Potential of Ultra-High-Resolution UAV Images with Centimeter GNSS Positioning for Plant Scale Crop Monitoring

To implement agricultural practices that are more respectful of the environment, precision agriculture methods for monitoring crop heterogeneity are becoming more and more spatially detailed. The objective of this study was to evaluate the potential of Ultra-High-Resolution UAV images with centimeter GNSS positioning for plant-scale monitoring. A Dji Phantom 4 RTK UAV with a 20 MPixel RGB camera was used, flying at an altitude of 25 m (0.7 cm resolution). This study was conducted on an experimental plot sown with maize. A centimeter-precision Trimble Geo7x GNSS receiver was used for the field measurements. After evaluating the precision of the UAV’s RTK antenna in static mode on the ground, the positions of 17 artificial targets and 70 maize plants were measured during a series of flights in different RTK modes. Agisoft Metashape software was used. The error in position of the UAV RTK antenna in static mode on the ground was less than one centimeter, in terms of both planimetry and elevation. The horizontal position error measured in flight on the 17 targets was less than 1.5 cm, while it was 2.9 cm in terms of elevation. Finally, according to the RTK modes, at least 81% of the corn plants were localized to within 5 cm of their position, and 95% to within 10 cm

Potential of Ultra-High-Resolution UAV Images with Centimeter GNSS Positioning for Plant Scale Crop Monitoring

To implement agricultural practices that are more respectful of the environment, precision agriculture methods for monitoring crop heterogeneity are becoming more and more spatially detailed. The objective of this study was to evaluate the potential of Ultra-High-Resolution UAV images with centimeter GNSS positioning for plant-scale monitoring. A Dji Phantom 4 RTK UAV with a 20 MPixel RGB camera was used, flying at an altitude of 25 m (0.7 cm resolution). This study was conducted on an experimental plot sown with maize. A centimeter-precision Trimble Geo7x GNSS receiver was used for the field measurements. After evaluating the precision of the UAV’s RTK antenna in static mode on the ground, the positions of 17 artificial targets and 70 maize plants were measured during a series of flights in different RTK modes. Agisoft Metashape software was used. The error in position of the UAV RTK antenna in static mode on the ground was less than one centimeter, in terms of both planimetry and elevation. The horizontal position error measured in flight on the 17 targets was less than 1.5 cm, while it was 2.9 cm in terms of elevation. Finally, according to the RTK modes, at least 81% of the corn plants were localized to within 5 cm of their position, and 95% to within 10 cm

Contribution of VSHR Pléiades images to the assessment of agricultural systems over a peri-urban region near Paris, France

Contribution of VSHR Pléiades images to the assessment of agricultural systems over a peri-urban region near Paris, France. Pléiades Day

Potential of combined Sentinel 1/ Sentinel 2 images for mapping topsoil organic carbon content over cropland taking into account soil roughness

Potential of combined Sentinel 1/ Sentinel 2 images for mapping topsoil organic carbon content over cropland taking into account soil roughness. EGU 2018, European Geophysical Union General Assembly 201

Potential of Sentinel-2 Satellite Images for Monitoring Green Waste Compost and Manure Amendments in Temperate Cropland

International audienceIncreasing attention has been placed on the agroecological impact of applying exogenous organic matter (EOM) amendments, such as green waste compost (GWC) and livestock manure, to agricultural landscapes. However, monitoring the frequency and locality of this practice poses a major challenge, as these events are typically unreported. The purpose of this study is to evaluate the utility of Sentinel-2 imagery for the detection of EOM amendments. Specifically, we investigated the spectral shift resulting from GWC and manure application at two spatial scales, satellite and proximal. At the satellite scale, multispectral Sentinel-2 image pairs were analyzed before and after EOM application to six cultivated fields in the Versailles Plain, France. At the proximal scale, multi-temporal spectral field measurements were taken of experimental plots consisting of 14 total treatments: EOM variety, amendment quantity (15, 30 and 60 t.ha−1) and tillage. The Sentinel-2 images showed significant spectral differences before and after EOM application. Exogenous Organic Matter Indices (EOMI) were developed and analyzed for separative performance. The best performing index was EOMI2, using the B4 and B12 Sentinel-2 spectral bands. At the proximal scale, simulated Sentinel-2 reflectance spectra, which were created using field measurements, successfully monitored all EOM treatments for three days, except for the buried green waste compost at a rate of 15 t.ha−1

Potential substitution of mineral N fertilizers by organic residues at the territory scale

INRA EGC et Pessac, Véolia EnvironnementPotential substitution of mineral N fertilizers by organic residues at the territory scale. 15. International Conferences of RAMIRAN (Network on Recycling of Agricultural, Municipal and Industrial Residues in Agriculture

Uncertainty of soil reflectance retrieval from SPOT and RapidEye multispectral satellite images using a per-pixel bootstrapped empirical line atmospheric correction over an agricultural region

Many authors have reported the use of empirical line regression between field target sites and image pixels in order to perform atmospheric correction of multispectral images. However few studies were dedicated to the specific reflectance retrieval for cultivated bare soils from multispectral satellite images, from a large number (≥15) of bare field targets spread over a region. Even fewer were oriented towards additional field targets for validation and uncertainty assessment of reflectance error. This study aimed at assessing ELM validation accuracy and uncertainty for predicting topsoil reflectance over a wide area (221 km2) with contrasting soils and tillage practices using a set of six multispectral images at very high (supermode SPOT5, 2.5 m), high (RapidEye, 6.5 m) and medium (SPOT4, 20 m) spatial resolutions. For each image and each spectral band, linear regression (LR) models were constructed through a series of 1000 bootstrap datasets of training/validation samples generated amongst a total of about 30 field sites used as targets, the reflectance measurements of which were made between −6 days/+7 days around acquisition date. The achieved models had an average coefficient of variation of validation errors of ∼14%, which indicates that the composition of training field sites does influence performance results of ELM. However, according to median LR-models, our approach mostly resulted in accurate predictions with low standard errors of estimation around 1-2% reflectance, validation errors of 2-3% reflectance, low validation bias ( |20°|). The predictions obtained from median LR-models through per-pixel bootstrapped ELM approach were as accurate as the ATCOR2 predictions with default parameters for the RapidEye image and were slightly more accurate and less biased for the SPOT4 images

Biases in the spatial estimation of pesticide loss to groundwater

Prediction of pesticide fate in soils is highly sensitive to parameters describing sorption and degradation processes, namely the Koc partioning coefficient between the soil solution and organic carbon fraction and the half-life DT50 for degradation. This paper explores the impact of getting Koc and DT50 values either from databases or from site-specific measurements on the predicted fate of atrazine, isoproturon and metamitron on the catchment scale. Pesticide fate on the scale of the Bruyères-et-Montbérault catchment, France, was predicted using the SEAMS software that couples a one-dimensional local-scale model of pesticide fate to a geographic information system. The results show that the use of database average values for Koc and DT50 underestimates the average risk of pesticide leaching calculated from site-specific Koc and DT50 values, whereas maximised risk scenarios based on extreme Koc and DT50 values may be overestimated when using database values. Whenever available, site-specific data should be preferred to limit bias in pesticide leaching risk assessments on the catchment scale

Potential of SPOT Multispectral Satellite Images for Mapping Topsoil Organic Carbon Content over Peri-Urban Croplands

International audienceThis study aims at identifying the potential of SPOT satellite images for predicting the topsoil soil organic carbon (SOC) content of bare cultivated soils over a large peri-urban area (221 km2) with both contrasted soils and SOC contents. Predictions were made from either field reflectance spectra, SPOT-simulated field reflectance spectra, or atmospherically corrected multispectral SPOT 2.5- and 20-m images. Field reflectance spectra were related to topsoil SOC contents by means of either partial least squares regression (PLSR) or multiple linear regression (MLR). Regression robustness was evaluated through a series of 1000 bootstrap data sets of calibration-validation samples generated among a total of 128 sampled sites. For satellite images, SOC contents were estimated from MLR bootstrap modeling on a smaller sample of pixels (∼30) that were bare soils at the time of acquisition. Field-based models obtained from SPOT-simulated spectra of regional sample sets composed of varied soils resulted in median validation root-mean-square errors (RMSE) of ∼4.6 to 4.9 g kg−1, while image-based models resulted in median validation RMSE of 4.8 g kg−1 but higher bias range and uncertainty. Postvalidation of SOC maps through an additional set of bare pixels led to RMSE values of ∼4.6 to 6.0 g kg−1. Although the resulting maps of SOC contents cannot deliver as accurate predictions as field spectra, they may enable prediction of rough classes of SOC contents with accuracies up to 60 to 70% when derived from image models, in possible agreement with the need to spatially monitor SOC classes over regional territories