Remote and Proximal Sensing-Derived Spectral Indices and Biophysical Variables for Spatial Variation Determination in Vineyards

Remote-sensing measurements are crucial for smart-farming applications, crop monitoring, and yield forecasting, especially in fields characterized by high heterogeneity. Therefore, in this study, Precision Viticulture (PV) methods using proximal- and remote-sensing technologies were exploited and compared in a table grape vineyard to monitor and evaluate the spatial variation of selected vegetation indices and biophysical variables throughout selected phenological stages (multi-seasonal data), from veraison to harvest. The Normalized Difference Vegetation Index and the Normalized Difference Red-Edge Index were calculated by utilizing satellite imagery (Sentinel-2) and proximal sensing (active crop canopy sensor Crop Circle ACS-470) to assess the correlation between the outputs of the different sensing methods. Moreover, numerous vegetation indices and vegetation biophysical variables (VBVs), such as the Modified Soil Adjusted Vegetation Index, the Normalized Difference Water Index, the Fraction of Vegetation Cover, and the Fraction of Absorbed Photosynthetically Active Radiation, were calculated, using the satellite data. The vegetation indices analysis revealed different degrees of correlation when using diverse sensing methods, various measurement dates, and different parts of the cultivation. The results revealed the usefulness of proximal- and remote-sensing-derived vegetation indices and variables and especially of Normalized Difference Vegetation Index and Fraction of Absorbed Photosynthetically Active Radiation in the monitoring of vineyard condition and yield examining, since they were demonstrated to have a very high degree of correlation (coefficient of determination was 0.87). The adequate correlation of the vegetation indices with the yield during the latter part of the veraison stage provides valuable information for the future estimation of production in broader areas.

Satellite Based Fraction of Absorbed Photosynthetically Active Radiation Is Congruent with Plant Diversity in India

A dynamic habitat index (DHI) based on satellite derived biophysical proxy (fraction of absorbed photosynthetically active radiation, FAPAR) was used to evaluate the vegetation greenness pattern across deserts to alpine ecosystems in India that account to different biodiversity. The cumulative (DHI-cum), minimum (DHI-min), and seasonal (DHI-sea) DHI were generated using Moderate Resolution Imaging Spectroradiometer (MODIS)-based FAPAR. The higher DHI-cum and DHI-min represented the biodiversity hotspots of India, whereas the DHI-sea was higher in the semi-arid, the Gangetic plain, and the Deccan peninsula. The arid and the trans-Himalaya are dominated with grassland or barren land exhibit very high DHI-sea. The inter-year correlation demonstrated an increase in vegetation greenness in the semi-arid region, and continuous reduction in greenness in the Northeastern region. The DHI components validated using field-measured plant richness data from four biogeographic regions (semi-arid, eastern Ghats, the Western Ghats, and Northeast) demonstrated good congruence. DHI-cum that represents the annual greenness strongly correlated with the plant richness (R2 = 0.90, p-value < 0.001), thereby emerging as a suitable indicator for assessing plant richness in large-scale biogeographic studies. Overall, the FAPAR-based DHI components across Indian biogeographic regions provided understanding of natural variability of the greenness pattern and its congruence with plant diversity.

Upscaling from Instantaneous to Daily Fraction of Absorbed Photosynthetically Active Radiation (FAPAR) for Satellite Products

The fraction of absorbed photosynthetically active radiation (FAPAR) is an essential climate variable (ECV) widely used for various ecological and climate models. However, all the current FAPAR satellite products correspond to instantaneous FAPAR values acquired at the satellite transit time only, which cannot represent the variations in photosynthetic processes over the diurnal period. Most studies have directly used the instantaneous FAPAR as a reasonable approximation of the daily integrated value. However, clearly, FAPAR varies a lot according to the weather conditions and amount of incoming radiation. In this paper, a temporal upscaling method based on the cosine of the solar zenith angle (SZA) at local noon ( c o s ( S Z A n o o n ) ) is proposed for converting instantaneous FAPAR to daily integrated FAPAR. First, the diurnal variations in FAPAR were investigated using PROSAIL (a model of Leaf Optical Properties Spectra (PROSPECT) integrating a canopy radiative transfer model (Scattering from Arbitrarily Inclined Leaves, SAIL)) simulations with different leaf area index (LAI) values corresponding to different latitudes. It was found that the instantaneous black sky FAPAR at 09:30 AM provided a good approximation for the daily integrated black sky FAPAR; this gave the highest correlation (R2 = 0.995) and lowest Root Mean Square Error (RMSE = 0.013) among the instantaneous black sky FAPAR values observed at different times. Secondly, the difference between the instantaneous black sky FAPAR values acquired at different times and the daily integrated black sky FAPAR was analyzed; this could be accurately modelled using the cosine value of solar zenith angle at local noon ( c o s ( S Z A n o o n ) ) for a given vegetation scene. Therefore, a temporal upscaling method for typical satellite products was proposed using a cos(SZA)-based upscaling model. Finally, the proposed cos(SZA)-based upscaling model was validated using both the PROSAIL simulated data and the field measurements. The validated results indicated that the upscaled daily black sky FAPAR was highly consistent with the daily integrated black sky FAPAR, giving very high mean R2 values (0.998, 0.972), low RMSEs (0.007, 0.014), and low rMAEs (0.596%, 1.378%) for the simulations and the field measurements, respectively. Consequently, the cos(SZA)-based method performs well for upscaling the instantaneous black sky FAPAR to its daily value, which is a simple but extremely important approach for satellite remote sensing applications related to FAPAR.

Quality Assessment of PROBA-V LAI, fAPAR and fCOVER Collection 300 m Products of Copernicus Global Land Service

The Copernicus Global Land Service (CGLS) provides global time series of leaf area index (LAI), fraction of absorbed photosynthetically active radiation (fAPAR) and fraction of vegetation cover (fCOVER) data at a resolution of 300 m and a frequency of 10 days. We performed a quality assessment and validation of Version 1 Collection 300 m products that were consistent with the guidelines of the Land Product Validation (LPV) subgroup of the Committee on Earth Observation System (CEOS) Working Group on Calibration and Validation (WGCV). The spatiotemporal patterns of Collection 300 m V1 LAI, fAPAR and fCOVER products are consistent with CGLS Collection 1 km V1, Collection 1 km V2 and Moderate Resolution Imagery Spectroradiometer Collection 6 (MODIS C6) products. The Collection 300 m V1 products have good precision and smooth temporal profiles, and the interannual variations are consistent with similar satellite products. The accuracy assessment using ground measurements mainly over crops shows an overall root mean square deviation of 1.01 (44.3%) for LAI, 0.12 (22.2%) for fAPAR and 0.21 (42.6%) for fCOVER, with positive mean biases of 0.36 (15.5%), 0.05 (10.3%) and 0.16 (32.2%), respectively. The products meet the CGLS user accuracy requirements in 69.1%, 62.5% and 29.7% of the cases for LAI, fAPAR and fCOVER, respectively. The CGLS will continue the production of Collection 300 m V1 LAI, fAPAR and fCOVER beyond the end of the PROBA-V mission by using Sentinel-3 OLCI as input data.

Evidence of Arithmetical Uncertainty in Estimation of Light and Water Use Efficiency

It was demonstrated that conventional resource use efficiency (RUE) estimation methodology is largely subject to arithmetic weakness. Extensive field research data on aboveground biomass (AGB), absorbed photosynthetically active radiation (APAR), and crop evapotranspiration (ETc) in maize, soybean, sorghum, and winter wheat confirmed this methodological bias for light use efficiency (LUE) and water use efficiency (WUE) estimation. LUE and WUE were derived using cumulated (data aggregates across samplings) and independent (data increments across samplings) approaches. Use of cumulated data yielded strong-but-false correlation between AGB and APAR or ETc, being a statistical artefact. RUE values from an independent approach were substantially lower than that from a cumulated approach with greater standard errors. Overall, a cumulated approach tends to oversimplify the complex interactions among carbon and resource coupling in agroecosystems, which is accurately represented when employing an independent approach instead.