Mapping upland crop–rice cropping systems for targeted sustainable intensification in South China

Upland crop-rice cropping systems(UCR)facilitate sustainable agricultural intensification.Accurate UCR cultivation mapping is needed to ensure food security,sustainable water management,and rural revitalization.However,datasets describing cropping systems are limited in spatial coverage and crop types.Mapping UCR is more challenging than crop identification and most existing approaches rely heavily on accurate phenology calendars and representative training samples,which limits its applications over large regions.We describe a novel algorithm(RRSS)for automatic mapping of upland crop-rice cropping systems using Sentinel-1 Synthetic Aperture Radar(SAR)and Sentinel-2 Multispectral Instrument(MSI)data.One indicator,the VV backscatter range,was proposed to discriminate UCR and another two indicators were designed by coupling greenness and pigment indices to further discriminate tobacco or oilseed UCR.The RRSS algorithm was applied to South China characterized by complex smallholder rice cropping systems and diverse topographic conditions.This study developed 10-m UCR maps of a major rice bowl in South China,the Xiang-Gan-Min(XGM)region.The performance of the RRSS algorithm was validated based on 5197 ground-truth reference sites,with an overall accuracy of 91.92%.There were7348 km^(2) areas of UCR,roughly one-half of them located in plains.The UCR was represented mainly by oilseed-UCR and tobacco-UCR,which contributed respectively 69%and 15%of UCR area.UCR patterns accounted for only one-tenth of rice production,which can be tripled by intensification from single rice cropping.Application to complex and fragmented subtropical regions suggested the spatiotemporal robustness of the RRSS algorithm,which could be further applied to generate 10-m UCR datasets for application at national or global scales.

Spatiotemporal analysis of vegetation variability and its relationship with climate change in China

This paper investigated spatiotemporal dynamic pattern of vegetation,climate factor,and their complex relationships from seasonal to inter-annual scale in China during the period 1982–1998 through wavelet transform method based on GIMMS data-sets.First,most vegetation canopies demonstrated obvious seasonality,increasing with latitudinal gradient.Second,obvious dynamic trends were observed in both vegetation and climate change,especially the positive trends.Over 70%areas were observed with obvious vegetation greening up,with vegetation degradation principally in the Pearl River Delta,Yangtze River Delta,and desert.Overall warming trend was observed across the whole country(>98%area),stronger in Northern China.Although over half of area(58.2%)obtained increasing rainfall trend,around a quarter of area(24.5%),especially the Central China and most northern portion of China,exhibited significantly negative rainfall trend.Third,significantly positive normalized difference vegetation index(NDVI)–climate relationship was generally observed on the de-noised time series in most vegetated regions,corresponding to their synchronous stronger seasonal pattern.Finally,at inter-annual level,the NDVI–climate relationship differed with climatic regions and their long-term trends:in humid regions,positive coefficients were observed except in regions with vegetation degradation;in arid,semiarid,and semihumid regions,positive relationships would be examined on the condition that increasing rainfall could compensate the increasing water requirement along with increasing temperature.This study provided valuable insights into the long-term vegetation–climate relationship in China with consideration of their spatiotemporal variability and overall trend in the global change process.

Relationship between spatial heterogeneity and wavelength in multisensor airborne images

Variogram has been utilized to exploring the spatial heterogeneity of remote sensing images,especially its association with spatial resolution.However,very few attentions have been drawn on evaluating the spatial heterogeneity of multisensor airborne imagery and its relationship with spectral wavelength.Therefore,an investigation was carried out on multisensor airborne images to determine the relation between spatial heterogeneity and spectral wavelength for woodland,grass,and urban landscapes by applying variogram modeling.The airborne thematic mapper(ATM),compact airborne spectrographic imager(CASI),and Specim AISA Eagle airborne images at Harwood Forest,Monks wood,Cambridge,and River Frome areas,UK,were utilized.Results revealed that(1)the red band contained greater spatial variability than near-infrared wavelengths and other visible wavebands;(2)there was a steep gradient at the red edge in reference to its spatial variability of multisensor airborne images;(3)only for natural landscape such as woodland and grass,near-infrared waveband contains greater within-scene variations than the blue and green bands;(4)compared with the discrepancy of spatial resolution introduced by multisensor images(ATM,CASI,and Eagle),the specific landscape and spectral bands were more important in determining heterogeneity by means of original visible,near-infrared bands,and normalized difference vegetation index(NDVI).These findings remained us to be caution when combining and interpreting spatial variability and spatial structures derived from airborne images with different spatial resolution and spectral wavelength.Additionally,the outcomes of this study also have considerable implications in terms of designing and choosing suitable images for different applications.