From Remotely Sensed Vegetation Onset to Sowing Dates: Aggregating Pixel-Level Detections into Village-Level Sowing Probabilities

Marinho, Eduardo; Vancutsem, Christelle; Fasbender, Dominique; Francois, Kayitakire; Pini, Giancarlo; Pekel, Jean-François

doi:10.3390/rs61110947

Cited by 7 publications

(5 citation statements)

References 29 publications

(51 reference statements)

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“…While numerous methods have been proposed to detect the timing of vegetation green-up, maturity, senescence, and dormancy (e.g., Zhang et al, 2003;Funk and Budde, 2009), only a few have related phenological information derived from RS time series to determine actual sowing dates (e.g. Marinho et al, 2014;Jain et al, 2016;Boschetti et al, 2017;Manfron et al, 2017).…”

Section: Factors Affecting Crop Lodgingmentioning

confidence: 99%

Remote sensing-based crop lodging assessment: Current status and perspectives

Chauhan

Darvishzadeh

Boschetti

et al. 2019

ISPRS Journal of Photogrammetry and Remote Sensing

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Rapid and quantitative assessment of crop lodging is important for understanding the causes of the phenomena, improving crop management, making better production and supporting loss estimates in general. Accurate information on the location and timing of crop lodging is valuable for farmers, agronomists, insurance loss adjusters, and policymakers. Lodging studies can be performed to assess the impact of lodging events or to model the risk of occurrence, both of which rely on information that can be acquired by field observations, from meteorological data and from remote sensing (RS). While studies applying RS data to assess crop lodging dates back three decades, there has been no comprehensive review of the status, potential, current approaches, and challenges in this domain. In this position paper, we review the trends in field/lab-based and RS-based studies for crop lodging assessment and discuss the strengths and weaknesses of current approaches. Theoretical background on crop lodging is presented, and the scope of RS in assessing plant characteristics associated with lodging is reviewed and discussed. The review focuses on RS-based studies, grouping them according to the platform deployed (i.e., ground-based, airborne and spaceborne), with an emphasis on analyzing the pros and cons of the technology. Finally, the challenges, research gaps, perspectives for future research, and an outlook on new sensors and platforms are presented to provide state-of-the-art and future scenarios of RS in lodging assessment. Our review reveals that the use of RS techniques in crop lodging assessment is still in an experimental stage. However, there is increasing interest within the RS scientific community (based on the increased rate of publications over time) to investigate its use for crop lodging detection and risk mapping. The existing satellitebased lodging assessment studies are very few, and the operational application of the current approaches over large spatial extents seems to be the biggest challenge. We identify opportunities for future studies that can develop quantitative models for estimating lodging severity and mapping lodging risk using RS data. 1.2. The role of remote sensing The past few decades have witnessed considerable growth in the use of sensors on-board Earth-Observation (EO) systems for agricultural monitoring applications. Today, crop biophysical properties such as leaf

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Section: Factors Affecting Crop Lodgingmentioning

confidence: 99%

Remote sensing-based crop lodging assessment: Current status and perspectives

Chauhan

Darvishzadeh

Boschetti

et al. 2019

ISPRS Journal of Photogrammetry and Remote Sensing

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“…Estimation of crop phenological metrics using RS data depends largely on greenness information captured by optical satellite sensors. Studies have found that remotely sensed crop phenological metrics, especially the start of growing season (SOS, green-up date), can provide spatially explicit information on crop development stages, including the seeding date [7,12,13]. Crop phenological metrics are usually derived from time-series vegetation indices (VIs) through curve fitting, and the indices are usually calculated from reflectance data in the visible and near-infrared (NIR) bands.…”

Section: Introductionmentioning

confidence: 99%

“…This agrometeorological approach is highly dependent on the availability of nearby weather stations, while in reality station networks tend to be quite sparse, and the estimation of crop development stages is poor over areas where there are no nearby weather stations. In this case, the estimated crop development stage is insufficient for field-scale crop modelling and management over large areas [12,34].…”

Section: Introductionmentioning

confidence: 99%

Field-Scale Crop Seeding Date Estimation from MODIS Data and Growing Degree Days in Manitoba, Canada

et al. 2019

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Information on crop seeding date is required in many applications; such as crop management and yield forecasting. This study presents a novel method to estimate crop seeding date at the field level from time-series 250-m Moderate Resolution Imaging Spectroradiometer (MODIS) data and growing degree days (GDD; base 5 °C; °C-days). The start of growing season (SOS) was first derived from time-series EVI2 (two-band Enhanced Vegetation Index) calculated from a MODIS 8-day composite surface reflectance product (MOD09Q1; Collection 6). Based on GDD; calculated from the Daymet gridded estimates of daily weather parameters; a simple model was developed to establish a linkage between the observed seeding date and the SOS. Calibration and validation of the model was conducted on three major crops; spring wheat; canola and oats; in the Province of Manitoba; Canada. The estimated SOS had a strong linear correlation with the observed seeding date; with a deviation of a few days depending on the year. The seeding date of the three crops can be calculated from the SOS by adjusting the number of days needed to accumulate GDD (AGDD) for emergence. The overall root-mean-square-difference (RMSD) of the estimated seeding date was less than 10 days. Validation showed that the accuracy of the estimated seeding date was crop-type independent. The developed method is useful for estimating the historical crop seeding date from remote sensing data in Canada; to support studies of the interactions among seeding date; crop management and crop yield under climate change. It is anticipated that this method can be adapted to other crops in other locations using the same or different satellite data.

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“…Remote sensing, particularly the use of optical satellite imagery, has become an emerging tool for monitoring phenological events of crops at regional to national scales [3][4][5]. Sensors, such as the Advanced Very High Resolution Radiometer (AVHRR) and the Moderate Resolution Imaging relationships and model-based crop phenology [27], but have not yet been used for validation of satellite-derived phenometrics.…”

Section: Introductionmentioning

confidence: 99%

Optimising Phenological Metrics Extraction for Different Crop Types in Germany Using the Moderate Resolution Imaging Spectrometer (MODIS)

Conrad

Doktor

2017

Remote Sensing

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Phenological metrics extracted from satellite data (phenometrics) have been increasingly used to access timely, spatially explicit information on crop phenology, but have rarely been calibrated and validated with field observations. In this study, we developed a calibration procedure to make phenometrics more comparable to ground-based phenological stages by optimising the settings of Best Index Slope Extraction (BISE) and smoothing algorithms together with thresholds. We used a six-year daily Moderate Resolution Imaging Spectrometer (MODIS) Normalized Difference Vegetation Index (NDVI) time series and 211 ground-observation records from four major crop species (winter wheat/barley, oilseed rape, and sugar beet) in central Germany. Results showed the superiority of the Savitzky-Golay algorithm in combination with BISE. The satellite-derived senescence dates matched ripeness stages of winter crops and the dates with maximum NDVI were closely related to the field-observed heading stage of winter cereals. We showed that the emergence of winter crops corresponded to the dates extracted with a threshold of 0.1, which translated into 8.89 days of root-mean-square error (RMSE) improvement compared to the standard threshold of 0.5. The method with optimised settings and thresholds can be easily transferred and applied to areas with similar growing conditions. Altogether, the results improve our understanding of how satellite-derived phenometrics can explain in situ phenological observations.

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From Remotely Sensed Vegetation Onset to Sowing Dates: Aggregating Pixel-Level Detections into Village-Level Sowing Probabilities

Cited by 7 publications

References 29 publications

Remote sensing-based crop lodging assessment: Current status and perspectives

Remote sensing-based crop lodging assessment: Current status and perspectives

Field-Scale Crop Seeding Date Estimation from MODIS Data and Growing Degree Days in Manitoba, Canada

Optimising Phenological Metrics Extraction for Different Crop Types in Germany Using the Moderate Resolution Imaging Spectrometer (MODIS)

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