Mapping Winter Wheat with Optical and SAR Images Based on Google Earth Engine in Henan Province, China

Li, Changchun; Weinan, Chen; Yi-lin, Wang; Wang, Yu; Ma, C.; Li, Yacong; Li, Jingbo; Zhai, Weiguang

doi:10.3390/rs14020284

Cited by 29 publications

(17 citation statements)

References 50 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The results obtained from the classification of Sentinel-2 data in the study area Knezha are similar to published studies from some other regions using the same or similar type of data. Li et al 37 achieved an overall accuracy of 86.7% identifying five different classes, including winter wheat in an agricultural region in China. The classification input data consisted of a time composite image from Sentinel-2 in the post-winter period.…”

Section: Resultsmentioning

confidence: 99%

Crop type mapping and LAI, fAPAR, and fCover prediction in winter wheat fields with Sentinel-2 data

Kamenova

2023

Ninth International Conference on Remote Sensing and Geoinformation of the Environment (RSCy2023)

View full text Add to dashboard Cite

The aim of this study is to map winter wheat fields in designated study area in Bulgaria and to analyze the spatial variation of various biophysical parameters of winter wheat crops in these fields using multispectral satellite imagery. The study uses Sentinel-2 data for image classification and to predict the Leaf Area Index (LAI), fraction of absorbed photosynthetically active radiation (fAPAR), and fraction of vegetation cover (fCover) of the crops. The study area is situated in the Danubian plain in Bulgaria, a heavily cultivated agricultural region in Europe. To distinguish winter wheat fields from other agricultural fields, classification techniques were applied using two methods, Support Vector Machines (SVM) and Random Forest (RF), to identify the winter wheat fields in different phonological phases during the growing season. Both classification methods performed with similar accuracy and showed high accuracies in classifying winter wheat using Sentinel-2 images at various phonological phases (F1 > 93%; tillering; F1 > 95%; stem elongation; F1 > 94%; anthesis). To predict the LAI, fAPAR, and fCover dynamics in the winter wheat fields, regression models were used, calibrated with vegetation indices and in situ data. Maps displaying the within-field variation of LAI, fAPAR, and fCover were created for two growth stages: tillering and stem elongation.

show abstract

Section: Resultsmentioning

confidence: 99%

Crop type mapping and LAI, fAPAR, and fCover prediction in winter wheat fields with Sentinel-2 data

Kamenova

2023

Ninth International Conference on Remote Sensing and Geoinformation of the Environment (RSCy2023)

View full text Add to dashboard Cite

show abstract

“…(Didan, 2015). The study area's crop land used also used for crop data from the livelihood shape (FEWS NET, 2018) with the International Geosphere-Biosphere Programme (IGBP) in order to calibrate the model using the (MOD13Q1-EVI) product's Enhanced Vegetation Index (EVI) ( C. Li et al, 2022;Xie & Fan, 2021)., which has better sensitivity over high biomass regions. For zonal statics of crop biomass index, the pixel value from the acquisitions of 16 day period observation with low clouds, low view angle, and the highest NDVI/EVI value is employed.…”

Section: Study Areamentioning

confidence: 99%

Modeling canopy water content in the assessment for rainfall induced surface and groundwater nitrate contamination index from cropland N-fertilizer in Bilate downstream

Assa

Bhowmick

Elias

2023

Preprint

View full text Add to dashboard Cite

Crop canopy water content and crop root zone soil water content have been predicted and observed for estimation of crop water balance for the rainy season nitrate runoff and leaching index of the Bilate watershed cropland. For the calibration of earth data observation, the watershed rain gauge station estimated the pattern of rainfall for the main cropping season of crop growth. Cropping season (Apr, May and Jun) monthly mean rainfall between (125mm/month to 165 mm/month) of agro metrological station has predicted time series crop canopy water in the analysis for crop land nitrate-nitrogen leaching/runoff index for the quantification of groundwater and surface runoff nitrate from the Bilate watershed farming zone. May and June were the months in which crops grew in the cropland of the study area, as indicated by the crop biomass statistics from MODIS 006 MOD13Q1-EVI of (Mine 0.19 and Max, 0.57) which is the average crop coefficient Kc for crop growth curve analyzed for model crop parameter. Hyper-spectral indices derived from enhanced vegetation indices (EVI) have been calculated for the analysis of crop zonal biomass statistics (kc) and integrated into the prediction model. The objective of the current research was to predict crop canopy water content in order to determine crop water balance for farmland Nitrogen Nitrate (NO− 3-N) runoff\leaching index for surface and groundwater pollution in the Bilate downstream. The overall predicted result of crop canopy water content has been validated with a regression coefficient (R2) with observed crop root zone soil water content. And the crop land nitrogen balance has been used to confirm the nitrate-nitrogen leaching and runoff index for the study area by comparing the current result with the crop land nitrogen balance.

show abstract

“…Considering the phenological characteristics of the whole life cycle of rice, we extracted five rice phenological parameters based on the backscatter coefficients of VH and VV time series (Figure 5), respectively, which are the transplanting date (TD), rice agronomy was defined as the length from rice TD to MD (Wang et al, 2022). SAR images have significant advantages in displaying texture features (Peña-Barragán et al, 2011;Li et al, 2022). Considering data redundancy and computational efficiency, three texture parameters (SAVG/sum average, VAR/variance and CONT/contrast) were selected for rice identification according to the feature importance calculated by the random forest algorithm (Table 3).…”

Section: Multi-characteristic Parameters Extractionmentioning

confidence: 99%

A new rice identification algorithm under complex terrain combining multi-characteristic parameters and homogeneous objects based on time series dual-polarization synthetic aperture radar

Wang²,

Sun³

et al. 2023

Front. Ecol. Evol.

View full text Add to dashboard Cite

Accurate mapping of rice-growing areas is essential to ascertain the spatial distribution of rice fields, and ensure food security. It is a challenging task to timely and accurate identify rice under the complex terrain due to its diversified land cover, small- or middle-sized rice fields with fragmented distribution. In this paper, the time series VV and VH backscatter coefficient datasets were first constructed based on 411 sentinel-1 synthetic aperture radar (SAR) images in Chongqing city with complex terrain. Then, the rice multi-characteristic parameters, including SAR backscatter features, composite features, rice phenological parameters, texture features and topographic features, were generated. On this basis, the homogeneous image objects were produced. Furthermore, a rice identification algorithm combining multi-characteristic parameters and homogeneous objects based on time series dual-polarization SAR (MPHO-DPSAR) was established. The research demonstrated that the MPHO-DPSAR algorithm can achieve accurate mapping of small and medium-sized and fragmented rice fields in regions under complex terrain according to the accuracy evaluation at three levels and the comparison with other three classical rice identification methods. The suitability and limitations of proposed MPHO-DPSAR algorithm were also discussed from the aspects of SAR data temporal and spatial resolution, rice phenology, and surface landscape complexity.

show abstract

Mapping Winter Wheat with Optical and SAR Images Based on Google Earth Engine in Henan Province, China

Cited by 29 publications

References 50 publications

Crop type mapping and LAI, fAPAR, and fCover prediction in winter wheat fields with Sentinel-2 data

Crop type mapping and LAI, fAPAR, and fCover prediction in winter wheat fields with Sentinel-2 data

Modeling canopy water content in the assessment for rainfall induced surface and groundwater nitrate contamination index from cropland N-fertilizer in Bilate downstream

A new rice identification algorithm under complex terrain combining multi-characteristic parameters and homogeneous objects based on time series dual-polarization synthetic aperture radar

Contact Info

Product

Resources

About