Estimating winter wheat biomass by assimilating leaf area index derived from fusion of Landsat-8 and MODIS data

Abstract. Winter wheat is a staple crop for global food security, and is the dominant vegetation cover for a significant fraction of Earth's croplands. As such, it plays an important role in carbon cycling and land–atmosphere interactions in these key regions. Accurate simulation of winter wheat growth is not only crucial for future yield prediction under a changing climate, but also for accurately predicting the energy and water cycles for winter wheat dominated regions. We modified the winter wheat model in the Community Land Model (CLM) to better simulate winter wheat leaf area index, latent heat flux, net ecosystem exchange of CO2, and grain yield. These included schemes to represent vernalization as well as frost tolerance and damage. We calibrated three key parameters (minimum planting temperature, maximum crop growth days, and initial value of leaf carbon allocation coefficient) and modified the grain carbon allocation algorithm for simulations at the US Southern Great Plains ARM site (US-ARM), and validated the model performance at eight additional sites across North America. We found that the new winter wheat model improved the prediction of monthly variation in leaf area index, reduced latent heat flux, and net ecosystem exchange root mean square error (RMSE) by 41 and 35 % during the spring growing season. The model accurately simulated the interannual variation in yield at the US-ARM site, but underestimated yield at sites and in regions (northwestern and southeastern US) with historically greater yields by 35 %.

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Spring top-dressings of ‘Nitro-Chalk’ and late sprays of a liquid N-fertilizer and a broad spectrum fungicide for consecutive crops of winter wheat at Saxmundham, Suffolk

The Journal of Agricultural Science ◽

10.1017/s0021859600056021 ◽

1978 ◽

Vol 90 (3) ◽

pp. 509-516 ◽

Cited By ~ 14

Author(s):

A. Penny ◽

F. V. Widdowson ◽

J. F. Jenkyn

Keyword(s):

Calcium Carbonate ◽

Winter Wheat ◽

Leaf Area Index ◽

Ammonium Nitrate ◽

Leaf Area ◽

Broad Spectrum ◽

N Fertilizer ◽

N Content ◽

Area Index ◽

Liquid Fertilizer

SummaryAn experiment at Saxmundham, Suffolk, during 1974–6, tested late sprays of a liquid N-fertilizer (ammonium nitrate/urea) supplying 50 kg N/ha, and a broad spectrum fungicide (benomyl and maneb with mancozeb) on winter wheat given, 0, 50, 100 or 150 kg N/ha as ‘Nitro-Chalk’ (ammonium nitrate/calcium carbonate) in springMildew (Erysiphe graminisf. sp. tritici) was most severe in 1974. It was increased by N and decreased by the fungicide in both 1974 and 1975, but was negligible in 1976. Septoria (S. nodorum) was very slight in 1974 and none was observed in 1976. It was much more severe in 1975, but was unaffected by the fungicide perhaps because this was applied too late.Yield and N content, number of ears and leaf area index were determined during summer on samples taken from all plots given 100 or 150 kg N/ha in spring; each was larger with 150 than with 100 kg N/ha. The effects of the liquid N-fertilizer on yield and N content varied, but leaf area index was consistently increased. None was affected consistently by the fungicide.Yields, percentages of N in, and amounts of N removed by grain and straw were greatly and consistently increased by each increment of ‘Nitro-Chalk’. Yields of grain were increased (average, 9%) by the liquid fertilizer in 1974 and 1975, and most where most ‘Nitro-Chalk’ had been given, but not in 1976 when the wheat ripened in July; however, both the percentage of N in and the amount of N removed by the grain were increased by the liquid fertilizer each year. The fungicide increased the response to the liquid N-fertilizer in 1974, but not in 1975 when Septoria was not controlled, nor in 1976 when leaf diseases were virtually absent.The weight of 1000 grains was increased by each increment of ‘Nitro-Chalk’ in 1975 but only by the first one (50 kg N/ha) in 1974 and 1976; it was very slightly increased by the liquid fertilizer and by fungicide each year.

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An optimized non-linear vegetation index for estimating leaf area index in winter wheat

Precision Agriculture ◽

10.1007/s11119-019-09648-8 ◽

2019 ◽

Vol 20 (6) ◽

pp. 1157-1176 ◽

Cited By ~ 6

Author(s):

Wei Feng ◽

Yapeng Wu ◽

Li He ◽

Xingxu Ren ◽

Yangyang Wang ◽

...

Keyword(s):

Winter Wheat ◽

Leaf Area Index ◽

Leaf Area ◽

Vegetation Index ◽

Area Index ◽

Non Linear

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Retrospective retrieval of long-term consistent global leaf area index (1981-2011) from combined AVHRR and MODIS data

Journal of Geophysical Research Atmospheres ◽

10.1029/2012jg002084 ◽

2012 ◽

Vol 117 (G4) ◽

pp. n/a-n/a ◽

Cited By ~ 105

Author(s):

Yang Liu ◽

Ronggao Liu ◽

Jing M. Chen

Keyword(s):

Leaf Area Index ◽

Leaf Area ◽

Area Index ◽

Modis Data

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Numerical Assessments of Leaf Area Index in Tropical Savanna Rangelands, South Africa Using Landsat 8 OLI Derived Metrics and In-Situ Measurements

Remote Sensing ◽

10.3390/rs11070829 ◽

2019 ◽

Vol 11 (7) ◽

pp. 829 ◽

Cited By ~ 3

Author(s):

Timothy Dube ◽

Santa Pandit ◽

Cletah Shoko ◽

Abel Ramoelo ◽

Dominic Mazvimavi ◽

...

Keyword(s):

South Africa ◽

Leaf Area Index ◽

Leaf Area ◽

National Park ◽

Dry Season ◽

Kruger National Park ◽

Landsat 8 ◽

Tropical Savanna ◽

Wet Season ◽

Area Index

Knowledge on rangeland condition, productivity patterns and possible thresholds of potential concern, as well as the escalation of risks in the face of climate change and variability over savanna grasslands is essential for wildlife/livestock management purposes. The estimation of leaf area index (LAI) in tropical savanna ecosystems is therefore fundamental for the proper planning and management of this natural capital. In this study, we assess the spatio-temporal seasonal LAI dynamics (dry and wet seasons) as a proxy for rangeland condition and productivity in the Kruger National Park (KNP), South Africa. The 30 m Landsat 8 Operational Land Imager (OLI) spectral bands, derived vegetation indices and a non-parametric approach (i.e., random forest, RF) were used to assess dry and wet season LAI condition and variability in the KNP. The results showed that RF optimization enhanced the model performance in estimating LAI. Moderately high accuracies were observed for the dry season (R2 of 0.63–0.72 and average RMSE of 0.60 m2/m2) and wet season (0.62–0.63 and 0.79 m2/m2). Derived thematic maps demonstrated that the park had high LAI estimates during the wet season when compared to the dry season. On average, LAI estimates ranged between 3 and 7 m2/m2 during the wet season, whereas for the dry season most parts of the park had LAI estimates ranging between 0.00 and 3.5 m2/m2. The findings indicate that Kruger National Park had high levels of productivity during the wet season monitoring period. Overall, this work shows the unique potential of Landsat 8-derived metrics in assessing LAI as a proxy for tropical savanna rangelands productivity. The result is relevant for wildlife management and habitat assessment and monitoring.

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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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