dd23f813d0634db70e61a825b9a70f75 remotesensing-17-00340.pdf 6891a7bee5fd59e7409294e0d4efdb11ad331cec remotesensing-17-00340.pdf 3adc0617ad39c4c261c106fa49ba3784844ad5a0fd2a5c8c3657cd28a33c91a3 remotesensing-17-00340.pdf Title: Evaluating Sentinel-2 for Monitoring Drought-Induced Crop Failure in Winter Cereals Subject: Extreme climate events can threaten food production and disrupt supply chains. For instance, the 2023 drought in Catalonia caused large areas of winter cereals to wilt and die early, yielding no grain. This study examined whether Sentinel-2 can detect total crop losses of winter cereals using ground truth data on crop failure. The methodology explored which Sentinel-2 phenological and greenness variables could best predict three drought impact classes: normal growth, moderate impact, and high impact, where the crop failed to produce grain. The results demonstrate that winter cereals affected by drought exhibit a premature decline in several vegetation indices. As a result, the best predictors for detecting total crop losses were metrics associated with the later stages of crop development. Specifically, the mean Normalized Difference Vegetation Index (NDVI) for the first half of May showed the highest correlation with drought impact classes (R2 = 0.66). This study is the first to detect total crop losses at the plantation level using field data combined with Sentinel-2 imagery. It also offers insights into rapid monitoring methods for crop failure, an event likely to become more frequent as the climate warms. Keywords: crop failure; agricultural drought; total crop loss; winter cereals; Sentinel-2 Author: Adrià Descals, Karen Torres, Aleixandre Verger and Josep Peñuelas Creator: LaTeX with hyperref Producer: pdfTeX-1.40.25 CreationDate: Mon Jan 20 09:58:12 2025 CET ModDate: Mon Jan 20 10:01:27 2025 CET Custom Metadata: no Metadata Stream: no Tagged: no UserProperties: no Suspects: no Form: none JavaScript: no Pages: 13 Encrypted: no Page size: 595.276 x 841.89 pts (A4) Page rot: 0 File size: 9396484 bytes Optimized: no PDF version: 1.7 name type encoding emb sub uni object ID ------------------------------------ ----------------- ---------------- --- --- --- --------- WNWTFR+VnURWPalladioL-Bold Type 1 Custom yes yes yes 10 0 QKZKVH+VnURWPalladioL Type 1 Custom yes yes yes 16 0 FFUBWM+URWPalladioL-Roma Type 1 Custom yes yes yes 21 0 CUTNLT+URWPalladioL-Bold Type 1 Custom yes yes yes 27 0 QZCEUH+URWPalladioL-Ital Type 1 Custom yes yes yes 32 0 RFPHKI+CMSY10 Type 1 Builtin yes yes yes 61 0 GPOMIB+Symbol Type 1C Custom yes yes yes 75 0 GPOMIC+Arial CID TrueType Identity-H yes yes yes 80 0 GPOLAC+PalatinoLinotype,Bold TrueType WinAnsi yes yes no 86 0 GPOLAD+PalatinoLinotype CID TrueType Identity-H yes yes yes 89 0 GPOLAE+PalatinoLinotype TrueType WinAnsi yes yes no 95 0 GPOLAG+PalatinoLinotype,Italic TrueType WinAnsi yes yes no 98 0 GPOLKF+PalatinoLinotype TrueType MacRoman yes yes no 101 0 GPOMIB+Symbol Type 1C Custom yes yes yes 115 0 GPOMIC+Arial CID TrueType Identity-H yes yes yes 120 0 GPOLAC+PalatinoLinotype,Bold TrueType WinAnsi yes yes no 126 0 GPOLAD+PalatinoLinotype CID TrueType Identity-H yes yes yes 129 0 GPOLAE+PalatinoLinotype TrueType WinAnsi yes yes no 135 0 GPOLAG+PalatinoLinotype,Italic TrueType WinAnsi yes yes no 138 0 GPOLKF+PalatinoLinotype TrueType MacRoman yes yes no 141 0 GPOLAC+PalatinoLinotype,Bold TrueType WinAnsi yes yes no 156 0 GPOLAD+PalatinoLinotype CID TrueType Identity-H yes yes yes 159 0 GPOLAE+PalatinoLinotype TrueType WinAnsi yes yes no 165 0 GPOLAG+PalatinoLinotype,Italic TrueType WinAnsi yes yes no 168 0 GPOLKF+PalatinoLinotype TrueType MacRoman yes yes no 171 0 GIGFZE+CMR10 Type 1 Builtin yes yes yes 178 0 GPOMIB+Symbol Type 1C Custom yes yes yes 190 0 GPOMIC+Arial CID TrueType Identity-H yes yes yes 195 0 GPPAFL+CambriaMath CID TrueType Identity-H yes yes yes 201 0 GPOLAC+PalatinoLinotype,Bold TrueType WinAnsi yes yes no 207 0 GPOLAE+PalatinoLinotype TrueType WinAnsi yes yes no 210 0 GPOLAG+PalatinoLinotype,Italic TrueType WinAnsi yes yes no 213 0 GPOLKF+PalatinoLinotype TrueType MacRoman yes yes no 216 0 QQXUPP+PazoMath Type 1 Builtin yes yes yes 223 0 GPOLAC+PalatinoLinotype,Bold TrueType WinAnsi yes yes no 238 0 GPOLAD+PalatinoLinotype CID TrueType Identity-H yes yes yes 241 0 GPOLAE+PalatinoLinotype TrueType WinAnsi yes yes no 247 0 GPOLAG+PalatinoLinotype,Italic TrueType WinAnsi yes yes no 250 0 GPOLKF+PalatinoLinotype TrueType MacRoman yes yes no 253 0 GPOLAC+PalatinoLinotype,Bold TrueType WinAnsi yes yes no 267 0 GPOLAD+PalatinoLinotype CID TrueType Identity-H yes yes yes 270 0 GPOLAE+PalatinoLinotype TrueType WinAnsi yes yes no 276 0 GPOLAG+PalatinoLinotype,Italic TrueType WinAnsi yes yes no 279 0 GPOLKF+PalatinoLinotype TrueType MacRoman yes yes no 282 0 GPOLAC+PalatinoLinotype,Bold TrueType WinAnsi yes yes no 297 0 GPOLAD+PalatinoLinotype CID TrueType Identity-H yes yes yes 300 0 GPOLAE+PalatinoLinotype TrueType WinAnsi yes yes no 306 0 GPOLAG+PalatinoLinotype,Italic TrueType WinAnsi yes yes no 309 0 GPOLKF+PalatinoLinotype TrueType MacRoman yes yes no 312 0 Jhove (Rel. 1.28.0, 2023-05-18) Date: 2025-02-19 02:42:08 CET RepresentationInformation: remotesensing-17-00340.pdf ReportingModule: PDF-hul, Rel. 1.12.4 (2023-03-16) LastModified: 2025-02-18 13:30:47 CET Size: 9396484 Format: PDF Version: 1.7 Status: Well-Formed and valid SignatureMatches: PDF-hul MIMEtype: application/pdf PDFMetadata: Objects: 465 FreeObjects: 1 IncrementalUpdates: 0 DocumentCatalog: PageLayout: SinglePage PageMode: UseNone Outlines: Item: Title: Introduction Destination: section.1 Item: Title: Materials and Methods Destination: section.2 Children: Item: Title: Study Area Destination: subsection.2.1 Item: Title: Overview of the Methodology Destination: subsection.2.2 Item: Title: Collection of Field Data Destination: subsection.2.3 Item: Title: Sentinel-2 Time Series Destination: subsection.2.4 Item: Title: Feature Extraction Destination: subsection.2.5 Item: Title: Feature Selection Destination: subsection.2.6 Item: Title: Results Destination: section.3 Item: Title: Discussion Destination: section.4 Item: Title: Conclusions Destination: section.5 Item: Title: References Destination: appendix.A. Info: Title: Evaluating Sentinel-2 for Monitoring Drought-Induced Crop Failure in Winter Cereals Author: Adrià Descals, Karen Torres, Aleixandre Verger and Josep Peñuelas Subject: Extreme climate events can threaten food production and disrupt supply chains. For instance, the 2023 drought in Catalonia caused large areas of winter cereals to wilt and die early, yielding no grain. This study examined whether Sentinel-2 can detect total crop losses of winter cereals using ground truth data on crop failure. The methodology explored which Sentinel-2 phenological and greenness variables could best predict three drought impact classes: normal growth, moderate impact, and high impact, where the crop failed to produce grain. The results demonstrate that winter cereals affected by drought exhibit a premature decline in several vegetation indices. As a result, the best predictors for detecting total crop losses were metrics associated with the later stages of crop development. Specifically, the mean Normalized Difference Vegetation Index (NDVI) for the first half of May showed the highest correlation with drought impact classes (R2 = 0.66). This study is the first to detect total crop losses at the plantation level using field data combined with Sentinel-2 imagery. It also offers insights into rapid monitoring methods for crop failure, an event likely to become more frequent as the climate warms. 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