Keke Duan , Anton Vrieling , Michael Schlund , Uday Bhaskar Nidumolu , Christina Ratcliff , Simon Collings , Andrew Nelson
{"title":"利用 Sentinel-2 和气象数据检测谷物产量损失并确定损失原因:南澳大利亚案例研究","authors":"Keke Duan , Anton Vrieling , Michael Schlund , Uday Bhaskar Nidumolu , Christina Ratcliff , Simon Collings , Andrew Nelson","doi":"10.1016/j.isprsjprs.2024.05.021","DOIUrl":null,"url":null,"abstract":"<div><p>Weather extremes affect crop production. Remote sensing can help to detect crop damage and estimate lost yield due to weather extremes over large spatial extents. We propose a novel scalable method to predict in-season yield losses at the sub-field level and attribute these to weather extremes. To assess our method’s potential, we conducted a proof-of-concept case study on winter cereal paddocks in South Australia using data from 2017 to 2022. To detect crop growth anomalies throughout the growing season, we aligned a two-band Enhanced Vegetation Index (EVI2) time series from Sentinel-2 with thermal time. The deviation between the expected and observed EVI2 time series was defined as the Crop Damage Index (CDI). We assessed the performance of the CDI within specific phenological windows to predict yield loss. Finally, by comparing instances of substantial increase in CDI with different extreme weather indicators, we explored which (combinations of) extreme weather events were likely responsible for the experienced yield reduction. We found that the use of thermal time diminished the temporal deviation of EVI2 time series between years, resulting in the effective construction of typical stress-free crop growth curves. Thermal-time-based EVI2 time series resulted in better prediction of yield reduction than those based on calendar dates. Yield reduction could be predicted before grain-filling (approximately two months before harvest) with an R<sup>2</sup> of 0.83 for wheat and 0.91 for barley. Finally, the combined analysis of CDI curves and extreme weather indices allowed for timely detection of weather-related causes of crop damage, which also captured the spatial variations of crop damage attribution at sub-field level. The proposed framework provides a basis for early warning of crop damage and attributing the damage to weather extremes in near real-time, which should help to adopt appropriate crop protection strategies.</p></div>","PeriodicalId":50269,"journal":{"name":"ISPRS Journal of Photogrammetry and Remote Sensing","volume":null,"pages":null},"PeriodicalIF":10.6000,"publicationDate":"2024-05-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S0924271624002193/pdfft?md5=a7c5d07fec49f19fc05bd71d62c4e830&pid=1-s2.0-S0924271624002193-main.pdf","citationCount":"0","resultStr":"{\"title\":\"Detection and attribution of cereal yield losses using Sentinel-2 and weather data: A case study in South Australia\",\"authors\":\"Keke Duan , Anton Vrieling , Michael Schlund , Uday Bhaskar Nidumolu , Christina Ratcliff , Simon Collings , Andrew Nelson\",\"doi\":\"10.1016/j.isprsjprs.2024.05.021\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><p>Weather extremes affect crop production. Remote sensing can help to detect crop damage and estimate lost yield due to weather extremes over large spatial extents. We propose a novel scalable method to predict in-season yield losses at the sub-field level and attribute these to weather extremes. To assess our method’s potential, we conducted a proof-of-concept case study on winter cereal paddocks in South Australia using data from 2017 to 2022. To detect crop growth anomalies throughout the growing season, we aligned a two-band Enhanced Vegetation Index (EVI2) time series from Sentinel-2 with thermal time. The deviation between the expected and observed EVI2 time series was defined as the Crop Damage Index (CDI). We assessed the performance of the CDI within specific phenological windows to predict yield loss. Finally, by comparing instances of substantial increase in CDI with different extreme weather indicators, we explored which (combinations of) extreme weather events were likely responsible for the experienced yield reduction. We found that the use of thermal time diminished the temporal deviation of EVI2 time series between years, resulting in the effective construction of typical stress-free crop growth curves. Thermal-time-based EVI2 time series resulted in better prediction of yield reduction than those based on calendar dates. Yield reduction could be predicted before grain-filling (approximately two months before harvest) with an R<sup>2</sup> of 0.83 for wheat and 0.91 for barley. Finally, the combined analysis of CDI curves and extreme weather indices allowed for timely detection of weather-related causes of crop damage, which also captured the spatial variations of crop damage attribution at sub-field level. 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Detection and attribution of cereal yield losses using Sentinel-2 and weather data: A case study in South Australia
Weather extremes affect crop production. Remote sensing can help to detect crop damage and estimate lost yield due to weather extremes over large spatial extents. We propose a novel scalable method to predict in-season yield losses at the sub-field level and attribute these to weather extremes. To assess our method’s potential, we conducted a proof-of-concept case study on winter cereal paddocks in South Australia using data from 2017 to 2022. To detect crop growth anomalies throughout the growing season, we aligned a two-band Enhanced Vegetation Index (EVI2) time series from Sentinel-2 with thermal time. The deviation between the expected and observed EVI2 time series was defined as the Crop Damage Index (CDI). We assessed the performance of the CDI within specific phenological windows to predict yield loss. Finally, by comparing instances of substantial increase in CDI with different extreme weather indicators, we explored which (combinations of) extreme weather events were likely responsible for the experienced yield reduction. We found that the use of thermal time diminished the temporal deviation of EVI2 time series between years, resulting in the effective construction of typical stress-free crop growth curves. Thermal-time-based EVI2 time series resulted in better prediction of yield reduction than those based on calendar dates. Yield reduction could be predicted before grain-filling (approximately two months before harvest) with an R2 of 0.83 for wheat and 0.91 for barley. Finally, the combined analysis of CDI curves and extreme weather indices allowed for timely detection of weather-related causes of crop damage, which also captured the spatial variations of crop damage attribution at sub-field level. The proposed framework provides a basis for early warning of crop damage and attributing the damage to weather extremes in near real-time, which should help to adopt appropriate crop protection strategies.
期刊介绍:
The ISPRS Journal of Photogrammetry and Remote Sensing (P&RS) serves as the official journal of the International Society for Photogrammetry and Remote Sensing (ISPRS). It acts as a platform for scientists and professionals worldwide who are involved in various disciplines that utilize photogrammetry, remote sensing, spatial information systems, computer vision, and related fields. The journal aims to facilitate communication and dissemination of advancements in these disciplines, while also acting as a comprehensive source of reference and archive.
P&RS endeavors to publish high-quality, peer-reviewed research papers that are preferably original and have not been published before. These papers can cover scientific/research, technological development, or application/practical aspects. Additionally, the journal welcomes papers that are based on presentations from ISPRS meetings, as long as they are considered significant contributions to the aforementioned fields.
In particular, P&RS encourages the submission of papers that are of broad scientific interest, showcase innovative applications (especially in emerging fields), have an interdisciplinary focus, discuss topics that have received limited attention in P&RS or related journals, or explore new directions in scientific or professional realms. It is preferred that theoretical papers include practical applications, while papers focusing on systems and applications should include a theoretical background.