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Graphical abstract Highlights • Developed a system that monitors VIs, fPAR, and LAI automatically. • Displayed linear responses to light intensities in the lab and rice field. • Agreed to a reference spectrometer and LAI-2200. • Tracked green LAI by integrating LED and micro-camera. Abstract Continuous monitoring of vegetation indices (VIs) the fraction of absorbed photosynthetically active radiation (fPAR) and leaf area index (LAI) through satellite remote sensing has advanced our understanding of biosphere–atmosphere interactions. Substantial efforts have been put into monitoring individual variables in the field, but options to concurrently monitor VIs, fPAR, and LAI in-situ have been lacking. In this paper, we present the Smart Surface Sensing System (4S), which automatically collects, transfers and processes VIs, fPAR and LAI data streams. The 4S consists of a microcomputer, controller and camera, a multi-spectral spectrometer built in with a light-emitting diode (LED) and an internet connection. Lab testing and field observations in a rice paddy site that experiences wet summer monsoon seasons confirmed the linear response of 4S to light intensities in the blue, green, red and near-infrared spectral channels, with wide ranging temperatures and humidity having only a minor impact on 4S throughout the growing season. Applied over an entire rice growing season (day of year [DOY] 120 - 248), VIs and fPAR from 4S were linearly related to corresponding VIs from a reference spectrometer (R2 = 0.98; NDVI, R2 = 0.96; EVI) and the LAI-2200 instrument (R2 = 0.76), respectively. Integration of gap fraction-based LAI from LED sensors and a green index from the micro-camera allowed tracking of the seasonality of green LAI. The continuous and diverse nature of 4S observations highlights its potential for evaluating satellite remote sensing products. We believe that 4S will be useful for the expansion of ecological sensing networks across multiple spatial and temporal scales. [ABSTRACT FROM AUTHOR]

• Ground nadir SIF were upscaled to EC footprint using satellite VIs. • SIF-GPP relationships did not significantly change after upscaling in croplands. • Different VIs, EC footprint models and satellite data led to marginal differences. • Cropland homogeneity contributed to the marginal change of SIF-GPP relationships. Ground solar-induced chlorophyll fluorescence (SIF) is important for the mechanistic understanding of the dynamics of vegetation gross primary production (GPP) at fine spatiotemporal scales. However, eddy covariance (EC) observations generally cover larger footprint areas than ground SIF observations (a bare fiber with nadir), and this footprint mismatch between nadir SIF and GPP could complicate the canopy SIF-GPP relationships. Here, we upscaled nadir SIF observations to EC footprint and investigated the change in SIF-GPP relationships after the upscaling in cropland. We included 13 site-years data in our study, with seven site-years corn, four site-years soybeans, and two site-years miscanthus, all located in the US Corn Belt. All sites' crop nadir SIF observations collected from the automated FluoSpec2 system (a hemispheric-nadir system) were upscaled to the GPP footprint-based SIF using vegetation indices (VIs) calculated from high spatiotemporal satellite reflectance data. We found that SIF-GPP relationships were not substantially changed after upscaling nadir SIF to GPP footprint at our crop sites planted with corn, soybean, and miscanthus, with R2 change after the upscaling ranging from -0.007 to 0.051 and root mean square error (RMSE) difference from -0.658 to 0.095 umol m −2 s −1 relative to original nadir SIF-GPP relationships across all the site-years. The variation of the SIF-GPP relationship within each species across different site-years was similar between the original nadir SIF and upscaled SIF. Different VIs, EC footprint models, and satellite data led to marginal differences in the SIF-GPP relationships when upscaling nadir SIF to EC footprint. Our study provided a methodological framework to correct this spatial mismatch between ground nadir SIF and GPP observations for croplands and potentially for other ecosystems. Our results also demonstrated that the spatial mismatch between ground nadir SIF and GPP might not significantly affect the SIF-GPP relationship in cropland that are largely homogeneous. [ABSTRACT FROM AUTHOR]

Conventional process-based crop simulation models and agro-land surface models require numerous forcing variables and input parameters. The regional application of these crop simulation models is complicated by factors concerning input data requirements and parameter uncertainty. In addition, the empirical remotely sensed regional scale crop yield estimation method does not enable growth process modeling. In this study, we developed a process-based rice yield estimation model by integrating an assimilate allocation module into the satellite remote sensing-derived and biophysical process-based Breathing Earth System Simulator (BESS). Normalized accumulated gross primary productivity ( G P P n o r m - a c c u ) was used as a scaler for growth development, and the relationships between G P P n o r m - a c c u and dry matter partitioning coefficients were determined from the eddy covariance and biometric measurements at the Cheorwon Rice paddy KoFlux site. Over 95% of the variation in the dry matter allocation coefficients of rice grain could be explained by G P P n o r m - a c c u . The dynamics of dry matter distribution among different rice components were simulated, and the annual grain yields were estimated. BESS-Rice simulated GPP and dry matter partitioning dynamics, and rice yields were evaluated against in-situ measurements at three paddy rice sites registered in KoFlux. The results showed that BESS-Rice performed well in terms of rice productivity estimation, with average root mean square error (RMSE) value of 2.2 g C m −2  d −1 (29.5%) and bias of –0.5 g C m −2  d −1 (–7.1%) for daily GPP, and an average RMSE value of 534.8 kg ha −1 (7.7%) and bias of 242.1 kg ha −1 (3.5%) for the annual yield, respectively. BESS-Rice is much simpler than conventional crop models and this helps to reduce the uncertainty related to the forcing variables and input parameters and can result in improved regional yield estimation. The process-based mechanism of BESS-Rice also enables an agronomic diagnosis to be made and the potential impacts of climate change on rice productivity to be investigated. [ABSTRACT FROM AUTHOR]

There remains limited information to characterize the solar-induced chlorophyll fluorescence (SIF)-gross primary production (GPP) relationship in C4 cropping systems. The annual C4 crop corn and perennial C4 crop miscanthus differ in phenology, canopy structure and leaf physiology. Investigating the SIF-GPP relationships in these species could deepen our understanding of SIF-GPP relationships within C4 crops. Using in situ canopy SIF and GPP measurements for both species along with leaf-level measurements, we found considerable differences in the SIF-GPP relationships between corn and miscanthus, with a stronger SIF-GPP relationship and higher slope of SIF-GPP observed in corn compared to miscanthus. These differences were mainly caused by leaf physiology. For miscanthus, high non-photochemical quenching (NPQ) under high light, temperature and water vapor deficit (VPD) conditions caused a large decline of fluorescence yield (Φ F), which further led to a SIF midday depression and weakened the SIF-GPP relationship. The larger slope in corn than miscanthus was mainly due to its higher GPP in mid-summer, largely attributed to the higher leaf photosynthesis and less NPQ. Our results demonstrated variation of the SIF-GPP relationship within C4 crops and highlighted the importance of leaf physiology in determining canopy SIF behaviors and SIF-GPP relationships. [ABSTRACT FROM AUTHOR]

Digital cover photography (DCP) has emerged as an indirect method to measure gap fraction of vegetation canopies. However, as with other photographic methods, determining camera relative exposure value (REV) and threshold for pixel classification, cause substantial uncertainties in gap fraction estimates. Here we propose a new method to improve the measurement of gap fraction under various solar zenith angles (SZAs), sky conditions, and canopy structures. This method computes gap fractions of ambiguous vegetation or sky pixels using an unsaturated raw image from DCP and a reconstructed sky image from the raw image, thus taking full advantage of the potential of raw image processing. This is combined with pre-classification of pixels that are unambiguously canopy and sky to greatly reduce light scattering effects that are likely to be present within the canopy. To test the sensitivity of the new method, we acquired images at one-hour intervals between 20 and 85° of SZAs under closed, half-closed, and open canopies with REV settings from 0 to −5. The new method showed little variation in gap fractions across the diverse SZAs in closed, half-closed, and open canopies. A perforated panel experiment, which was used to test the accuracy of the estimated gap fractions, confirmed that the new method accurately estimated gap fractions across a range of hole size, gap fractions and SZAs. We conclude that the new method opens new opportunities to estimate gap fractions accurately regardless of solar positions from open to closed canopies, and is a significant advance for accurate and precise monitoring of canopy cover and leaf area index (LAI), and for calibrating and evaluating satellite remote sensing LAI products. [ABSTRACT FROM AUTHOR]

Continuous field leaf area index (LAI) measurement has become increasingly important for the validation of remote sensing LAI products. A seasonal field campaign was carried out to take continuous LAI measurements over paddy rice fields in NE China in 2012. Three indirect optical methods, LAI-2200, digital hemispherical photography (DHP), and AccuPAR, were compared with a destructive sampling method conducted concurrently. Corrections for the clumping effect were applied to the effective plant area indices (PAI eff ) estimated from the indirect optical measurements. Both LAI-2200 and DHP produce consistent PAI eff estimates over the season ( R 2 = 0.76, RMSE = 0.97). The clumping index (CI) values obtained from DHP generally decrease with plant growth and range between 0.63 and 0.74 during the peak growing period from day of year (DOY) 191−230. The CI values retrieved from DHP photos generally decrease with increasing view angles. The optical PAI and LAI values estimated from LAI-2200 and DHP correspond very well with the destructive values before DOY 230 ( R 2 = 0.75, RMSE = 1.15 for PAI and R 2 = 0.78, RMSE = 0.74 for LAI), and the relative errors are less than 10% and 5%, respectively, for the two instruments. Omitting ring 5 for LAI-2200 generates very accurate PAI and LAI estimations during the peak season. Nevertheless, AccuPAR underestimates the PAI eff , PAI, and LAI values obtained from other methods (up to 30%). After DOY 231, the capability to detect PAI decreases significantly for both destructive and optical methods due to the leaf senescence and the DHP classification difficulty. In general, rice PAI could be accurately estimated with LAI-2200 and DHP before senescence if the clumping effect could be properly taken into account. The seasonal continuous LAI measurements obtained from this study are valuable for the validation of remote sensing LAI products. [ABSTRACT FROM AUTHOR]

• Fully evaluated the ecosys model in simulating carbon budget over the U.S. Midwest. • Ecosys captured the dynamics of carbon fluxes and pools at site and regional scales. • Ecosys captured the response of carbon cycle dynamics to environmental variabilities. • Mass-balance approach can be used to estimate cropland soil carbon dynamics. As one of the major agricultural production areas in the world, the United States (U.S.) Midwest plays a vital role in the global food supply and agricultural ecosystem services. Although significant efforts have been made in modeling the carbon cycle dynamics over this area, large uncertainty still exists in the previous simulations in terms of reproducing individual components of the carbon cycle and their responses to environmental variability. Here we evaluated the performance of an advanced agroecosystem model, ecosys , in simulating carbon budgets over the U.S. Midwest, considering both the magnitude of carbon flux/yield and its response to environmental (climate and soil) variability. We conducted model simulations and evaluations at 7 cropland eddy-covariance sites as well as over 293 counties of Illinois, Indiana, and Iowa in the U.S. Midwest. The site-level simulations showed that ecosys captured both the magnitude and seasonal patterns of carbon fluxes (i.e., net ecosystem carbon exchange (NEE), ecosystem gross primary production (GPP), and ecosystem respiration (Reco)), leaf area index (LAI), and dynamic plant carbon allocation processes, with R2 equal to 0.92, 0.87, 0.87, and 0.78 for GPP, NEE, Reco, and LAI, respectively across all the sites compared with the observations. For regional scale simulations, ecosys reproduced the spatial distribution and interannual variability of corn and soybean yields with the constraints of observed yields and a new remotely sensed GPP product, with R2 of multi-year averaged simulated and observed yield equal 0.83 and 0.80 for corn and soybean, respectively. The simulated responses of carbon cycle dynamics to environmental variability were consistent with that from the empirical observations at both site and regional scales. Our results demonstrated the applicability of ecosys in simulating the carbon cycle and soil carbon dynamics of the U.S. Midwestern agroecosystems under different climate and soil conditions. [ABSTRACT FROM AUTHOR]