Rice Mapping Research Articles

A New Methodology for Rice Area Monitoring With COSMO-SkyMed HH–VV PingPong Mode SAR Data

In this paper, a novel approach is proposed to exploit a time series of COSMO-SkyMed (CSK) HH-VV SAR images to map rice fields and to estimate the sowing dates. The approach relies on multi-polarization features, i.e., the squared modulus of the HH and VV channels and the polarization ratio, extracted from CSK SAR scenes. The key step consists of extracting a rice training signature related to the multipolarization features. This signature allows estimating the sowing date that, at once, is used to refine the rice map obtained by the conventional interpretation of the CSK time series in terms of the scattering mechanisms of the different growing cycles. Experiments, carried out on a time series of 32 CSK images, collected from the Mekong Delta region, South Vietnam, confirm the soundness of the proposed methodology which is shown to provide results comparable to the ones obtained by a literature approach that exploits a similar dataset.

Accuracies of support vector machine and random forest in rice mapping with Sentinel-1A, Landsat-8 and Sentinel-2A datasets

SVM and RF are widely used in rice mapping. However, their performance with single and different combinations of satellite datasets is rarely reported. Hence we report their rice mapping accuracies for two seasons using Sentinel-1A, Landsat-8 and Sentinel-2A images. The VH and VV polarizations of Sentinel-1A, and two spectral indices (SIs) of Landsat-8 and Sentine1-2A were used to obtain seven datasets (VH, VV, SI, VHVV, VHSI, VVSI and VHVVSI), and on which SVM and RF were applied and accuracies were assessed. VHSI showed the highest overall accuracy for both algorithms in both years. SVM with VHSI had a slightly higher accuracy (90.8%) than RF with VHSI (89.2%) in 2015 while in 2016 RF with VHSI showed a slightly higher accuracy (95.2%) than SVM with VHSI (93.4%). Although they produced equivalent accuracies within years, RF is more sensitive to additional data, given a 6.0% increase from 2015 to 2016 with VHSI.

OsMAPKKK63 is involved in salt stress response and seed dormancy control

ABSTRACTApproximately 75 MAP kinase kinase kinases (MAPKKKs) have been identified in the rice genome. However, only a few of them have been functionally characterized. In this paper, we report the function of a rice MAPKKK, OsMAPKKK63. OsMAPKKK63 was found to be induced by several abiotic stresses, including high salinity, chilling and drought. Our data indicate that OsMAPKKK63 possesses in vitro kinase activity and that it interacts with rice MAP kinase kinase OsMKK1 and OsMKK6. The two rice MKKs are known mediator of the salt stress response, implying that OsMAPKKK63 may be involved in the high salinity response. Our analysis of an OsMAPKKK63 knockout mutant indeed demonstrated that it is necessary for normal response to high salt. On the other hand, OsMAPKKK63 OX lines exhibited viviparous phenotype in both rice and Arabidopsis. The result suggests that OsMAPKKK63 may also be involved in seed dormancy control.

Mapping and Monitoring Rice Agriculture with Multisensor Temporal Mixture Models

Rice is the staple food for more than half of humanity. Accurate prediction of rice harvests is therefore of considerable global importance for food security and economic stability, especially in the developing world. Landsat sensors have collected coincident thermal and optical images for the past 35+ years, and so can provide both retrospective and near-realtime constraints on the spatial extent of rice planting and the timing of rice phenology. Thermal and optical imaging capture different physical processes, and so provide different types of information for phenologic mapping. Most analyses use only one or the other data source, omitting potentially useful information. We present a novel approach to the mapping and monitoring of rice agriculture which leverages both optical and thermal measurements. The approach relies on Temporal Mixture Models (TMMs) derived from parallel Empirical Orthogonal Function (EOF) analyses of Landsat image time series. Analysis of each image time series is performed in two stages: (1) spatiotemporal characterization, and (2) temporal mixture modeling. Characterization evaluates the covariance structure of the data, culminating in the selection of temporal endmembers (EMs) representing the most distinct phenological cycles of either vegetation abundance or surface temperature. Modeling uses these EMs as the basis for linear TMMs which map the spatial distribution of each EM phenological pattern across study area. The two metrics we analyze in parallel are (1) fractional vegetation abundance (Fv) derived from spectral mixture analysis (SMA) of optical reflectance, and (2) land surface temperature (LST) derived from brightness temperature (Tb). These metrics are chosen on the basis of being straightforward to compute for any (cloud-free) Landsat 4-8 image in the global archive. We demonstrate the method using a 90 × 120 km area in the Sacramento Valley of California. Satellite Tb retrievals are corrected to LST using a standardized atmospheric correction approach and pixelwise fractional emissivity estimates derived from SMA. LST and Tb time series are compared to field station data in 2016 and 2017. Uncorrected Tb is observed to agree with the upper bound of the envelope of air temperature observations to within 3 °C on average. As expected, LST estimates are 3 to 5 °C higher. Soil T, air T, Tb and LST estimates can all be represented as linear transformations of the same seasonal cycle. The 3D temporal feature spaces of Fv and LST clearly resolve 5 and 7 temporal EM phenologies, respectively, with strong clustering distinguishing rice from other vegetation. Results from parallel EOF analyses of coincident Fv and LST image time series over the 2016 and 2017 growing seasons suggest that TMMs based on single year Fv datasets can provide accurate maps of crop timing, while TMMs based on dual year LST datasets can provide comparable maps of year-to-year crop conversion. We also test a partial-year model midway through the 2018 growing season to illustrate a potential real-time monitoring application. Field validation confirms the monitoring model provides an upper bound estimate of spatial extent and relative timing of the rice crop accurate to 89%, even with an unusually sparse set of usable Landsat images.

Dehydration-responsive alterations in the chloroplast proteome and cell metabolomic profile of rice reveals key stress adaptation responses

Chloroplast is a semi-autonomous organelle in plants and other photosynthetic eukaryotes, playing a fundamental role of regulating photosynthesis. It is also responsible for sustaining essential biosynthetic reactions including synthesis of amino acids, fatty acids and terpenes. Photosynthesis, the conversion of light energy into chemical energy, serves as the sensor of environmental changes and augments different cellular functions to initiate adaptive responses. However, the molecular processes and regulatory mechanisms of dehydration tolerance adopted by chloroplast remain largely unknown. To gain a better understanding of dehydration response, a chloroplast proteome map of rice was developed. Four-week-old rice seedlings were subjected to dehydration by withholding water for 9 d, and the magnitude of dehydration-induced damage to the chloroplast was monitored. The iTRAQ-based quantitative proteome analysis led to the identification of 40 differentially regulated proteins (DRPs). The DRPs were presumably involved in a wide array of metabolic processes including chloroplast energy metabolism, photosynthesis and defense response. Furthermore, dehydration-induced changes in the metabolite profile and network analysis revealed a high abundance of branched chain amino acids and sugar that might reduce osmotic potential, thereby protecting cellular integrity. The proteomics approach revealed altered status of major photosynthesis related proteins, while cell metabolite profile demonstrated alteration of tricarboxylic acid cycle intermediates, indicating dehydration-triggered alterations in ATP production and energy metabolism. Altogether, these results demonstrated that the global regulation of chloroplast proteome is intimately linked to cellular metabolic rewiring of adaptive responses, which may favor genetic manipulation of crop species for better adaptation.

In-season early mapping of rice area and flooding dynamics from optical and SAR satellite data

ABSTRACT Rice mapping products were derived from Sentinel-1A and Landsat-8 OLI multi-temporal imagery over Northern Italy at the early stages of the 2015 growing season. A rule-based algorithm was applied to synthetic statistical metrics (TSDs-Temporal Spectra Descriptors) computed from temporal datasets of optical spectral indices and SAR backscattering coefficient. Temporal series are available up to the tillering/full canopy cover stage which is identified as the optimum timing for delivering in-season information on rice area (i.e. mid July). The approach relies on a-priori knowledge on crop dynamics to adapt time horizons for TSD computation and thresholds to local conditions. Output products consist of maps of rice cultivated areas, rice seeding techniques (dry and flooded rice) and flooding practices. Validation showed rice mapping overall accuracy to be 87.8% with commission and omission errors of 3.5% and 24.7%, respectively. Mapping of rice seeding technique showed good agreement with farmer declarations aggregated at the municipality scale (dry rice r 2 = 0.71 and flooded rice r 2 = 0.91). Finally, flood maps have an overall accuracy above 70%. Geo-products on rice areas and flooding occurrence are relevant information for water management at regional scale especially during summer in presence of multiple crops and water shortage.

Decreasing Rice Cropping Intensity in Southern China from 1990 to 2015

Assessing changes in rice cropping systems is essential for ensuring food security, greenhouse gas emissions, and sustainable water management. However, due to the insufficient availability of images with moderate to high spatial resolution, caused by frequent cloud cover and coarse temporal resolution, high-resolution maps of rice cropping systems at a large scale are relatively limited, especially in tropical and subtropical regions. This study combined the difference of Normalized Difference Vegetation Index (dNDVI) method and the Normalized Difference Vegetation Index (NDVI) threshold method to monitor changes in rice cropping systems of Southern China using Landsat images, based on the phenological differences between different rice cropping systems. From 1990–2015, the sown area of double cropping rice (DCR) in Southern China decreased by 61054.5 km2, the sown area of single cropping rice (SCR) increased by 20,110.7 km2, the index of multiple cropping decreased from 148.3% to 129.3%, and the proportion of DCR decreased by 20%. The rice cropping systems in Southern China showed a “double rice shrinking and single rice expanding” change pattern from north to south, and the most dramatic changes occurred in the Middle-Lower Yangtze Plain. This study provided an efficient strategy that can be applied to moderate to high resolution images with deficient data availability, and the resulting maps can be used as data support to adjust agricultural structures, formulate food security strategies, and compile a greenhouse gas emission inventory.

Incorporating crop phenological trajectory and texture for paddy rice detection with time series MODIS, HJ-1A and ALOS PALSAR imagery

ABSTRACT Repeated monitoring of paddy rice is essential for government agencies and policy makers to maintain the balance of supply and demand for rice. Recent studies have mostly concentrated on the mapping of paddy rice with temporal satellite imagery during growing seasons. Given the phenological variation within paddy rice fields and spectral confusion between paddies and other vegetation classes, our ability to identify paddy rice fields with temporal imagery remains limited. The objective of this study is to develop new phenology and textural-based strategies to detect paddies with HJ-1A, MODIS and PALSAR FNF imagery. Two phenology-based strategies that track the seasonal trajectory of crops and one textural-based strategy that contains image surface characteristics are presented. With the proposed strategies, temporal, spectral and textural features were investigated for paddy rice detection. The results indicate that the phenology-based strategies could reveal the phenological variation within paddy rice and significantly improved the detection accuracy. Seasonal amplitude, grey level co-occurrence matrix entropy and spectral features of the heading stage were proven to be important in identifying paddy rice. It was concluded that the combination of HJ-1A, MODIS and PALSAR FNF imagery are promising in facilitating the rapid mapping of paddy rice at a regional scale.

Sen4Rice: A Processing Chain for Differentiating Early and Late Transplanted Rice Using Time-Series Sentinel-1 SAR Data With Google Earth Engine

Accurate spatio-temporal information about rice growth is an important factor for agronomic management and regional grain yield estimation. In this letter, a unified framework for monitoring and mapping of rice using dense time-series of Sentinel-1 synthetic aperture radar (SAR) images is proposed. A processing chain for such dense time-series Sentinel-1 images is developed with the Google Earth Engine’s cloud computing platform. A dense time-series analysis of backscatter response of rice with different management practices is analyzed. Subsequently, the early and late transplanted rice is classified using a clustering algorithm within this platform. The proposed approach is used to monitor different cultivars of rice in three districts in the state of West Bengal, which is one of the major rice growing regions in India. The classification accuracy is assessed across 150 validation points spanning multiple blocks for the 2017 monsoon season. The Sentinel-1 SAR images acquired up to the early vegetative stage for rice have provided satisfactory classification accuracy with an overall accuracy >85% with $\kappa \sim 0.86$ across different management practices throughout the region.

Mapping Rice Planted Area Using a New Normalized EVI and SAVI (NVI) Derived From Landsat-8 OLI

Obtaining annually updated data of actually planted rice paddy is essential for evaluating food security and estimating methane emission. Paddy field in southern China generally displays unique phenological landscape changes from exposed soils, shallow flooding water, to rice plants during the whole growth period in a year. A phenology-based algorithm was developed to map the rice planted paddies in the Poyang Lake Plain (PLP), China, in 2014, a typical region in Central China single- and double-rice cropping belt. This algorithm, or a normalized vegetation index, was based on the normalization of Landsat-8 Operational Land Imager-derived enhanced vegetation index and soil-adjusted vegetation index. It highlighted the temporal differences in vegetation cover and background soil between two critical growth phases of paddy rice, i.e., the flooding to transplanting stage and reproductive to ripening stage. There was estimated to be 7148.31 km2 in the PLP, with an overall accuracy of 96.8% and the kappa coefficient of 0.97. Comparison between Landsat-detected results and the statistics of paddy field at the county level showed a high determination coefficient with the $R^{2}$ of 0.88. The phenology-based algorithm greatly facilitates rice farming monitoring at the regional scale.

Mapping paddy rice in Jiangsu Province, China, based on phenological parameters and a decision tree model

Timely and accurate mapping of rice planting areas is crucial under China’s current cropping structure. This study proposes a new paddy rice mapping method by combining phenological parameters and a decision tree model. Six phenological parameters were developed to identify paddy rice areas based on the analysis of the Moderate Resolution Imaging Spectroradiometer (MODIS) Enhanced Vegetation Index (EVI) time series and the Land Surface Water Index (LSWI) time series. The six phenological parameters considered the performance of different land cover types during specific phenological phases (EVI1 and EVI2), one-half of or the entire rice growing cycle (LSWI1 and LSWI2), and the shape of the LSWI time series (KurtosisLSWI and SkewnessLSWI). A hierarchical decision tree model was designed to classify paddy rice areas according to the potential separability of different land cover types in paired phenological parameter spaces. Results showed that the decision tree model was more sensitive to LSWI1, LSWI2, and SkewnessLSWI than the other phenological parameters. A paddy rice map of Jiangsu Province for 2015 was generated with an optimal threshold set of (0.4, 0.42, 9, 19, 1.5, –1.7, 0.0) with a total accuracy of 93.9%. The MODIS-derived paddy rice map generally agreed with the paddy land fraction map from the National Land Cover Dataset project, but there were regional discrepancies because of their different definitions of land use and the inability of MODIS to map paddy rice at a fragmental level. The MODIS-derived paddy rice map showed high correlation (R2= 0.85) with county-level agricultural statistics. The results of this study indicate that the phenological parameter-based paddy rice mapping algorithm could be applied at larger spatial scales.

Method for Mapping Rice Fields in Complex Landscape Areas Based on Pre-Trained Convolutional Neural Network from HJ-1 A/B Data

Accurate and timely information about rice planting areas is essential for crop yield estimation, global climate change and agricultural resource management. In this study, we present a novel pixel-level classification approach that uses convolutional neural network (CNN) model to extract the features of enhanced vegetation index (EVI) time series curve for classification. The goal is to explore the practicability of deep learning techniques for rice recognition in complex landscape regions, where rice is easily confused with the surroundings, by using mid-resolution remote sensing images. A transfer learning strategy is utilized to fine tune a pre-trained CNN model and obtain the temporal features of the EVI curve. Support vector machine (SVM), a traditional machine learning approach, is also implemented in the experiment. Finally, we evaluate the accuracy of the two models. Results show that our model performs better than SVM, with the overall accuracies being 93.60% and 91.05%, respectively. Therefore, this technique is appropriate for estimating rice planting areas in southern China on the basis of a pre-trained CNN model by using time series data. And more opportunity and potential can be found for crop classification by remote sensing and deep learning technique in the future study.

Regional scale mapping of fractional rice cropping change using a phenology-based temporal mixture analysis

ABSTRACTMonitoring changes of paddy rice is challenging due to its diverse cropping patterns and spectral variation. To investigate the spatio-temporal changes of rice cropping, we used the 10-day composited Moderate Resolution Imaging Spectroradiometer (MODIS) Normalized Difference Vegetation Index (NDVI) time series data with a spatial resolution of 250 m to map the sub-pixel rice spatial distributions in the Hunan Province, the top one region in rice planting area in southern of China. A method of improved phenology-based temporal mixture analysis (PTMA) was presented to identify early, middle, and late rice cropping patterns. The results show that the PTMA is effective to extract rice cropping. The nine rice cropping patterns were classified as early, middle, and late rice cropping, and fractional rice cropping within 250 m pixels was obtained to analyse the internal changes. Both the local planting conditions and different forms of rice cultivation were compared with statistical data. Overall, MODIS-estimated fractional rice agreed well with field samples at the pixel level and statistical data at the county level, which demonstrates the effectiveness of the PTMA method for mapping rice in these hilly regions with small-size paddy rice field. The changes show that single-cropping rice and double-cropping rice have been frequently transferred in space, which could be important information to support agricultural decision-making.

Object-based rice mapping using time-series and phenological data

Remote sensing techniques are often used in mapping rice, but high quality time-series remote sensing data are difficult to obtain due to the cloudy weather of rice growing areas and long satellite revisit interval. As such, rice mapping is usually based on mono-temporal Landsat TM/ETM+ data, which have large uncertainties due to the spectral similarity of different vegetation types. Moreover, conventional pixel-based classification method is unable to meet the required accuracy for rice mapping. Therefore, this study proposes a new strategy for mapping rice in cloud-prone areas using fused data of Landsat-8 OLI time-series and phenological parameters, based on the object-based method. We determine the critical growth stages of paddy rice from observed phenological data and MODIS-NDVI time-series data. The spatial and temporal adaptive reflectance fusion model (STARFM) is used to blend the MODIS and Landsat data to obtain a multi-temporal Landsat-like dataset for classification. Finally, an object-oriented algorithm is used to extract rice paddies from the Landsat-like, time-series dataset. The validation experiments show that the proposed method can provide high accuracy rice mapping, with an overall accuracy of 92.38% and a kappa coefficient of 0.85.

A Rice Mapping Method Based on Time-Series Landsat Data for the Extraction of Growth Period Characteristics

The rapid and accurate acquisition of rice cultivation information is very important for the management and assessment of rice agriculture and for research on food security, the use of agricultural water resources, and greenhouse gas emissions. Rice mapping methods based on phenology have been widely used but further studies are needed to clearly quantify the rice characteristics during the growth cycle. This paper selected the area where rice agriculture has undergone tremendous changes as the observation object. The rice areas were mapped in three time periods during the period from 1993 to 2016 by combining the characteristics of the harvested areas, flooded areas, and the time interval when harvesting and flooding occurred. An error matrix was used to determine the mapping accuracy. After exclusion of clouds and cloud shadows, the overall accuracy of the paddy fields was higher than 90% (90.5% and 93.5% in period 1 and period 3, respectively). Mixed pixels, image quality, and image acquisition time are important factors affecting the accuracy of rice mapping. The rapid economic development led to an adjustment of people’s diets and presumably this is the main reason why rice cultivation is no longer the main agricultural production activity in the study area.

Mapping Paddy Rice Area and Yields Over Thai Binh Province in Viet Nam From MODIS, Landsat, and ALOS-2/PALSAR-2

This study uses multiple satellite datasets to map paddy rice areas and yields for the Thai Binh Province, Viet Nam, over the summer growing season of 2015. The major datasets used are: first, surface reflectance and vegetation indices (VI) by fusing the optical observations from the Landsat sensors and the MODerate Resolution Imaging Spectroradiometer; and second, the L-band radar data from the PALSAR-2 sensor onboard the Advanced Land Observing Satellite 2. We find that although the fused VI time series are not necessarily beneficial for paddy rice mapping, the fusion datasets reduce observational gaps and allow us to better identify peak VI values and derive their empirical relationships with crop-cutting yield data ( $R^{2}= {\text{0.4}}$ for all the rice types, and $R^{2}= {\text{0.69}}$ for the dominant rice type −58% of all the sampled fields). The L-band radar data have slightly lower performance in rice mapping than the optical satellite data, while it has much less contribution to yield estimation than the optical data. Furthermore, our study suggests the geolocation errors of satellite images be taken into account when selecting small sample areas for crop cutting. This practice will ensure the representativeness of crop-cutting sample areas with regard to satellite observations and thus better linkages between field data and satellite pixels for yield modeling. We also highlight the need of crop-cutting data from multiple years and/or at different regions to account for the spatial and temporal variations of harvest index to improve the spatially explicit rice yield estimates through satellite observations.

Field-based rice classification in Wuhua county through integration of multi-temporal Sentinel-1A and Landsat-8 OLI data

Rice is one of the most important cereals in the world. With the change of agricultural land, it is urgently necessary to update information about rice planting areas. This study aims to map rice planting areas with a field-based approach through the integration of multi-temporal Sentinel-1A and Landsat-8 OLI data in Wuhua County of South China where has many basins and mountains. This paper, using multi-temporal SAR and optical images, proposes a methodology for the identification of rice-planting areas. This methodology mainly consists of SSM applied to time series SAR images for the calculation of a similarity measure, image segmentation process applied to the pan-sharpened optical image for the searching of homogenous objects, and the integration of SAR and optical data for the elimination of some speckles. The study compares the per-pixel approach with the per-field approach and the results show that the highest accuracy (91.38%) based on the field-based approach is 1.18% slightly higher than that based on the pixel-based approach for VH polarization, which is brought by eliminating speckle noise through comparing the rice maps of these two approaches. Therefore, the integration of Sentinel-1A and Landsat-8 OLI images with a field-based approach has great potential for mapping rice or other crops’ areas.

Evaluation of One-Class Support Vector Classification for Mapping the Paddy Rice Planting Area in Jiangsu Province of China from Landsat 8 OLI Imagery

Identification of paddy fields is essential for monitoring the rice cultivated area and predicting rice productivity. Timely and accurate extraction of rice distribution can bring vital information for national food security, agricultural policy formulation, and regional environmental sustainability. Conventional classification methods usually suffered from low accuracy, multi-class training samples, or demanding imagery requirements. This paper proposes to use one-class support vector classification (OCSVC) to extract rice cultivated area with Landsat Optical Land Imager (OLI) imagery. Instead of sampling and training all land cover types as performed by multi-class classification methods, OCSVC only used the training samples of target class (rice) for rice mapping. The performance of OCSVC was evaluated in terms of the classification accuracy of rice mapping and rice acreage estimation based on high-resolution imagery, field survey data and rice acreage data from government reports for Jiangsu Province, China. At the county-level, OCSVC was also compared with the commonly used multi-class support vector classification (MCSVC), decision tree classification (DTC), and vegetation index-based thresholding (VIT). Our results demonstrated that OCSVC produced a comparable overall accuracy to DTC and outperformed MCSVC and VIT. The computational efficiency of OCSVC increased approximately ten times as compared to MCSVC. The OCSVC produced the best correlation between its classified area and reported area among the four classification methods evaluated. When applied to the provincial level, the classification overall accuracy for OCSVC was 88.54%. The detected rice planting area for Jiangsu Province was 22,602 km2, which was consistent with the statistics from the National Bureau of Statistics (22,948 km2). This OCSVC-based mapping strategy provides a practical and efficient way to detect the rice planting extent with Landsat imagery at a large scale.

Classification and Mapping of Paddy Rice by Combining Landsat and SAR Time Series Data

Rice is an important food resource, and the demand for rice has increased as population has expanded. Therefore, accurate paddy rice classification and monitoring are necessary to identify and forecast rice production. Satellite data have been often used to produce paddy rice maps with more frequent update cycle (e.g., every year) than field surveys. Many satellite data, including both optical and SAR sensor data (e.g., Landsat, MODIS, and ALOS PALSAR), have been employed to classify paddy rice. In the present study, time series data from Landsat, RADARSAT-1, and ALOS PALSAR satellite sensors were synergistically used to classify paddy rice through machine learning approaches over two different climate regions (sites A and B). Six schemes considering the composition of various combinations of input data by sensor and collection date were evaluated. Scheme 6 that fused optical and SAR sensor time series data at the decision level yielded the highest accuracy (98.67% for site A and 93.87% for site B). Performance of paddy rice classification was better in site A than site B, which consists of heterogeneous land cover and has low data availability due to a high cloud cover rate. This study also proposed Paddy Rice Mapping Index (PMI) considering spectral and phenological characteristics of paddy rice. PMI represented well the spatial distribution of paddy rice in both regions. Google Earth Engine was adopted to produce paddy rice maps over larger areas using the proposed PMI-based approach.

Estimation of Rice Evapotranspiration Using Reflective Images of Landsat Satellite in Sefidrood Irrigation and Drainage Network

More accurate estimation of crop evapotranspiration (ETc) in a regional scale has always been one of the most important challenges. Temporal and spatial monitoring of ETc using satellite images can help to enhance accuracy of estimations. In this study, the (ETc)rice maps were produced by using statistical/experimental methods based on crop coefficient (Kc) maps derived from vegetation index (VI). Kc was estimated using four methods, including linear relationship between Kc and VI (Kc-VI), calibrated model of Kc-VI, linear relationship between Kcb (the basal crop coefficient) and VI (Kcb-VI), and calibrated model of Kcb-VI. The results showed that calibrated model of Kc-VI had a better performance compared to the other methods, with normalized root mean square errors (NRMSE), mean absolute error and root mean square error being 5.7%, 0.05 mm/d and 0.06 mm/d, respectively. (ETc)rice maps were produced by using calibrated model of Kc-VI and reference evapotranspiration (ET0) from FAO Penman-Monteith method. The NRMSE was 21.3% for using FAO Penman-Monteith method. Therefore, calibrated Kc-VI model in combining with ET0 based on the Landsat 7 ETM+ images could be provided a good estimation of (ETc)rice in regional scale, and can be applied to estimate water requirement due to the free and facilitate access.