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Reliable cropland parcel data are vital for agricultural monitoring, yield estimation, and agricultural intensification assessments. However, the inherently high landscape fragmentation and irregularly shaped cropland associated with smallholder farming systems restrict the accuracy of cropland parcels extraction. In this study, we proposed an adaptive image segmentation method with the automated selection of optimal scale (MSAOS) to extract cropland parcels in heterogeneous agricultural landscapes. The MSAOS method includes three major components: (1) coarse segmentation to divide the whole images into homogenous and heterogeneous regions, (2) fine segmentation to determine the optimal segmentation scale based on average local variance function, and (3) region merging to merge and dissolve the over-segmented objects with small area. The potential cropland objects derived from MSAOS were combined with random forest to generate the final cropland parcels. The MSAOS method was evaluated over different agricultural regions in China, and derived results were assessed by benchmark cropland parcels interpreted from high-spatial resolution images. Results showed the texture features of Homogeneity and Entropy are the most important features for MSAOS to extract potential cropland parcels, with the highest separability index of 0.28 and 0.26, respectively. MSAOS-derived cropland parcels had high agreement with the reference dataset over eight tiles in Qichun county, with average F1 scores of 0.839 and 0.779 for the area-based classification evaluation (Fab) and object-based segmentation evaluation (Fob), respectively. The further evaluation of MSAOS on different tiles of four provinces exhibited the similar results (Fab = 0.857 and Fob = 0.775) with that on eight test tiles, suggesting the good transferability of the MSAOS over different agricultural regions. Furthermore, MSAOS outperformed other widely-used approaches in terms of the accuracy and integrity of the extracted cropland parcels. These results indicate the great potential of using MSAOS for image segmentation in conjunction with random forest classification to effectively extract cropland parcels in smallholder farming systems.

Calotropis gigantea is often found in mining areas with heavy metal pollution. However, little is known about the physiological and molecular response mechanism of C. gigantea to Cd stress. In the present study, Cd tolerance characteristic of C. gigantea and the potential mechanisms were explored. Seed germination test results showed that C. gigantea had a certain Cd tolerance capacity. Biochemical and transcriptomic analysis indicated that the roots and leaves of C. gigantea had different responses to early Cd stress. A total of 176 and 1618 DEGs were identified in the roots and leaves of C. gigantea treated with Cd compared to the control samples, respectively. Results indicated that oxidative stress was mainly initiated in the roots of C. gigantea, whereas the leaves activated several Cd detoxification processes to cope with Cd, including the upregulation of genes involved in Cd transport (i.e., absorption, efflux, or compartmentalization), cell wall remodeling, antioxidant system, and chelation. This study provides preliminary information to understand how C. gigantea respond to Cd stress, which is useful for evaluating the potential of C. gigantea in the remediation of Cd-contaminated soils.

The rice-crayfish field (i.e., RCF), a newly emerging rice cultivation pattern, has greatly expanded in China in the last decade due to its significant ecological and economic benefits. The spatial distribution of RCFs is an important dataset for crop planting pattern adjustment, water resource management and yield estimation. Here, an object- and topology-based analysis (OTBA) method, which considers spectral-spatial features and the topological relationship between paddy fields and their enclosed ditches, was proposed to identify RCFs. First, we employed an object-based method to extract crayfish breeding ditches using very high-resolution images. Subsequently, the paddy fields that provide fodder for crayfish were identified according to the topological relationship between the paddy field and circumjacent crayfish ditch. The extracted ditch objects together with those paddy fields were merged to derive the final RCFs. The performance of the OTBA method was carefully evaluated using the RCF and non-RCF samples. Moreover, the effects of different spatial resolutions, spectral bands and temporal information on RCF identification were comprehensively investigated. Our results suggest the OTBA method performed well in extracting RCFs, with an overall accuracy of 91.77%. Although the mapping accuracies decreased as the image spatial resolution decreased, satisfactory RCF mapping results (>80%) can be achieved at spatial resolutions greater than 2 m. Additionally, we demonstrated that the mapping accuracy can be improved by more than 10% when near-infrared (NIR) band information was involved, indicating the necessity of the NIR band when selecting images to derive reliable RCF maps. Furthermore, the images acquired in the rice growth phase are recommended to maximize the differences of spectral characteristics between paddy fields and ditches. These promising findings suggest that the OTBA approach performs well for mapping RCFs in areas with fragmented agricultural landscapes, which provides fundamental information for further agricultural land use and water resources management.

Despite desert ecosystem being crucial to our understanding of natural geography, species evolution and global climate change, there is limited information on the dynamics of their composition and the diversity of endophytic fungi communities driven by plant identity and organ differentiation. Here, an extensive investigation of endophytic fungal microbiome in root, stem, and leaf organs associated with five xerophyte shrubs in an extremely arid desert, Northwest China, were examined. The fungal community dominated by Dothideomycetes and Pleosporales. Shrub species strongly drive the niche-based processes of endophytic fungi across the root, stem and leaf compartments. The diversity and composition of endophytic fungi in stem showed higher variability among plant species than leaf and root. The fungal communities in root libraries were more diverse and exhibited a remarkable differentiation of community composition. We further demonstrated the significant host preferences and tissue specificity of desert endophytic fungi, and unique specific taxa were also observed. The co-occurrence network revealed the coexistence of fungal endophytes in arid desert, and the root fungal network harbored the highest interspecies connectivity. Members of Pleosporales were the most common keystone species in the root fungal network. This is the first report of mycobiota in both plant species and organ differentiation in an extremely arid desert ecosystem.

Global biophysical products at decametric resolution derived from Sentinel-2 imagery have emerged as a promising dataset for fine-scale ecosystem modeling and agricultural monitoring. Evaluating uncertainties of different Sentinel-2 biophysical products over various regions and vegetation types is pivotal in the application of land surface models. In this study, we quantified the performance of Sentinel-2-derived Leaf Area Index (LAI), Fraction of Absorbed Photosynthetically Active Radiation (FAPAR), and Fractional Vegetation Cover (FVC) estimates using global ground observations with consistent measurement criteria. Our results show that the accuracy of vegetation and non-vegetated classification based on Sentinel-2 surface reflectance products is greater than 95%, which indicates the vegetation identification is favorable for the practical application of biophysical estimates, as several LAI, FAPAR, and FVC retrievals were derived for non-vegetated pixels. The rate of best retrievals is similar between LAI and FAPAR estimates, both accounting for 87% of all vegetation pixels, while it is almost 100% for FVC estimates. Additionally, the Sentinel-2 FAPAR and FVC estimates agree well with ground-measurements-derived (GMD) reference maps, whereas a large discrepancy is observed for Sentinel-2 LAI estimates by comparing with both GMD effective LAI (LAIe) and actual LAI (LAI) reference maps. Furthermore, the uncertainties of Sentinel-2 LAI, FAPAR and FVC estimates are 1.09 m2/m2, 1.14 m2/m2, 0.13 and 0.17 through comparisons to ground LAIe, LAI, FAPAR, and FVC measurements, respectively. Given the temporal difference between Sentinel-2 observations and ground measurements, Sentinel-2 LAI estimates are more consistent with LAIe than LAI values. The robustness of evaluation results can be further improved as long as more multi-temporal ground measurements across different regions are obtained. Overall, this study provides fundamental information about the performance of Sentinel-2 LAI, FAPAR, and FVC estimates, which imbues our confidence in the broad applications of these decametric products.