop residues and soils. A new reflectance ratio water index that used the two bands located on the shoulders of the cellulose absorption feature to estimate scene water conditions was proposed and tested with data from corn, soybean, and wheat fields. The ratio water index was used to describe the changes in the slope of crop residue cover vs. CAI and improve the predictions of crop residue cover. These results indicate that spatial and temporal adjustments in the spectral estimates of crop residue cover are possible. Current mutispectral imaging systems will not provide reliable estimates of crop residue cover when scene water content varies. Hyperspectral data are not required, because the three narrow bands that are used for both CAI and the scene moisture correction could be incorporated in advanced multispectral sensors. Thus, regional surveys of soil conservation practices that affect soil carbon dynamics may be feasible using either advanced multispectral or hyperspectral imaging systems.

Leachate Collection Efficiency of Zero‐tension Pan and Passive Capillary Fiberglass Wick Lysimeters

Lysimeter leachate collection efficiencies (LCEs), which are the measured leachate volume divided by estimated percolation water, are needed to convert measured leachate volumes to actual leachate fluxes. In this study, LCE of zero‐tension pan and passive capillary fiberglass wick lysimeters were evaluated and directly compared. A total of 18 pan and 18 wick lysimeters were installed 1.2 m below the soil surface in tilled and no‐till plots. From May 1995 to April 2000 the lysimeter LCEs were evaluated using a water‐balance method with evapotranspiration (ET) estimated by the Penman‐Monteith equation. On average, wick lysimeters collected 2.7 times more leachate than did pan lysimeters, and tillage had no effect on the 5‐yr total leachate volume at the 5% significance level. If the anomalous 1997 leaching year with an exceptionally warm and wet winter was excluded, wick and pan lysimeters collected about 50 and 20% of precipitation, respectively, from both tillage systems. The average 4‐yr LCE for wick lysimeters was 101% and that for the pan lysimeters was 40%. The much higher LCEs for both pan and wick lysimeters during the 1997 leaching year were thought to be the result of over‐sampling of leachate during the exceptionally wet and warm winter. Errors of ET estimates associated with estimating crop residue cover and water stress adjustment parameters were small. Errors in LCE estimates can be mathematically shown to be in the same range as those of ET estimates.

Estimating crop residue cover by blue fluorescence imaging

Crop residues, the portion of the crop left in the field after harvest, can be an important management factor in controlling soil erosion. Current methods for quantifying crop residue cover use tedious manual sampling methods or visual comparisons with photographs. There is a need for new methods to quantify residue cover that are rapid, accurate, and objective. Scenes with known amounts of crop residue were illuminated in the lab with long-wave ultraviolet (UV) radiation and fluorescence images were measured and recorded with a video camera equipped with a mieo-channel-plate image intensifier and fitted with a 453–488 nm bandpass filter. Six agricultural soils were used as backgrounds for the weathered soybean residue. Residue cover was determined from the proportion of the pixels in the image with fluorescence values greater than a threshold. Soil pixels gave the lowest fluorescence or brightness responses in the images and the residues the highest, so that brightness values of the scene spanned nearly the full range of the 8-bit video data. The images were classified in brightness categories that related to within 2% (absolute units) of measured residue cover regardless of the soil type or moisture condition (dry vs. wet). Therefore, fluorescence images can be used to provide percent residue cover in the lab, but portable equipment and procedures for use in. the field still need to be developed.