Understanding the Land Surface Phenology and Gross Primary Production of Sugarcane Plantations by Eddy Flux Measurements, MODIS Images, and Data-Driven Models

Crop productivity estimation by integrating multisensor satellite, in situ, and eddy covariance data into efficiency-based model

Accurate and quantitative regional estimates of the carbon budget require an integration of eddy covariance (EC) flux-tower observations and remote sensing in ecosystem models. In this study, a simple remote sensing driven light use efficiency (LUE) model was used to estimate the primary productivity for major cropping systems using multi-temporal satellite data over the Saharanpur district in India.The model is based on radiation absorption and its conversion into biomass. The LUE model was implemented for major crop rotations derived from the time-series of Sentinel-2 and Landsat 8 with monthly satellite-based spatially explicit fields of photosynthetically active radiation (PAR), fraction of absorbed PAR (fAPAR) and down-regulated light use efficiency. Incident PAR and fAPAR were estimated on monthly basis from the ground-calibrated empirical equation using INSAT-3D insolation product and remote sensing-based vegetation indices, respectively. Spatial LUE maps created by down-regulating maximum LUE (EC tower-based) with water and temperature stressors derived from land surface water index (LSWI) and EC-based cardinal temperature, respectively. LUE-based modeled GPP over the sugarcane-wheat system was found higher than the rice-wheat system in Saharanpur district. This is because C4 crop (sugarcane) has very high photosynthetic efficiency compared to C3 crops (rice and wheat). Modeled GPP over the sugarcane-wheat system was found in good agreement with observed EC tower-based GPP (Index of Agreement = 0.93). Further regionally calibrated remote sensing-based LUE model well captures gross photosynthesis rates (GPP) over cropland ecosystem compared to globally modeled MODIS GPP product.

Prediction of Oil Palm Yield Using Machine Learning in the Perspective of Fluctuating Weather and Soil Moisture Conditions: Evaluation of a Generic Workflow

Current development in precision agriculture has underscored the role of machine learning in crop yield prediction. Machine learning algorithms are capable of learning linear and nonlinear patterns in complex agro-meteorological data. However, the application of machine learning methods for predictive analysis is lacking in the oil palm industry. This work evaluated a supervised machine learning approach to develop an explainable and reusable oil palm yield prediction workflow. The input data included 12 weather and three soil moisture parameters along with 420 months of actual yield records of the study site. Multisource data and conventional machine learning techniques were coupled with an automated model selection process. The performance of two top regression models, namely Extra Tree and AdaBoost was evaluated using six statistical evaluation metrics. The prediction was followed by data preprocessing and feature selection. Selected regression models were compared with Random Forest, Gradient Boosting, Decision Tree, and other non-tree algorithms to prove the R2 driven performance superiority of tree-based ensemble models. In addition, the learning process of the models was examined using model-based feature importance, learning curve, validation curve, residual analysis, and prediction error. Results indicated that rainfall frequency, root-zone soil moisture, and temperature could make a significant impact on oil palm yield. Most influential features that contributed to the prediction process are rainfall, cloud amount, number of rain days, wind speed, and root zone soil wetness. It is concluded that the means of machine learning have great potential for the application to predict oil palm yield using weather and soil moisture data.

Large spatial variation and stagnation of cropland gross primary production increases the challenges of sustainable grain production and food security in China

Sustainable crop grain production and food security is a grand societal challenge. Substantial investments in China's agriculture have been made in the past decades, but our knowledge on cropland gross primary production in China remains limited. Here we analyzed gross primary production (GPP), solar-induced chlorophyll fluorescence (SIF), terrestrial water storage, crop grain production, and agricultural investment and policy during 2000-2018. We found that based on croplands in 2000, approximately 52 × 10⁶ ha (~37%) had continuous increasing trends in GPP during 2000-2018, which were mainly located in northern China. GPP for 63% of croplands was stagnant, declined, or had no significant change. At the national scale, annual cropland GPP increased during 2000-2008 but became stagnant in 2009-2018, which was inconsistent with the interannual trend in the crop grain production data for 2009-2018. The spatial mismatch between crop production and water availability became worse. The major grain exporting provinces, mostly located in water-stressed regions, experienced increased water resource constraints, which posed a challenge for sustainable grain production. The stagnant cropland GPP and increasing water resource constraints highlight the urgent need for sustainable management for crop production and food security in China.

Dynamics of CO2 fluxes and controlling environmental factors in sugarcane (C4)-wheat (C3) ecosystem of dry sub-humid region in India

In this study, CO₂ exchange over sugarcane and wheat growing season was quantified by continuous measurement of CO₂ fluxes using eddy covariance (EC) system from January 2014 to June 2015. We also elaborated on the response of CO₂ fluxes to environmental variables. The results show that the ecosystem has seasonal and diurnal dynamics of CO₂ with a distinctive U-shaped curve in both growing seasons with maximal CO₂ absorption reaching up to -8.94 g C m^-2 day^-1 and -6.08 g C m^-2 day^-1 over sugarcane and wheat crop, respectively. The ecosystem as a whole acted as a carbon sink during the active growing season while it exhibits a carbon source prior to sowing and post-harvesting of crops. The cumulative net ecosystem exchange (NEE), gross primary productivity (GPP), and ecosystem respiration (R_eco) were -923.04, 3316.65, and 2433.18 g C m^-2 over the sugarcane growing season while the values were -192.30, 621.47, and 488.34 g C m^-2 over the wheat growing season. The sesbania (green manure) appeared to be a carbon source once it is incorporated into soil. The response of day-time NEE to photosynthetically active radiation (PAR) under two vapor pressure deficit (VPD) sections (0-20 h Pa and 20-40 h Pa) seems more effective over sugarcane (R² = 0.41-0.61) as compared to the wheat crop (R² = 0.25-0.40). A decrease in net CO₂ uptake was observed under higher VPD conditions. Similarly, night-time NEE was exponentially related to temperature at different soil moisture conditions and showed higher response to optimum soil moisture conditions for sugarcane (R² = 0.87, 0.33 ≤ SWC < 0.42 m³ m^-3) and wheat (R² = 0.75, 0.31 ≤ SWC < 0.37 m³ m^-3) crop seasons. The response of daily averaged NEE to environmental variables through path analysis indicates that PAR was the dominant predictor with the direct path coefficient of -0.65 and -0.74 over sugarcane and wheat growing season, respectively. Satellite-based GPP products from Moderate Resolution Imaging Spectroradiometer (GPP_MOD) and Vegetation Photosynthetic model (GPP_VPM) were also compared with the GPP obtained from EC (GPP_EC) technique. The seasonal dynamics of GPP_EC and GPP_VPM agreed well with each other. This study covers the broad aspects ranging from micro-meteorology to remote sensing over C4-C3 cropping system.

Spatiotemporal Changes of Winter Wheat Planted and Harvested Areas, Photosynthesis and Grain Production in the Contiguous United States from 2008–2018

Winter wheat is a main cereal crop grown in the United States of America (USA), and the USA is the third largest wheat exporter globally. Timely and reliable in-season forecast and year-end estimation of winter wheat grain production in the USA are needed for regional and global food security. In this study, we assessed the consistency between the agricultural statistical reports and satellite-based data for winter wheat over the contiguous US (CONUS) at both the county and national scales. First, we compared the planted area estimates from the National Agricultural Statistics Service (NASS) and the Cropland Data Layer (CDL) from 2008–2018. Second, we investigated the relationship between gross primary production (GPP) estimated by the vegetation photosynthesis model (VPM) and grain production from the NASS. Lastly, we explored the in-season utility of GPPVPM in monitoring seasonal production. Strong spatiotemporal consistency of planted areas was found between the NASS and CDL datasets. However, in the Southern Great Plains, both the CDL and NASS planted acreage were noticeable larger (>20%) than the NASS harvested area, where some winter wheat fields were used as forage for cattle grazing. County-level GPPVPM was linearly related with grain production of winter wheat, with an R2 value of 0.68 across the CONUS. The relationships between grain production and GPPVPM in those counties without a substantial difference (<20%) between planted and harvested area were much stronger and their harvest index (HIGPP) values ranged from 0.2–0.3. GPPVPM in May could explain about 70–90% of the variance of winter wheat grain production. Our findings highlight the potential of GPPVPM in winter wheat monitoring, especially for those high harvested/planted ratio, which could provide useful data to guide planning and marketing for decision makers, stakeholders, and the public.

Monitoring Forest Phenology in a Changing World

Plant phenology is strongly interlinked with ecosystem processes and biodiversity. Like many other aspects of ecosystem functioning, it is affected by habitat and climate change, with both global change drivers altering the timings and frequency of phenological events. As such, there has been an increased focus in recent years to monitor phenology in different biomes. A range of approaches for monitoring phenology have been developed to increase our understanding on its role in ecosystems, ranging from the use of satellites and drones to collection traps, each with their own merits and limitations. Here, we outline the trade-offs between methods (spatial resolution, temporal resolution, cost, data processing), and discuss how their use can be optimised in different environments and for different goals. We also emphasise emerging technologies that will be the focus of monitoring in the years to follow and the challenges of monitoring phenology that still need to be addressed. We conclude that there is a need to integrate studies that incorporate multiple monitoring methods, allowing the strengths of one to compensate for the weaknesses of another, with a view to developing robust methods for upscaling phenological observations from point locations to biome and global scales and reconciling data from varied sources and environments. Such developments are needed if we are to accurately quantify the impacts of a changing world on plant phenology.