Improving Spring Maize Yield Estimation at Field Scale by Assimilating Time-Series HJ-1 CCD Data into the WOFOST Model Using a New Method with Fast Algorithms

Establishing timely and high accurate models for crop yield estimation is of great significance for crop management and as well as decision makers. The arm of this study is to gain an approach of the method, depending on crop growth model and entropy method, to estimate spring maize yield with multi-temporal remotely sensed Landsat TM/ETM+ data at main growth and development stages of spring maize. The experiment had been conducted in Junchuan Farm of Northeast China. In this paper, the combined weights of the single-temporal estimation models were calculated by applying the entropy method (EM), and a combination forecasting (CF) model was developed. In order to improve the rationality of CF-EM and the accuracy of yield estimation, especially to follow the law of crop growth, the combination forecasting of combined weights method (CF-CM) was developed. The results showed that the yield estimation model based on CF-CM could increase the precision of the yield estimation model based on single-temporal remote images, the correlation coefficient was remarkably improved, and the value was increased by 0.09. The combined weights in the CF-CM were proposed for selecting the favorable coefficient of the subjective weight and objective weight, and that was of great importance for some key aspects: supplying usefulness information, how to raise maize yield and selecting key temporal satellite images to estimate maize yield. The CF-CM model discussed in this paper is feasible and effective to estimate spring maize yield.

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A NEW METHOD FOR ANALYZING IMAGES AND TIME SERIES USING A POTENTIAL TRANSFORMATION

Reliability and Quality of Complex Systems ◽

10.21685/2307-4205-2019-1-2 ◽

2019 ◽

Author(s):

O. B. Butusov ◽

◽

V. V. Dikusar ◽

◽

Keyword(s):

Time Series ◽

New Method

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Transfer mutual information: A new method for measuring information transfer to the interactions of time series

Physica A Statistical Mechanics and its Applications ◽

10.1016/j.physa.2016.10.027 ◽

2017 ◽

Vol 467 ◽

pp. 517-526 ◽

Cited By ~ 4

Author(s):

Xiaojun Zhao ◽

Pengjian Shang ◽

Aijing Lin

Keyword(s):

Time Series ◽

Mutual Information ◽

Information Transfer ◽

New Method

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A New Method for Fusion of Measured and Model-derived Solar Radiation Time-series

Energy Procedia ◽

10.1016/j.egypro.2014.02.182 ◽

2014 ◽

Vol 48 ◽

pp. 1617-1626 ◽

Cited By ~ 16

Author(s):

Theresa Mieslinger ◽

Felix Ament ◽

Kaushal Chhatbar ◽

Richard Meyer

Keyword(s):

Time Series ◽

Solar Radiation ◽

New Method ◽

Radiation Time

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Improving the estimation of soil-available nutrients at the sub-field scale using time-series UAV observations

Remote Sensing Letters ◽

10.1080/2150704x.2020.1763498 ◽

2020 ◽

Vol 11 (8) ◽

pp. 739-747

Author(s):

Jihua Meng ◽

Zhiqiang Cheng

Keyword(s):

Time Series ◽

Field Scale ◽

Available Nutrients ◽

Soil Available Nutrients

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Fusing Geostationary Satellite Observations with Harmonized Landsat-8 and Sentinel-2 Time Series for Monitoring Field-Scale Land Surface Phenology

Remote Sensing ◽

10.3390/rs13214465 ◽

2021 ◽

Vol 13 (21) ◽

pp. 4465

Author(s):

Yu Shen ◽

Xiaoyang Zhang ◽

Weile Wang ◽

Ramakrishna Nemani ◽

Yongchang Ye ◽

...

Keyword(s):

Time Series ◽

Land Surface ◽

Cloud Cover ◽

Landsat 8 ◽

Field Scale ◽

Spatiotemporal Resolution ◽

Essential Information ◽

Land Surface Phenology ◽

The Earth ◽

Sentinel 2

Accurate and timely land surface phenology (LSP) provides essential information for investigating the responses of terrestrial ecosystems to climate changes and quantifying carbon and surface energy cycles on the Earth. LSP has been widely investigated using daily Visible Infrared Imaging Radiometer Suite (VIIRS) or Moderate Resolution Imaging Spectroradiometer (MODIS) observations, but the resultant phenometrics are frequently influenced by surface heterogeneity and persistent cloud contamination in the time series observations. Recently, LSP has been derived from Landsat-8 and Sentinel-2 time series providing detailed spatial pattern, but the results are of high uncertainties because of poor temporal resolution. With the availability of data from Advanced Baseline Imager (ABI) onboard a new generation of geostationary satellites that observe the earth every 10–15 min, daily cloud-free time series could be obtained with high opportunities. Therefore, this study investigates the generation of synthetic high spatiotemporal resolution time series by fusing the harmonized Landsat-8 and Sentinel-2 (HLS) time series with the temporal shape of ABI data for monitoring field-scale (30 m) LSP. The algorithm is verified by detecting the timings of greenup and senescence onsets around north Wisconsin/Michigan states, United States, where cloud cover is frequent during spring rainy season. The LSP detections from HLS-ABI are compared with those from HLS or ABI alone and are further evaluated using PhenoCam observations. The result indicates that (1) ABI could provide ~3 times more high-quality observations than HLS around spring greenup onset; (2) the greenup and senescence onsets derived from ABI and HLS-ABI are spatially consistent and statistically comparable with a median difference less than 1 and 10-days, respectively; (3) greenup and senescence onsets derived from HLS data show sharp boundaries around the orbit-overlapped areas and shifts of ~13 days delay and ~15 days ahead, respectively, relative to HLS-ABI detections; and (4) HLS-ABI greenup and senescence onsets align closely to PhenoCam observations with an absolute average difference of less than 2 days and 5 days, respectively, which are much better than phenology detections from ABI or HLS alone. The result suggests that the proposed approach could be implemented the monitor of 30 m LSP over regions with persistent cloud cover.

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Field-scale soil moisture bridges the spatial-scale gap between drought monitoring and agricultural yields

10.5194/hess-2020-364 ◽

2020 ◽

Author(s):

Noemi Vergopolan ◽

Sitian Xiong ◽

Lyndon Estes ◽

Niko Wanders ◽

Nathaniel W. Chaney ◽

...

Keyword(s):

Soil Moisture ◽

Spatial Scale ◽

Root Zone ◽

Maize Yield ◽

Remotely Sensed ◽

Drought Monitoring ◽

Surface Model ◽

Field Scale ◽

Yield Data ◽

Scale Modeling

Abstract. Soil moisture is highly variable in space, and its deficits (i.e. droughts) plays an important role in modulating crop yields and its variability across landscapes. Limited hydroclimate and yield data, however, hampers drought impact monitoring and assessment at the farmer field-scale. This study demonstrates the potential of field-scale soil moisture simulations to advance high-resolution agricultural yield prediction and drought monitoring at the smallholder farm field-scale. We present a multi-scale modeling approach that combines HydroBlocks, a physically-based hyper-resolution Land Surface Model (LSM), and machine learning. We applied HydroBlocks to simulate root zone soil moisture and soil temperature in Zambia at 3-hourly 30-m resolution. These simulations along with remotely sensed vegetation indices, meteorological conditions, and data describing the physical properties of the landscape (topography, land cover, soil properties) were combined with district-level maize data to train a random forest model (RF) to predict maize yields at the district- and field-scale (250-m) levels. Our model predicted yields with a coefficient of variation (R2) of 0.61, Mean Absolute Error (MAE) of 349 kg ha−1, and mean normalized error of 22 %. We captured maize losses due to the 2015/2016 El Niño drought at similar levels to losses reported by the Food and Agriculture Organization (FAO). Our results revealed that soil moisture is the strongest and most reliable predictor of maize yield, driving its spatial and temporal variability. Consequently, soil moisture was also the most effective indicator of drought impacts in crops when compared with precipitation, soil and air temperatures, and remotely-sensed NDVI-based drought indices. By combining field-scale root zone soil moisture estimates with observed maize yield data, this research demonstrates how field-scale modeling can help bridge the spatial scale discontinuity gap between drought monitoring and agricultural impacts.

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A New Method to Detect Outliers in High-frequency Time Series

International Journal of Statistics and Probability ◽

10.5539/ijsp.v8n1p16 ◽

2018 ◽

Vol 8 (1) ◽

pp. 16

Author(s):

Ilaria Lucrezia Amerise ◽

Agostino Tarsitano

Keyword(s):

Time Series ◽

High Frequency ◽

Time Series Data ◽

New Method ◽

Series Data ◽

Absolute Difference ◽

Data Preparation ◽

Simple Method ◽

Pass Through ◽

Nonparametric Procedure

The objective of this research is to develop a fast, simple method for detecting and replacing extreme spikes in high-frequency time series data. The method primarily consists  of a nonparametric procedure that pursues a balance between fidelity to observed data and smoothness. Furthermore, through examination of the absolute difference between original and smoothed values, the technique is also able to detect and, where necessary, replace outliers with less extreme data. Unlike other filtering procedures found in the literature, our method does not require a model to be specified for the data. Additionally, the filter makes only a single pass through the time series. Experiments  show that the new method can be validly used as a data preparation tool to ensure that time series modeling is supported by clean data, particularly in a complex context such as one with high-frequency data.

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