Estimating the crop leaf area index using hyperspectral remote sensing

Liu, Ke; Zhou, Qingbo; Wu, Wenbin; Xia, Tian; Tang, Huajun

doi:10.1016/s2095-3119(15)61073-5

Cited by 86 publications

(41 citation statements)

References 91 publications

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“…Suitable radiative transfer models for the analysis of these new spectral capabilities are therefore required. Many studies confirmed that the use of many contiguous spectral bands lead to more accurate variable retrievals or provides greater potential for reducing estimation uncertainties which consequently improves the accuracy and stability of the resulting biophysical and biochemical products indirectly [42,76,101,102]. Currently, a Scientific Processor for managed vegetation is under development, which will include forest and agricultural modules.…”

Section: Applications Of the Prosail Modelmentioning

confidence: 99%

Evaluation of the PROSAIL Model Capabilities for Future Hyperspectral Model Environments: A Review Study

et al. 2018

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Upcoming satellite hyperspectral sensors require powerful and robust methodologies for making optimum use of the rich spectral data. This paper reviews the widely applied coupled PROSPECT and SAIL radiative transfer models (PROSAIL), regarding their suitability for the retrieval of biophysical and biochemical variables in the context of agricultural crop monitoring. Evaluation was carried out using a systematic literature review of 281 scientific publications with regard to their (i) spectral exploitation, (ii) vegetation type analyzed, (iii) variables retrieved, and (iv) choice of retrieval methods. From the analysis, current trends were derived, and problems identified and discussed. Our analysis clearly shows that the PROSAIL model is well suited for the analysis of imaging spectrometer data from future satellite missions and that the model should be integrated in appropriate software tools that are being developed in this context for agricultural applications. The review supports the decision of potential users to employ PROSAIL for their specific data analysis and provides guidelines for choosing between the diverse retrieval techniques.

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Section: Applications Of the Prosail Modelmentioning

confidence: 99%

Evaluation of the PROSAIL Model Capabilities for Future Hyperspectral Model Environments: A Review Study

et al. 2018

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“…Since the N content of sugar beet tops is not only related to the two-dimensional leaf area but also its density, multiplying NDVI times canopy height results in a "leaf volume" instead of a leaf area index [145]. In experiments with other crop types with different leaf and growth architectures, NDVI measurements were related to plant height in alfalfa (Medicago sativa L.), where plant height and surface area coverage develop nearly simultaneously but were not related to plant height in grasses, where surface coverage was nearly continuous [146].…”

Section: Use Of Sensors and Ndvimentioning

confidence: 99%

A Review of Methods to Improve Nitrogen Use Efficiency in Agriculture

2017

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Management of nitrogen (N) is a challenging task and several methods individually and in combination are in use to manage its efficiency. However, nitrogen use efficiency (NUE) has not been improved to a level, only 33%, as predicted by the researchers while developing nitrogen management tools and methods. The primary objective of this review article is to evaluate methods and tools available to manage nitrogen. Several methods, soil testing, plant tissue testing, spectral response, fertilizer placement and timing and vegetative indexes (leaf area index, and NDVI) through drones, handheld sensors, and satellite imagery were reviewed on the subject of user-friendly and effectiveness towards NUE. No single method was found sufficient to counter the nitrogen loss. Some methods were found time consuming and unsynchronized with N uptake behavior of particular crop, for example, plant tissue testing. Use of precision agriculture tools, such as GreenSeeker, Holland Crop Circle, drone, and satellite imagery, were found better compared to conventional methods such as soil testing, but these tools can only be used when the crop is up. Therefore, N management is possible only through inseason N application methods. When 70% of the applied nitrogen is used by the crops within 25-30 days after planting, for example, corn and potatoes, it is required to apply major N rates through inseason approach and some N at planting using soil test reports. In conclusion, this article strongly advocates using two or more methods in combination when managing N.

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“…Indirect methods adopt devices such as plant canopy analyzers (e.g., LAI-2000, LI-COR, Inc., Lincoln, NE, USA), digital hemispherical photography (DHP), and remote sensing [8]. Remote sensing technology is cost-effective and non-destructive and, thus, a prevalent technology for estimating LAI [9].…”

Section: Introductionmentioning

confidence: 99%

Retrieving Soybean Leaf Area Index from Unmanned Aerial Vehicle Hyperspectral Remote Sensing: Analysis of RF, ANN, and SVM Regression Models

et al. 2017

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Leaf area index (LAI) is an important indicator of plant growth and yield that can be monitored by remote sensing. Several models were constructed using datasets derived from SRS and STR sampling methods to determine the optimal model for soybean (multiple strains) LAI inversion for the whole crop growth period and a single growth period. Random forest (RF), artificial neural network (ANN), and support vector machine (SVM) regression models were compared with a partial least-squares regression (PLS) model. The RF model yielded the highest precision, accuracy, and stability with V-R 2 , SD R 2 , V-RMSE, and SD RMSE values of 0.741, 0.031, 0.106, and 0.005, respectively, over the whole growth period based on STR sampling. The ANN model had the highest precision, accuracy, and stability (0.452, 0.132, 0.086, and 0.009, respectively) over a single growth phase based on STR sampling. The precision, accuracy, and stability of the RF, ANN, and SVM models were improved by inclusion of STR sampling. The RF model is suitable for estimating LAI when sample plots and variation are relatively large (i.e., the whole growth period or more than one growth period). The ANN model is more appropriate for estimating LAI when sample plots and variation are relatively low (i.e., a single growth period).

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Estimating the crop leaf area index using hyperspectral remote sensing

Cited by 86 publications

References 91 publications

Evaluation of the PROSAIL Model Capabilities for Future Hyperspectral Model Environments: A Review Study

Evaluation of the PROSAIL Model Capabilities for Future Hyperspectral Model Environments: A Review Study

A Review of Methods to Improve Nitrogen Use Efficiency in Agriculture

Retrieving Soybean Leaf Area Index from Unmanned Aerial Vehicle Hyperspectral Remote Sensing: Analysis of RF, ANN, and SVM Regression Models

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