Increasing landscape heterogeneity by restoring semi‐natural elements to reverse farmland biodiversity declines is not always economically feasible or acceptable to farmers due to competition for land. We hypothesized that increasing the heterogeneity of the crop mosaic itself, hereafter referred to as crop heterogeneity, can have beneficial effects on within‐field plant diversity. Using a unique multi‐country dataset from a cross‐continent collaborative project covering 1,451 agricultural fields within 432 landscapes in Europe and Canada, we assessed the relative effects of compositional and configurational crop heterogeneity on within‐field plant diversity components. We also examined how these relationships were modulated by the position within the field. We found strong positive effects of configurational crop heterogeneity on within‐field plant alpha and gamma diversity in field interiors. These effects were as high as the effect of semi‐natural cover. In field borders, effects of crop heterogeneity were limited to alpha diversity. We suggest that a heterogeneous crop mosaic may overcome the high negative impact of management practices on plant diversity in field interiors, whereas in field borders, where plant diversity is already high, landscape effects are more limited. Synthesis and applications. Our study shows that increasing configurational crop heterogeneity is beneficial to within‐field plant diversity. It opens up a new effective and complementary way to promote farmland biodiversity without taking land out of agricultural production. We therefore recommend adopting manipulation of crop heterogeneity as a specific, effective management option in future policy measures, perhaps adding to agri‐environment schemes, to contribute to the conservation of farmland plant diversity.

The water cloud model (WCM) can be inverted to estimate leaf area index (LAI) using the intensity of backscatter from synthetic aperture radar (SAR) sensors. Published studies have demonstrated that the WCM can accurately estimate LAI if the model is effectively calibrated. However, calibration of this model requires access to field measures of LAI as well as soil moisture. In contrast, machine learning (ML) algorithms can be trained to estimate LAI from satellite data, even if field moisture measures are not available. In this study, a support vector machine (SVM) was trained to estimate the LAI for corn, soybeans, rice, and wheat crops. These results were compared to LAI estimates from the WCM. To complete this comparison, in situ and satellite data were collected from seven Joint Experiment for Crop Assessment and Monitoring (JECAM) sites located in Argentina, Canada, Germany, India, Poland, Ukraine and the United States of America (U.S.A.). The models used C-Band backscatter intensity for two polarizations (like-polarization (VV) and cross-polarization (VH)) acquired by the RADARSAT-2 and Sentinel-1 SAR satellites. Both the WCM and SVM models performed well in estimating the LAI of corn. For the SVM, the correlation (R) between estimated LAI for corn and LAI measured in situ was reported as 0.93, with a root mean square error (RMSE) of 0.64 ${\mathrm{m}}^2{\mathrm{m}}^{-2}$ and mean absolute error (MAE) of 0.51 ${\mathrm{m}}^2{\mathrm{m}}^{-2}$. The WCM produced an R-value of 0.89, with only slightly higher errors (RMSE of 0.75 ${\mathrm{m}}^2{\mathrm{m}}^{-2}$ and MAE of 0.61 ${\mathrm{m}}^2{\mathrm{m}}^{-2}$) when estimating corn LAI. For rice, only the SVM model was tested, given the lack of soil moisture measures for this crop. In this case, both high correlations and low errors were observed in estimating the LAI of rice using SVM (R of 0.96, RMSE of 0.41 ${\mathrm{m}}^2{\mathrm{m}}^{-2}$ and MAE of 0.30 ${\mathrm{m}}^2{\mathrm{m}}^{-2}$). However, the results demonstrated that when the calibration points were limited (in this case for soybeans), the WCM outperformed the SVM model. This study demonstrates the importance of testing different modeling approaches over diverse agro-ecosystems to increase confidence in model performance.

Annual and within-season crop type monitoring and mapping is an important ongoing consideration for governments, global agricultural monitoring organizations and private interests worldwide. Successful country-wide operational remote sensing-based inventories are well-established utilizing optical-only and optical/single frequency Synthetic Aperture Radar (SAR) combinations of data. However, the drawbacks of these data combinations are the requirement of multiple sources of imagery throughout the entire growing season, which impedes within-season analysis, and cloud cover effects on the optical data. Currently, C-band SAR data are available with continuous global coverage from Sentinel-1A & B, RADARSAT-2 and from the expected launch of the RADARSAT Constellation Mission (RCM). With current and expected launches of several other frequency (L-, P-, etc.) SAR missions over the next few years (SAOCOM, NISAR, etc.) the opportunity for continuous, multi-frequency SAR coverage edges toward reality. The JECAM SAR Inter-Comparison Experiment is a multi-year, multi-partner project that aims to compare global methods for SAR-based crop monitoring and inventory. The third component of this experiment is the assessment of multi-frequency SAR data for crop classification and mapping. Earth observation (EO) data acquisitions of Sentinel-1A & B, RADARSAT-2/RCM, ALOS-2, SAOCOM1 and TerraSAR-X/TanDEM-X have been planned and requested for the 2019 growing season to complement within field surveys being conducted across the globe. In preparation for these data, this research analyzed ALOS-2, TerraSAR-X and RADARSAT-2 data for crop mapping at the JECAM Canada-Carman site using two dates of multi-frequency SAR data, in comparison to a traditional full season optical/SAR dataset. The multi-frequency data had similar overall accuracies as the optical/SAR dataset, and improved on several individual agricultural class accuracies.