Remote Sensing

Net primary productivity (NPP) plays a central role in biological carbon pump (BCP) and global carbon cycling, but NPP estimates in high-latitude regions remain highly uncertain despite their disproportional contribution to the global carbon sink. Previous studies identified discrepancies among satellite- and in situ-derived NPP estimates, but the drivers of disagreement remain poorly resolved due to limited high-frequency observations. Here, we present continuous depth-resolved estimates of in situ NPP from the Central Labrador Sea (56°N) in the subpolar Northwest Atlantic, reconstructed from daily bio-optical profiles collected between May and November 2016, and a 40-year dataset of photosynthesis parameters derived from ¹⁴C photosynthesis-irradiance (P-I) incubation experiments. Comparing with two satellite-based models showed satellite models overestimated NPP by factors of 2.5–4 from the study period. Statistical analyses identified the P-I parameterization as the most influential contributor to the disagreement between satellite and in situ NPP estimates. Given the importance of NPP to estimates of the biological carbon pump, our findings highlight the need for regionally appropriate P-I parameterization in NPP models, particularly in high-latitude regions.

Portable sun photometers are useful for atmospheric characterization at satellite calibration and validation (CAL/VAL) sites where permanent refFerence instruments are unavailable, but conventional MICROTOPS II operation requires manual solar pointing and is not well suited to unattended, temporally regular observations. This study developed an automated solar tracking and supervisory control system for MICROTOPS II and evaluated reference-based calibration transfer against a collocated AErosol RObotic NETwork (AERONET) CIMEL CE-318 for CAS500-4. Two Model 540 instruments were operated during three field days at the Jeonju_RDA AERONET site. A custom Python (version 3.10.20) controller automated serial communication, location and time synchronization, fixed interval scanning, data retrieval, calibration constant logging, and local network monitoring. Calibration parameters were estimated from two complete observation days and then frozen before evaluation on the remaining day in a threefold leave-one-day-out (LODO) design. The pooled out-of-fold (OOF) dataset contained 147 pooled OOF matched pairs for each aerosol channel and 147 matched pairs for precipitable water. CE-318-based calibration reduced the RMSE for NASCM540 to 0.0088–0.0188 across 340–870 nm and reduced PKNUM540 RMSE to 0.0117 at 440 nm and 0.0080 at 500 nm; the largest reduction was 89.8% for PKNUM540 500 nm aerosol optical depth (AOD). Residual performance was weakest at 870 nm for PKNUM540 (RMSE = 0.0282, R² = 0.280). Cross-validated temperature compensation improved 1020 nm AOD estimates (RMSE 0.0234 to 0.0199) and precipitable water estimates (0.1177 to 0.1100 cm) but did not improve AOD estimates at 870 nm. The results demonstrate that automated acquisition and independent-day-wise calibration transfer can substantially reduce systematic error while revealing wavelength specific limitations that must be resolved before broader seasonal or spatial transfer for satellite CAL/VAL.

The required amount of water for crop production differs from region to region, mainly due to the decreasing “effectiveness” of rainfall in maintaining plant growth due to increasing evaporation. Effective rainfall is related to the available moisture in the plant’s root zone, allowing the plant to germinate, emerge, and maintain its growth. There is, thus, a need to design agrometeorological and rain gauge networks for monitoring suitable crops, leading to sustainable agriculture. However, the minimum density needed for a rain-gauge network depends on the variability in space and time of the intended precipitation fields, and a variety of factors, including topographical features, accessibility, and ease of maintenance, influence the choice of rain-gauge locations. The number of rain gauges, rather than their location, is what optimizes a rain gauge network for various time scales, such as daily or monthly data. To map mean daily or mean monthly precipitation over watersheds or medium-sized agroclimatic zones and monitor crops for sustainable agriculture, this study focuses on the number of grid cells leading to the design of a rain gauge network, specifically a regular rain gauge grid scenario, considering the areal extent of land suitable for agriculture based on the agroclimatic zoning methodology. The universal kriging approach, an interpolation method based on geostatistical analysis, is used to calculate the required number of grid cells for the network’s density. Geostatistical analysis focuses on estimating and selecting the semi-variogram (SV) to determine the range or the decorrelation distance between rain gauges for precipitation estimation, superimposed on a grid mesh that encompasses the area’s agricultural land, such as Thessaly in Greece, as delineated by the agroclimatic zoning methodology. The daily and monthly mean climatological precipitation database is used, based on the CHIRPS-v3 dataset. The results indicate that the best fit for both daily and monthly mean precipitation is provided by the Spherical SV model, and the corresponding maximum range, which also represents the decorrelation distance, is calculated at 28.162 km for both mean daily precipitation and mean monthly precipitation, respectively. In summary, the size of the selected grid cells, based on the computed range of the selected SV, is 28 × 28 km² for the region of Thessaly, where one rain gauge is expected to be installed in each grid, leading to a total number of 18 grid cells.