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Summary Accurate quantification of dissolved organic carbon (DOC) in surface and soil pore waters is crucial for understanding changes in water resources under the influence of climate and land use changes. Sampling and laboratory analysis of DOC content at a sufficient temporal frequency are especially difficult to achieve for natural DOC sources like the extensive boreal and arctic mire landscapes due to their remoteness. Therefore, the goals of this paper are (1) to investigate the performance of a portable, high-resolution ultraviolet–visible light spectroscopic method for determining the DOC content of surface and soil pore water samples from a boreal mire complex and (2) to compare the spectroscopic method with other DOC measurement techniques, e.g., the wet heated persulfate oxidation method and a laboratory, expulsion-based spectrophotometric method and (3) to assess different multivariate models that relate absorbance measurements with DOC contents. The study indicates that high-resolution spectroscopic measurements provide a simple, robust and non-destructive method for measuring DOC content. These measurements are of short duration (<1 min) and the sample analysis is portable, rendering this method particularly advantageous for in situ investigations at remote field locations. The study also demonstrates that if absorbances at specific wavelengths are used as proxies for DOC concentration, it is recommended to create site-specific calibration models that include more than one wavelength to achieve the optimal accuracy of the proxy-based DOC quantification. [ABSTRACT FROM AUTHOR]

• Alternate wetting and drying did not negatively impact canopy water use. • Estimated T:ET ranged between 0.43 and 0.56 for rice grown in arkansas. • Despite increased humidity, VPD improved the relationship between GPP and ET. Improved water management is a growing need in areas where rice production is intensive. In the state of Arkansas and other portions of the US, new irrigation practices are being implemented to conserve water during rice cultivation. The goal of this study was to evaluate canopy water use in two commercial rice fields using different irrigation practices across three growing seasons. Canopy water use was assessed across multiple metrics, including different representations of water use efficiency (WUE) as well as their contributing terms, gross primary production (GPP) and evapotranspiration (ET). Furthermore, we validated and employed a methodology for estimating transpiration from ET using the concept of underlying water use efficiency (uWUE) that includes a sensitivity to vapor pressure deficit (VPD). Periodic drying associated with the alternate wetting and drying irrigation practice did not result in decreased GPP, ET, or transpiration (T). Our findings indicated that approximately 43 to 56 % of ET is released as T during the growing season. The uWUE method improved the relationship between GPP and ET by accounting for the limitation of VPD on GPP during the afternoon periods. [ABSTRACT FROM AUTHOR]

• ET was measured in an arctic wetland over two contrasting summer seasons. • Ground and tower-based estimates of ET were compared using upscaling analysis. • Chamber method strongly underestimated fluxes measured with eddy covariance. • Performance of the chamber method was influenced by hydrometeorological conditions. Evapotranspiration (ET) is an important hydrological flux with a strong influence on greenhouse gas emissions from thawing permafrost. This study examines the suitability of the closed chamber method in characterising the spatial heterogeneity of ET fluxes in a typical polygonal tundra landscape in the Lena River Delta, northern Siberia. Actual evapotranspiration is compared across scales using: (1) ground-based chamber measurements to observe ET at the microsite scale; and (2) tower-based eddy covariance (EC) measurements which provide spatially averaged ET observations at the ecosystem level. Adopting an upscaling approach using EC estimates as a benchmark, the authors assess the suitability of the closed chamber method in an arctic wetland environment. A short closure time (40 s) and a linear model to describe the change in chamber headspace water vapour concentration were employed to estimate ET rates from the chamber measurements. Using a correction factor chamber fluxes were successfully scaled to the EC data. Yet, findings suggest that the performance of the closed chamber method is highly sensitive to the prevailing hydrometeorological conditions, and it is likely that the sorption and desorption of water molecules to the inside of the chamber and tubing has a strong impact on results. A number of methodological issues are presented in this paper which question the use of closed chamber measurements as a stand-alone tool for measuring ET in arctic wetland environments. However, when paired alongside a trusted benchmark, chambers can provide valuable information on ET at the microsite scale. [ABSTRACT FROM AUTHOR]

Sustainable intensification of rice farming is crucial to meeting human food needs while reducing environmental impacts. Rice production represents 8% of all anthropogenic emissions of CH 4 , a potent greenhouse gas. Cultivation practices that minimize the number of days the rice fields are flooded, such as irrigation using the alternate wetting and drying (AWD) technique instead of continuous flooding (DF) can potentially reduce CH 4 emissions. Ratoon cropping, wherein a second crop of rice is grown from the harvested stubble of the first crop, can produce additional yield with minimal labor but may generate more CH 4 than single cropping. The objectives of this study were first to test different water management regimes for their impact on yield and CH 4 emissions, and second to investigate CH 4 emissions from a ratoon crop and perform an exploratory economic analysis of ratoon cropping. Two adjacent fields in Lonoke County, Arkansas were compared under different irrigation treatments from 2015 through 2020; the 2020 season also included a ratoon crop. Field-scale CH 4 emissions were measured using the eddy covariance method at each field. AWD reduced CH 4 emissions by 79.5% on average in comparison to DF for the main seasons. Across the field-seasons, the emissions from the main crop ranged from 77.2 to 132.5 kg CH 4 -C ha−1 under DF and from 7.1 to 40.7 kg CH 4 -C ha−1 under AWD. The ratoon crop generated emissions from 39.7 to 50.7 kg CH 4 -C ha−1, up to a 3.6-fold increase from the main crop of the same year. CH 4 emissions from the ratoon crop in this study were much lower than those found in previous ratoon studies. The ratoon crop yield was 13% that of the main crop yield on average but there was no significant difference in yield between irrigation treatments for the main seasons. • Alternate wetting and drying reduced average rice CH 4 emissions by 79.5%. • Ratoon crop CH 4 emissions were 24.6–160.0% higher than main crop emissions. • The ratoon crop yield was 13% that of the main crop yield on average. • Yield did not significantly differ between main season irrigation treatments. [ABSTRACT FROM AUTHOR]

The construction of contour levees for rice irrigation represents a major landscape management activity with impacts on irrigation water use efficiency, crop management decisions, and food production. However, levee distribution information traditionally relies on local field surveys because remote sensing approaches are complicated by irregular spacing, shape, and landscape variability within the field. In this paper the authors develop a deep learning approach capable of identifying rice fields with contour style levee irrigation practices from open-source aerial imagery. To generate a levee-identification scheme, a hybrid ResNet/Unet model is built from the commonly known Residual Network (ResNet) architecture for multi-layer deep learning strategies. The model takes a 320 × 320 RGB aerial landscape image from the US National Agricultural Imagery Program as input along with label data to then generate a probability map of the distribution of farm fields that use contour levees within the image. In performing this task, the model generates a 0.991 receiver operating characteristic curve score. The model continues to perform well under the introduction of clouds, data augmentation, or minor reductions in spatial resolution. Throughout these tests, the model performed within 0.2 of its original score, except for when the image quality was reduced to 60 m wherein the model score dropped to 0.691. Via these tests the model demonstrates potential to function well given different spatial extents or potential satellite remote sensing with moderate (10 m) resolutions. This model provides a proof-of-concept for the use of aerial imagery and a deep learning strategy for irrigation-type mapping practices. • Contour levee identification method using aerial images yields ROC score of 0.991. • A ResNet/UNet hybrid provides ample performance for remote sensing applications. • The method is insensitive to simulated cloud cover up to 40% opacity. • Performance degrades with a decrease in resolution from 1 m to less than 10 m. • The introduction of dense static noise could present a challenge for the model. [ABSTRACT FROM AUTHOR]

Although rice cultivation is one of the most important agricultural sources of methane (CH 4) and contributes ∼8% of total global anthropogenic emissions, large discrepancies remain among estimates of global CH 4 emissions from rice cultivation (ranging from 18 to 115 Tg CH 4 yr−1) due to a lack of observational constraints. The spatial distribution of paddy-rice emissions has been assessed at regional-to-global scales by bottom-up inventories and land surface models over coarse spatial resolution (e.g., > 0.5°) or spatial units (e.g., agro-ecological zones). However, high-resolution CH 4 flux estimates capable of capturing the effects of local climate and management practices on emissions, as well as replicating in situ data, remain challenging to produce because of the scarcity of high-resolution maps of paddy-rice and insufficient understanding of CH 4 predictors. Here, we combine paddy-rice methane-flux data from 23 global eddy covariance sites and MODIS remote sensing data with machine learning to 1) evaluate data-driven model performance and variable importance for predicting rice CH 4 fluxes; and 2) produce gridded up-scaling estimates of rice CH 4 emissions at 5000-m resolution across Monsoon Asia, where ∼87% of global rice area is cultivated and ∼ 90% of global rice production occurs. Our random-forest model achieved Nash-Sutcliffe Efficiency values of 0.59 and 0.69 for 8-day CH 4 fluxes and site mean CH 4 fluxes respectively, with land surface temperature, biomass and water-availability-related indices as the most important predictors. We estimate the average annual (winter fallow season excluded) paddy rice CH 4 emissions throughout Monsoon Asia to be 20.6 ± 1.1 Tg yr−1 for 2001–2015, which is at the lower range of previous inventory-based estimates (20–32 CH 4 Tg yr−1). Our estimates also suggest that CH 4 emissions from paddy rice in this region have been declining from 2007 through 2015 following declines in both paddy-rice growing area and emission rates per unit area, suggesting that CH 4 emissions from paddy rice in Monsoon Asia have likely not contributed to the renewed growth of atmospheric CH 4 in recent years. • We developed data-driven models for upscaling CH4 fluxes from Eddy Covariance data. • Paddy rice CH 4 emissions across Monsoon Asia were 20.6 ± 1.1 Tg yr-1 during 2001–2015. • Paddy rice CH 4 emissions across Monsoon Asia declined after 2007 through 2015. • Land Surface temperature was identified as the most important predictor. [ABSTRACT FROM AUTHOR]

Rice has a large water demand and its cultivation is responsible for 8% of global anthropogenic methane (CH 4) emissions. In this context, on-farm practices to increase water use efficiency and decrease greenhouse gas (GHG) emissions are increasingly being considered by rice producers. In this study, four water saving practices implemented in ten Arkansas rice farms during two consecutive years were evaluated: Multiple-Inlet Rice Irrigation (MIRI), zero grade (land leveling), Furrow-Irrigated Rice (FIR), and Alternate Wetting and Drying (AWD). The sustainability of these practices at the field level was evaluated with two decision support tools, Fieldprint Platform (FPP) and Cool Farm Tool (CFT). From a GHG perspective, both tools agreed that FIR and AWD were the best practices to decrease CH 4 emissions. A sensitivity analysis with FPP was performed to detect which variables and management practices most affect metric scores. FIR was the best practice regarding GHG emissions due to the decrease in CH 4 emissions; however, when considering energy use, FIR and zero grade increased energy use due to greater chemical applications. Initiatives such as AWD irrigation, burning the straw or other straw removal practices when practicing a rice-rice rotation, or making decisions about crop planting based on differences in soil type inside the farm should be encouraged to decrease CH 4 emissions. The energy cost of agrochemical production had a large impact whereby even 10–20% reductions in application rates can significantly reduce the energy use. These decision support tools offer farmers an opportunity to evaluate their practices and increase farm sustainability; however, policy makers need to incentivize conservation practices and encourage creation of farmer networks to support adoption of sustainability practices. • Rice cultivation practices were assessed using farm-level decision support tools. • Methane is the main contributor to the total GHG emissions. • Alternate wetting and drying or furrow irrigation decrease methane emissions by 74%. • The use of surface water decreases energy use by 5% compared to a mix of sources. • A 20% decrease of the urea rate decreases total energy use by 9%. [ABSTRACT FROM AUTHOR]

• Measured ET in AWD and conventional DF irrigated rice fields was similar. • As canopy developed through the growing season, ET observations became more similar regardless of irrigation treatment. • The PM-AET method was able to suitably estimate ET across irrigation regimes. As rice production is water intensive, establishing an accurate field–scale water budget is paramount for sustainable use of local water resources. This study's goal was to quantify and characterize half-hourly and seasonal evapotranspiration (ET) in two commercial, zero–grade rice fields in the U.S. Mid–South over three growing seasons. During each growing season, irrigation regimes for the studied fields differed between alternate wetting and drying (AWD) and delayed flooding (DF). The 2015 growing season enabled a direct comparison of the effects of AWD and DF on ET, while during the 2016 and 2017 seasons both fields were simultaneously under AWD and DF, respectively. The DF method is the region's most common irrigation practice, and it prescribes a continuous flood to be maintained for the majority of the growing season after the plants have reached the 5-leaf growth stage (40–50 days after planting). In contrast, after holding this initial flooding for 3 weeks, AWD allows for field drying to promote the capture of seasonal rains to reduce irrigation water withdrawal and associated water pumping costs. In this study, ET was estimated using gap–filled eddy covariance observations and two variations of the Penman–Monteith equation. These methods determined growing season ET values between 560 mm and 636 mm. This study found that there were no significant differences in cumulative ET or yield when comparing AWD to DF practices. Furthermore, AWD elicited no change in ET during periods of drying when compared to DF. By this metric, AWD did not induce drought stress within the plants. We conclude that the main benefit of the AWD practice is to take advantage of seasonal rainfall to offset pumping costs and pressure on irrigation water requirements while maintaining yields comparable to conventional irrigation practices. [ABSTRACT FROM AUTHOR]

Planned satellite launches will provide open access and operational L-band radar data streams at space-time resolutions not previously available. To further prepare, the Uninhabited Aerial Vehicle Synthetic Aperture Radar (UAVSAR) platform was used to observe cropland sites across the southern United States to support the development of L-band (24 cm) prototype science products. Time series flights flew over four independent areas during the growing season of 2019 while crop ground measurements were collected. Major crops include corn, cotton, pasture, peanut, rice, and soybean. A suite of cropland classification experiments applied a set of machine learning (ML) algorithms (random forest, feedforward fully connected neural network, support vector machine), the recently developed Multi-temporal Binary Tree Classification (MBTC), and a phenology (Coefficient of Variation; CoV) approach to synergistically assess performance, scattering mechanisms, and limitations. Specific objectives of this research application included 1.) evaluation of L-band mapping performance across multiple independent agricultural production areas with field scale training data, and 2.) assessment of the CoV approach for the generation of prototype NISAR Level 2 science products. Collectively, SAR terms with sensitivity to volume scattering performed well and consistently across CoV mapping experiments achieving accuracy greater than 80% for cropland vs not cropland. Dynamic phenology classes, such as herbaceous wetlands, had some confusion with CoV agriculture requiring further regionalized training optimization. Volume scattering and cross-pol terms were most useful across the different ML techniques with overall accuracy and Kappa consistently over 90% and 0.85, respectively, for crop type by late growth stages for L-band observations. As expected, time series information was more valuable compared to any single ML technique, site, or crop schema. Ultimately, as more SAR platforms launch, the user community should leverage physical contributions of different wavelengths and polarizations along with growing open access time series for efficient and meaningful agricultural products. • CoV and machine learning methods are deployed to understand L-band crop Monitoring. • CoV achieves an overall accuracy greater than 80% for the NISAR L2 product. • Time series and the cross-pol term are the most useful for discrimination. [ABSTRACT FROM AUTHOR]

• New Free Convection Limit approaches (FCL) are developed for turbulent fluxes. • FCL are tested over three surfaces of major interest in agriculture. • Similar surface energy balance closures obtained using FCL and reference methods. Free convection limit (FCL) approaches to estimate surface fluxes are of interest given the evidence that they may extend up to near neutral stability conditions. For measurements taken in the inertial sublayer, the formulation based on surface renewal theory and the analysis of small eddies (SRSE) to estimate the sensible heat flux (H) was extended to latent heat flux (LE) with the aim to derive their FCL approaches. For sensible heat flux (H FCL), the input requirements are traces of the fast-response (such as 10–20 Hz) air temperature and the zero-plane displacement. For latent heat flux (LE FCL), input requirements are fast response traces of water vapor density, mean temperature of the air, the available net surface energy (Rn-G, where Rn and G are the net radiation and soil heat flux, respectively) and the zero-plane displacement. Taking eddy covariance (EC) as a reference method, the performance of the FCL method was tested over a growing cotton field that involved three contrasting surfaces: partly mulched bare soil, a sparse canopy and a homogeneous canopy. Using traces at 10 Hz and 20 Hz, H FCL overestimated and underestimated the EC sensible heat flux (H EC), respectively. In general, LE FCL tended to slightly underestimate LE EC. The surface energy balance closure show that (H EC + LE EC) underestimated (Rn-G) in a range of 19% (homogeneous canopy) and 8% (sparse canopy). Given that, in general (H FCL + LE FCL) was closer to (Rn-G) than (H EC + LE EC), the FCL method may be recommended for field applications, especially when the wind speed is not available. [ABSTRACT FROM AUTHOR]

• GPP and ET was highest from the first week of flowering to after cutout. • Average crop ET was 3.5 mm d−1 and peak water use was 7.6 mm d−1. • WUE at 0.3 kg lint cotton m−3 water was 1.5–2 times that of more arid regions. An eddy covariance (EC) system was used over a production-scale, US mid-south cotton field in order to improve understanding of carbon and water flux dynamics in cotton production. Measurement outcomes of NEE and ET from agricultural cropping systems in the humid US mid-south may differ from measurements made in arid production regions due to variation in precipitation, relative humidity, and cloud cover. Local measurements are needed for timely irrigation water management decisions and to assess their impact on the carbon cycle. Measurements were made in a 63-ha field in Northeast Arkansas in the 2016 and 2017 growing seasons (May-October). Average daily NEE during the growing season was driven largely by gross primary production (GPP) measured at -1.80 ± 0.26 g C m−2 d-1 ranging between -13.5 and 2.4 g m−2 d-1 during 2016 and -1.97 ± 0.26 g C m−2 d-1 ranging between -11.1 and 5.9 g m−2 d-1 during 2017. Across both years the average daily GPP during the growing season was 7.65 ± 0.45 g C m−2 d-1 during 2016 and 8.33 ± 0.37 g C m−2 d-1 during 2017. GPP was lowest during emergence and post-harvest and highest from the first week of flowering (FF) stage to about 3 weeks after cutout (cessation of vegetative expansion and production of new fruiting sites). Measured ET was lowest early and late in the season, and highest from FF until cutout. Across both seasons, average ET was 3.5 ± 0.1 mm d-1, more specifically 3.3 ± 0.1 mm d-1 during 2016 and 3.6 ± 0.1 mm d-1 during 2017. Peak water use was 7.6 mm d-1 during both growing seasons. Measured ET values were similar to results from lysimeter studies conducted in humid southeastern US climates, but lower than those observed in studies in arid regions. The water use efficiency of harvest yield per evapotranspiration flux was 0.26 kg lint cotton m-3 water and 0.34 kg lint m-3 water in 2016 and 2017, respectively which 1.5–2 times those of other studies, likely due to higher yields and lower evapotranspiration measured in this study. The findings suggest that US mid-south cotton production should continue to be managed differently than other more arid US cotton production regions. [ABSTRACT FROM AUTHOR]