Comparing satellites and vegetation indices for cover crop biomass estimation

Swoish, Michael; Filho, Jose Franco Da Cunha Leme; Reiter, Mark S.; Campbell, James B.; Thomason, Wade E.

doi:10.1016/j.compag.2022.106900

Cited by 20 publications

(11 citation statements)

References 32 publications

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“…The distribution of the sampled biomass per crop type, the median and standard deviation values of dry aboveground biomass, the water content, and weed percentages of the field measurements are presented in Table 2. The average biomass for all the crop types is in line with what was reported by other authors [43,62] with values of DAM ranging from 0.69 to 5.08 t•ha −1 . We observed notable variations in DAM values in different groups of plants.…”

Section: Cover Crop Characteristicssupporting

confidence: 91%

“…To achieve the main objective of this study, which is devising a robust methodology to estimate winter fallow cover crop biomass in France using Sentinel-2 data, our initial step involved an analysis to determine the most effective combinations of spectral bands, vegetation indices (VIs), and statistical models. Employing a cross-validation approach akin to methodologies previously utilized by Goffart et al [43] and Swoish et al [62], we used the last image before in situ sampling. Subsequently, we scrutinized all VIs and spectral bands through a multivariate approach using different machine learning models, to harness the full spectral data encapsulated within Sentinel-2 imagery considering the last available image before the sampling.…”

Section: Methodsmentioning

confidence: 99%

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Estimating Winter Cover Crop Biomass in France Using Optical Sentinel-2 Dense Image Time Series and Machine Learning

do Nascimento Bendini,

Fieuzal,

Carrere

et al. 2024

Remote Sensing

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Cover crops play a pivotal role in mitigating climate change by bolstering carbon sequestration through biomass production and soil integration. However, current methods for quantifying cover crop biomass lack spatial precision and objectivity. Thus, our research aimed to devise a remote-sensing-based approach to estimate cover crop biomass across various species and mixtures during fallow periods in France. Leveraging Sentinel-2 optical data and machine learning algorithms, we modeled biomass across 50 fields representative of France’s diverse cropping practices and climate types. Initial tests using traditional empirical relationships between vegetation indices/spectral bands and dry biomass revealed challenges in accurately estimating biomass for mixed cover crop categories due to spectral interference from grasses and weeds, underscoring the complexity of modeling diverse agricultural conditions. To address this challenge, we compared several machine learning algorithms (Support Vector Machine, Random Forest, and eXtreme Gradient Boosting) using spectral bands and vegetation indices from the latest available image before sampling as input. Additionally, we developed an approach that incorporates dense optical time series of Sentinel-2 data, generated using a Radial Basis Function for interpolation. Our findings demonstrated that a Random Forest model trained with dense time series data during the cover crop development period yielded promising results, with an average R-squared (r2) value of 0.75 and root mean square error (RMSE) of 0.73 t·ha−1, surpassing results obtained from methods using single-image snapshots (r2 of 0.55). Moreover, our approach exhibited robustness in accounting for factors such as crop species diversity, varied climatic conditions, and the presence of weed vegetation—essential for approximating real-world conditions. Importantly, its applicability extends beyond France, holding potential for global scalability. The availability of data for model calibration across diverse regions and timeframes could facilitate broader application.

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Section: Cover Crop Characteristicssupporting

confidence: 91%

Section: Methodsmentioning

confidence: 99%

Estimating Winter Cover Crop Biomass in France Using Optical Sentinel-2 Dense Image Time Series and Machine Learning

do Nascimento Bendini,

Fieuzal,

Carrere

et al. 2024

Remote Sensing

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“…Several authors have reported higher cover crop biomass predictability when using remote sensing indices in single-species studies [ 18 , 51 , 52 ]. Higher R 2 values ranging from 0.53 to 0.76 for mixed cover crop species was reported by [ 53 ] using Planet imagery. They used several large fields to predict cover crop biomass compared to small plots in the current study, where few pixels were inside each plot.…”

Section: Discussionmentioning

confidence: 67%

“…Based on the Planet dataset, our third hypothesis was rejected, and biomass estimation was affected by mixed cover crop species. A previous study using Landsat-8, Sentinel-2 and PlanetScope sensors reported that VIs from PlanetScope sensors were most accurate (lower root mean square error and higher R 2 ) for mixed cover crop biomass estimation [ 53 ] when individual sampling sites within the fields were considered.…”

Section: Discussionmentioning

confidence: 99%

Mixed-Species Cover Crop Biomass Estimation Using Planet Imagery

Kharel

Bhandari

Mubvumba

et al. 2023

Sensors

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Cover crop biomass is helpful for weed and pest control, soil erosion control, nutrient recycling, and overall soil health and crop productivity improvement. These benefits may vary based on cover crop species and their biomass. There is growing interest in the agricultural sector of using remotely sensed imagery to estimate cover crop biomass. Four small plot study sites located at the United States Department of Agriculture Agricultural Research Service, Crop Production Systems Research Unit farm, Stoneville, MS with different cereals, legumes, and their mixture as fall-seeded cover crops were selected for this analysis. A randomized complete block design with four replications was used at all four study sites. Cover crop biomass and canopy-level hyperspectral data were collected at the end of April, just before cover crop termination. High-resolution (3 m) PlanetScope imagery (Dove satellite constellation with PS2.SD and PSB.SD sensors) was collected throughout the cover crop season from November to April in the 2021 and 2022 study cycles. Results showed that mixed cover crop increased biomass production up to 24% higher compared to single species rye. Reflectance bands (blue, green, red and near infrared) and vegetation indices derived from imagery collected during March were more strongly correlated with biomass (r = 0–0.74) compared to imagery from November (r = 0.01–0.41) and April (r = 0.03–0.57), suggesting that the timing of imagery acquisition is important for biomass estimation. The highest correlation was observed with the near-infrared band (r = 0.74) during March. The R2 for biomass prediction with the random forest model improved from 0.25 to 0.61 when cover crop species/mix information was added along with Planet imagery bands and vegetation indices as biomass predictors. More study with multiple timepoint biomass, hyperspectral, and imagery collection is needed to choose appropriate bands and estimate the biomass of mix cover crop species.

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“…NDVI index can quantify vegetation greenness, understand vegetation density, and assess plant health changes. NDVI index is calculated using red and near-infrared spectra of the multispectral cameras or from GreenSeeker sensor measurements [34,35], and it can be used to quantify vegetation greenness, understand vegetation density, and assess plant health changes. Due to its versatility and ease of extraction, it is a frequently used vegetation index among farmers and researchers.…”

Section: Introductionmentioning

confidence: 99%

Yield Prediction Using NDVI Values from GreenSeeker and MicaSense Cameras at Different Stages of Winter Wheat Phenology

Zsebő,

Bede,

Kukorelli

et al. 2024

Drones

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This work aims to compare and statistically analyze Normalized Difference Vegetation Index (NDVI) values provided by GreenSeeker handheld crop sensor measurements and calculate NDVI values derived from the MicaSense RedEdge-MX Dual Camera, to predict in-season winter wheat (Triticum aestivum L.) yield, improving a yield prediction model with cumulative growing degree days (CGDD) and days from sowing (DFS) data. The study area was located in Mosonmagyaróvár, Hungary. A small-scale field trial in winter wheat was constructed as a randomized block design including Environmental: N-135.3, P2O5-77.5, K2O-0; Balance: N-135.1, P2O5-91, K2O-0; Genezis: N-135, P2O5-75, K2O-45; and Control: N, P, K 0 kg/ha. The crop growth was monitored every second week between April and June 2022 and 2023, respectively. NDVI measurements recorded by GreenSeeker were taken at three pre-defined GPS points for each plot; NDVI values based on the MicaSense camera Red and NIR bands were calculated for the same points. Results showed a significant difference (p ≤ 0.05) between the Control and treated areas by GreenSeeker measurements and Micasense-based calculated NDVI values throughout the growing season, except for the heading stage. At the heading stage, significant differences could be measured by GreenSeeker. However, remotely sensed images did not show significant differences between the treated and Control parcels. Nevertheless, both sensors were found suitable for yield prediction, and 226 DAS was the most appropriate date for predicting winter wheat’s yield in treated plots based on NDVI values and meteorological data.

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Comparing satellites and vegetation indices for cover crop biomass estimation

Cited by 20 publications

References 32 publications

Estimating Winter Cover Crop Biomass in France Using Optical Sentinel-2 Dense Image Time Series and Machine Learning

Estimating Winter Cover Crop Biomass in France Using Optical Sentinel-2 Dense Image Time Series and Machine Learning

Mixed-Species Cover Crop Biomass Estimation Using Planet Imagery

Yield Prediction Using NDVI Values from GreenSeeker and MicaSense Cameras at Different Stages of Winter Wheat Phenology

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