Improving remotely-sensed crop monitoring by NDVI-based crop phenology estimators for corn and soybeans in Iowa and Illinois, USA

Seo, Byong Seol; Lee, Jihye; Lee, Kyung-Do; Hong, Su Young; Kang, Sinkyu

doi:10.1016/j.fcr.2019.03.015

Cited by 86 publications

(43 citation statements)

References 61 publications

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“…The commonly used MODIS-based 250-m products are suitable for many regions, such as the Great Plains of the US, which have large field sizes (mean field size of 19.3 ha [ 42 ]), and countries in Europe [ 4 , 47 , 52 ], which have small field sizes (two-thirds of Europeans fields are less than 5 ha [ 53 ]). Many methods (pixel-based crop planting ratio, phenological information, among others) have been proposed to improve the accuracy of MODIS in agricultural applications [ 28 , 41 , 46 , 52 , 54 ], such as crop map masks and phenological information adjustment used in this study. The NDVI is the most commonly used vegetation index, calculated from the two bands of the MODIS 250-m reflectivity products.…”

Section: Discussionmentioning

confidence: 99%

Prediction of Crop Yield Using Phenological Information Extracted from Remote Sensing Vegetation Index

Pan

Zhu

et al. 2021

Sensors

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Phenology is an indicator of crop growth conditions, and is correlated with crop yields. In this study, a phenological approach based on a remote sensing vegetation index was explored to predict the yield in 314 counties within the US Corn Belt, divided into semi-arid and non-semi-arid regions. The Moderate Resolution Imaging Spectroradiometer (MODIS) data product MOD09Q1 was used to calculate the normalized difference vegetation index (NDVI) time series. According to the NDVI time series, we divided the corn growing season into four growth phases, calculated phenological information metrics (duration and rate) for each growth phase, and obtained the maximum correlation NDVI (Max-R2). Duration and rate represent crop growth days and rate, respectively. Max-R2 is the NDVI value with the most significant correlation with corn yield in the NDVI time series. We built three groups of yield regression models, including univariate models using phenological metrics and Max-R2, and multivariate models using phenological metrics, and multivariate models using phenological metrics combined with Max-R2 in the whole, semi-arid, and non-semi-arid regions, respectively, and compared the performance of these models. The results show that most phenological metrics had a statistically significant (p < 0.05) relationship with corn yield (maximum R2 = 0.44). Models established with phenological metrics realized yield prediction before harvest in the three regions with R2 = 0.64, 0.67, and 0.72. Compared with the univariate Max-R2 models, the accuracy of models built with Max-R2 and phenology metrics improved. Thus, the phenology metrics obtained from MODIS-NDVI accurately reflect the corn characteristics and can be used for large-scale yield prediction. Overall, this study showed that phenology metrics derived from remote sensing vegetation indexes could be used as crop yield prediction variables and provide a reference for data organization and yield prediction with physical crop significance.

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Section: Discussionmentioning

confidence: 99%

Prediction of Crop Yield Using Phenological Information Extracted from Remote Sensing Vegetation Index

Pan

Zhu

et al. 2021

Sensors

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“…The differences between the real optical features and the transferred optical features of the synchronous response relationship were identified based on these geo-parcels by using a rebuilt transformer network to optimize the classification model.Model optimization based on the terrestrial measurement spectrum: The terrestrial measurement spectra of the crops were collected by Analytica Spectral Devices (ASD) based on the geo-parcels. Furthermore, the differences between the terrestrial measurement spectrum and the learned optical features could be learned from the translation network, and then the real spectra could be built based on the new model.Crop class prediction for geo-parcels with missing spectral information: For the geo-parcels whose spectra were affected by mountains, mist, etc., the crop classes could be estimated by machine learning (XGBoost) [31] based on auxiliary data and geographic patterns (spatial/temporal continuity).Crop monitoring: On the basis of the crop distribution map obtained in the previous steps and the field crop site monitoring data, vegetation indices could be calculated for the purpose of remote sensing-based crop yield estimation and other applications [32,33]. …”

Section: Methodsmentioning

confidence: 99%

“…Crop monitoring: On the basis of the crop distribution map obtained in the previous steps and the field crop site monitoring data, vegetation indices could be calculated for the purpose of remote sensing-based crop yield estimation and other applications [32,33].…”

Section: Methodsmentioning

confidence: 99%

Synchronous Response Analysis of Features for Remote Sensing Crop Classification Based on Optical and SAR Time-Series Data

Sun

Luo

et al. 2019

Sensors

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Accurate crop classification is the basis of agricultural research, and remote sensing is the only effective measuring technique to classify crops over large areas. Optical remote sensing is effective in regions with good illumination; however, it usually fails to meet requirements for highly accurate crop classification in cloud-covered areas and rainy regions. Synthetic aperture radar (SAR) can achieve active data acquisition by transmitting signals; thus, it has strong resistance to cloud and rain interference. In this study, we designed an improved crop planting structure mapping framework for cloudy and rainy regions by combining optical data and SAR data, and we revealed the synchronous-response relationship of these two data types. First, we extracted geo-parcels from optical images with high spatial resolution. Second, we built a recurrent neural network (RNN)-based classifier suitable for remote sensing images on the geo-parcel scale. Third, we classified crops based on the two datasets and established the network. Fourth, we analyzed the synchronous response relationships of crops based on the results of the two classification schemes. This work is the basis for the application of remote sensing data for the fine mapping and growth monitoring of crop planting structures in cloudy and rainy areas in the future.

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“…This way it provides a sort of synthesis of both domains and might therefore be considered an information-rich indicator. As documented in scientific literature for wheat and maize, the timing of NDVI peak (DOY_VImax) occurs around the booting or heading date [37,38], from shortly before flowering to the time of flowering (silking of maize [39]).…”

Section: Smoothing and Extraction Of Phenologymentioning

confidence: 99%

A Crop Group-Specific Pure Pixel Time Series for Europe

et al. 2019

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Long timeseries of Earth observation data for the characterization of agricultural crops across large scales are of high interest to crop modelers, scientists, and decision makers in the fields of agricultural and environmental policy as well as crop monitoring and food security. They are particularly important for regression-based crop monitoring systems that rely on historic information. The major challenge lies in identifying pixels from satellite imagery that represent pure enough crop signals. Here, we present a data-driven semi-automatic approach to identify pure pixels of two crop groups (i.e., winter and spring crops and summer crops) based on a MODIS–NDVI timeseries. We applied this method to the European Union at a 250 m spatial resolution. Pre-processed and smoothed, daily normalized difference vegetation index (NDVI) data (2001–2017) were used to first extract the phenological data. To account for regional characteristics (varying climate, agro-management, etc.), these data were clustered by administrative units and by year using a Gaussian mixture model. The number of clusters was pre-defined using data from regional agricultural acreage statistics. After automatic labelling, clusters were filtered based on agronomic knowledge and phenological information extracted from the same timeseries. The resulting pure pixels were validated with two different datasets, one based on high-resolution Sentinel-2 data (5 sites, 2 years) and one based on a regional crop map (1 site, 7 years). For the winter and spring crop class, pixel purity amounted to 93% using the first validation dataset and to 73% using the second one, averaged over the different years. For summer crops, the respective values were 61% (91% without one critical validation site) and 72%. The phenological analyses revealed a clear trend towards an earlier NDVI peak (approximately −0.28 days/year) for winter and spring crops across Europe. We expect that this dataset will be useful for various applications, from crop model calibration to operational crop monitoring and yield forecasting.

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Improving remotely-sensed crop monitoring by NDVI-based crop phenology estimators for corn and soybeans in Iowa and Illinois, USA

Cited by 86 publications

References 61 publications

Prediction of Crop Yield Using Phenological Information Extracted from Remote Sensing Vegetation Index

Prediction of Crop Yield Using Phenological Information Extracted from Remote Sensing Vegetation Index

Synchronous Response Analysis of Features for Remote Sensing Crop Classification Based on Optical and SAR Time-Series Data

A Crop Group-Specific Pure Pixel Time Series for Europe

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