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.

Evaluating Maize Residue Cover Using Machine Learning and Remote Sensing in the Meadow Soil Region of Northeast China

The management of crop residues in farmland is crucial for increasing soil organic matter and reducing soil erosion. Identifying the regional extent of crop residue cover (CRC) is vital for implementing conservation tillage and formulating agricultural subsidy policies. The Google Earth Engine (GEE) and remote sensing images from 2019 to 2023 were used to obtain spectral characteristics before the maize seedling stage in Northeast China, followed by constructing the CRC estimation models using machine learning algorithms. To avoid the impact of multicollinearity among data, three machine learning algorithms—ridge regression (RR), partial least squares regression (PLSR), and least absolute shrinkage and selection operator (LASSO)—were employed. By comparing the accuracy of these methods, the most accurate model was determined and applied to subsequent CRC estimation. Based on the estimated CRC and Conservation Technology Information Center definitions of tillage practices, the conservation tillage mapping was completed, and the spatiotemporal distribution characteristics were thoroughly analyzed. The following findings were demonstrated: (1) the PLSR-based model outperformed RR (Pearson’s correlation coefficient (r) = 0.8875, R2 = 0.7877, RMSE = 6.99%) and LASSO (r = 0.8903, R2 = 0.7926, RMSE = 6.88%) with higher accuracy (r = 0.9264, R2 = 0.8582, RMSE = 4.93%). (2) Over the five years, the average no-tillage (NT) proportion in the study area was 15.9%, reduced tillage (RT) was 17.8%, and conventional tillage (CT) was 66.3%. In 2020 and 2022, NT rates were significantly higher at 27.5% and 15.5%, while RT were 15.7% and 30.0%, respectively. (3) Compared to the Sanjiang and Liaohe Plains (RT = 1907 km2 and 1336 km2, and NT = 559 km2 and 585 km2, respectively), the Songnen Plain exhibited higher conservation tillage rates (where RT was 3791 km2 and NT was 1265 km2). This provides crucial scientific evidence for the management and planning of conservation tillage, thereby optimizing farmland production planning, enhancing production efficiency, and promoting the development of sustainable agricultural production systems.

Maize crop residue cover mapping using Sentinel-2 MSI data and random forest algorithms

The return of crop residues to cultivated fields has numerous agronomic and soil quality benefits and, therefore, the areal extent of crop residue cover (CRC) could provide a rapid measure of the sustainability of agricultural production systems in a region. Recognizing the limitations of traditional CRC methods, a new method is proposed for estimating the spatial and temporal distribution of maize residue cover (MRC) in the Jilin Province, NE China. The method used random forest (RF) algorithms, 13 tillage indices and 9 textural feature indicators derived from Sentinel-2 data. The tillage indices with the best predictive performance were STI and NDTI (R2 of 0.85 and 0.84, respectively). Among the texture features, the best-fitting was Band8AMean-5*5 (R2 of 0.56 and 0.54 for the line-transect and photographic methods, respectively). Based on MSE and InNodePurity, the optimal combination of RF algorithm for the line-transect method was STI, NDTI, NDI7, NDRI5, SRNDI, NDRI6, NDRI7 and Band3Mean-3*3. Likewise, the optimal combination of RF algorithm for the photographic method was STI, NDTI, NDI7, SRNDI, NDRI6, NDRI5, NDRI9 and Band3Mean-3*3. Regional distribution of MRC in the Jilin Province, estimated using the RF prediction model, was higher in the central and southeast sections than in the northwest. That distribution was in line with the spatial heterogeneity of maize yield in the region. These findings showed that the RF algorithm can be used to map regional MRC and, therefore, represents a useful tool for monitoring regional-scale adoption of conservation agricultural practices.

Estimation of Maize Residue Cover Using Remote Sensing Based on Adaptive Threshold Segmentation and CatBoost Algorithm

Maize residue cover (MRC) is an important parameter to quantify the degree of crop residue cover in the field and its spatial distribution characteristics. It is also a key indicator of conservation tillage. Rapid and accurate estimation of maize residue cover (MRC) and spatial mapping are of great significance to increasing soil organic carbon, reducing wind and water erosion, and maintaining soil and water. Currently, the estimation of maize residue cover in large areas suffers from low modeling accuracy and poor working efficiency. Therefore, how to improve the accuracy and efficiency of maize residue cover estimation has become a research hotspot. In this study, adaptive threshold segmentation (Yen) and the CatBoost algorithm are integrated and fused to construct a residue coverage estimation method based on multispectral remote sensing images. The maize planting areas in and around Sihe Town in Jilin Province, China, were selected as typical experimental regions, and the unmanned aerial vehicle (UAV) was employed to capture maize residue cover images of sample plots within the area. The Yen algorithm was applied to calculate and analyze maize residue cover. The successive projections algorithm (SPA) was used to extract spectral feature indices from Sentinel-2A multispectral images. Subsequently, the CatBoost algorithm was used to construct a maize residue cover estimation model based on spectral feature indices, thereby plotting the spatial distribution map of maize residue cover in the experimental area. The results show that the image segmentation based on the Yen algorithm outperforms traditional segmentation methods, with the highest Dice coefficient reaching 81.71%, effectively improving the accuracy of maize residue cover recognition in sample plots. By combining the spectral index calculation with the SPA algorithm, the spectral features of the images are effectively extracted, and the spectral feature indices such as NDTI and STI are determined. These indices are significantly correlated with maize residue cover. The accuracy of the maize residue cover estimation model built using the CatBoost model surpasses that of traditional machine learning models, with a maximum determination coefficient (R2) of 0.83 in the validation set. The maize residue cover estimation model constructed based on the Yen and CatBoost algorithms effectively enhances the accuracy and reliability of estimating maize residue cover in large areas using multispectral imagery, providing accurate and reliable data support and services for precision agriculture and conservation tillage.

Automated Crop Residue Estimation via Unsupervised Techniques Using High-Resolution UAS RGB Imagery

Crop Residue Cover (CRC) is crucial for enhancing soil quality and mitigating erosion in agricultural fields. Accurately estimating CRC in near real-time presents challenges due to the limitations of traditional and remote sensing methods. This study addresses the challenge of accurately estimating CRC using unsupervised algorithms on high-resolution Unmanned Aerial System (UAS) imagery. We employ two methods to perform CRC estimation: (1) K-means unsupervised algorithm and (2) Principal Component Analysis (PCA) along with the Otsu thresholding technique. The advantages of these methods lie in their independence from human intervention for any supervised training stage. Additionally, these methods are rapid and suitable for near real-time estimation of CRC as a decision-making support in agricultural management. Our analysis reveals that the K-means method, with an R2=0.79, achieves superior accuracy in CRC estimation over the PCA-Otsu method with an R2=0.46. The accuracy of CRC estimation for both corn and soybean crops is significantly higher in winter than in spring, attributable to the more weathered state of crop residue. Furthermore, CRC estimations in corn fields exhibit a stronger correlation, likely due to the larger size of corn residue which enhances detectability in images. Nevertheless, the variance in CRC estimation accuracy between corn and soybean fields is minimal. Furthermore, CRC estimation achieves the highest correlation in no-till fields, while the lowest correlation is observed in conventionally tilled fields, a difference likely due to the soil disturbance during plowing in conventional tillage practices.

A 30-m annual corn residue coverage dataset from 2013 to 2021 in Northeast China

Crop residue cover plays a key role in the protection of black soil by covering the soil in the non-growing season against wind erosion and chopping for returning to the soil to increase organic matter in the future. Although there are some studies that have mapped the crop residue coverage by remote sensing technique, the results are mainly on a small scale, limiting the generalizability of the results. In this study, we present a novel corn residue coverage (CRC) dataset for Northeast China spanning the years 2013–2021. The aim of our dataset is to provide a basis to describe and monitor CRC for black soil protection. The accuracy of our estimation results was validated against previous studies and measured data, demonstrating high accuracy with a coefficient of determination (R2) of 0.7304 and root mean square error (RMSE) of 0.1247 between estimated and measured CRC in field campaigns. In addition, it is the first of its kind to offer the longest time series, enhancing its significance in long-term monitoring and analysis.

Crop Residue Cover Percentage Estimation from RGB Images Using Transfer Learning and Ensemble Ordinal Regression

Highlights A transfer learning strategy improved residue estimates from high-resolution RGB imagery. The best method used probabilistic estimates of expert classifiers to estimate residue cover. This research confirms the utility of RGB imagery to quantify residue cover in agricultural fields. Abstract. Plant residue on the soil surface increases the sustainability of food and fiber production in agricultural systems. Automated assessments of residue cover based on imagery have the potential to reduce labor and human bias associated with in-field measurements. We evaluate the capacity of a transfer learning strategy to improve the determination of residue level from high-resolution RGB images. The imagery for the study was collected from 88 field locations in 40 row crop fields in five Missouri counties between mid-April and early July in 2018 and 2019. At each field location, 50 contiguous 0.3 m × 0.2 m region of interest (ROI) images (ground sampling distance of 0.014 cm pixel--1) were extracted from imagery, resulting in a dataset of 4,400 ROI images; 3,000 were used for cross-validation and training (data collected in 2018) and 1,400 were used for testing (data collected in 2019). The percentage residue for each ROI image (ground truth) was determined by a bullseye grid method (n = 100). Features were extracted from ROI images using the VGGNet-16 CNN model, a pre-trained convolutional neural network model. We extracted 1,472 features per ROI using a global averaging and pooling strategy. The optimum feature set was identified using recursive feature elimination using a support vector machine (RFE-SVM). To estimate crop residue percentage using selected features, expert two-class SVMs were trained to separate adjacent levels of residue cover, where the rationale of the ensemble was to allow each of the two-class SVMs to find the hyperplanes that maximize the margin between the corresponding two consecutive classes. Based on the distance of the samples to these hyperplanes, probabilistic estimates of the data-point belonging to the class were computed. With the combined knowledge of probabilistic estimates from each expert classifier, the percentage crop residue cover of each ROI image was calculated. We tested our approach with 3-, 4-, 5-, and 8-class problems, achieving the best results with the 8-class problem with r2 = 0.93 at the ROI level, r2 = 0.97 at the field-location level, and minimal bias in residue estimates in low residue conditions. These results are superior to other reported estimates of percent residue derived from imagery. This research confirms the utility of high-resolution RGB imagery to quantify residue cover in agricultural systems. Keywords: Convolutional neural network, Soil erosion, Support vector machine, Transfer learning.

Climate Smart Irrigation Practices for Improving Water Productivity in India: A Comprehensive Review

India, supporting 17% of the global population, from limited land (2.4% of the world's total) and freshwater resources (4%), faces severe water scarcity issues. The country experiences heightened challenges due to a monsoon climate leading to floods and droughts. Irrigation efficiency in India is 35-40% and irrigated area ~48.9%. To meet the demands of a large or growing population in limited land and water resources, climate-smart irrigation practices (CSIPs) are imperative. These practices not only increase yield but also precisely supply water, reduce water application volume, and enhance soil health under changing climate conditions. Precision water management technologies includes; advanced agro-techniques, micro-irrigation, conservation agriculture, crop diversification, integrated farming systems, and water harvesting. Micro-irrigation, encompassing drip and sprinkler systems, emerges as a critical solution for efficient water use. Techniques like Surface Drip Irrigation and Sub-surface Drip Irrigation (SSDI) not only save water but also enhance nutrient transport and reduce labor costs. The automation of micro-irrigation through sensors and wireless communication revolutionizes traditional practices, ensuring precise water management and boosting agricultural productivity. In addition, advanced agro-techniques, including laser land leveling, furrow-irrigated raised beds, aerobic rice cultivation, system of rice intensification, ground cover cum rice production system and Saguna rice technique have good potential to save water and improve water productivity. Implementing these advanced agro-techniques not only conserves water but also contributes to sustainable agriculture by improving overall water productivity, reducing environmental impact, and enhancing crop productivity. The integration of conservation agriculture (minimum soil disturbance, crop residue cover and crop diversification), integrated farming systems (combine diverse agricultural activities synergistically), and water harvesting is imperative for sustainable water management. This review paper systematically compiles climate-smart irrigation practices, including precision water management, combined with conservation agriculture, crop diversification, integrated farming systems, and water harvesting. This review paper offers researchers a comprehensive understanding of different CSIPs, assessing their impact on water conservation, increased crop and water productivity, and sustainability amid climate change. Farmers can gain practical understandings of CSIPs, while policymakers obtain essential information for addressing national water mission goals.

Comparison of Five Spectral Indices and Six Imagery Classification Techniques for Assessment of Crop Residue Cover Using Four Years of Landsat Imagery

Determining residue cover on agricultural land is an important task. Residue cover helps reduce soil erosion and helps sequester carbon. Many studies have used either spectral indices or classification techniques to map residue cover using satellite imagery. Unfortunately, most of these studies use only a few spectral indices or classification techniques and generally only study an area for a single year with a certain level of success. This manuscript presents an investigation of five spectral indices and six classification techniques over four years to determine if a single spectral index or classification technique performs consistently better than the others. A second objective is to determine whether using the coefficient of determination (R2) from the relationship between residue cover and a spectral index is a reasonable substitute for calculating accuracy. Field visits were conducted for each of the years studied and used to create the correlations with the spectral indices and as ground truth for the classification techniques. It was found that no spectral index/classification technique is consistently better than all the others. Classification techniques tended to be more accurate in 2011 and 2013, while spectral indices tended to be more accurate in 2015 and 2018. The combination of spectral indices/classification techniques outperformed the individual approach. For the second objective, it was found that R2 is not a great indicator of accuracy. Root mean square error (RMSE) is a better indicator of accuracy than R2. However, simply calculating the accuracy would be the best of all.

Monitoring the impacts of crop residue cover on agricultural productivity and soil chemical and physical characteristics

To the best of our knowledge, the impacts of crop residue cover (CRC) on agricultural productivity and soil fertility have not been studied by previous researchers. In this regard, this study aims to apply an integrated approach of remote sensing and geospatial analysis to detect CRC and monitor the effects of CRC on agricultural productivity, as well as soil chemical and physical characteristics. To achieve this, a series of Landsat images and 275 ground control points (GCPs) collected from the study areas for the years 2013, 2015, and 2021 were used. A convolutional neural network (CNN), a class of artificial neural network has commonly applied to analyze visual imagery, was employed in this study for CRC detection in two classes (Not-CRC and CRC) for the years 2013, 2015, and 2021. To assess the effects of CRC, the Normalized Difference Vegetation Index (NDVI) was applied to Landsat image series for the years 2015 (22 images), 2019 (20 images), and 2022 (23 images). Furthermore, this study evaluates the impacts of CRC on soil fertility based on collected field observation data. The results show a high performance (Accuracy of > 0.95) of the CNN for CRC detection and mapping. The findings also reveal positive effects of CRC on agricultural productivity, indicating an increase in vegetation density by about 0.1909 and 0.1377 for study areas 1 and 2, respectively, from 2015 to 2022. The results also indicate an increase in soil chemical and physical characteristics, including EC, PH, Na, Mg, HCO3, K, silt, sand, and clay from 2015 to 2022, based on physical examination. In general, the findings underscore that the value of an integrated approach of remote sensing and geospatial analysis for detecting CRC and monitoring its impacts on agricultural productivity and soil fertility. This research can offer valuable insight to researchers and decision-makers in the field of soil science, land management and agriculture.

Factors Influencing Ammonia Concentrations above Corn Fields after Dairy Manure Application

Ammonia-nitrogen (NH3-N) loss from agriculture decreases crop yield potential and environmental quality. Incorporating animal manures by chisel plowing (CP) can reduce NH3 loss but may increase crop residue loss compared to lower disturbance incorporation methods and vertical tillage (VT). Few studies have evaluated VT efficacy for incorporating manure and reducing NH3 concentrations compared to traditional tillage tools, such as CP. Six trials during 2013 to 2016 were conducted to evaluate the impacts of manure incorporation method (CP, VT, or broadcast) and weather conditions at the time of application on NH3-N concentrations at a dairy research farm in central Wisconsin, USA. Passive samplers measured NH3-N concentrations at 30-cm above the ground during the first 0 to 24 and 24 to 48 h post-manure application/incorporation. Average NH3-N concentrations for CP and VT were 44 to 86% of broadcast and similar for most trials, while crop residue coverage for VT was greater than CP (39 and 22% of control plots, respectively). Concentrations of NH3-N were correlated with the amount of plot area covered by manure for the first (r = 0.56, p < 0.0001) and second measurement periods (r = 0.85, p < 0.0001). Results show that VT had comparable NH3-N concentration reductions to CP while conserving more crop residue.

Quantification of the effects of long-term straw return on soil organic matter spatiotemporal variation: a case study in a typical black soil region

Abstract. The straw return practice is essential to soil organic matter (SOM) accumulation in the black soil area with high carbon sequestration potential. However, due to lacking accurate spatial distribution of straw return, few studies have carried out rigorous research on the impact of long-term straw return on SOM spatiotemporal variation on a regional scale. This study was carried out across an approximately 3000 km2 area in Lishu County, northeastern China, a typical agricultural plain. Based on a total of 619 soil samples and 16 environmental covariates, the study mapped the spatial distributions of SOM in 2006 and 2018 by random forest (RF) and evaluated the effects of the interaction of soil properties, land use, and straw return on SOM spatial–temporal variation. The results show that in the context of long-term straw return, the mean SOM content increased from 18.93 to 20.84 g kg−1 during 2006–2018. And 74.49 % of the region had a significant increase (maximum of 24.41 g kg−1) of SOM. The severest SOM loss occurred in the northwest due to the light texture and the transition from paddy fields to dryland. Nevertheless, for areas from paddy fields to dryland, the SOM loss decreased with the increased cumulative crop residue coverage (CRC). The SOM even increased by 1.79 g kg−1 when the cumulative crop residue coverage reached 0.60–1.00. In addition, soil with higher initial SOM and sand content had a lower response to straw return. The study revealed that straw return is beneficial to carbon sink in farmland and is a better way to prevent a carbon source caused by the conservation of paddy field to dryland.

Overcoming agricultural sustainability challenges in water-limited environments through soil health and water conservation: insights from the Ogallala Aquifer Region, USA

ABSTRACTA rapid decline in water availability for crop production has driven substantial changes in cropping systems in the arid and semi-arid regions, including transitions from irrigated to dryland cropping. Management decisions play a critical role in the sustainability of agricultural systems facing transitions. Specifically, adopting practices that increase crop water use efficiency, improve soil health, and conserve water in the soil profile could improve agricultural sustainability. This review discusses published literature on the challenges associated with crop production and highlights management strategies to sustain soil health, enhance agricultural production, and farm profitability in the Ogallala Aquifer region to elucidate pathways to agricultural sustainability in water-limited environments around the world. We searched for published papers discussing soil health and water conservation practices, including conservation tillage, crop residue management, crop diversification, cover cropping, and livestock integration in cropping systems. These studies demonstrate adopting conservation systems can increase soil organic carbon (SOC) storage, water infiltration, soil microbial activities, water use efficiency, and decrease N fertilizer inputs compared to conventional systems. Integrating more than one soil and water conservation practice can complement to enhance soil health and sustainability of dryland or limited-irrigation agriculture in the Ogallala Aquifer region and similar agroecosystems across the world.

Modeling the Corn Residue Coverage after Harvesting and before Sowing in Northeast China by Random Forest and Soil Texture Zoning

Crop residue cover is vital for reducing soil erosion and improving soil fertility, which is an important way of conserving tillage to protect the black soil in Northeast China. How much the crop residue covers on cropland is of significance for black soil protection. Landsat-8 and Sentinel-2 images were used to estimate corn residue coverage (CRC) in Northeast China in this study. The estimation model of CRC was established for improving CRC estimation accuracy by the optimal combination of spectral indices and textural features, based on soil texture zoning, using the random forest regression method. Our results revealed that (1) the optimization C5 of spectral indices and textural features improves the CRC estimation accuracy after harvesting and before sowing with determination coefficients (R2) of 0.78 and 0.73, respectively; (2) the random forest improves the CRC estimation accuracy after harvesting and before sowing with R2 of 0.81 and 0.77, respectively; (3) considering the spatial heterogeneity of the soil background and the usage of soil texture zoning models increase the accuracy of CRC estimation after harvesting and before sowing with R2 of 0.84 and 0.81, respectively. In general, the CRC estimation accuracy after harvesting was better than that before sowing. The results revealed that the corn residue coverage in most of the study area was 0.3 to 0.6 and was mainly distributed in the Songnen Plain. By the estimated corn residue coverage results, the implementation of conservation tillage practices is identified, which is vital for protecting the black soil in Northeast China.

Effects of Selected Manure Sources on Runoff, Soil Loss, and Nutrient Transport

Highlights Surface water runoff was reduced following manure additions. Sediment yield was reduced following manure additions. Nutrient loss was increased following manure additions. Abstract. Soil erosion models are becoming increasingly accurate and now can simulate a variety of surface covers which can lessen erosion by reducing detachment and movement of soil particles. Currently, there is limited data on the effects of animal manure on soil erosion. Most process-based models credit animal manure as crop residue cover. Thus, there is a need for bench studies under laboratory conditions, as well as field studies to determine the effects manure applications have on runoff and sediment loss. Rainfall simulation studies were conducted to evaluate the influence of different animal manures on runoff, sediment yield, and nutrient loss using undisturbed soil monoliths placed in perforated trays. The soil was collected from fields managed under long-term no-till and a conventional till for over 15 years. Poultry litter, solid dairy manure, liquid swine manure, liquid dairy manure applied to the experimental units were compared to a no manure treatment. Generally, sediment yield and runoff amounts were reduced following manure application regardless of source (averaged across all rainfall events and manure rates), with reductions generally occurring with increasing rates of manure. When evaluating each rainfall event separately, runoff increased with increasing rates of manure for the liquid dairy manure only in the initial rainfall simulation events due to surface sealing. However, the effects of surface sealing decreased from the first rainfall simulation event to the third event. Sediment yield tended to be higher in conventional till versus no-till (only evaluated with liquid dairy manure); however, the addition of manure tended to be more effective at reducing sediment loss with conventional till. Dissolved reactive P, NH4-N, and NO3-N levels in the runoff effluent increased for all of the manure sources with increasing rates of manure. These results suggest that manure applications can reduce soil loss, especially from the conventionally-tilled treatment. Keywords: Manure, Rainfall simulation, Runoff, Soil erosion.

Optimizing Landsat Next Shortwave Infrared Bands for Crop Residue Characterization

This study focused on optimizing the placement of shortwave infrared (SWIR) bands for pixel-level estimation of fractional crop residue cover (fR) for the upcoming Landsat Next mission. We applied an iterative wavelength shift approach to a database of crop residue field spectra collected in Beltsville, Maryland, USA (n = 916) and computed generalized two- and three-band spectral indices for all wavelength combinations between 2000 and 2350 nm, then used these indices to model field-measured fR. A subset of the full dataset with a Normalized Difference Vegetation Index (NDVI) < 0.3 threshold (n = 643) was generated to evaluate green vegetation impacts on fR estimation. For the two-band wavelength shift analyses applied to the NDVI < 0.3 dataset, a generalized normalized difference using 2226 nm and 2263 nm bands produced the top fR estimation performance (R2 = 0.8222; RMSE = 0.1296). These findings were similar to the established two-band Shortwave Infrared Normalized Difference Residue Index (SINDRI) (R2 = 0.8145; RMSE = 0.1324). Performance of the two-band generalized normalized difference and SINDRI decreased for the full-NDVI dataset (R2 = 0.5865 and 0.4144, respectively). For the three-band wavelength shift analyses applied to the NDVI < 0.3 dataset, a generalized ratio-based index with a 2031–2085–2216 nm band combination, closely matching established Cellulose Absorption Index (CAI) bands, was top performing (R2 = 0.8397; RMSE = 0.1231). Three-band indices with CAI-type wavelengths maintained top fR estimation performance for the full-NDVI dataset with a 2036–2111–2217 nm band combination (R2 = 0.7581; RMSE = 0.1548). The 2036–2111–2217 nm band combination was also top performing in fR estimation (R2 = 0.8690; RMSE = 0.0970) for an additional analysis assessing combined green vegetation cover and surface moisture effects. Our results indicate that a three-band configuration with band centers and wavelength tolerances of 2036 nm (±5 nm), 2097 nm (±14 nm), and 2214 (±11 nm) would optimize Landsat Next SWIR bands for fR estimation.

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.

Improving soil organic carbon predictions from a Sentinel–2 soil composite by assessing surface conditions and uncertainties

Soil organic carbon (SOC) prediction from remote sensing is often hindered by disturbing factors at the soil surface, such as photosynthetic active and non–photosynthetic active vegetation, variation in soil moisture or surface roughness. With the increasing amount of freely available satellite data, recent studies have focused on stabilizing the soil reflectance by building reflectance composites using time series of images. Although composite imagery has demonstrated its potential in SOC prediction, it is still not well established if the resulting composite spectra mirror the reflectance fingerprint of the optimal conditions to predict topsoil properties (i.e. a smooth, dry and bare soil).We have collected 303 photos of soil surfaces in the Belgian loam belt where five main classes of surface conditions were distinguished: smooth seeded soils, soil crusts, partial cover by a growing crop, moist soils and crop residue cover. Reflectance spectra were then extracted from the Sentinel–2 images coinciding with the date of the photos. After the growing crop was removed by an NDVI < 0.25, the Normalized Burn Ratio (NBR2) was calculated to characterize the soil surface, and a threshold of NBR2 < 0.05 was found to be able to separate dry bare soils from soils in unfavorable conditions i.e. wet soils and soils covered by crop residues. Additionally, we found that normalizing the spectra (i.e. dividing the reflectance of each band by the mean reflectance of all spectral bands) allows for cancelling the albedo shift between soil crusts and smooth soils in seed–bed conditions. We then built the exposed soil composite from Sentinel–2 imagery for southern Belgium and part of Noord-Holland and Flevoland in the Netherlands (covering the spring periods of 2016–2021). We used the mean spectra per pixel to predict SOC content by means of a Partial Least Squares Regression Model (PLSR) with 10–fold cross–validation. The uncertainty of the models was assessed via the prediction interval ratio (PIR). The cross validation of the model gave satisfactory results (mean of 100 bootstraps: model efficiency coefficient (MEC) = 0.48 ± 0.07, RMSE = 3.5 ± 0.3 g C kg–1, RPD = 1.4 ±0.1 and RPIQ = 1.9 ± 0.3). The resulting SOC prediction maps show that the uncertainty of prediction decreases when the number of scenes per pixel increases, and reaches a minimum when at least six scenes per pixel are used (mean PIR of all pixels is 12.4 g C kg–1, while mean SOC predicted is 14.1 g C kg–1). The results of a validation against an independent data set showed a median difference of 0.5 g C kg–1 ± 2.8 g C kg–1 SOC between the measured (average SOC content 13.5 g C kg–1) and predicted SOC contents at field scale. Overall, this compositing method shows both realistic within field and regional SOC patterns.

The potential of conservation agriculture to improve nitrogen fixation in cowpea under the semi-arid conditions of Kenya

Low-cost but productive crop intensification options are needed to assist smallholder farmers in the tropics to move away from poverty. This study assessed the capacity of cowpea (Vigna unguiculata (L.) Walp.) to perform biological N fixation (BNF) under no-tillage practices, crop residue cover and intercropping with maize (Zea mays L.). The study was performed during the long rains of 2017 at Kiboko experimental station, located in semi-arid eastern Kenya. The research field trials had been running for three years (6 cropping seasons) by the time the sampling took place. The experimental set up was a split-plot design: main plots being tillage system (no till + maize stover retention (CA), and conventional tillage to 15 cm depth without mulch retention (CT)), sub-plot being cropping system (maize-cowpea intercrop, maize monocrop, and cowpea monocrop). Cowpea plants were sampled at 50% flowering stage and at physiological maturity to investigate biomass production and %N derived from the atmosphere (%Ndfa) through the 15N natural abundance technique, using maize as a reference control. Results showed that the number of nodules per plant was higher in CA treatments rather than in CT although not significant (p&amp;gt;0.05). Mean cowpea grain yield at harvest varied between 472 – 590 kg ha‐1 in intercrops whereas grain yield in monocrops was between 1465 - 1618 kg ha-1. Significant differences were however recorded between treatments with CT monocropped cowpea at flowering recording the highest mean %Ndfa (62%) and CT intercrop the lowest (52%). At harvest stage CA inter recorded the highest %Ndfa (54%) while CT intercrop the lowest (41%). The %Ndfa was higher (p&amp;lt;0.05) at flowering (between 57- 69%) compared with 45 - 64 % at harvest stage. Overall cowpeas in intercrops derived between 17.8 - 22.8 kg ha-1 of their total N from atmospheric dinitrogen fixation while monocrops between 54.9 - 55.2 kg ha-1. The effect of CA on BNF was positive but not significantly different from CT. These results suggest that CA has the potential to enhance the BNF process but there is a need to explore in future alternative spatial arrangement and variety choice in intercropping of cowpea and maize to optimize the BNF process.

Evaluation of the impact of crop residue on fractional vegetation cover estimation by vegetation indices over conservation tillage cropland: a simulation study

ABSTRACT Accurate estimation of fractional vegetation cover (FVC) is of great significance to agricultural production. Crop residue management affect crop residue cover (CRC) over croplands. Crop and crop residue on the soil surface both contribute to overall canopy reflectance. Few studies, however, have examined the effect of crop residue on vegetation indices (VIs) and estimated FVC. The present study evaluated the response of eight commonly used VIs to crop residues and FVC uncertainty caused by crop residue based on the dimidiate pixel model (DPM) by using simulated reflectance of low-tilled cropland via a three-dimensional radiative transfer model. The absolute difference (AD) was used to quantify the spectral difference between crop residues and soils in red and near infrared wavelengths. Increases in normalized difference VI (NDVI), ratio VI (RVI), transformed soil-adjusted VI (TSAVI), and normalized difference phenology index (NDPI) were observed when green crops were mixed with crop residue that had negative ADs with soils, but decreases in enhance VI (EVI), perpendicular VI (PVI), SAVI, and litter-soil-adjusted VI (L-SAVI) were observed when crop residue was present under medium and high vegetation cover. The presence of crop residue with a positive AD with soils reduced NDVI, RVI, TSAVI, and NDPI while increased the other VIs. Crop residue had the least impact on EVI- and SAVI-based DPMs, with FVC-estimated uncertainty less than 0.1, followed by the NDPI- and L-SAVI-based model, while DPMs based on NDVI- and RVI performed poorly. Each VI-based DPM’s estimated uncertainty was highly correlated with AD values. Furthermore, the majority of the VI-based models were sensitive to solar position except for the NDPI-based model. Our findings highlight the need of considering the impact of crop residue on FVC retrieval over low-tilled cropland in future research.