scholarly journals Prior Season Crop Type Masks for Winter Wheat Yield Forecasting: A US Case Study

2018 ◽  
Vol 10 (10) ◽  
pp. 1659 ◽  
Author(s):  
Inbal Becker-Reshef ◽  
Belen Franch ◽  
Brian Barker ◽  
Emilie Murphy ◽  
Andres Santamaria-Artigas ◽  
...  
Keyword(s):  
Time Series ◽  
Winter Wheat ◽  
Spatial Resolution ◽  
Vegetation Index ◽  
Time Series Data ◽  
Crop Yields ◽  
Wheat Yield ◽  
Series Data ◽  
Crop Type ◽  
Winter Wheat Yield

Monitoring and forecasting crop yields is a critical component of understanding and better addressing global food security challenges. Detailed spatial information on crop-type distribution is fundamental for in-season crop condition monitoring and yields forecasting over large agricultural areas, as it enables the extraction of crop-specific signals. Yet, the availability of such data within the growing season is often limited. Within this context, this study seeks to develop a practical approach to extract a crop-specific signal for yield forecasting in cases where crop rotations are prevalent, and detailed in-season information on crop type distribution is not available. We investigated the possibility of accurately forecasting winter wheat yields by using a counter-intuitive approach, which coarsens the spatial resolution of out-of-date detailed winter wheat masks and uses them in combination with easily accessibly coarse spatial resolution remotely sensed time series data. The main idea is to explore an optimal spatial resolution at which crop type changes will be negligible due to crop rotation (so a previous seasons’ mask, which is more readily available can be used) and an informative signal can be extracted, so it can be correlated to crop yields. The study was carried out in the United States of America (USA) and utilized multiple years of NASA Moderate Resolution Imaging Spectroradiometer (MODIS) data, US Department of Agriculture (USDA) National Agricultural Statistics Service (NASS) detailed wheat masks, and a regression-based winter wheat yield model. The results indicate that, in places where crop rotations were prevalent, coarsening the spatial scale of a crop type mask from the previous season resulted in a constant per-pixel wheat proportion over multiple seasons. This enables the consistent extraction of a crop-specific vegetation index time series that can be used for in-season monitoring and yield estimation over multiple years using a single mask. In the case of the USA, using a moderate resolution crop type mask from a previous season aggregated to 5 km resolution, resulted in a 0.7% tradeoff in accuracy relative to the control case where annually-updated detailed crop-type masks were available. These findings suggest that when detailed in-season data is not available, winter wheat yield can be accurately forecasted (within 10%) prior to harvest using a single, prior season crop mask and coarse resolution Normalized Difference Vegetation Index (NDVI) time series data.

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

2021 ◽  
Vol 13 (22) ◽  
pp. 4660
Author(s):  
Fa Zhao ◽  
Guijun Yang ◽  
Hao Yang ◽  
Yaohui Zhu ◽  
Yang Meng ◽  
...  
Keyword(s):  
Time Series ◽  
Winter Wheat ◽  
Vegetation Index ◽  
Time Series Data ◽  
Short Term Memory ◽  
Combination Method ◽  
Series Data ◽  
Medium Term ◽  
Short Term ◽  
Deep Recurrent Neural Network

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.

scholarly journals Degree Approximation-Based Fuzzy Partitioning Algorithm and Applications in Wheat Production Prediction

Symmetry ◽  
2018 ◽  
Vol 10 (12) ◽  
pp. 768 ◽  
Author(s):  
Rachna Jain ◽  
Nikita Jain ◽  
Shivani Kapania ◽  
Le Son
Keyword(s):  
Time Series ◽  
Time Series Data ◽  
Crop Yields ◽  
Wheat Yield ◽  
Fuzzy Time Series ◽  
Series Data ◽  
Yield Production ◽  
Fuzzy Partitioning ◽  
Predicted Values ◽  
Mean Function

Recently, prediction modelling has become important in data analysis. In this paper, we propose a novel algorithm to analyze the past dataset of crop yields and predict future yields using regression-based approximation of time series fuzzy data. A framework-based algorithm, which we named DAbFP (data algorithm for degree approximation-based fuzzy partitioning), is proposed to forecast wheat yield production with fuzzy time series data. Specifically, time series data were fuzzified by the simple maximum-based generalized mean function. Different cases for prediction values were evaluated based on two-set interval-based partitioning to get accurate results. The novelty of the method lies in its ability to approximate a fuzzy relation for forecasting that provides lesser complexity and higher accuracy in linear, cubic, and quadratic order than the existing methods. A lesser complexity as compared to dynamic data approximation makes it easier to find the suitable de-fuzzification process and obtain accurate predicted values. The proposed algorithm is compared with the latest existing frameworks in terms of mean square error (MSE) and average forecasting error rate (AFER).

scholarly journals Fusing MODIS and AVHRR products to generate a global 1-km continuous NDVI time series covering four decades

2021 ◽  
Author(s):  
Xiaobin Guan ◽  
Huanfeng Shen ◽  
Yuchen Wang ◽  
Dong Chu ◽  
Xinghua Li ◽  
...  
Keyword(s):  
Time Series ◽  
Spatial Resolution ◽  
Vegetation Index ◽  
Time Series Data ◽  
Series Data ◽  
Spatiotemporal Resolution ◽  
Modis Data ◽  
Ndvi Time Series ◽  
Fusion Framework

Abstract. Satellite normalized difference vegetation index (NDVI) time-series data are an essential data source for numerous ecological and environmental applications. Although various long-term global NDVI products have been produced with different characteristics over the past decades, there is still an apparent trade-off between the spatiotemporal resolution and time coverage. The Advanced Very High-Resolution Radiometer (AVHRR) instrument can provide the only continuous time series with the longest time coverage since the early 1980s, but with the drawback of a coarse spatial resolution and poor data quality compared to the observations of later instruments. To address this issue, a spatio-temporal fusion-based long-term NDVI product (STFLNDVI) since 1982 was generated in this study, with a 1-km spatial resolution and a monthly temporal resolution. A multi-step processing fusion framework was employed to combine the superior characteristics of Moderate Resolution Imaging Spectroradiometer (MODIS) and AVHRR products, respectively. Simulated and real-data assessments both confirm the ideal accuracy of the fusion result with regard to the spatial distribution and temporal variation. Only a few relatively unsatisfactory results are found due to the poor relationship between the original AVHRR and MODIS data. The evaluations also show that the proposed fusion framework can obtain stable results similar to MODIS data in different years and seasons, even when the temporal distance between the fusion data and the reference data is large. We believe that the STFLNDVI product will be of great significance to characterize the spatial patterns and long-term variations of global vegetation. The NDVI product is available at DOI: http://doi.org/10.5281/zenodo.4734593 (Guan et al., 2021).

scholarly journals Analysis of Influencing Factors on Winter Wheat Yield Estimations Based on a Multisource Remote Sensing Data Fusion

2021 ◽  
Vol 37 (5) ◽  
pp. 991-1003
Author(s):  
Yan Li ◽  
Yan Zhao Ren ◽  
Wan Lin Gao ◽  
Sha Tao ◽  
Jing Dun Jia ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Winter Wheat ◽  
Spatial Resolution ◽  
Vegetation Index ◽  
Time Window ◽  
Wheat Yield ◽  
Remote Sensing Data ◽  
Sensing Data ◽  
Winter Wheat Yield ◽  
Window Selection

HighlightsThe potential of fusing GF-1 WFV and MODIS data by the ESTARFM algorithm was demonstrated.A better time window selection method for estimating yields was provided.A better vegetation index suitable for yield estimation based on spatiotemporally fused data was identified.The effect of the spatial resolution of remote sensing data on yield estimations was visualized.Abstract. The accurate estimation of crop yields is very important for crop management and food security. Although many methods have been developed based on single remote sensing data sources, advances are still needed to exploit multisource remote sensing data with higher spatial and temporal resolution. More suitable time window selection methods and vegetation indexes, both of which are critical for yield estimations, have not been fully considered. In this article, the Chinese GaoFen-1 Wide Field View (GF-1 WFV) and Terra Moderate Resolution Imaging Spectroradiometer (MODIS) data were fused by the Enhanced Spatial and Temporal Adaptive Reflectance Fusion Model (ESTARFM) to generate time-series data with a high spatial resolution. Then, two time window selection methods involving distinguishing or not distinguishing the growth stages during the monitoring period, and three vegetation indexes, the normalized difference vegetation index (NDVI), two-band enhanced vegetation index (EVI2) and wide dynamic range vegetation index (WDRVI), were intercompared. Furthermore, the yield estimations obtained from two different spatial resolutions of fused data and MODIS data were analyzed. The results indicate that taking the growth stage as the time window unit division basis can allow a better estimation of winter wheat yield; and that WDRVI is more suitable for yield estimations than NDVI or EVI2. This study demonstrates that the spatial resolution has a great influence on yield estimations; further, this study identifies a better time window selection method and vegetation index for improving the accuracy of yield estimations based on a multisource remote sensing data fusion. Keywords: Remote sensing, Spatiotemporal data fusion, Winter wheat, Yield estimation.

scholarly journals Spatial Pattern of Agricultural Productivity Trends in Malawi

2020 ◽  
Vol 12 (4) ◽  
pp. 1313
Author(s):  
Leah M. Mungai ◽  
Joseph P. Messina ◽  
Sieglinde Snapp
Keyword(s):  
Remote Sensing ◽  
Time Series ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Time Series Data ◽  
Agricultural Productivity ◽  
Household Survey ◽  
Maize Yield ◽  
Series Data ◽  
Maize Crop

This study aims to assess spatial patterns of Malawian agricultural productivity trends to elucidate the influence of weather and edaphic properties on Moderate Resolution Imaging Spectroradiometer (MODIS)-Normalized Difference Vegetation Index (NDVI) seasonal time series data over a decade (2006–2017). Spatially-located positive trends in the time series that can’t otherwise be accounted for are considered as evidence of farmer management and agricultural intensification. A second set of data provides further insights, using spatial distribution of farmer reported maize yield, inorganic and organic inputs use, and farmer reported soil quality information from the Malawi Integrated Household Survey (IHS3) and (IHS4), implemented between 2010–2011 and 2016–2017, respectively. Overall, remote-sensing identified areas of intensifying agriculture as not fully explained by biophysical drivers. Further, productivity trends for maize crop across Malawi show a decreasing trend over a decade (2006–2017). This is consistent with survey data, as national farmer reported yields showed low yields across Malawi, where 61% (2010–11) and 69% (2016–17) reported yields as being less than 1000 Kilograms/Hectare. Yields were markedly low in the southern region of Malawi, similar to remote sensing observations. Our generalized models provide contextual information for stakeholders on sustainability of productivity and can assist in targeting resources in needed areas. More in-depth research would improve detection of drivers of agricultural variability.

scholarly journals Automatic Land-Cover Mapping using Landsat Time-Series Data based on Google Earth Engine

2019 ◽  
Vol 11 (24) ◽  
pp. 3023 ◽  
Author(s):  
Shuai Xie ◽  
Liangyun Liu ◽  
Xiao Zhang ◽  
Jiangning Yang ◽  
Xidong Chen ◽  
...  
Keyword(s):  
Time Series ◽  
Land Cover ◽  
Vegetation Index ◽  
Time Series Data ◽  
Land Cover Classification ◽  
Google Earth ◽  
Thematic Mapper ◽  
Series Data ◽  
Land Cover Mapping ◽  
Google Earth Engine

The Google Earth Engine (GEE) has emerged as an essential cloud-based platform for land-cover classification as it provides massive amounts of multi-source satellite data and high-performance computation service. This paper proposed an automatic land-cover classification method using time-series Landsat data on the GEE cloud-based platform. The Moderate Resolution Imaging Spectroradiometer (MODIS) land-cover products (MCD12Q1.006) with the International Geosphere–Biosphere Program (IGBP) classification scheme were used to provide accurate training samples using the rules of pixel filtering and spectral filtering, which resulted in an overall accuracy (OA) of 99.2%. Two types of spectral–temporal features (percentile composited features and median composited monthly features) generated from all available Landsat Thematic Mapper (TM) and Enhanced Thematic Mapper Plus (ETM+) data from the year 2010 ± 1 were used as input features to a Random Forest (RF) classifier for land-cover classification. The results showed that the monthly features outperformed the percentile features, giving an average OA of 80% against 77%. In addition, the monthly features composited using the median outperformed those composited using the maximum Normalized Difference Vegetation Index (NDVI) with an average OA of 80% against 78%. Therefore, the proposed method is able to generate accurate land-cover mapping automatically based on the GEE cloud-based platform, which is promising for regional and global land-cover mapping.

scholarly journals An Improved CASA Model for Estimating Winter Wheat Yield from Remote Sensing Images

2019 ◽  
Vol 11 (9) ◽  
pp. 1088 ◽  
Author(s):  
Yulong Wang ◽  
Xingang Xu ◽  
Linsheng Huang ◽  
Guijun Yang ◽  
Lingling Fan ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Time Series ◽  
Winter Wheat ◽  
Crop Yield ◽  
Satellite Remote Sensing ◽  
Wheat Yield ◽  
Growing Season ◽  
Remote Sensing Images ◽  
Casa Model ◽  
Winter Wheat Yield

The accurate and timely monitoring and evaluation of the regional grain crop yield is more significant for formulating import and export plans of agricultural products, regulating grain markets and adjusting the planting structure. In this study, an improved Carnegie–Ames–Stanford approach (CASA) model was coupled with time-series satellite remote sensing images to estimate winter wheat yield. Firstly, in 2009 the entire growing season of winter wheat in the two districts of Tongzhou and Shunyi of Beijing was divided into 54 stages at five-day intervals. Net Primary Production (NPP) of winter wheat was estimated by the improved CASA model with HJ-1A/B satellite images from 39 transits. For the 15 stages without HJ-1A/B transit, MOD17A2H data products were interpolated to obtain the spatial distribution of winter wheat NPP at 5-day intervals over the entire growing season of winter wheat. Then, an NPP-yield conversion model was utilized to estimate winter wheat yield in the study area. Finally, the accuracy of the method to estimate winter wheat yield with remote sensing images was verified by comparing its results to the ground-measured yield. The results showed that the estimated yield of winter wheat based on remote sensing images is consistent with the ground-measured yield, with R2 of 0.56, RMSE of 1.22 t ha−1, and an average relative error of −6.01%. Based on time-series satellite remote sensing images, the improved CASA model can be used to estimate the NPP and thereby the yield of regional winter wheat. This approach satisfies the accuracy requirements for estimating regional winter wheat yield and thus may be used in actual applications. It also provides a technical reference for estimating large-scale crop yield.

Sign in / Sign up

Export Citation Format

Share Document