Remote sensing for estimating and mapping single and basal crop coefficientes: A review on spectral vegetation indices approaches

Pôças, Isabel; Calera, Alfonso; Campos, Isidro; Cunha, Mário

doi:10.1016/j.agwat.2020.106081

Cited by 121 publications

(88 citation statements)

References 125 publications

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“…Remote sensing technology, especially hyperspectral remote sensing technology, mainly uses various methods and indicators based on spectral characteristics to achieve monitoring purposes. On the basis of this idea, many spectral indices have been developed to estimate soil water and salt content, which can extract useful information from complex spectral reflectance to eliminate the effects of soil heterogeneity, vegetation coverage, and weather conditions [54]. However, a spectral index developed on the basis of specific data in a specific area has obvious limitations, poor portability, and often cannot achieve good inversion results in other areas.…”

Section: Discussion On Optimized Spectral Indicesmentioning

confidence: 99%

Possibility of Zhuhai-1 Hyperspectral Imagery for Monitoring Salinized Soil Moisture Content Using Fractional Order Differentially Optimized Spectral Indices

Kahaer

Tashpolat

Shi

et al. 2020

Water

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The possibility of quantitative inversion of salinized soil moisture content (SMC) from Zhuhai-1 hyperspectral imagery and the application effect of fractional order differentially optimized spectral indices were discussed, which provided new research ideas for improving the accuracy of hyperspectral remote sensing inversion. The hyperspectral data from indoor and Zhuhai-1 remote sensing imagery were resampled to the same spectral scale. The soil hyperspectral data were processed by fractional order differential preprocessing method and optimized spectral indices method, and the Pearson correlation coefficient (PCC/r) analysis was made with SMC data. The sensitive optimized spectral indices were used to establish the ground hyperspectral estimation model, and a variety of modeling methods were used to select the best SMC inversion model. The results were as follows: the maximum one-dimensional r between SMC and the 466–938 nm band was −0.635, the maximum one-dimensional r with the 0.5-order absorbance spectrum was 0.665, and the maximum two-dimensional r with the difference index (DI) calculated by the 0.5-order absorbance spectrum was ±0.72. The maximum three-dimensional r with the triangle vegetation index (TVI) calculated from the 0.5-order absorbance spectrum reached 0.755, which exceeded the one-dimensional r extreme value of 400–2400 nm. The TreeNet gradient boosting machine (TGBM) regression model had the highest modeling accuracy, with a calibration coefficient of determination (R2C) = 0.887, calibration root mean square error (RMSEC) = 2.488%, standard deviation (SD) = 6.733%, and r = 0.942. However, the partial least squares regression (PLSR) model had the strongest predictive ability, with validation coefficient of determination (R2V) = 0.787, validation root mean square error (RMSEV) = 3.247%, and relative prediction deviation (RPD) = 2.071. The variable importance in projection (VIP) method could not only improve model efficiency but also increased model accuracy. R2C of the optimal PLSR model was 0.733, RMSEC was 3.028%, R2V was 0.805, RMSEV was 3.100%, RPD was 1.976, and Akaike information criterion (AIC) was 151.050. The three-band optimized spectral indices with fractional differential pretreatment could to a certain extent break through the limitation of visible near-infrared spectrum in SMC estimation due to the lack of shortwave infrared spectra, which made it possible to quantitatively retrieve saline SMC on the basis of Zhuhai-1 hyperspectral imagery.

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Section: Discussion On Optimized Spectral Indicesmentioning

confidence: 99%

Possibility of Zhuhai-1 Hyperspectral Imagery for Monitoring Salinized Soil Moisture Content Using Fractional Order Differentially Optimized Spectral Indices

Kahaer

Tashpolat

Shi

et al. 2020

Water

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“…As an approach to overcome this challenge, the Food and Agriculture Organization (FAO) recommends the use of reference evapotranspiration (ET 0 ), which represents evapotranspiration from actively growing green grass under adequate watering with fixed crop height, albedo, and surface resistance conditions of 0.12 m, 0.23, and 70 S m −1 , respectively [1]. ET 0 is applied to represent the effects of atmospheric conditions and acts as a reference from which to estimate crop evapotranspiration (ET c ) and potential evapotranspiration (ET p ) from field to regional scales [3,[7][8][9].…”

Section: Introductionmentioning

confidence: 99%

A Novel Approach for the Simulation of Reference Evapotranspiration and Its Partitioning

Wang

et al. 2021

Agriculture

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To estimate the irrigation volume required for agriculture and improve water resources utilization efficiency, it is essential to obtain an estimate of reference evapotranspiration (ET0) and its components (e.g., reference transpiration, T0 and reference soil evaporation, E0). This study updated a soil-plant-atmosphere continuum (SPAC) evapotranspiration model and its associated components to obtain a reference-based SPAC model of reference evapotranspiration (R-SPAC), and it applied the model to an agricultural ecosystem. Model simulations of mean hourly ET0 were benchmarked against those of the Penman-Monteith method by the Food and Agriculture Organization (FAO-PM) throughout the growing season. The resulting good correlation obtained (R2 = 0.96, agreement index, I = 0.98, root-mean-square deviation (RMSD) = 0.05 mm h−1) validated the accuracy of the R-SPAC model. Sensitivity analysis was used to explore uncertainties and errors for ET0, T0, and E0 caused by input variables. The results showed that net radiation and shortwave radiation at the study site were the main drivers of ET0 for both the FAO-PM and R-SPAC models. The study showed that the proposed R-SPAC model can be used for predicting ET0 and for exploring interactions between climate, crop type, and soil in determining evapotranspiration under various future environment conditions.

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“…The latter has been used in qualitative and quantitative studies of vegetation on a temporal and spatial scale [ 3 , 4 ]. Vegetation indices are structured from a limited set of wavelengths; VIs attempt to maximize sensitivity to a biophysical parameter while minimizing adverse effects (e.g., atmospheric composition and variations in canopy background) [ 5 ].…”

Section: Introductionmentioning

confidence: 99%

“…Some of the spectral indices are used to predict more than one parameter at a time, on scales ranging from leaf level to canopy level, and even on global scales. For example, one of the most common VI, the Normalized Difference Vegetation Index (NDVI), is used in applications to assess biomass, phenology, Leaf Area Index, plant growth, among others [ 2 , 5 ]. Another spectral index is the Double Difference Index (DDI), developed to evaluate chlorophyll levels, but it has also presented a high correlation with the equivalent water thickness [ 6 , 7 ].…”

Section: Introductionmentioning

confidence: 99%

Retrieval of Vegetation Indices Related to Leaf Water Content from a Single Index: A Case Study of Eucalyptus globulus (Labill.) and Pinus radiata (D. Don.)

Villacrés

Fuentes

Reszka

et al. 2021

Plants

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The vegetation indices derived from spectral reflectance have served as an indicator of vegetation’s biophysical and biochemical parameters. Some of these indices are capable of characterizing more than one parameter at a time. This study examines the feasibility of retrieving several spectral vegetation indices from a single index under the assumption that all these indices are correlated with water content. The models used are based on a linear regression adjusted with least squares. The spectral signatures of Eucalyptus globulus and Pinus radiata, which constitute 97.5% of the forest plantation in Valparaiso region in Chile, have been used to test and validate the proposed approach. The linear models were fitted with an independent data set from which their performance was assessed. The results suggest that from the Leaf Water Index, other spectral indices can be recovered with a root mean square error up to 0.02, a bias of 1.12%, and a coefficient of determination of 0.77. The latter encourages using a sensor with discrete wavelengths instead of a continuum spectrum to estimate the forestry’s essential parameters.

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Remote sensing for estimating and mapping single and basal crop coefficientes: A review on spectral vegetation indices approaches

Cited by 121 publications

References 125 publications

Possibility of Zhuhai-1 Hyperspectral Imagery for Monitoring Salinized Soil Moisture Content Using Fractional Order Differentially Optimized Spectral Indices

Possibility of Zhuhai-1 Hyperspectral Imagery for Monitoring Salinized Soil Moisture Content Using Fractional Order Differentially Optimized Spectral Indices

A Novel Approach for the Simulation of Reference Evapotranspiration and Its Partitioning

Retrieval of Vegetation Indices Related to Leaf Water Content from a Single Index: A Case Study of Eucalyptus globulus (Labill.) and Pinus radiata (D. Don.)

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