Assessment of Leaf Area Index Models Using Harmonized Landsat and Sentinel-2 Surface Reflectance Data over a Semi-Arid Irrigated Landscape
Leaf area index (LAI) is an essential indicator of crop development and growth. For many agricultural applications, satellite-based LAI estimates at the farm-level often require near-daily imagery at medium to high spatial resolution. The combination of data from different ongoing satellite missions...
| Main Authors: | , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2020-09-01
|
| Series: | Remote Sensing |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2072-4292/12/19/3121 |
| _version_ | 1797552805624414208 |
|---|---|
| author | Roya Mourad Hadi Jaafar Martha Anderson Feng Gao |
| author_facet | Roya Mourad Hadi Jaafar Martha Anderson Feng Gao |
| author_sort | Roya Mourad |
| collection | DOAJ |
| description | Leaf area index (LAI) is an essential indicator of crop development and growth. For many agricultural applications, satellite-based LAI estimates at the farm-level often require near-daily imagery at medium to high spatial resolution. The combination of data from different ongoing satellite missions, Sentinel 2 (ESA) and Landsat 8 (NASA), provides this opportunity. In this study, we evaluated the leaf area index generated from three methods, namely, existing vegetation index (VI) relationships applied to Harmonized Landsat-8 and Sentinel-2 (HLS) surface reflectance produced by NASA, the SNAP biophysical model, and the THEIA L2A surface reflectance products from Sentinel-2. The intercomparison was conducted over the agricultural scheme in Bekaa (Lebanon) using a large set of in-field LAIs and other biophysical measurements collected in a wide variety of canopy structures during the 2018 and 2019 growing seasons. The major studied crops include herbs (e.g., cannabis: <i>Cannabis sativa</i>, mint: <i>Mentha</i>, and others), potato (<i>Solanum tuberosum</i>), and vegetables (e.g., bean: <i>Phaseolus vulgaris</i>, cabbage: <i>Brassica oleracea</i>, carrot: <i>Daucus carota</i> subsp. <i>sativus</i>, and others). Additionally, crop-specific height and above-ground biomass relationships with LAIs were investigated. Results show that of the empirical VI relationships tested, the EVI2-based HLS models statistically performed the best, specifically, the LAI models originally developed for wheat (RMSE:1.27), maize (RMSE:1.34), and row crops (RMSE:1.38). LAI derived through European Space Agency’s (ESA) Sentinel Application Platform (SNAP) biophysical processor underestimated LAI and provided less accurate estimates (RMSE of 1.72). Additionally, the S2 SeLI LAI algorithm (from SNAP biophysical processor) produced an acceptable accuracy level compared to HLS-EVI2 models (RMSE of 1.38) but with significant underestimation at high LAI values. Our findings show that the LAI-VI relationship, in general, is crop-specific with both linear and non-linear regression forms. Among the examined indices, EVI2 outperformed other vegetation indices when all crops were combined, and therefore it can be identified as an index that is best suited for a unified algorithm for crops in semi-arid irrigated regions with heterogeneous landscapes. Furthermore, our analysis shows that the observed height-LAI relationship is crop-specific and essentially linear with an <i>R</i><sup>2</sup> value of 0.82 for potato, 0.79 for wheat, and 0.50 for both cannabis and tobacco. The ability of the linear regression to estimate the fresh and dry above-ground biomass of potato from both observed height and LAI was reasonable, yielding <i>R</i><sup>2</sup>: ~0.60. |
| first_indexed | 2024-03-10T16:06:17Z |
| format | Article |
| id | doaj.art-0a6dad31fc494a1aa2645c4396f859af |
| institution | Directory Open Access Journal |
| issn | 2072-4292 |
| language | English |
| last_indexed | 2024-03-10T16:06:17Z |
| publishDate | 2020-09-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Remote Sensing |
| spelling | doaj.art-0a6dad31fc494a1aa2645c4396f859af2023-11-20T14:48:39ZengMDPI AGRemote Sensing2072-42922020-09-011219312110.3390/rs12193121Assessment of Leaf Area Index Models Using Harmonized Landsat and Sentinel-2 Surface Reflectance Data over a Semi-Arid Irrigated LandscapeRoya Mourad0Hadi Jaafar1Martha Anderson2Feng Gao3Department of Agriculture, Faculty of Agricultural and Food Sciences, American University of Beirut, Bliss St., Beirut 2020-1100, LebanonDepartment of Agriculture, Faculty of Agricultural and Food Sciences, American University of Beirut, Bliss St., Beirut 2020-1100, LebanonUSDA ARS, Hydrology and Remote Sensing Lab, Beltsville, MD 20705-2350, USAUSDA ARS, Hydrology and Remote Sensing Lab, Beltsville, MD 20705-2350, USALeaf area index (LAI) is an essential indicator of crop development and growth. For many agricultural applications, satellite-based LAI estimates at the farm-level often require near-daily imagery at medium to high spatial resolution. The combination of data from different ongoing satellite missions, Sentinel 2 (ESA) and Landsat 8 (NASA), provides this opportunity. In this study, we evaluated the leaf area index generated from three methods, namely, existing vegetation index (VI) relationships applied to Harmonized Landsat-8 and Sentinel-2 (HLS) surface reflectance produced by NASA, the SNAP biophysical model, and the THEIA L2A surface reflectance products from Sentinel-2. The intercomparison was conducted over the agricultural scheme in Bekaa (Lebanon) using a large set of in-field LAIs and other biophysical measurements collected in a wide variety of canopy structures during the 2018 and 2019 growing seasons. The major studied crops include herbs (e.g., cannabis: <i>Cannabis sativa</i>, mint: <i>Mentha</i>, and others), potato (<i>Solanum tuberosum</i>), and vegetables (e.g., bean: <i>Phaseolus vulgaris</i>, cabbage: <i>Brassica oleracea</i>, carrot: <i>Daucus carota</i> subsp. <i>sativus</i>, and others). Additionally, crop-specific height and above-ground biomass relationships with LAIs were investigated. Results show that of the empirical VI relationships tested, the EVI2-based HLS models statistically performed the best, specifically, the LAI models originally developed for wheat (RMSE:1.27), maize (RMSE:1.34), and row crops (RMSE:1.38). LAI derived through European Space Agency’s (ESA) Sentinel Application Platform (SNAP) biophysical processor underestimated LAI and provided less accurate estimates (RMSE of 1.72). Additionally, the S2 SeLI LAI algorithm (from SNAP biophysical processor) produced an acceptable accuracy level compared to HLS-EVI2 models (RMSE of 1.38) but with significant underestimation at high LAI values. Our findings show that the LAI-VI relationship, in general, is crop-specific with both linear and non-linear regression forms. Among the examined indices, EVI2 outperformed other vegetation indices when all crops were combined, and therefore it can be identified as an index that is best suited for a unified algorithm for crops in semi-arid irrigated regions with heterogeneous landscapes. Furthermore, our analysis shows that the observed height-LAI relationship is crop-specific and essentially linear with an <i>R</i><sup>2</sup> value of 0.82 for potato, 0.79 for wheat, and 0.50 for both cannabis and tobacco. The ability of the linear regression to estimate the fresh and dry above-ground biomass of potato from both observed height and LAI was reasonable, yielding <i>R</i><sup>2</sup>: ~0.60.https://www.mdpi.com/2072-4292/12/19/3121vegetation indicesmachine learningHarmonized Landsat and Sentinel-2 (HLS)Sentinel Application Platform Software (SNAP) |
| spellingShingle | Roya Mourad Hadi Jaafar Martha Anderson Feng Gao Assessment of Leaf Area Index Models Using Harmonized Landsat and Sentinel-2 Surface Reflectance Data over a Semi-Arid Irrigated Landscape Remote Sensing vegetation indices machine learning Harmonized Landsat and Sentinel-2 (HLS) Sentinel Application Platform Software (SNAP) |
| title | Assessment of Leaf Area Index Models Using Harmonized Landsat and Sentinel-2 Surface Reflectance Data over a Semi-Arid Irrigated Landscape |
| title_full | Assessment of Leaf Area Index Models Using Harmonized Landsat and Sentinel-2 Surface Reflectance Data over a Semi-Arid Irrigated Landscape |
| title_fullStr | Assessment of Leaf Area Index Models Using Harmonized Landsat and Sentinel-2 Surface Reflectance Data over a Semi-Arid Irrigated Landscape |
| title_full_unstemmed | Assessment of Leaf Area Index Models Using Harmonized Landsat and Sentinel-2 Surface Reflectance Data over a Semi-Arid Irrigated Landscape |
| title_short | Assessment of Leaf Area Index Models Using Harmonized Landsat and Sentinel-2 Surface Reflectance Data over a Semi-Arid Irrigated Landscape |
| title_sort | assessment of leaf area index models using harmonized landsat and sentinel 2 surface reflectance data over a semi arid irrigated landscape |
| topic | vegetation indices machine learning Harmonized Landsat and Sentinel-2 (HLS) Sentinel Application Platform Software (SNAP) |
| url | https://www.mdpi.com/2072-4292/12/19/3121 |
| work_keys_str_mv | AT royamourad assessmentofleafareaindexmodelsusingharmonizedlandsatandsentinel2surfacereflectancedataoverasemiaridirrigatedlandscape AT hadijaafar assessmentofleafareaindexmodelsusingharmonizedlandsatandsentinel2surfacereflectancedataoverasemiaridirrigatedlandscape AT marthaanderson assessmentofleafareaindexmodelsusingharmonizedlandsatandsentinel2surfacereflectancedataoverasemiaridirrigatedlandscape AT fenggao assessmentofleafareaindexmodelsusingharmonizedlandsatandsentinel2surfacereflectancedataoverasemiaridirrigatedlandscape |