Cubesat Constellations Provide Enhanced Crop Phenology And Digital Agricultural Insights Using Daily Leaf Area Index Retrievals.

2021 ◽  
Author(s):  
Kasper Johansen ◽  
Matteo G. Ziliani ◽  
Rasmus Houborg ◽  
Trenton E. Franz ◽  
Matthew F. McCabe
Keyword(s):  
High Resolution ◽  
Leaf Area Index ◽  
Leaf Area ◽  
Management Practices ◽  
Irrigation Scheduling ◽  
Growth Stages ◽  
Landsat 8 ◽  
Maize Crop ◽  
Area Index ◽  
Agricultural Revolution

Abstract Satellite remote sensing has great potential to deliver on the promise of a data-driven agricultural revolution, with emerging space-based platforms providing spatiotemporal insights into precision-level attributes such as crop water use, vegetation health and condition and crop response to management practices. Using a harmonized collection of high-resolution Planet CubeSat, Sentinel-2, Landsat-8 and additional coarser resolution imagery from MODIS and VIIRS, we exploit a multi-satellite data fusion and machine learning approach to deliver a radiometrically calibrated and gap-filled time-series of daily leaf area index (LAI) at an unprecedented spatial resolution of 3 m. The insights available from such high-resolution CubeSat-based LAI data are demonstrated through tracking the growth cycle of a maize crop and identifying observable within-field spatial and temporal variations across key phenological stages. Daily LAI retrievals peaked at the tasseling stage, demonstrating their value for fertilizer and irrigation scheduling. An evaluation of satellite-based retrievals against field-measured LAI data collected from both rain-fed and irrigated fields shows high correlation and captures the spatiotemporal development of intra- and inter-field variations. Novel agricultural insights related to individual vegetative and reproductive growth stages were obtained, showcasing the capacity for new high-resolution CubeSat platforms to deliver actionable intelligence for precision agricultural and related applications.

scholarly journals Estimation of Corn Leaf Area Index and Ground Cover With Vegetation Indices as a Result of Irrigation Dose

2020 ◽  
Vol 12 (12) ◽  
pp. 234
Author(s):  
C. D. Papanikolaou ◽  
M. A. Sakellariou-Makrantonaki
Keyword(s):  
Leaf Area Index ◽  
Leaf Area ◽  
Soil Cover ◽  
Vegetation Indices ◽  
Irrigation Treatment ◽  
Irrigation Scheduling ◽  
Growth Stages ◽  
Area Index ◽  
The Third ◽  
Average Maximum

New technologies have been implemented in the agricultural sector to improve crop production and resource management including irrigation water. A three-year long project was conducted to determine whether vegetation indices (VIs) could be used to estimate the leaf area index (LAI) as well as the soil cover fraction (fc) at different crop growth stages in order to use these parameters in a future study concerning irrigation through drones. A low cost unmanned aerial vehicle (drone) and a multispectral camera were used to calculate different VIs of corn (Zea mays). The irrigation scheduling based on the FAO Penman-Monteith equation and the drip irrigation method were used. Three treatments were organized in three replications and the irrigation doses were equal to 100%, 75%, and 50% of the daily evapotranspiration respectively. The Simple and Multiple Regression analysis were used and different equations were formed where the VIs were the predictor variables and the LAI and fc the predicted ones. According to the two-year period data (2018-2019), during 2018 the average maximum LAI in the full irrigation treatment (100%) was 4.1. In the medium irrigation treatment (75%) the LAI was 4.0. The LAI in the third treatment (50%) was 3.9. In 2019, the LAI was 3.7 (100% treatment). In the second treatment, the LAI was 3.3. The LAI in the third treatment was 3.0. According to the results different VIs and prediction equations could be used to estimate the LAI values with high accuracy with the in situ measurements as well as the soil cover fraction.

scholarly journals Wheat Leaf Area Index Prediction Using Data Fusion Based on High-Resolution Unmanned Aerial Vehicle Imagery

2021 ◽  
Author(s):  
Shuang Wu ◽  
Lei Deng ◽  
Lijie Guo ◽  
Yanjie Wu
Keyword(s):  
High Resolution ◽  
Data Fusion ◽  
Leaf Area Index ◽  
Leaf Area ◽  
Unmanned Aerial Vehicle ◽  
Prediction Accuracy ◽  
Sensor Data ◽  
Support Vector ◽  
Area Index ◽  
Aerial Vehicle

Abstract Background: Leaf Area Index (LAI) is half of the amount of leaf area per unit horizontal ground surface area. Consequently, accurate vegetation extraction in remote sensing imagery is critical for LAI estimation. However, most studies do not fully exploit the advantages of Unmanned Aerial Vehicle (UAV) imagery with high spatial resolution, such as not removing the background (soil and shadow, etc.). Furthermore, the advancement of multi-sensor synchronous observation and integration technology allows for the simultaneous collection of canopy spectral, structural, and thermal data, making it possible for data fusion.Methods: To investigate the potential of high-resolution UAV imagery combined with multi-sensor data fusion in LAI estimation. High-resolution UAV imagery was obtained with a multi-sensor integrated MicaSense Altum camera to extract the wheat canopy's spectral, structural, and thermal features. After removing the soil background, all features were fused, and LAI was estimated using Random Forest and Support Vector Machine Regression.Result: The results show that: (1) the soil background reduced the accuracy of the LAI prediction, and soil background could be effectively removed by taking advantage of high-resolution UAV imagery. After removing the soil background, the LAI prediction accuracy improved significantly, R2 raised by about 0.27, and RMSE fell by about 0.476. (2) The fusion of multi-sensor synchronous observation data improved LAI prediction accuracy and achieved the best accuracy (R2 = 0.815 and RMSE = 1.023). (3) When compared to other variables, 23 CHM, NRCT, NDRE, and BLUE are crucial for LAI estimation. Even the simple Multiple Linear Regression model could achieve high prediction accuracy (R2 = 0.679 and RMSE = 1.231), providing inspiration for rapid and efficient LAI prediction.Conclusions: The method of this study can be transferred to other sites with more extensive areas or similar agriculture structures, which will facilitate agricultural production and management.

Integrating remote sensing and climate data with process-based models to map forest productivity within west-central Alberta's boreal forest: Ecoleap-West

2006 ◽  
Vol 82 (2) ◽  
pp. 159-176 ◽  
Author(s):  
R J Hall ◽  
F. Raulier ◽  
D T Price ◽  
E. Arsenault ◽  
P Y Bernier ◽  
...  
Keyword(s):  
Climate Change ◽  
Remote Sensing ◽  
Leaf Area Index ◽  
Leaf Area ◽  
Management Practices ◽  
Forest Productivity ◽  
Process Models ◽  
Growth And Yield ◽  
Area Index ◽  
Study Region

Forest yield forecasting typically employs statistically derived growth and yield (G&Y) functions that will yield biased growth estimates if changes in climate seriously influence future site conditions. Significant climate warming anticipated for the Prairie Provinces may result in increased moisture deficits, reductions in average site productivity and changes to natural species composition. Process-based stand growth models that respond realistically to simulated changes in climate can be used to assess the potential impacts of climate change on forest productivity, and hence can provide information for adapting forest management practices. We present an application of such a model, StandLEAP, to estimate stand-level net primary productivity (NPP) within a 2700 km2 study region in western Alberta. StandLEAP requires satellite remote-sensing derived estimates of canopy light absorption or leaf area index, in addition to spatial data on climate, topography and soil physical characteristics. The model was applied to some 80 000 stand-level inventory polygons across the study region. The resulting estimates of NPP correlate well with timber productivity values based on stand-level site index (height in metres at 50 years). This agreement demonstrates the potential to make site-based G&Y estimates using process models and to further investigate possible effects of climate change on future timber supply. Key words: forest productivity, NPP, climate change, process-based model, StandLEAP, leaf area index, above-ground biomass

Using Hemispherical Photography to Determine Leaf Area Index of Landscape Trees in Urban Green Space of Dongguan, South China

2014 ◽  
Vol 955-959 ◽  
pp. 4034-4038
Author(s):  
Luo Jian Mo ◽  
Wen Bin Li ◽  
Yong Chang Ye ◽  
Yong Wen Zhou ◽  
Song Song Liu ◽  
...  
Keyword(s):  
Leaf Area Index ◽  
Leaf Area ◽  
Green Space ◽  
Urban Green Space ◽  
Growth Stages ◽  
Hemispherical Photography ◽  
Area Index ◽  
Urban Green ◽  
Large Trees ◽  
Landscape Trees

Transect sampling method was used to measure structural attributes of landscape trees in urban green space of three city parks and one residential greenbelt in Dongguan. Leaf area index (LAI) of the landscape trees in each urban green space was determined using hemispherical photography. Average DBH (diameter at the breast height) and CW(crown width) in Wenhua Square were the largest due to the presence of heritage large trees, while the landscape trees were species diverse in Renmin Park. A comparison of LAI in the green space gave a result in descending order: Renmin Park > Wenhua Square > Jinhuwan greenbelt > Yuanmei Park. The case of Renmin Park indicated that when a green space consisted of various structural attributes, landscape trees in different growth stages tended to have large LAI. Findings of our study suggested that a diversity of trees with potentially different LAI should be selected when planning urban green space.

scholarly journals Peatland leaf-area index and biomass estimation with ultra-high resolution remote sensing

2020 ◽  
Vol 57 (7) ◽  
pp. 943-964
Author(s):  
Aleksi Räsänen ◽  
Sari Juutinen ◽  
Margaret Kalacska ◽  
Mika Aurela ◽  
Pauli Heikkinen ◽  
...  
Keyword(s):  
Remote Sensing ◽  
High Resolution ◽  
Leaf Area Index ◽  
Leaf Area ◽  
Biomass Estimation ◽  
Area Index

scholarly journals Quantifying Uncertainty and Bridging the Scaling Gap in the Retrieval of Leaf Area Index by Coupling Sentinel-2 and UAV Observations

2020 ◽  
Vol 12 (11) ◽  
pp. 1843 ◽  
Author(s):  
Andrew Revill ◽  
Anna Florence ◽  
Alasdair MacArthur ◽  
Stephen Hoad ◽  
Robert Rees ◽  
...  
Keyword(s):  
Spatial Heterogeneity ◽  
Leaf Area Index ◽  
Leaf Area ◽  
Growth Stages ◽  
Two Stage ◽  
Area Index ◽  
Field Surveys ◽  
Red Edge ◽  
Calibration Approach ◽  
Sentinel 2

Leaf area index (LAI) estimates can inform decision-making in crop management. The European Space Agency’s Sentinel-2 satellite, with observations in the red-edge spectral region, can monitor crops globally at sub-field spatial resolutions (10–20 m). However, satellite LAI estimates require calibration with ground measurements. Calibration is challenged by spatial heterogeneity and scale mismatches between field and satellite measurements. Unmanned Aerial Vehicles (UAVs), generating high-resolution (cm-scale) LAI estimates, provide intermediary observations that we use here to characterise uncertainty and reduce spatial scaling discrepancies between Sentinel-2 observations and field surveys. We use a novel UAV multispectral sensor that matches Sentinel-2 spectral bands, flown in conjunction with LAI ground measurements. UAV and field surveys were conducted on multiple dates—coinciding with different wheat growth stages—that corresponded to Sentinel-2 overpasses. We compared chlorophyll red-edge index (CIred-edge) maps, derived from the Sentinel-2 and UAV platforms. We used Gaussian processes regression machine learning to calibrate a UAV model for LAI, based on ground data. Using the UAV LAI, we evaluated a two-stage calibration approach for generating robust LAI estimates from Sentinel-2. The agreement between Sentinel-2 and UAV CIred-edge values increased with growth stage—R2 ranged from 0.32 (stem elongation) to 0.75 (milk development). The CIred-edge variance between the two platforms was more comparable later in the growing season due to a more homogeneous and closed wheat canopy. The single-stage Sentinel-2 LAI calibration (i.e., direct calibration from ground measurements) performed poorly (mean R2 = 0.29, mean NRMSE = 17%) when compared to the two-stage calibration using the UAV data (mean R2 = 0.88, mean NRMSE = 8%). The two-stage approach reduced both errors and biases by >50%. By upscaling ground measurements and providing more representative model training samples, UAV observations provide an effective and viable means of enhancing Sentinel-2 wheat LAI retrievals. We anticipate that our UAV calibration approach to resolving spatial heterogeneity would enhance the retrieval accuracy of LAI and additional biophysical variables for other arable crop types and a broader range of vegetation cover types.

scholarly journals Influence of Spiral Nematodes (Scutellonema spp.) on Maize Performance and Growth under Natural Field Infestation in Mwea, Kenya

2020 ◽  
Vol 2020 ◽  
pp. 1-9
Author(s):  
Samuel Maina ◽  
Rossa Nyoike Ng’endo
Keyword(s):  
Food Security ◽  
Leaf Area Index ◽  
Leaf Area ◽  
Plant Height ◽  
Management Practices ◽  
Block Design ◽  
Negative Relationship ◽  
Stress Factors ◽  
Area Index ◽  
Number Of Leaves

Maize (Zea mays L.) is a significant food security crop in Kenya and it serves as the main source of nutrition and calories among the small-holder farmers. The overall maize yields per hectare have been fluctuating in the past few years posing a great risk to food security. Among the stress factors associated with maize yield loss include plant-feeding nematodes. In this regard, this study was conducted to evaluate the impacts of plant-parasitic nematodes specifically Scutellonema spp. under field conditions on maize performance in Mwea, Kenya. The field trials were laid out in a randomized complete block design with each treatment comprising of four replicates. The treatments included maize plots without nematicide (MPWN) and control plots treated with nematicide. The experiments were conducted in two trials. Soil samples were taken at a 0–20 cm depth at monthly intervals during 2018–2019. During the two trials, MPWN recorded significantly lower plant height and number of leaves per plant. Correlation analysis revealed a significant negative relationship between Scutellonema abundance with leaf area index, plant height, and number of functional leaves in MPWN during the 2019 trial. This implies that high population of Scutellonema perhaps has the potential to affect leaf area index, plant height, number of leaves per plant, which are aspects that in turn influence maize productivity. Therefore, holistic sustainable management practices to control Scutellonema spp. in maize fields such as use of organic amendments, resistant maize cultivars, and antagonistic organisms are crucial in order to alleviate negative impacts linked to Scutellonema infestation.

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

2019 ◽  
Vol 11 (7) ◽  
pp. 829 ◽  
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.

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

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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