Abstract
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forecasting winter wheat yields by using a counter-intuitive approach, which coarsens the spatial resolution of out-of-date detailed winter wheat masks and uses them in combination with easily accessibly coarse spatial resolution remotely sensed time series data.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.
Highlights
In recent years, there has been a dramatic increase in the demand for up-to-date, reliable global agricultural information [1,2,3,4,5]
Their utility for crop yield forecasting has long been recognized and demonstrated across a wide range of scales and geographic regions [7,8,9,10,11] it is widely acknowledged that remotely sensed data could be better utilized within current operational monitoring systems, so there is a critical need for research focused on developing practical robust methods for agricultural monitoring and for crop production forecasting using available and accessible data [1,12,13]
An approach utilizing a single static crop-type mask derived for a preceding season/s, with freely available coarse resolution Normalized Difference Vegetation Index (NDVI) time series was evaluated in the US
Summary
There has been a dramatic increase in the demand for up-to-date, reliable global agricultural information [1,2,3,4,5]. Sensed data from space offers a practical means for generating such information, as they provide consistent, global, timely, cost-effective, and synoptic information on crop condition and distribution Their utility for crop yield forecasting has long been recognized and demonstrated across a wide range of scales and geographic regions [7,8,9,10,11] it is widely acknowledged that remotely sensed data could be better utilized within current operational monitoring systems, so there is a critical need for research focused on developing practical robust methods for agricultural monitoring and for crop production forecasting using available and accessible data [1,12,13]. Stratifying a region into different crop types—a process commonly termed as crop masking—is an important step in developing Earth Observations (EO)-based yield forecasting models [14,15] Such masks enable the isolation of the remotely sensed (RS) crop-specific signal throughout the growing season, reducing the noise on the signal from other land cover or crop types. A general cropland mask can be used to isolate a general cropland signal but does not provide a crop-specific signal, which is preferable for yield forecasting in areas with a diversity of crops
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