Meta-analysis of influential factors on crop yield estimation by remote sensing

Huang, Jing; Han, Dawei

doi:10.1080/01431161.2014.890761

Cited by 23 publications

(10 citation statements)

References 64 publications

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“…If in absolute values, yields are overestimated compared to official agricultural statistics of the Kollo department, the analysis of the standardized values has shown a good agreement in terms of year-to-year variability reproduction, translating into a high correlation with statistics. In their recent meta-analysis [8] found that for four studies conducted in Senegal, Burkina Faso and Niger using NOAA AVHRR data, the correlation coefficients between NDVI alone and millet yield were comprised between 0.75 and 0.94 which is comparable to the present work (r=0.82). However, caution in the interpretations has to be taken particularly because (1) although the size of the study area considered in these studies is similar to that of the present study (i.e.…”

Section: Estimation Of Pearl Millet Yieldssupporting

confidence: 83%

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Crop Monitoring Using Vegetation and Thermal Indices for Yield Estimates: Case Study of a Rainfed Cereal in Semi-Arid West Africa

Leroux

Baron

Zoungrana³

et al. 2016

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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For the semi-arid Sahelian region, climate variability is one of the most important risks of food insecurity. Field experimentations as well as crop modelling are helpful tools for the monitoring and the understanding of yields at local scale. However, extrapolation of these methods at a regional scale remains a demanding task. Remote sensing observations appear as a good alternative or addition to existing crop monitoring systems. In this study, a new approach based on the combination of vegetation and thermal indices for rainfed cereal yield assessment in the Sahelian region was investigated. Empirical statistical models were developed between MODIS NDVI and LST variables and the crop model SARRA-H simulated aboveground biomass and harvest index in order to assess each component of the yield equation. The resulting model was successfully applied at the Niamey Square Degree (NSD) site scale with yield estimations close to the official agricultural statistics of Niger for a period of 11 years (2000-2011) (r=0.82, pvalue<0.05). The combined NDVI and LST indices based model was found to clearly outperform the model based on NDVI alone (r=0.59, pvalue<0.10). In areas where access to ground measurements is difficult, a simple, robust and timely satellite-based model combining vegetation and 2 thermal indices from MODIS and calibrated using crop model outputs, can be pertinent. In particular, such a model can provide an assessment of the year-to-year yield variability shortly after harvest for regions with agronomic and climate characteristics close to those of the NSD study area.

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Section: Estimation Of Pearl Millet Yieldssupporting

confidence: 83%

“…Yield estimation systems based on crop modelling allow accurate quantitative assessments (e.g. AGRHYMET in West Africa; the AGRI4CAST action in Europe), but are confronted with input data availability and spatial consistency constraints [8], [9].…”

Section: Introductionmentioning

confidence: 99%

Crop Monitoring Using Vegetation and Thermal Indices for Yield Estimates: Case Study of a Rainfed Cereal in Semi-Arid West Africa

Leroux

Baron

Zoungrana³

et al. 2016

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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“…High-resolution monitoring of vegetation health conditions using remote sensing observations provides valuable information that is widely used for a variety of purposes, such as drought monitoring (AghaKouchak et al, 2015), ecological health assessments (Li et al, 2014), and crop yield forecasting (Huang and Han, 2014;Johnson, 2016). Vegetation health and growth dynamics are influenced by a myriad of factors such as the timing and amount of rainfall, changes in evaporative demand due to anomalous weather conditions, and the availability of sufficient root zone soil moisture to meet the vegetation's water requirements during different stages of its growth.…”

Section: Introductionmentioning

confidence: 99%

Exploring seasonal and regional relationships between the Evaporative Stress Index and surface weather and soil moisture anomalies across the United States

Otkin

Zhong

Lorenz

et al. 2018

Hydrol. Earth Syst. Sci.

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Abstract. This study uses correlation analyses to explore relationships between the satellite-derived Evaporative Stress Index (ESI) – which depicts standardized anomalies in an actual to reference evapotranspiration (ET) fraction – and various land and atmospheric variables that impact ET. Correlations between the ESI and forcing variable anomalies calculated over sub-seasonal timescales were computed at weekly and monthly intervals during the growing season. Overall, the results revealed that the ESI is most strongly correlated to anomalies in soil moisture and 2 m dew point depression. Correlations between the ESI and precipitation were also large across most of the US; however, they were typically smaller than those associated with soil moisture and vapor pressure deficit. In contrast, correlations were much weaker for air temperature, wind speed, and radiation across most of the US, with the exception of the south-central US where correlations were large for all variables at some point during the growing season. Together, these results indicate that changes in soil moisture and near-surface atmospheric vapor pressure deficit are better predictors of the ESI than precipitation and air temperature anomalies are by themselves. Large regional and seasonal dependencies were also observed for each forcing variable. Each of the regional and seasonal correlation patterns were similar for ESI anomalies computed over 2-, 4-, and 8-week time periods; however, the maximum correlations increased as the ESI anomalies were computed over longer time periods and also shifted toward longer averaging periods for the forcing variables.

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“…Accurate yield prediction methods can provide support for good decision-making in agricultural planning, budgeting and input [1][2][3]. The normalized difference vegetation index (NDVI) is an indicator that reflects the greenness and productivity of vegetation, and it is closely related to the growth and yield of crops [4]. Accurate NDVI prediction can lead to very effective forecasts for crop yields [3,[5][6][7][8].…”

Section: Introductionmentioning

confidence: 99%

Short and Medium-Term Prediction of Winter Wheat NDVI Based on the DTW–LSTM Combination Method and MODIS Time Series Data

Zhao

Yang

et al. 2021

Remote Sensing

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The normalized difference vegetation index (NDVI) is an important agricultural parameter that is closely correlated with crop growth. In this study, a novel method combining the dynamic time warping (DTW) model and the long short-term memory (LSTM) deep recurrent neural network model was developed to predict the short and medium-term winter wheat NDVI. LSTM is well-suited for modelling long-term dependencies, but this method may be susceptible to overfitting. In contrast, DTW possesses good predictive ability and is less susceptible to overfitting. Therefore, by utilizing the combination of these two models, the prediction error caused by overfitting is reduced, thus improving the final prediction accuracy. The combined method proposed here utilizes the historical MODIS time series data with an 8-day time resolution from 2015 to 2020. First, fast Fourier transform (FFT) is used to decompose the time series into two parts. The first part reflects the inter-annual and seasonal variation characteristics of winter wheat NDVI, and the DTW model is applied for prediction. The second part reflects the short-term change characteristics of winter wheat NDVI, and the LSTM model is applied for prediction. Next, the results from both models are combined to produce a final prediction. A case study in Hebei Province that predicts the NDVI of winter wheat at five prediction horizons in the future indicates that the DTW–LSTM model proposed here outperforms the LSTM model according to multiple evaluation indicators. The results of this study suggest that the DTW–LSTM model is highly promising for short and medium-term NDVI prediction.

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Meta-analysis of influential factors on crop yield estimation by remote sensing

Cited by 23 publications

References 64 publications

Crop Monitoring Using Vegetation and Thermal Indices for Yield Estimates: Case Study of a Rainfed Cereal in Semi-Arid West Africa

Crop Monitoring Using Vegetation and Thermal Indices for Yield Estimates: Case Study of a Rainfed Cereal in Semi-Arid West Africa

Exploring seasonal and regional relationships between the Evaporative Stress Index and surface weather and soil moisture anomalies across the United States

Short and Medium-Term Prediction of Winter Wheat NDVI Based on the DTW–LSTM Combination Method and MODIS Time Series Data

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