Assimilation of the leaf area index and vegetation temperature condition index for winter wheat yield estimation using Landsat imagery and the CERES-Wheat model

Xie, Yi; Wang, Pengxin; Bai, Xuejiao; Khan, Jahangir; Zhang, Shuyu; Li, Li; Wang, Lei

doi:10.1016/j.agrformet.2017.06.015

Cited by 110 publications

(44 citation statements)

References 42 publications

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“…Using the PSO algorithm, Jin et al [85] assimilated the combined EVI × RVI data from the HJ-1A/B + RADARSAT-2 images into the AquaCrop for yield estimation of the winter wheat, and the RMSE of the estimated yield with the measured data was 0.81 t/ha, which was comparable with our result. With an ensemble Kalman filter (EnKF) algorithm, Xie et al [86] compared the accuracies of five assimilation schemes, which assimilated the LAI and soil moisture at different wheat growth stages into the CERES-wheat for the yield estimation. The results showed that the assimilation of LAI at the jointing and heading-filling stages into CERES-wheat obtained the higher accuracy, with a RMSE of 548.97 kg/ha, which was better than our estimated result [86].…”

Section: Discussionmentioning

confidence: 99%

“…With an ensemble Kalman filter (EnKF) algorithm, Xie et al [86] compared the accuracies of five assimilation schemes, which assimilated the LAI and soil moisture at different wheat growth stages into the CERES-wheat for the yield estimation. The results showed that the assimilation of LAI at the jointing and heading-filling stages into CERES-wheat obtained the higher accuracy, with a RMSE of 548.97 kg/ha, which was better than our estimated result [86]. Adding the GLDAS/Noah-derived surface incoming solar radiation as an input, MODIS-based LAI was also assimilated using a sequential update algorithm into the Soil Water Atmosphere Plant model, and the results of the yield-estimation were improved by 14-26%, with the absolute errors of <7%, which was better than our result [87].…”

Section: Discussionmentioning

confidence: 99%

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Winter Wheat Production Estimation Based on Environmental Stress Factors from Satellite Observations

et al. 2018

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Abstract:The rapid and accurate estimation of wheat production at a regional scale is crucial for national food security and sustainable agricultural development. This study developed a new gross primary productivity (GPP) estimation model (denoted as the [ACPM]), based on the effects of light, heat, soil moisture, and nitrogen content (N) on the light-use efficiency of winter wheat. The ACPM model used the quantic additivity of the environmental factors to improve the minimum form or multiple multiplication form in the previous model and thus characterized the joint effects of heat, soil moisture, and N on crop photosynthesis performance. The key parameters (i.e., light) were determined from the photosynthetically active radiation product of the Himawari-8 sensor and the fraction of photosynthetically active radiation product of Moderate Resolution Imaging Spectroradiometer (MODIS). The heat was determined from the land temperature products of MODIS. The soil moisture was obtained from the inversion using a visible and shortwave infrared drought index (VSDI), whereas the N stress of winter wheat was detected using the newly developed modified ratio vegetation index (MRVI), which could accurately obtain the spatiotemporal distribution of the leaf chlorophyll content of winter wheat. The ACPM and two other previous models (named the GPP1 and GPP2 models) were applied on the Himawari-8 and MODIS images in Hengshui City. The evaluation results, based on the ground measurement, indicated that the ACPM models exhibited the best estimate of dry aboveground biomass (DAM) and the wheat yield in Hengshui City, with errors of <10% and <12% for the DAM and yield, respectively. Considering the easy operation of the ACPM model and the accessibility of the corresponding satellite images, the Agriculture Crop Photosynthesis Model (ACPM) can be expected to provide information on the winter wheat shortfalls and surplus ahead of the availability of official statistical data.

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Winter Wheat Production Estimation Based on Environmental Stress Factors from Satellite Observations

et al. 2018

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“…Another possible reason for this underestimation is the insensitivity of the VIs to the actual photosynthetic activities of crops. Thus, many previous studies have focused on using greenness-based VI metrics along with other climatology data to quantify yield variations and may underestimate the yield loss effects (Idso, Jackson, & Reginato, 1977;Lobell, Asner, Ortiz-Monasterio, & Benning, 2003;Prasad, Chai, Singh, & Kafatos, 2006;Quarmby et al, 1993;Xie et al, 2017). On the other hand, compared with VIs, chlorophyll fluorescence originals from the photosynthetic apparatus, so the background has a smaller impact on the fluorescence signal (Baker, 2008).…”

Section: Potential Of Sif For Heat Stress Monitoring In Wheatmentioning

confidence: 99%

Satellite sun‐induced chlorophyll fluorescence detects early response of winter wheat to heat stress in the Indian Indo‐Gangetic Plains

Song

Guanter

Guan

et al. 2018

Global Change Biology

183

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Extremely high temperatures represent one of the most severe abiotic stresses limiting crop productivity. However, understanding crop responses to heat stress is still limited considering the increases in both the frequency and severity of heat wave events under climate change. This limited understanding is partly due to the lack of studies or tools for the timely and accurate monitoring of crop responses to extreme heat over broad spatial scales. In this work, we use novel spaceborne data of sun-induced chlorophyll fluorescence (SIF), which is a new proxy for photosynthetic activity, along with traditional vegetation indices (Normalized Difference Vegetation Index NDVI and Enhanced Vegetation Index EVI) to investigate the impacts of heat stress on winter wheat in northwestern India, one of the world's major wheat production areas. In 2010, an abrupt rise in temperature that began in March adversely affected the productivity of wheat and caused yield losses of 6% compared to previous year. The yield predicted by satellite observations of SIF decreased by approximately 13.9%, compared to the 1.2% and 0.4% changes in NDVI and EVI, respectively. During early stage of this heat wave event in early March 2010, the SIF observations showed a significant reduction and earlier response, while NDVI and EVI showed no changes and could not capture the heat stress until late March. The spatial patterns of SIF anomalies closely tracked the temporal evolution of the heat stress over the study area. Furthermore, our results show that SIF can provide large-scale, physiology-related wheat stress response as indicated by the larger reduction in fluorescence yield (SIF ) than fraction of photosynthetically active radiation during the grain-filling phase, which may have eventually led to the reduction in wheat yield in 2010. This study implies that satellite observations of SIF have great potential to detect heat stress conditions in wheat in a timely manner and assess their impacts on wheat yields at large scales.

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“…Timely and accurate updates of crop planting area are very important for assessing national food security, for agricultural management, and for the evaluation of ecological functions [1][2][3]. Regional crop-type mapping provides basic data for crop growth monitoring and yield forecasting [4][5][6].…”

Section: Introductionmentioning

confidence: 99%

Mapping Winter Crops in China with Multi-Source Satellite Imagery and Phenology-Based Algorithm

et al. 2019

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Timely and accurate mapping of winter crop planting areas in China is important for food security assessment at a national level. Time-series of vegetation indices, such as the normalized difference vegetation index (NDVI), are widely used for crop mapping, as they can characterize the growth cycle of crops. However, with the moderate spatial resolution optical imagery acquired by Landsat and Sentinel-2, it is difficult to obtain complete time-series curves for vegetation indices due to the influence of the revisit cycle of the satellite and weather conditions. Therefore, in this study, we propose a method for compositing the multi-temporal NDVI, in order to map winter crop planting areas with the Landsat-7 and -8 and Sentinel-2 optical images. The algorithm composites the multi-temporal NDVI into three key values, according to two time-windows-a period of low NDVI values and a period of high NDVI values-for the winter crops. First, we identify the two time-windows, according to the time-series of the NDVI obtained from daily Moderate Resolution Imaging Spectroradiometer observations. Second, the 30 m spatial resolution multi-temporal NDVI curve, derived from the Landsat-7 and -8 and Sentinel-2 optical images, is composited by selecting the maximal value in the high NDVI value period, and the minimal and median values in the low NDVI value period, using an algorithm of the Google Earth Engine. Third, a decision tree classification method is utilized to perform the winter crop classification at a pixel level. The results indicate that this method is effective for the large-scale mapping of winter crops. In the study area, the area of winter crops in 2018 was determined to be 207,641 km 2 , with an overall accuracy of 96.22% and a kappa coefficient of 0.93. The method proposed in this paper is expected to contribute to the rapid and accurate mapping of winter crops in large-scale applications and analyses.Resolution Imaging Spectroradiometer (MODIS) Land Cover Collections [12], and the 1 km Global Land Cover 2000 (GLC2000) [13]). However, more specific information on winter crops is still limited in existing land-cover products [2,14]. On the other hand, the mapping of winter crop planting areas using the traditional agricultural census approach requires a good deal of manpower, money, and time [15]. Further, the timeliness of the traditional approach is poor. For example, the Chinese government generally publishes this information at least six months after the crops have been harvested. Furthermore, the quality of the survey data tends to suffer from the influence of human errors [3].Remote sensing techniques provide a very effective means to map crops [2,11,16,17], due to their fast responses, periodic observations, wide field of view, and low cost [18][19][20]. However, there are still some challenges associated with large-scale (e.g., provincial-or national-scale) winter crop mapping by remote sensing. MODIS data was the major data source for large-scale crop mapping in previous studies, due to its high revisit...

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Assimilation of the leaf area index and vegetation temperature condition index for winter wheat yield estimation using Landsat imagery and the CERES-Wheat model

Cited by 110 publications

References 42 publications

Winter Wheat Production Estimation Based on Environmental Stress Factors from Satellite Observations

Winter Wheat Production Estimation Based on Environmental Stress Factors from Satellite Observations

Satellite sun‐induced chlorophyll fluorescence detects early response of winter wheat to heat stress in the Indian Indo‐Gangetic Plains

Mapping Winter Crops in China with Multi-Source Satellite Imagery and Phenology-Based Algorithm

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