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Remote Sensing of Crop Residue and Non-photosynthetic Vegetation

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A special issue of Remote Sensing (ISSN 2072-4292). This special issue belongs to the section "Biogeosciences Remote Sensing".

Deadline for manuscript submissions: closed (31 May 2021) | Viewed by 32533

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Special Issue Editors

Dr. W. Dean Hively

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Guest Editor

Research Physical Scientist, U.S. Geological Survey, Lower Mississippi-Gulf Water Science Center, Hydrologic Transport and Response Branch, Land-Change Research Unit. Posted to U.S. Department of Agriculture, Hydrology and Remote Sensing Laboratory, Beltsville, MD 20705, USA
Interests: crop residue; winter cover crops; agricultural conservation; sustainable agriculture

Dr. Craig Daughtry

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Guest Editor

Hydrology and Remote Sensing Laboratory, US Department of Agriculture Agricultural Research Service, Beltsville, MD 20705, USA
Interests: research on the agronomic, physical, and spectral properties of plants and soils; research to assess crop residue cover and soil tillage intensity; research to measure and model the spatial variability of crops and soils at multiple scales
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Dr. Miguel Quemada

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Guest Editor

Universidad Poltécnica de Madrid, School of Agricultural Engineering and CEIGRAM, Madrid, Spain
Interests: crop residue cover; winter cover crops; conservation agriculture; soil carbon sequestration and nitrogen retention; remote sensing monitoring of agricultural systems

Special Issue Information

Dear Colleagues,

Maintenance of crop residue cover (CRC) on the soil surface can provide important benefits to the environmental performance of cropping systems by maintaining a protective mulch on the soil surface, helping to reduce erosion, nutrient loss, evaporation, and soil temperature. Remote sensing techniques have been developed to detect crop residue, thereby monitoring the adoption of conservation tillage practices. Multispectral and hyperspectral data have been used to measure CRC using broad spectral contrasts between shortwave infrared (SWIR) and near-infrared (NIR) reflectance as well as narrow contrasts measuring cellulose absorption in the SWIR. However, challenges remain in the development of a robust operational use of remote sensing to map CRC and tillage intensity. These challenges include requirements for scene-specific calibration, the influence of soil and residue moisture content on spectral features, diversity in residue and soil characteristics, and interference from green vegetation. Additionally, the range of capabilities in proximal, airborne, and spaceborne sensors is broad. Similarly, remote sensing of non-photosynthetic vegetation provides valuable information for rangeland management, our understanding of vegetation dynamics, and monitoring of carbon fluxes in the broader agricultural landscape, but scientific challenges remain to be overcome before its robust operational use.

The goal of this Special Issue is to advance remote sensing applications to address these concerns. Manuscripts are solicited that address the following potential topics, as well as others fitting to the subject area:

inter-image calibration of CRC measurements from various multispectral and hyperspectral data sources;
effect of scene moisture content on accurate determination of CRC;
effect of background soil characteristics on accurate determination of CRC, such as influence of kaolinite adsorption features on SWIR features associated with cellulose and lignin;
integration of multiple satellite and airborne data sources (VIS-NIR, SWIR, SAR, LiDAR);
high-resolution imagery applications using proximal and unmanned aerial vehicle sensor systems;
time series analysis of springtime field management;
integration of conservation implementation records and modeling applications; and
robust applications measuring non-photosynthetic vegetation in a broad agricultural row crop, rangeland, and forested landscape.

Dr. W. Dean Hively
Dr. Craig T. Daughtry
Dr. Miguel Quemada
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Remote Sensing is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2700 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

crop residue
conservation tillage
cellulose
lignin
non-photosynthetic vegetation

Published Papers (8 papers)

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31 pages, 6962 KiB

Open AccessArticle

Evaluation of SWIR Crop Residue Bands for the Landsat Next Mission

by Wells Dean Hively, Brian T. Lamb, Craig S. T. Daughtry, Guy Serbin, Philip Dennison, Raymond F. Kokaly, Zhuoting Wu and Jeffery G. Masek

Remote Sens. 2021, 13(18), 3718; https://doi.org/10.3390/rs13183718 - 17 Sep 2021

Cited by 16 | Viewed by 4016

Abstract

This research reports the findings of a Landsat Next expert review panel that evaluated the use of narrow shortwave infrared (SWIR) reflectance bands to measure ligno-cellulose absorption features centered near 2100 and 2300 nm, with the objective of measuring and mapping non-photosynthetic vegetation (NPV), crop residue cover, and the adoption of conservation tillage practices within agricultural landscapes. Results could also apply to detection of NPV in pasture, grazing lands, and non-agricultural settings. Currently, there are no satellite data sources that provide narrowband or hyperspectral SWIR imagery at sufficient volume to map NPV at a regional scale. The Landsat Next mission, currently under design and expected to launch in the late 2020’s, provides the opportunity for achieving increased SWIR sampling and spectral resolution with the adoption of new sensor technology. This study employed hyperspectral data collected from 916 agricultural field locations with varying fractional NPV, fractional green vegetation, and surface moisture contents. These spectra were processed to generate narrow bands with centers at 2040, 2100, 2210, 2260, and 2230 nm, at various bandwidths, that were subsequently used to derive 13 NPV spectral indices from each spectrum. For crop residues with minimal green vegetation cover, two-band indices derived from 2210 and 2260 nm bands were top performers for measuring NPV (R² = 0.81, RMSE = 0.13) using bandwidths of 30 to 50 nm, and the addition of a third band at 2100 nm increased resistance to atmospheric correction residuals and improved mission continuity with Landsat 8 Operational Land Imager Band 7. For prediction of NPV over a full range of green vegetation cover, the Cellulose Absorption Index, derived from 2040, 2100, and 2210 nm bands, was top performer (R² = 0.77, RMSE = 0.17), but required a narrow (≤20 nm) bandwidth at 2040 nm to avoid interference from atmospheric carbon dioxide absorption. In comparison, broadband NPV indices utilizing Landsat 8 bands centered at 1610 and 2200 nm performed poorly in measuring fractional NPV (R² = 0.44), with significantly increased interference from green vegetation. Full article

(This article belongs to the Special Issue Remote Sensing of Crop Residue and Non-photosynthetic Vegetation)

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24 pages, 67729 KiB

Open AccessArticle

A Comparative Approach of Fuzzy Object Based Image Analysis and Machine Learning Techniques Which Are Applied to Crop Residue Cover Mapping by Using Sentinel-2 Satellite and UAV Imagery

by Payam Najafi, Bakhtiar Feizizadeh and Hossein Navid

Remote Sens. 2021, 13(5), 937; https://doi.org/10.3390/rs13050937 - 3 Mar 2021

Cited by 22 | Viewed by 3892

Abstract

Conservation tillage methods through leaving the crop residue cover (CRC) on the soil surface protect it from water and wind erosions. Hence, the percentage of the CRC on the soil surface is very critical for the evaluation of tillage intensity. The objective of this study was to develop a new methodology based on the semiautomated fuzzy object based image analysis (fuzzy OBIA) and compare its efficiency with two machine learning algorithms which include: support vector machine (SVM) and artificial neural network (ANN) for the evaluation of the previous CRC and tillage intensity. We also considered the spectral images from two remotely sensed platforms of the unmanned aerial vehicle (UAV) and Sentinel-2 satellite, respectively. The results indicated that fuzzy OBIA for multispectral Sentinel-2 image based on Gaussian membership function with overall accuracy and Cohen’s kappa of 0.920 and 0.874, respectively, surpassed machine learning algorithms and represented the useful results for the classification of tillage intensity. The results also indicated that overall accuracy and Cohen’s kappa for the classification of RGB images from the UAV using fuzzy OBIA method were 0.860 and 0.779, respectively. The semiautomated fuzzy OBIA clearly outperformed machine learning approaches in estimating the CRC and the classification of the tillage methods and also it has the potential to substitute or complement field techniques. Full article

(This article belongs to the Special Issue Remote Sensing of Crop Residue and Non-photosynthetic Vegetation)

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19 pages, 2360 KiB

Open AccessArticle

Estimates of Conservation Tillage Practices Using Landsat Archive

by Peter C. Beeson, Craig S.T. Daughtry and Steven A. Wallander

Remote Sens. 2020, 12(16), 2665; https://doi.org/10.3390/rs12162665 - 18 Aug 2020

Cited by 15 | Viewed by 4802

Abstract

The USDA Environmental Quality Incentives Program (EQIP) provides financial assistance to encourage producers to adopt conservation practices. Historically, one of the most common practices is conservation tillage, primarily the use of no-till planting. The objectives of this research were to determine crop residue using remote sensing, an indicator of tillage intensity, without using training data and examine its performance at the field level. The Landsat Thematic Mapper Series platforms can provide global temporal and spatial coverage beginning in the mid-1980s. In this study, we used the Normalized Difference Tillage Index (NDTI), which has proved to be robust and accurate in studies built upon training datasets. We completed 10 years of residue maps for the 150,000 km² study area in South Dakota, North Dakota, and Minnesota and validated the results against field-level survey data. The overall accuracy was between 64% and 78% with additional improvement when survey points with suspect geolocation and satellite tillage estimates with fewer than four dates of Landsat images were excluded. This study demonstrates that, with Landsat Archive available at no cost, researchers can implement retrospective, untrained estimates of conservation tillage with sufficient accuracy for some applications. Full article

(This article belongs to the Special Issue Remote Sensing of Crop Residue and Non-photosynthetic Vegetation)

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12 pages, 5370 KiB

Open AccessArticle

The Impact of Non-Photosynthetic Vegetation on LAI Estimation by NDVI in Mixed Grassland

by Dandan Xu, Deshuai An and Xulin Guo

Remote Sens. 2020, 12(12), 1979; https://doi.org/10.3390/rs12121979 - 19 Jun 2020

Cited by 24 | Viewed by 3269

Abstract

Leaf area index (LAI) is widely used for algorithms and modelling in the field of ecology and land surface processes. At a global scale, normalized difference vegetation index (NDVI) products generated by different remote sensing satellites, have provided more than 40 years of time series data for LAI estimation. NDVI saturation issues are reported in agriculture and forest ecosystems at high LAI values, creating a challenge when using NDVI to estimate LAI. However, NDVI saturation is not reported on LAI estimation in grasslands. Previous research implies that non-photosynthetic vegetation (NPV) reduces the accuracy of LAI estimation from NDVI and other vegetation indices. A question arises: is the absence of NDVI saturation in grasslands a result of low LAI value, or is it caused by NPV? This study aims to explore whether there is an NDVI saturation issue in mixed grassland, and how NPV may influence LAI estimation by NDVI. In addition, in-situ measured plant area index (PAI) by sensors that detect light interception through the vegetation canopy (e.g., Li-cor LAI-2000), the most widely used field LAI collection method, might create bias in LAI estimation or validation using NDVI. Thus, this study also aims to quantify the contribution of green vegetation (GV) and NPV on in-situ measured PAI. The results indicate that NDVI saturation (using the portion of NDVI only contributed by GV) exists in grassland at high LAI (LAI threshold is much lower than that reported for other ecosystems in the literature), and that the presence of NPV can override the saturation effects of NDVI used to estimate green LAI. The results also show that GV and NPV in mixed grassland explain, respectively, the 60.33% and 39.67% variation of in-situ measured PAI by LAI-2000. Full article

(This article belongs to the Special Issue Remote Sensing of Crop Residue and Non-photosynthetic Vegetation)

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21 pages, 3712 KiB

Open AccessArticle

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

by Yanling Ding, Hongyan Zhang, Zhongqiang Wang, Qiaoyun Xie, Yeqiao Wang, Lin Liu and Christopher C. Hall

Remote Sens. 2020, 12(9), 1470; https://doi.org/10.3390/rs12091470 - 6 May 2020

Cited by 28 | Viewed by 4637

Abstract

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 R²_cv of 0.63 and RMSE_cv 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 R²_cv = 0.66 and RMSE_cv = 6.427%. An RF model with predictors of MSI bands and textural features estimated CRC with an R²_cv = 0.61 and RMSE_cv = 6.415%. The estimation was improved by an SVR model with the same input predictors (R²_cv = 0.67, RMSE_cv = 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 R²_cv = 0.69 and RMSE_cv = 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. Full article

(This article belongs to the Special Issue Remote Sensing of Crop Residue and Non-photosynthetic Vegetation)

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18 pages, 3470 KiB

Open AccessArticle

Assessing Soil Cover Levels during the Non-Growing Season Using Multitemporal Satellite Imagery and Spectral Unmixing Techniques

by Ahmed Laamrani, Pamela Joosse, Heather McNairn, Aaron A. Berg, Jennifer Hagerman, Kathryn Powell and Mark Berry

Remote Sens. 2020, 12(9), 1397; https://doi.org/10.3390/rs12091397 - 28 Apr 2020

Cited by 13 | Viewed by 3062

Abstract

Growing cover or winter crops and retaining crop residue on agricultural lands are considered beneficial management practices to address soil health and water quality. Remote sensing is a valuable tool to assess and map crop residue cover and cover crops. The objective of this study is to evaluate the performance of linear spectral unmixing for estimating soil cover in the non-growing season (November–May) over the Canadian Lake Erie Basin using seasonal multitemporal satellite imagery. Soil cover ground measurements and multispectral Landsat-8 imagery were acquired for two areas throughout the 2015–2016 non-growing season. Vertical soil cover photos were collected from up to 40 residue and 30 cover crop fields for each area (e.g., Elgin and Essex sites) when harvest, cloud, and snow conditions permitted. Images and data were reviewed and compiled to represent a complete coverage of the basin for three time periods (post-harvest, pre-planting, and post-planting). The correlations between field measured and satellite imagery estimated soil covers (e.g., residue and green) were evaluated by coefficient of determination (R²) and root mean square error (RMSE). Overall, spectral unmixing of satellite imagery is well suited for estimating soil cover in the non-growing season. Spectral unmixing using three-endmembers (i.e., corn residue-soil-green cover; soybean residue-soil-green cover) showed higher correlations with field measured soil cover than spectral unmixing using two- or four-endmembers. For the nine non-growing season images analyzed, the residue and green cover fractions derived from linear spectral unmixing using corn residue-soil-green cover endmembers were highly correlated with the field-measured data (mean R² of 0.70 and 0.86, respectively). The results of this study support the use of remote sensing and spectral unmixing techniques for monitoring performance metrics for government initiatives, such as the Canada-Ontario Lake Erie Action Plan, and as input for sustainability indicators that both require knowledge about non-growing season land management over a large area. Full article

(This article belongs to the Special Issue Remote Sensing of Crop Residue and Non-photosynthetic Vegetation)

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12 pages, 3445 KiB

Open AccessLetter

Detection and Classification of Non-Photosynthetic Vegetation from PRISMA Hyperspectral Data in Croplands

by Monica Pepe, Loredana Pompilio, Beniamino Gioli, Lorenzo Busetto and Mirco Boschetti

Remote Sens. 2020, 12(23), 3903; https://doi.org/10.3390/rs12233903 - 28 Nov 2020

Cited by 35 | Viewed by 4959

Abstract

This study introduces a first assessment of the capabilities of PRISMA (PRecursore IperSpettrale della Missione Applicativa)—the new hyperspectral satellite sensor of the Italian Space Agency (ASI)—for Non-Photosynthetic Vegetation (NPV) monitoring, a topic which is becoming very relevant in the field of sustainable agriculture, being an indicator of crop residue (CR) presence in the field. Data-sets collected during the mission validation phase in croplands are used for mapping the NPV presence and for modelling the diagnostic absorption band of cellulose around 2.1

μ

m with an Exponential Gaussian Optimization approach, in the perspective of the prediction of the abundance of crop residues. Results proved that PRISMA data are suitable for these tasks, and call for further investigation to achieve quantitative estimates of specific biophysical variables, also in the framework of other hyperspectral missions. Full article

(This article belongs to the Special Issue Remote Sensing of Crop Residue and Non-photosynthetic Vegetation)

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12 pages, 2836 KiB

Open AccessLetter

Semi-Automated Roadside Image Data Collection for Characterization of Agricultural Land Management Practices

by Neal Pilger, Aaron Berg and Pamela Joosse

Remote Sens. 2020, 12(14), 2342; https://doi.org/10.3390/rs12142342 - 21 Jul 2020

Cited by 4 | Viewed by 2370

Abstract

Land cover management practices, including the adoption of cover crops or retaining crop residue during the non-growing season, has important impacts on soil health. To broadly survey these practices, a number of remotely sensed products are available but issues with cloud cover and access to agriculture fields for validation purposes may limit the collection of data over large regions. In this study, we describe the development of a mobile roadside survey procedure for obtaining ground reference data for the remote sensing of agricultural land use practices. The key objective was to produce a dataset of geo-referenced roadside digital images that can be used in comparison to in-field photos to measure agricultural land use and land cover associated with crop residue and cover cropping in the non-growing season. We found a very high level of correspondence (>90% level of agreement) between the mobile roadside survey to in-field ground verification data. Classification correspondence was carried out with a portion of the county-level census image data against 114 in-field manually categorized sites with a level of agreement of 93%. The few discrepancies were in the differentiation of residue levels between 30–60% and >60%, both of which may be considered as achieving conservation practice standards. The described mobile roadside image capture system has advantages of relatively low cost and insensitivity to cloudy days, which often limits optical remote sensing acquisitions during the study period of interest. We anticipate that this approach can be used to reduce associated field costs for ground surveys while expanding coverage areas and that it may be of interest to industry, academic, and government organizations for more routine surveys of agricultural soil cover during periods of seasonal cloud cover. Full article

(This article belongs to the Special Issue Remote Sensing of Crop Residue and Non-photosynthetic Vegetation)

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