A physically based approach in retrieving vegetation Leaf Area Index from Landsat surface reflectance data

<p>In a recent paper we investigated how different catchment and climate properties influence transit time distributions. This was done by employing a physically-based spatially explicit 3D model in a virtual catchment running many different scenarios with different combinations of catchment and climate properties. We found that the velocity distribution of water fluxes through a catchment is more sensitive to certain properties while other factors appear less relevant. Now we expanded the approach by adding vegetation to the model and thus introducing new hydrologic processes (transpiration and evaporation) to the simulated water cycle. On the one hand we wanted to know how these new processes would influence transit times of the water fluxes to the stream, on the other hand we were interested in how exactly differences in the vegetation itself (e.g. rooting depth and leaf area index) would alter the various flux velocities (including transit times of transpiration and evaporation). It was very interesting to observe that streamflow in forested areas appeared to become older on average. We also found that transpiration was generally younger if the vegetation had shallower roots and/or a larger leaf area index. The biggest difference in the age of evaporation was detected for different amounts of subsequent precipitation (evaporation was generally younger in a wetter climate). In conclusion, we found that forests influence the age of the different water fluxes within a catchment. According to our results the overall hydrologic cycle is decelerated when adding vegetation to a model that otherwise only simulates evaporation.</p><p>Still, in order to make meaningful predictions on the age of hydrologic fluxes, it is not constructive to single out specific catchment and climate properties. The multitude of influences from different parameters makes it very challenging to find rules and underlying principles in the integrated catchment response. Therefore it is necessary to look at the individual parameters and their potential interactions and interdependencies in a bottom-up approach.</p>

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Long-Time-Series Global Land Surface Satellite Leaf Area Index Product Derived From MODIS and AVHRR Surface Reflectance

IEEE Transactions on Geoscience and Remote Sensing ◽

10.1109/tgrs.2016.2560522 ◽

2016 ◽

Vol 54 (9) ◽

pp. 5301-5318 ◽

Cited By ~ 108

Author(s):

Zhiqiang Xiao ◽

Shunlin Liang ◽

Jindi Wang ◽

Yang Xiang ◽

Xiang Zhao ◽

...

Keyword(s):

Time Series ◽

Leaf Area Index ◽

Leaf Area ◽

Land Surface ◽

Surface Reflectance ◽

Area Index ◽

Long Time Series ◽

Global Land ◽

Long Time

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Assessment of Leaf Area Index Models Using Harmonized Landsat and Sentinel-2 Surface Reflectance Data over a Semi-Arid Irrigated Landscape

Remote Sensing ◽

10.3390/rs12193121 ◽

2020 ◽

Vol 12 (19) ◽

pp. 3121

Author(s):

Roya Mourad ◽

Hadi Jaafar ◽

Martha Anderson ◽

Feng Gao

Keyword(s):

Linear Regression ◽

Leaf Area Index ◽

Leaf Area ◽

Landsat 8 ◽

Surface Reflectance ◽

Above Ground Biomass ◽

Area Index ◽

Ground Biomass ◽

Semi Arid ◽

Sentinel 2

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: Cannabis sativa, mint: Mentha, and others), potato (Solanum tuberosum), and vegetables (e.g., bean: Phaseolus vulgaris, cabbage: Brassica oleracea, carrot: Daucus carota subsp. sativus, 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 R2 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 R2: ~0.60.

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Spatially and Temporally Continuous Leaf Area Index Mapping for Crops through Assimilation of Multi-resolution Satellite Data

Remote Sensing ◽

10.3390/rs11212517 ◽

2019 ◽

Vol 11 (21) ◽

pp. 2517 ◽

Cited By ~ 2

Author(s):

Huaan Jin ◽

Weixing Xu ◽

Ainong Li ◽

Xinyao Xie ◽

Zhengjian Zhang ◽

...

Keyword(s):

Time Series ◽

Leaf Area Index ◽

Leaf Area ◽

Landsat 8 ◽

Area Index ◽

Spatio Temporal ◽

Fine Resolution ◽

Modis Lai ◽

Temporal Profiles ◽

Reflectance Data

As a key parameter that represents the structural characteristics and biophysical changes of crop canopy, the leaf area index (LAI) plays a significant role in monitoring crop growth and mapping yield. A considerable amount of farmland is dispersed with strong spatial heterogeneity. The existing time series satellite LAI products fail to capture spatial distributions and growth changes of crops due to coarse spatial resolutions and spatio-temporal discontinuities. Therefore, it becomes crucial for fine resolution LAI mapping in time series over crop areas. A two-stage data assimilation scheme was developed for dense time series LAI mapping in this study. A LAI dynamic model was first constructed using multi-year MODIS LAI data. This model coupled with the PROSAIL radiative transfer model, and MOD09A1 reflectance data were used to retrieve temporal LAI profiles at the 500 m resolution with the assistance of the very fast simulated annealing (VFSA) algorithm. Then, the LAI dynamics at the 500 m scale were incorporated as prior information into the Landsat 8 OLI reflectance data for time series LAI mapping at the 30 m resolution. Finally, the spatio-temporal continuities and retrieval accuracies of assimilated LAI values were assessed at the 500 m and 30 m resolutions respectively, using the MODIS LAI product, fine resolution LAI reference map and field measurements. The results indicated that the assimilated the LAI estimations at the 500 m scale effectively eliminated the spatio-temporal discontinuities of the MODIS LAI product and displayed reasonable temporal profiles and spatial integrity of LAI. Moreover, the 30 m resolution LAI retrievals showed more abundant spatial details and reasonable temporal profiles than the counterparts at the 500 m scale. The determination coefficient R2 between the estimated and field LAI values was 0.76 with a root mean square error (RMSE) value of 0.71 at the 30 m scale. The developed method not only improves the spatio-temporal continuities of the LAI at the 500 m scale, but also obtains 30 m resolution LAI maps with fine spatial and temporal consistencies, which can be expected to meet the needs of analysis on crop dynamic changes and yield mapping in fragmented and highly heterogeneous areas.

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Remote estimation of canopy leaf area index and chlorophyll content in Moso bamboo (Phyllostachys edulis (Carrière) J. Houz.) forest using MODIS reflectance data

Annals of Forest Science ◽

10.1007/s13595-018-0721-y ◽

2018 ◽

Vol 75 (1) ◽

Cited By ~ 2

Author(s):

Xiaojun Xu ◽

Huaqiang Du ◽

Guomo Zhou ◽

Fangjie Mao ◽

Xuejian Li ◽

...

Keyword(s):

Leaf Area Index ◽

Leaf Area ◽

Chlorophyll Content ◽

Moso Bamboo ◽

Phyllostachys Edulis ◽

Area Index ◽

Remote Estimation ◽

Reflectance Data

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Using the Negative Soil Adjustment Factor of Soil Adjusted Vegetation Index (SAVI) to Resist Saturation Effects and Estimate Leaf Area Index (LAI) in Dense Vegetation Areas

Sensors ◽

10.3390/s21062115 ◽

2021 ◽

Vol 21 (6) ◽

pp. 2115

Author(s):

Zhijun Zhen ◽

Shengbo Chen ◽

Tiangang Yin ◽

Eric Chavanon ◽

Nicolas Lauret ◽

...

Keyword(s):

Time Series ◽

Linear Regression ◽

Leaf Area Index ◽

Leaf Area ◽

Vegetation Index ◽

Surface Reflectance ◽

Adjustment Factor ◽

Area Index ◽

Dense Vegetation ◽

Saturation Effects

Saturation effects limit the application of vegetation indices (VIs) in dense vegetation areas. The possibility to mitigate them by adopting a negative soil adjustment factor X is addressed. Two leaf area index (LAI) data sets are analyzed using the Google Earth Engine (GEE) for validation. The first one is derived from observations of MODerate resolution Imaging Spectroradiometer (MODIS) from 16 April 2013, to 21 October 2020, in the Apiacás area. Its corresponding VIs are calculated from a combination of Sentinel-2 and Landsat-8 surface reflectance products. The second one is a global LAI dataset with VIs calculated from Landsat-5 surface reflectance products. A linear regression model is applied to both datasets to evaluate four VIs that are commonly used to estimate LAI: normalized difference vegetation index (NDVI), soil adjusted vegetation index (SAVI), transformed SAVI (TSAVI), and enhanced vegetation index (EVI). The optimal soil adjustment factor of SAVI for LAI estimation is determined using an exhaustive search. The Dickey-Fuller test indicates that the time series of LAI data are stable with a confidence level of 99%. The linear regression results stress significant saturation effects in all VIs. Finally, the exhaustive searching results show that a negative soil adjustment factor of SAVI can mitigate the SAVIs’ saturation in the Apiacás area (i.e., X = −0.148 for mean LAI = 5.35), and more generally in areas with large LAI values (e.g., X = −0.183 for mean LAI = 6.72). Our study further confirms that the lower boundary of the soil adjustment factor can be negative and that using a negative soil adjustment factor improves the computation of time series of LAI.

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