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

Guan, Kaiyu; Ли, Жан; Rao, Lakshman Nagraj; Gao, Feng; Xie, Donghui; Zeng, Zhenzhong

doi:10.1109/jstars.2018.2834383

Cited by 28 publications

(21 citation statements)

References 60 publications

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“…However, by effectively combining information from multiple Landsat-and MODIS-like satellites, these limitations can be mitigated [9]. For example, a recent study by Guan et al [40] used a Landsat-MODIS data fusion approach to improve paddy rice yield estimation in Vietnam. As of today, Landsat-7 is still in operation, even though it has suffered a failure in scan-line-correction since May 2003.…”

Section: Introductionmentioning

confidence: 99%

Assessing the Variability of Corn and Soybean Yields in Central Iowa Using High Spatiotemporal Resolution Multi-Satellite Imagery

et al. 2018

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The utility of remote sensing data in crop yield modeling has typically been evaluated at the regional or state level using coarse resolution (>250 m) data. The use of medium resolution data (10–100 m) for yield estimation at field scales has been limited due to the low temporal sampling frequency characteristics of these sensors. Temporal sampling at a medium resolution can be significantly improved, however, when multiple remote sensing data sources are used in combination. Furthermore, data fusion approaches have been developed to blend data from different spatial and temporal resolutions. This paper investigates the impacts of improved temporal sampling afforded by multi-source datasets on our ability to explain spatial and temporal variability in crop yields in central Iowa (part of the U.S. Corn Belt). Several metrics derived from vegetation index (VI) time-series were evaluated using Landsat-MODIS fused data from 2001 to 2015 and Landsat-Sentinel2-MODIS fused data from 2016 and 2017. The fused data explained the yield variability better, with a higher coefficient of determination (R2) and a smaller relative mean absolute error than using a single data source alone. In this study area, the best period for the yield prediction for corn and soybean was during the middle of the growing season from day 192 to 236 (early July to late August, 1–3 months before harvest). These findings emphasize the importance of high temporal and spatial resolution remote sensing data in agricultural applications.

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Section: Introductionmentioning

confidence: 99%

Assessing the Variability of Corn and Soybean Yields in Central Iowa Using High Spatiotemporal Resolution Multi-Satellite Imagery

et al. 2018

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“…Furthermore, reliable radar-based yield estimates require at least three images -one from early-mid vegetative, at heading, and prior to harvest (Shao et al 2001;Li et al 2003). However, recent studies with crop cuts suggested that even at different polarization modes, linear relationships with backscatter are insignificant and other data sources or methods are more suitable (Guan et al 2018).…”

Section: Empirical Modelsmentioning

confidence: 99%

“…LUE has been routinely used to provide regionwide rice yield estimates to varying degrees of success. Guan et al (2018) fused Landsat-MODIS EVI product to estimate rice yield in Vietnam at a moderate accuracy (relative RMSE: 17-19%). In another study, it was used to predict NPP of rice in the Continental United States at a relatively poor accuracy (R 2 = 0.53, RMSE = 3.42 t/ha) (Marshall, Tu, and Brown 2018).…”

Section: Semi-empirical Modelsmentioning

confidence: 99%

“…Government agencies typically collect yield statistics at administrative levels. Accuracies of yield estimation models are compared against these by aggregating pixel-level estimates into administrative-level averages, using mostly RMSE, MAE, and R 2 measurements as error or agreement measures (Gilardelli et al 2019;Guan et al 2018). Although these models can be readily linked to spatial data, there are several unresolved issues related to models themselves and other parameters in the models, such as uncertainties in paddy cultivation area and modeling approach, that can degrade the reliability of estimated rice yields.…”

Section: Linking Models To Spatial Informationmentioning

confidence: 99%

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Remote sensing-based estimation of rice yields using various models: A critical review

Torre

Gao

Macinnis‐Ng

2021

Geo-spatial Information Science

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Reliable estimation of region-wide rice yield is vital for food security and agricultural management. Field-scale models have increased our understanding of rice yield and its estimation under theoretical environmental conditions. However, they offer little information on spatial variability effects on farm-scale yield. Remote Sensing (RS) is a useful tool to upscale yield estimates from farm scales to regional levels. Much research used RS with rice models for reliable yield estimation. As several countries start to operationalize rice monitoring systems, it is needed to synthesize current literature to identify knowledge gaps, to improve estimation accuracies, and to optimize processing. This paper critically reviewed significant developments in using geospatial methods, imagery, and quantitative models to estimate rice yield. First, essential characteristics of rice were discussed as detected by optical and radar sensors, band selection, sensor configuration, spatial resolution, mapping methods, and biophysical variables of rice derivable from RS data. Second, various empirical, process-based, and semi-empirical models that used RS data for spatial estimation of yield were critically assessed -discussing how major types of models, RS platforms, data assimilation algorithms, canopy state variables, and RS variables can be integrated for yield estimation. Lastly, to overcome current constraints and to improve accuracies, several possibilities were suggested -adding new modeling modules, using alternative canopy variables, and adopting novel modeling approaches. As rice yields are expected to decrease due to global warming, geospatial rice yield estimation techniques are indispensable tools for climate change assessments. Future studies should focus on resolving the current limitations of estimation by precise delineation of rice cultivars, by incorporating dynamic harvesting indices based on climatic drivers, using innovative modeling approaches with machine learning.

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“…One recent study used several fused images on key dates to map paddy rice fields in Hunan, China by supervised random tree (RT) classifier [25]. However, it can be seen that most studies used image statistics-based approaches, e.g., the supervised classifiers like maximum likelihood classify (MLC) [27], support vector machine (SVM) [23,26], random forest [29], and unsupervised classifiers like iterative self-organizing data analysis technique (ISODATE) [30]. Those approaches usually rely on image statistics and/or training sample collection and/or visual interpretation, which is time-consuming, labor-intensive and region-dependent.…”

Section: Introductionmentioning

confidence: 99%

Mapping Paddy Rice Planting Area in Northeastern China Using Spatiotemporal Data Fusion and Phenology-Based Method

et al. 2019

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Accurate paddy rice mapping with fine spatial detail is significant for ensuring food security and maintaining sustainable environmental development. In northeastern China, rice is planted in fragmented and patchy fields and its production has reached over 10% of the total amount of rice production in China, which has brought the increasing need for updated paddy rice maps in the region. Existing methods for mapping paddy rice are often based on remote sensing techniques by using optical images. However, it is difficult to obtain high quality time series remote sensing data due to the frequent cloud cover in rice planting area and low temporal sampling frequency of satellite imagery. Therefore, paddy rice maps are often developed using few Landsat or time series MODIS images, which has limited the accuracy of paddy rice mapping. To overcome these limitations, we presented a new strategy by integrating a spatiotemporal fusion algorithm and phenology-based algorithm to map paddy rice fields. First, we applied the spatial and temporal adaptive reflectance fusion model (STARFM) to fuse the Landsat and MODIS data and obtain multi-temporal Landsat-like images. From the fused Landsat-like images and the original Landsat images, we derived time series vegetation indices (VIs) with high temporal and high spatial resolution. Then, the phenology-based algorithm, considering the unique physical features of paddy rice during the flooding and transplanting phases/open-canopy period, was used to map paddy rice fields. In order to prove the effectiveness of the proposed strategy, we compared our results with those from other three classification strategies: (1) phenology-based classification based on original Landsat images only, (2) phenology-based classification based on original MODIS images only and (3) random forest (RF) classification based on both Landsat and Landsat-like images. The validation experiments indicate that our fusion-and phenology-based strategy could improve the overall accuracy of classification by 6.07% (from 92.12% to 98.19%) compared to using Landsat data only, and 8.96% (from 89.23% to 98.19%) compared to using MODIS data, and 4.66% (from93.53% to 98.19%) compared to using the RF algorithm. The results show that our new strategy, by integrating the spatiotemporal fusion algorithm and phenology-based algorithm, can provide an effective and robust approach to map paddy rice fields in regions with limited available images, as well as the areas with patchy and fragmented fields.

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

Cited by 28 publications

References 60 publications

Assessing the Variability of Corn and Soybean Yields in Central Iowa Using High Spatiotemporal Resolution Multi-Satellite Imagery

Assessing the Variability of Corn and Soybean Yields in Central Iowa Using High Spatiotemporal Resolution Multi-Satellite Imagery

Remote sensing-based estimation of rice yields using various models: A critical review

Mapping Paddy Rice Planting Area in Northeastern China Using Spatiotemporal Data Fusion and Phenology-Based Method

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