The spatial quantification of green leaf area index (LAI_green), the total green photosynthetically active leaf area per ground area, is a crucial biophysical variable for agroecosystem monitoring. The Sentinel-2 mission is with (1) a temporal resolution lower than a week, (2) a spatial resolution of up to 10 m, and (3) narrow bands in the red and red-edge region, a highly promising mission for agricultural monitoring. The aim of this work is to define an easy implementable LAI_green index for the Sentinel-2 mission. Two large and independent multi-crop datasets of in situ collected LAI_green measurements were used. Commonly used LAI_green indices applied on the Sentinel-2 10 m × 10 m pixel resulted in a validation R² lower than 0.6. By calculating all Sentinel-2 band combinations to identify high correlation and physical basis with LAI_green, the new Sentinel-2 LAI_green Index (SeLI) was defined. SeLI is a normalized index that uses the 705 nm and 865 nm centered bands, exploiting the red-edge region for low-saturating absorption sensitivity to photosynthetic vegetation. A R² of 0.708 (root mean squared error (RMSE) = 0.67) and a R² of 0.732 (RMSE = 0.69) were obtained with a linear fitting for the calibration and validation datasets, respectively, outperforming established indices. Sentinel-2 LAI_green maps are presented.

The atmospheric contribution constitutes about 90 percent of the signal measured by satellite sensors over oceanic and inland waters. Over open ocean waters, the atmospheric contribution is relatively easy to correct as it can be assumed that water-leaving radiance in the near-infrared (NIR) is equal to zero and it can be performed by applying a relatively simple dark-pixel-correction-based type of algorithm. Over inland and coastal waters, this assumption cannot be made since the water-leaving radiance in the NIR is greater than zero due to the presence of water components like sediments and dissolved organic particles. The aim of this study is to determine the most appropriate atmospheric correction processor to be applied on Sentinel-2 MultiSpectral Imagery over several types of inland waters. Retrievals obtained from different atmospheric correction processors (i.e., Atmospheric correction for OLI ‘lite’ (ACOLITE), Case 2 Regional Coast Colour (here called C2RCC), Case 2 Regional Coast Colour for Complex waters (here called C2RCCCX), Image correction for atmospheric effects (iCOR), Polynomial-based algorithm applied to MERIS (Polymer) and Sen2Cor or Sentinel 2 Correction) are compared against in situ reflectance measured in lakes and reservoirs in the Valencia region (Spain). Polymer and C2RCC are the processors that give back the best statistics, with coefficients of determination higher than 0.83 and mean average errors less than 0.01. An evaluation of the performance based on water types and single bands–classification based on ranges of in situ chlorophyll-a concentration and Secchi disk depth values- showed that performance of these set of processors is better for relatively complex waters. ACOLITE, iCOR and Sen2Cor had a better performance when applied to meso- and hyper-eutrophic waters, compare with oligotrophic. However, other considerations should also be taken into account, like the elevation of the lakes above sea level, their distance from the sea and their morphology.

Regression models based on spectral indices are typically empirical formulae enabling the mapping of biophysical parameters derived from Earth Observation (EO) data. Due to its empirical nature, it remains nevertheless uncertain to what extent a selected regression model is the most appropriate one, until all band combinations and curve fitting functions are assessed. This paper describes the application of a Spectral Index (SI) assessment toolbox in the Automated Radiative Transfer Models Operator (ARTMO) package. ARTMO enables semi-automatic retrieval and mapping of biophysical parameters from optical remote sensing observations. The SI toolbox facilitates the assessment of biophysical parameter retrieval accuracy of established as well as new and generic SIs. For instance, based on the SI formulation used, all possible band combinations of formulations with up to ten bands can be defined and evaluated. Several options are available in the SI assessment: calibration/validation data partitioning, the addition of noise and the definition of curve fitting models. To illustrate its functioning, all two-band combinations according to simple ratio (SR) and normalized difference (ND) formulations as well as various fitting functions (linear, exponential, power, logarithmic, polynomial) have been assessed. HyMap imaging spectrometer (430–2490 nm) data obtained during the SPARC-2003 campaign in Barrax, Spain, have been used to extract leaf area index (LAI) and leaf chlorophyll content (LCC) estimates. For both SR and ND formulations the most sensitive regions have been identified for two-band combinations of green (539–570 nm) with longwave SWIR (2421–2453 nm) for LAI (r2: 0.83) and far-red (692 nm) with NIR (1340 nm) or shortwave SWIR (1661–1686 nm) for LCC (r2: 0.93). Polynomial, logarithmic and linear fitting functions led to similar best correlations, though spatial differences emerged when applying the functions to HyMap imagery. We suggest that a systematic SI assessment is a strong requirement in the quality assurance approach for accurate biophysical parameter retrieval.

Sentinel-2 offers great potential for monitoring water quality in inland and coastal waters. However, atmospheric correction in these waters is challenging, and there is no standardized approach yet, but different methods coexist under constant development. The atmospheric correction Case 2 Regional Coast Colour (C2RCC) processor has been recently updated with the C2X-COMPLEX (C2XC). This study is one of the first attempts at exploring its performance, in comparison with C2RCC and C2X, in inland and coastal waters in the east of the Iberian Peninsula, in retrieving water surface reflectance and estimating chlorophyll-a ([Chl-a]), total suspended matter ([TSM]), and Secchi disk depth (Z_SD). The relationship between in situ Z_SD and Kd_z90max product (i.e., the depth of the water column from which 90% of the water-leaving irradiance is derived) of the C2RCC processors demonstrated the potential of this product for estimating water clarity (r > 0.75). However, [TSM] and [Chl-a] derived from the different processors with default calibration factors were not suitable within the targeted scenarios, requiring recalibration based on optical water types or a shift to dynamic algorithm blending approaches. This would benefit from switching between C2RCC and C2XC, which extends the potential for improving surface reflectance estimates to a wide range of scenarios and suggests a promising future for C2-Nets in operational monitoring of water quality.