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The present study assessed the seasonal and inter-annual variation in cropping pattern and crop acreage during summer (mid-February to May) and winter (November to January) seasons of 2017-2018, 2018-2019, and 2019-2020 cropping years over the Gosaba Block of Indian Sundarbans. Multi-temporal Sentinel-2 images acquired during the critical crop growth stages during a particular season were taken together for classification instead of a single image classification approach. This study employed the random forest algorithm for image classification for seasonal crop-fallow mapping over three consecutive years. The estimated post-monsoon (winter) crop coverage was less than that in the pre-monsoon (summer) season. In the post-monsoon season crop coverage varied from 3% to 12% of the net cultivable area, whereas pre-monsoon season crop coverage varied from 23% to 42%. The estimated area under boro rice decreased in 2018-19 and 2019-20 with a simultaneous increase in non-rice crops. Seasonal fallow land showed wide inter-annual variation. The amount and distribution of rainfall during the last part of the monsoon season and in the following months had a strong influence on the cropping pattern of the Sundarbans region. Results clearly showed that winter crop coverage gradually decreased over the study period depending on the rainfall pattern in the monsoon season and in the following months. During summer season, the area under non-rice crops notably increased at the cost of the area under boro rice. These findings clearly indicated the farmers' preferences for cultivating crops according to the climatic variability. The present study may be helpful for cropping intensification in the threatened regions for achieving sustainability. [ABSTRACT FROM AUTHOR]

Tuber initiation and tuber bulking stages are critical part of various phenological phases for potato production. Tuber initiation covers the period from the formation of spherical rhizome ends, the flowering and the start of tuber bulking. In general, the tuberization spans from 3 to 5 weeks after emergence and ends with the row closer i.e. canopies in adjacent rows touch each other across the furrow. Hence, this rapid growth seeks critical agronomic management practices such as irrigation and fertilization. It majorly influences the growth of stems, foliar area, dry weight and number of tubers particularly at the phase of tuber initiation. During these phenological stages, potato crops are susceptible to the infestation of late blight diseases caused by Phytophthora infestans and largely affects the potato production. Thus identifying the crop risk using remote sensing approaches can provide an efficient potato growth monitoring framework. In the context of monitoring crop dynamics, quad-pol Synthetic Aperture Radar (SAR) data has proven to be effective due to its sensitivity towards dielectric and geometric properties. In addition to SAR data, optical remote sensing data derived vegetation information can provide an improved insight into crop growth when combined with SAR data. In this research, quad-pol RADARSAT-2 and Sentinel-2 optical data are analyzed to monitor potato tuberization phase over Bardhaman district in the state ofWest Bengal, which is one of the major potato growing regions in India. The proposed approach uses polarimetric parameters such as backscatter intensities, ratio (HH/VV, VH/VV, linear depolarization ratio), and co-pol correlation (ρHH–VV) along with the vegetation indices derived from the Sentinel-2 data for understanding the spatio-temporal dynamics. The initial results show a promising accuracy in monitoring the dynamics of potato tuberization. Integration of such earth observation (EO) data, in conjunction with in-situ field measurements, might significantly enhance the current capabilities for crop monitoring. [ABSTRACT FROM AUTHOR]

Spatio-temporal variability of crop growth descriptors is of prime importance for crop risk assessment and yield gap analysis. The incorporation of three bands (viz., B5, B6, B7) in 'red-edge' position (i.e., 705 nm, 740 nm, 783 nm) in Sentinel-2 with 10–20 m spatial resolution images with five days revisit period have unfolded opportunity for meticulous crop monitoring. In the present study, the potential of Sentinel-2 have been appraised for monitoring phenological stages of potato over Bardhaman district in the state of West Bengal, India. Due to the competency of Vegetation indices (VI) to evaluate the status of crop growth; we have used the Normalized Difference Vegetation Index (NDVI), the Green Normalized Difference Vegetation Index (GNDVI), and the Normalized Difference Index45 (NDI45) for crop monitoring. Time series analysis of the VIs exhibited increasing trend as the crop started approaching maturity and attained a maximum value during the tuber development stage and started decreasing as the crop advances to senescence. Inter-field variability of VIs highlighted the need of crop monitoring at high spatial resolution. Among the three vegetation indices, the GNDVI (r = 0.636), NDVI (r = 0.620) had the highest correlation with biomass and Plant Area Index (PAI), respectively. NDI45 had comparatively a lower correlation (r = 0.572 and 0.585 for PAI and biomass, respectively) with both parameters as compared to other two indices. It is interesting to note that the use of Sentinel-2 Green band (B3) instead of the Red band (B4) in GNDVI resulted in 2.5% increase of correlation with biomass. However, the improvement in correlations between NDI45 with crop biophysical parameters is not apparent in this particular study with the inclusion of the Vegetation Red Edge band (B5) in VI. Nevertheless, the strong correlation of VIs with biomass and PAI asserted proficiency of Sentinel-2 for crop monitoring and potential for crop biophysical parameter retrieval with optimum accuracy. [ABSTRACT FROM AUTHOR]

The article offers information on micrometeorological measurements in agricultural research. It mentions the transformations in the micrometeorological measurement systems with development of wide range of sensor based instruments capable of recording and integrating digital values of biophysical parameters though fast response data loggers.