Sensitivity of Sentinel-1 Backscatter Signal to Vegetation Dynamics over Mozambique: A comparison with MODIS data Article Swipe
Carina Villegas-Lituma
,
Mariëtte Vreugdenhil
,
Samuel Massart
,
Pavan Muguda Sanjeevamurthy
,
Bernhard Raml
,
Wolfgang Wagner
·
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.5194/egusphere-egu24-11025
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.5194/egusphere-egu24-11025
Sentinel-1, a pair of Synthetic Aperture Radar (SAR) sensors, provides valuable all-weather and day-night imaging capabilities, enabling continuous monitoring of vegetation dynamics even in the presence of cloud cover. SAR sensors excel at penetrating vegetation canopies and providing information on crucial factors like vegetation structure, biomass, and moisture content. However, most remote sensing vegetation studies have primarily relied on optical data, benefiting from longer historical datasets but facing challenges due to atmospheric interference, limited temporal resolution. Moreover, there is no research assessing the sensitivity of optical and radar data to the dynamics of vegetation in Mozambique. This study investigates the sensitivity of the Sentinel-1 (S-1) backscatter signal to vegetation dynamics over Mozambique. We compare it with the Normalized Difference Vegetation Index (NDVI) from MODIS data and explore its relationship with precipitation variability and droughts across different land covers, including forest, cropland, herbaceous vegetation, and herbaceous wetland in Mozambique.The Sentinel-1 VV and VH polarized images were used to calculate the Cross Ratio (CR=VH/VV). Temporal behaviors of CR S-1 and MODIS NDVI were analyzed from 2017 to 2022, examining seasonal patterns, inter-annual variability, trends, and outliers. NDVI anomalies were calculated to identify the spatial and temporal occurrence of agricultural droughts, while CHIRPS precipitation data was utilized to detect fluctuations in the CR S-1 and NDVI time series relative to precipitation. The analysis revealed distinct seasonality in the CR time series data across all land cover types. Notably, croplands, herbaceous vegetation, and herbaceous wetlands exhibited a consistent increase in CR during winter months, followed by a decline during summer months. In contrast, forests displayed an inverse trend, with CR decreasing in winter and increasing in summer. Furthermore, no pronounced CR patterns were observed in herbaceous wetlands during 2019, coinciding with the agricultural drought between 2018 and 2019. Additionally, the seasonality of MODIS NDVI time series remained consistent across all land cover types, with no noticeable differences. It was observed that fluctuations in NDVI time series preceded those in CR for these specific land cover types, suggesting a potential correlation with photosynthetic activity and subsequent biomass production. Significantly, this trend was found to be opposite in forested areas. Overall, the CR trend exhibited a clear correlation with rainfall seasonality across the various land cover types, except for forests where an inverse relationship was observed. On the other hand, NDVI demonstrated a higher sensitivity to changes in precipitation across the different land cover categories.These findings highlight the unique sensitivity of Sentinel-1 SAR data in capturing the intricate dynamics of vegetation across diverse land cover types in Mozambique, providing valuable complementary information to traditional optical data sources.
Related Topics
Concepts
Metadata
- Type
- preprint
- Language
- en
- Landing Page
- https://doi.org/10.5194/egusphere-egu24-11025
- OA Status
- gold
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4392578414
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
-
https://openalex.org/W4392578414Canonical identifier for this work in OpenAlex
- DOI
-
https://doi.org/10.5194/egusphere-egu24-11025Digital Object Identifier
- Title
-
Sensitivity of Sentinel-1 Backscatter Signal to Vegetation Dynamics over Mozambique: A comparison with MODIS dataWork title
- Type
-
preprintOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2024Year of publication
- Publication date
-
2024-03-08Full publication date if available
- Authors
-
Carina Villegas-Lituma, Mariëtte Vreugdenhil, Samuel Massart, Pavan Muguda Sanjeevamurthy, Bernhard Raml, Wolfgang WagnerList of authors in order
- Landing page
-
https://doi.org/10.5194/egusphere-egu24-11025Publisher landing page
- Open access
-
YesWhether a free full text is available
- OA status
-
goldOpen access status per OpenAlex
- OA URL
-
https://doi.org/10.5194/egusphere-egu24-11025Direct OA link when available
- Concepts
-
Backscatter (email), Remote sensing, Sensitivity (control systems), Vegetation (pathology), Environmental science, SIGNAL (programming language), Geography, Computer science, Telecommunications, Engineering, Medicine, Wireless, Pathology, Programming language, Electronic engineeringTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
0Total citation count in OpenAlex
- Related works (count)
-
10Other works algorithmically related by OpenAlex
Full payload
| id | https://openalex.org/W4392578414 |
|---|---|
| doi | https://doi.org/10.5194/egusphere-egu24-11025 |
| ids.doi | https://doi.org/10.5194/egusphere-egu24-11025 |
| ids.openalex | https://openalex.org/W4392578414 |
| fwci | 0.0 |
| type | preprint |
| title | Sensitivity of Sentinel-1 Backscatter Signal to Vegetation Dynamics over Mozambique: A comparison with MODIS data |
| biblio.issue | |
| biblio.volume | |
| biblio.last_page | |
| biblio.first_page | |
| topics[0].id | https://openalex.org/T13890 |
| topics[0].field.id | https://openalex.org/fields/19 |
| topics[0].field.display_name | Earth and Planetary Sciences |
| topics[0].score | 0.9645000100135803 |
| topics[0].domain.id | https://openalex.org/domains/3 |
| topics[0].domain.display_name | Physical Sciences |
| topics[0].subfield.id | https://openalex.org/subfields/1902 |
| topics[0].subfield.display_name | Atmospheric Science |
| topics[0].display_name | Remote Sensing and Land Use |
| is_xpac | False |
| apc_list | |
| apc_paid | |
| concepts[0].id | https://openalex.org/C30354325 |
| concepts[0].level | 3 |
| concepts[0].score | 0.7693654894828796 |
| concepts[0].wikidata | https://www.wikidata.org/wiki/Q204667 |
| concepts[0].display_name | Backscatter (email) |
| concepts[1].id | https://openalex.org/C62649853 |
| concepts[1].level | 1 |
| concepts[1].score | 0.6839499473571777 |
| concepts[1].wikidata | https://www.wikidata.org/wiki/Q199687 |
| concepts[1].display_name | Remote sensing |
| concepts[2].id | https://openalex.org/C21200559 |
| concepts[2].level | 2 |
| concepts[2].score | 0.6767788529396057 |
| concepts[2].wikidata | https://www.wikidata.org/wiki/Q7451068 |
| concepts[2].display_name | Sensitivity (control systems) |
| concepts[3].id | https://openalex.org/C2776133958 |
| concepts[3].level | 2 |
| concepts[3].score | 0.6710764169692993 |
| concepts[3].wikidata | https://www.wikidata.org/wiki/Q7918366 |
| concepts[3].display_name | Vegetation (pathology) |
| concepts[4].id | https://openalex.org/C39432304 |
| concepts[4].level | 0 |
| concepts[4].score | 0.5572063326835632 |
| concepts[4].wikidata | https://www.wikidata.org/wiki/Q188847 |
| concepts[4].display_name | Environmental science |
| concepts[5].id | https://openalex.org/C2779843651 |
| concepts[5].level | 2 |
| concepts[5].score | 0.49349892139434814 |
| concepts[5].wikidata | https://www.wikidata.org/wiki/Q7390335 |
| concepts[5].display_name | SIGNAL (programming language) |
| concepts[6].id | https://openalex.org/C205649164 |
| concepts[6].level | 0 |
| concepts[6].score | 0.3317873179912567 |
| concepts[6].wikidata | https://www.wikidata.org/wiki/Q1071 |
| concepts[6].display_name | Geography |
| concepts[7].id | https://openalex.org/C41008148 |
| concepts[7].level | 0 |
| concepts[7].score | 0.2002289891242981 |
| concepts[7].wikidata | https://www.wikidata.org/wiki/Q21198 |
| concepts[7].display_name | Computer science |
| concepts[8].id | https://openalex.org/C76155785 |
| concepts[8].level | 1 |
| concepts[8].score | 0.1267472803592682 |
| concepts[8].wikidata | https://www.wikidata.org/wiki/Q418 |
| concepts[8].display_name | Telecommunications |
| concepts[9].id | https://openalex.org/C127413603 |
| concepts[9].level | 0 |
| concepts[9].score | 0.0734722912311554 |
| concepts[9].wikidata | https://www.wikidata.org/wiki/Q11023 |
| concepts[9].display_name | Engineering |
| concepts[10].id | https://openalex.org/C71924100 |
| concepts[10].level | 0 |
| concepts[10].score | 0.0 |
| concepts[10].wikidata | https://www.wikidata.org/wiki/Q11190 |
| concepts[10].display_name | Medicine |
| concepts[11].id | https://openalex.org/C555944384 |
| concepts[11].level | 2 |
| concepts[11].score | 0.0 |
| concepts[11].wikidata | https://www.wikidata.org/wiki/Q249 |
| concepts[11].display_name | Wireless |
| concepts[12].id | https://openalex.org/C142724271 |
| concepts[12].level | 1 |
| concepts[12].score | 0.0 |
| concepts[12].wikidata | https://www.wikidata.org/wiki/Q7208 |
| concepts[12].display_name | Pathology |
| concepts[13].id | https://openalex.org/C199360897 |
| concepts[13].level | 1 |
| concepts[13].score | 0.0 |
| concepts[13].wikidata | https://www.wikidata.org/wiki/Q9143 |
| concepts[13].display_name | Programming language |
| concepts[14].id | https://openalex.org/C24326235 |
| concepts[14].level | 1 |
| concepts[14].score | 0.0 |
| concepts[14].wikidata | https://www.wikidata.org/wiki/Q126095 |
| concepts[14].display_name | Electronic engineering |
| keywords[0].id | https://openalex.org/keywords/backscatter |
| keywords[0].score | 0.7693654894828796 |
| keywords[0].display_name | Backscatter (email) |
| keywords[1].id | https://openalex.org/keywords/remote-sensing |
| keywords[1].score | 0.6839499473571777 |
| keywords[1].display_name | Remote sensing |
| keywords[2].id | https://openalex.org/keywords/sensitivity |
| keywords[2].score | 0.6767788529396057 |
| keywords[2].display_name | Sensitivity (control systems) |
| keywords[3].id | https://openalex.org/keywords/vegetation |
| keywords[3].score | 0.6710764169692993 |
| keywords[3].display_name | Vegetation (pathology) |
| keywords[4].id | https://openalex.org/keywords/environmental-science |
| keywords[4].score | 0.5572063326835632 |
| keywords[4].display_name | Environmental science |
| keywords[5].id | https://openalex.org/keywords/signal |
| keywords[5].score | 0.49349892139434814 |
| keywords[5].display_name | SIGNAL (programming language) |
| keywords[6].id | https://openalex.org/keywords/geography |
| keywords[6].score | 0.3317873179912567 |
| keywords[6].display_name | Geography |
| keywords[7].id | https://openalex.org/keywords/computer-science |
| keywords[7].score | 0.2002289891242981 |
| keywords[7].display_name | Computer science |
| keywords[8].id | https://openalex.org/keywords/telecommunications |
| keywords[8].score | 0.1267472803592682 |
| keywords[8].display_name | Telecommunications |
| keywords[9].id | https://openalex.org/keywords/engineering |
| keywords[9].score | 0.0734722912311554 |
| keywords[9].display_name | Engineering |
| language | en |
| locations[0].id | doi:10.5194/egusphere-egu24-11025 |
| locations[0].is_oa | True |
| locations[0].source | |
| locations[0].license | cc-by |
| locations[0].pdf_url | |
| locations[0].version | acceptedVersion |
| locations[0].raw_type | posted-content |
| locations[0].license_id | https://openalex.org/licenses/cc-by |
| locations[0].is_accepted | True |
| locations[0].is_published | False |
| locations[0].raw_source_name | |
| locations[0].landing_page_url | https://doi.org/10.5194/egusphere-egu24-11025 |
| indexed_in | crossref |
| authorships[0].author.id | https://openalex.org/A5093473921 |
| authorships[0].author.orcid | https://orcid.org/0009-0005-2118-7358 |
| authorships[0].author.display_name | Carina Villegas-Lituma |
| authorships[0].author_position | first |
| authorships[0].raw_author_name | Carina Villegas-Lituma |
| authorships[0].is_corresponding | False |
| authorships[1].author.id | https://openalex.org/A5061832135 |
| authorships[1].author.orcid | https://orcid.org/0000-0003-4913-0255 |
| authorships[1].author.display_name | Mariëtte Vreugdenhil |
| authorships[1].author_position | middle |
| authorships[1].raw_author_name | Mariette Vreugdenhil |
| authorships[1].is_corresponding | False |
| authorships[2].author.id | https://openalex.org/A5053286823 |
| authorships[2].author.orcid | https://orcid.org/0009-0009-1312-5797 |
| authorships[2].author.display_name | Samuel Massart |
| authorships[2].author_position | middle |
| authorships[2].raw_author_name | Samuel Massart |
| authorships[2].is_corresponding | False |
| authorships[3].author.id | https://openalex.org/A5003803532 |
| authorships[3].author.orcid | https://orcid.org/0009-0007-9688-7732 |
| authorships[3].author.display_name | Pavan Muguda Sanjeevamurthy |
| authorships[3].author_position | middle |
| authorships[3].raw_author_name | Pavan Muguda Sanjeevamurthy |
| authorships[3].is_corresponding | False |
| authorships[4].author.id | https://openalex.org/A5014382146 |
| authorships[4].author.orcid | |
| authorships[4].author.display_name | Bernhard Raml |
| authorships[4].author_position | middle |
| authorships[4].raw_author_name | Bernhard Raml |
| authorships[4].is_corresponding | False |
| authorships[5].author.id | https://openalex.org/A5066754586 |
| authorships[5].author.orcid | https://orcid.org/0000-0001-7704-6857 |
| authorships[5].author.display_name | Wolfgang Wagner |
| authorships[5].author_position | last |
| authorships[5].raw_author_name | Wolfgang Wagner |
| authorships[5].is_corresponding | False |
| has_content.pdf | False |
| has_content.grobid_xml | False |
| is_paratext | False |
| open_access.is_oa | True |
| open_access.oa_url | https://doi.org/10.5194/egusphere-egu24-11025 |
| open_access.oa_status | gold |
| open_access.any_repository_has_fulltext | False |
| created_date | 2025-10-10T00:00:00 |
| display_name | Sensitivity of Sentinel-1 Backscatter Signal to Vegetation Dynamics over Mozambique: A comparison with MODIS data |
| has_fulltext | False |
| is_retracted | False |
| updated_date | 2025-11-06T03:46:38.306776 |
| primary_topic.id | https://openalex.org/T13890 |
| primary_topic.field.id | https://openalex.org/fields/19 |
| primary_topic.field.display_name | Earth and Planetary Sciences |
| primary_topic.score | 0.9645000100135803 |
| primary_topic.domain.id | https://openalex.org/domains/3 |
| primary_topic.domain.display_name | Physical Sciences |
| primary_topic.subfield.id | https://openalex.org/subfields/1902 |
| primary_topic.subfield.display_name | Atmospheric Science |
| primary_topic.display_name | Remote Sensing and Land Use |
| related_works | https://openalex.org/W1984559991, https://openalex.org/W1799242668, https://openalex.org/W2502358642, https://openalex.org/W3165883639, https://openalex.org/W1847813119, https://openalex.org/W2068956457, https://openalex.org/W2105579152, https://openalex.org/W2744709723, https://openalex.org/W2005239049, https://openalex.org/W4254698188 |
| cited_by_count | 0 |
| locations_count | 1 |
| best_oa_location.id | doi:10.5194/egusphere-egu24-11025 |
| best_oa_location.is_oa | True |
| best_oa_location.source | |
| best_oa_location.license | cc-by |
| best_oa_location.pdf_url | |
| best_oa_location.version | acceptedVersion |
| best_oa_location.raw_type | posted-content |
| best_oa_location.license_id | https://openalex.org/licenses/cc-by |
| best_oa_location.is_accepted | True |
| best_oa_location.is_published | False |
| best_oa_location.raw_source_name | |
| best_oa_location.landing_page_url | https://doi.org/10.5194/egusphere-egu24-11025 |
| primary_location.id | doi:10.5194/egusphere-egu24-11025 |
| primary_location.is_oa | True |
| primary_location.source | |
| primary_location.license | cc-by |
| primary_location.pdf_url | |
| primary_location.version | acceptedVersion |
| primary_location.raw_type | posted-content |
| primary_location.license_id | https://openalex.org/licenses/cc-by |
| primary_location.is_accepted | True |
| primary_location.is_published | False |
| primary_location.raw_source_name | |
| primary_location.landing_page_url | https://doi.org/10.5194/egusphere-egu24-11025 |
| publication_date | 2024-03-08 |
| publication_year | 2024 |
| referenced_works_count | 0 |
| abstract_inverted_index.a | 1, 241, 251, 332, 358, 385 |
| abstract_inverted_index.CR | 165, 209, 224, 245, 264, 275, 324, 355 |
| abstract_inverted_index.In | 256 |
| abstract_inverted_index.It | 312 |
| abstract_inverted_index.On | 379 |
| abstract_inverted_index.VH | 151 |
| abstract_inverted_index.VV | 149 |
| abstract_inverted_index.We | 112 |
| abstract_inverted_index.an | 260, 374 |
| abstract_inverted_index.at | 32 |
| abstract_inverted_index.be | 348 |
| abstract_inverted_index.by | 250 |
| abstract_inverted_index.in | 23, 94, 146, 207, 222, 244, 266, 270, 279, 317, 323, 350, 390, 407, 419 |
| abstract_inverted_index.is | 78 |
| abstract_inverted_index.it | 114 |
| abstract_inverted_index.no | 79, 273, 309 |
| abstract_inverted_index.of | 3, 19, 26, 84, 92, 101, 164, 195, 296, 403, 412 |
| abstract_inverted_index.on | 39, 58 |
| abstract_inverted_index.to | 70, 89, 107, 156, 174, 188, 204, 216, 347, 388, 425 |
| abstract_inverted_index.S-1 | 166, 210 |
| abstract_inverted_index.SAR | 29, 405 |
| abstract_inverted_index.all | 229, 304 |
| abstract_inverted_index.and | 12, 36, 46, 86, 125, 132, 143, 150, 167, 182, 192, 211, 237, 268, 291, 338 |
| abstract_inverted_index.but | 66 |
| abstract_inverted_index.due | 69 |
| abstract_inverted_index.for | 325, 371 |
| abstract_inverted_index.its | 127 |
| abstract_inverted_index.the | 24, 82, 90, 99, 102, 116, 158, 190, 208, 223, 286, 294, 354, 365, 380, 393, 400, 409 |
| abstract_inverted_index.was | 202, 313, 345, 377 |
| abstract_inverted_index.2017 | 173 |
| abstract_inverted_index.2018 | 290 |
| abstract_inverted_index.NDVI | 169, 184, 212, 298, 318, 383 |
| abstract_inverted_index.This | 96 |
| abstract_inverted_index.data | 88, 124, 201, 227, 406, 428 |
| abstract_inverted_index.even | 22 |
| abstract_inverted_index.from | 62, 122, 172 |
| abstract_inverted_index.have | 55 |
| abstract_inverted_index.land | 136, 230, 305, 328, 367, 395, 416 |
| abstract_inverted_index.like | 42 |
| abstract_inverted_index.most | 50 |
| abstract_inverted_index.over | 110 |
| abstract_inverted_index.pair | 2 |
| abstract_inverted_index.that | 315 |
| abstract_inverted_index.this | 343 |
| abstract_inverted_index.time | 213, 225, 299, 319 |
| abstract_inverted_index.used | 155 |
| abstract_inverted_index.were | 154, 170, 186, 277 |
| abstract_inverted_index.with | 115, 129, 263, 285, 308, 335, 361 |
| abstract_inverted_index.(S-1) | 104 |
| abstract_inverted_index.(SAR) | 7 |
| abstract_inverted_index.2019, | 283 |
| abstract_inverted_index.2019. | 292 |
| abstract_inverted_index.2022, | 175 |
| abstract_inverted_index.Cross | 159 |
| abstract_inverted_index.Index | 120 |
| abstract_inverted_index.MODIS | 123, 168, 297 |
| abstract_inverted_index.Radar | 6 |
| abstract_inverted_index.Ratio | 160 |
| abstract_inverted_index.clear | 359 |
| abstract_inverted_index.cloud | 27 |
| abstract_inverted_index.cover | 231, 306, 329, 368, 396, 417 |
| abstract_inverted_index.data, | 60 |
| abstract_inverted_index.excel | 31 |
| abstract_inverted_index.found | 346 |
| abstract_inverted_index.hand, | 382 |
| abstract_inverted_index.other | 381 |
| abstract_inverted_index.radar | 87 |
| abstract_inverted_index.study | 97 |
| abstract_inverted_index.there | 77 |
| abstract_inverted_index.these | 326 |
| abstract_inverted_index.those | 322 |
| abstract_inverted_index.trend | 344, 356 |
| abstract_inverted_index.types | 418 |
| abstract_inverted_index.where | 373 |
| abstract_inverted_index.while | 198 |
| abstract_inverted_index.(NDVI) | 121 |
| abstract_inverted_index.CHIRPS | 199 |
| abstract_inverted_index.across | 134, 228, 303, 364, 392, 414 |
| abstract_inverted_index.areas. | 352 |
| abstract_inverted_index.cover. | 28 |
| abstract_inverted_index.detect | 205 |
| abstract_inverted_index.during | 246, 253, 282 |
| abstract_inverted_index.except | 370 |
| abstract_inverted_index.facing | 67 |
| abstract_inverted_index.higher | 386 |
| abstract_inverted_index.images | 153 |
| abstract_inverted_index.longer | 63 |
| abstract_inverted_index.relied | 57 |
| abstract_inverted_index.remote | 51 |
| abstract_inverted_index.series | 214, 226, 300, 320 |
| abstract_inverted_index.signal | 106 |
| abstract_inverted_index.summer | 254 |
| abstract_inverted_index.trend, | 262 |
| abstract_inverted_index.types, | 307, 330, 369 |
| abstract_inverted_index.types. | 232 |
| abstract_inverted_index.unique | 401 |
| abstract_inverted_index.winter | 247, 267 |
| abstract_inverted_index.between | 289 |
| abstract_inverted_index.biomass | 340 |
| abstract_inverted_index.changes | 389 |
| abstract_inverted_index.compare | 113 |
| abstract_inverted_index.covers, | 137 |
| abstract_inverted_index.crucial | 40 |
| abstract_inverted_index.decline | 252 |
| abstract_inverted_index.diverse | 415 |
| abstract_inverted_index.drought | 288 |
| abstract_inverted_index.explore | 126 |
| abstract_inverted_index.factors | 41 |
| abstract_inverted_index.forest, | 139 |
| abstract_inverted_index.forests | 258, 372 |
| abstract_inverted_index.imaging | 14 |
| abstract_inverted_index.inverse | 261, 375 |
| abstract_inverted_index.limited | 73 |
| abstract_inverted_index.months, | 248 |
| abstract_inverted_index.months. | 255 |
| abstract_inverted_index.optical | 59, 85, 427 |
| abstract_inverted_index.sensing | 52 |
| abstract_inverted_index.sensors | 30 |
| abstract_inverted_index.spatial | 191 |
| abstract_inverted_index.studies | 54 |
| abstract_inverted_index.summer. | 271 |
| abstract_inverted_index.trends, | 181 |
| abstract_inverted_index.various | 366 |
| abstract_inverted_index.wetland | 145 |
| abstract_inverted_index.Aperture | 5 |
| abstract_inverted_index.However, | 49 |
| abstract_inverted_index.Notably, | 233 |
| abstract_inverted_index.Overall, | 353 |
| abstract_inverted_index.Temporal | 162 |
| abstract_inverted_index.activity | 337 |
| abstract_inverted_index.analysis | 218 |
| abstract_inverted_index.analyzed | 171 |
| abstract_inverted_index.biomass, | 45 |
| abstract_inverted_index.canopies | 35 |
| abstract_inverted_index.content. | 48 |
| abstract_inverted_index.datasets | 65 |
| abstract_inverted_index.distinct | 220 |
| abstract_inverted_index.droughts | 133 |
| abstract_inverted_index.dynamics | 21, 91, 109, 411 |
| abstract_inverted_index.enabling | 16 |
| abstract_inverted_index.findings | 398 |
| abstract_inverted_index.followed | 249 |
| abstract_inverted_index.forested | 351 |
| abstract_inverted_index.identify | 189 |
| abstract_inverted_index.increase | 243 |
| abstract_inverted_index.moisture | 47 |
| abstract_inverted_index.observed | 278, 314 |
| abstract_inverted_index.opposite | 349 |
| abstract_inverted_index.patterns | 276 |
| abstract_inverted_index.preceded | 321 |
| abstract_inverted_index.presence | 25 |
| abstract_inverted_index.provides | 9 |
| abstract_inverted_index.rainfall | 362 |
| abstract_inverted_index.relative | 215 |
| abstract_inverted_index.remained | 301 |
| abstract_inverted_index.research | 80 |
| abstract_inverted_index.revealed | 219 |
| abstract_inverted_index.seasonal | 177 |
| abstract_inverted_index.sensors, | 8 |
| abstract_inverted_index.sources. | 429 |
| abstract_inverted_index.specific | 327 |
| abstract_inverted_index.temporal | 74, 193 |
| abstract_inverted_index.utilized | 203 |
| abstract_inverted_index.valuable | 10, 422 |
| abstract_inverted_index.wetlands | 239, 281 |
| abstract_inverted_index.Moreover, | 76 |
| abstract_inverted_index.Synthetic | 4 |
| abstract_inverted_index.anomalies | 185 |
| abstract_inverted_index.assessing | 81 |
| abstract_inverted_index.behaviors | 163 |
| abstract_inverted_index.calculate | 157 |
| abstract_inverted_index.capturing | 408 |
| abstract_inverted_index.contrast, | 257 |
| abstract_inverted_index.cropland, | 140 |
| abstract_inverted_index.day-night | 13 |
| abstract_inverted_index.different | 135, 394 |
| abstract_inverted_index.displayed | 259 |
| abstract_inverted_index.droughts, | 197 |
| abstract_inverted_index.examining | 176 |
| abstract_inverted_index.exhibited | 240, 357 |
| abstract_inverted_index.highlight | 399 |
| abstract_inverted_index.including | 138 |
| abstract_inverted_index.intricate | 410 |
| abstract_inverted_index.observed. | 378 |
| abstract_inverted_index.outliers. | 183 |
| abstract_inverted_index.patterns, | 178 |
| abstract_inverted_index.polarized | 152 |
| abstract_inverted_index.potential | 333 |
| abstract_inverted_index.primarily | 56 |
| abstract_inverted_index.providing | 37, 421 |
| abstract_inverted_index.Difference | 118 |
| abstract_inverted_index.Normalized | 117 |
| abstract_inverted_index.Sentinel-1 | 103, 148, 404 |
| abstract_inverted_index.Vegetation | 119 |
| abstract_inverted_index.benefiting | 61 |
| abstract_inverted_index.calculated | 187 |
| abstract_inverted_index.challenges | 68 |
| abstract_inverted_index.coinciding | 284 |
| abstract_inverted_index.consistent | 242, 302 |
| abstract_inverted_index.continuous | 17 |
| abstract_inverted_index.croplands, | 234 |
| abstract_inverted_index.decreasing | 265 |
| abstract_inverted_index.herbaceous | 141, 144, 235, 238, 280 |
| abstract_inverted_index.historical | 64 |
| abstract_inverted_index.increasing | 269 |
| abstract_inverted_index.monitoring | 18 |
| abstract_inverted_index.noticeable | 310 |
| abstract_inverted_index.occurrence | 194 |
| abstract_inverted_index.pronounced | 274 |
| abstract_inverted_index.structure, | 44 |
| abstract_inverted_index.subsequent | 339 |
| abstract_inverted_index.suggesting | 331 |
| abstract_inverted_index.vegetation | 20, 34, 43, 53, 93, 108, 413 |
| abstract_inverted_index.(CR=VH/VV). | 161 |
| abstract_inverted_index.Mozambique, | 420 |
| abstract_inverted_index.Mozambique. | 95, 111 |
| abstract_inverted_index.Sentinel-1, | 0 |
| abstract_inverted_index.all-weather | 11 |
| abstract_inverted_index.atmospheric | 71 |
| abstract_inverted_index.backscatter | 105 |
| abstract_inverted_index.correlation | 334, 360 |
| abstract_inverted_index.information | 38, 424 |
| abstract_inverted_index.penetrating | 33 |
| abstract_inverted_index.production. | 341 |
| abstract_inverted_index.resolution. | 75 |
| abstract_inverted_index.seasonality | 221, 295, 363 |
| abstract_inverted_index.sensitivity | 83, 100, 387, 402 |
| abstract_inverted_index.traditional | 426 |
| abstract_inverted_index.variability | 131 |
| abstract_inverted_index.vegetation, | 142, 236 |
| abstract_inverted_index.Furthermore, | 272 |
| abstract_inverted_index.agricultural | 196, 287 |
| abstract_inverted_index.demonstrated | 384 |
| abstract_inverted_index.differences. | 311 |
| abstract_inverted_index.fluctuations | 206, 316 |
| abstract_inverted_index.inter-annual | 179 |
| abstract_inverted_index.investigates | 98 |
| abstract_inverted_index.relationship | 128, 376 |
| abstract_inverted_index.variability, | 180 |
| abstract_inverted_index.Additionally, | 293 |
| abstract_inverted_index.capabilities, | 15 |
| abstract_inverted_index.complementary | 423 |
| abstract_inverted_index.interference, | 72 |
| abstract_inverted_index.precipitation | 130, 200, 391 |
| abstract_inverted_index.Mozambique.The | 147 |
| abstract_inverted_index.Significantly, | 342 |
| abstract_inverted_index.photosynthetic | 336 |
| abstract_inverted_index.categories.These | 397 |
| abstract_inverted_index.precipitation. The | 217 |
| cited_by_percentile_year | |
| countries_distinct_count | 0 |
| institutions_distinct_count | 6 |
| sustainable_development_goals[0].id | https://metadata.un.org/sdg/15 |
| sustainable_development_goals[0].score | 0.6200000047683716 |
| sustainable_development_goals[0].display_name | Life in Land |
| citation_normalized_percentile.value | 0.03151555 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | False |