Remotely Sensed Rice Yield Prediction Using Multi-Temporal NDVI Data Derived from NOAA's-AVHRR

Abstract

Grain-yield prediction using remotely sensed data have been intensively studied in wheat and maize, but such information is limited in rice, barley, oats and soybeans. The present study proposes a new framework for rice-yield prediction, which eliminates the influence of the technology development, fertilizer application, and management improvement and can be used for the development and implementation of provincial rice-yield predictions. The technique requires the collection of remotely sensed data over an adequate time frame and a corresponding record of the region's crop yields. Longer normalized-difference-vegetation-index (NDVI) time series are preferable to shorter ones for the purposes of rice-yield prediction because the well-contrasted seasons in a longer time series provide the opportunity to build regression models with a wide application range. A regression analysis of the yield versus the year indicated an annual gain in the rice yield of 50 to 128 kg ha−1. Stepwise regression models for the remotely sensed rice-yield predictions have been developed for five typical rice-growing provinces in China. The prediction models for the remotely sensed rice yield indicated that the influences of the NDVIs on the rice yield were always positive. The association between the predicted and observed rice yields was highly significant without obvious outliers from 1982 to 2004. Independent validation found that the overall relative error is approximately 5.82%, and a majority of the relative errors were less than 5% in 2005 and 2006, depending on the study area. The proposed models can be used in an operational context to predict rice yields at the provincial level in China. The methodologies described in the present paper can be applied to any crop for which a sufficient time series of NDVI data and the corresponding historical yield information are available, as long as the historical yield increases significantly.

Development of a remote sensing-based rice yield forecasting model

“…9). In fact, our findings were similar to other studies, such as: (i) Nuarsa et al (2011) found R 2 ≈ 0.93 over Bali Province, Indonesia; (ii) Rahman et al (2009;2012) observed reasonable relationships (i.e., R 2 ≈ 0.56 for aus rice and R 2 ≈ 0.89 for aman rice) over Bangladesh; (iii) Chang (2012) reported good agreements (i.e., R 2 in the range 0.57 to 0.61) over Shi-ko, Taiwan; (iv) Huang et al (2013) predicted the rice yield over five rice growing provinces of China and observed good results (i.e., R 2 in the range 0.84 to 0.97, and overall RE of 5.82%); (v) Noureldin et al (2013) Despite good agreements, it would be worthwhile to note that our forecasting would hold if the rice crop not be affected by natural disturbances (that include cyclone, insect outbreak, etc.). In addition, approximately 14 to 24% of disagreements between the ground-based and forecasted rice yield estimates could be attributed by other factors, such as (i) satellite images might be affected by atmospheric effects (e.g., cloud), which degrade the quality of the acquired data and thus the developed crop-yield model (Mkhabela et al, 2011); (ii) variation in climatic conditions at microlevel during the growing season could potentially impact the agreement level of rice yield (Son et al, 2013;Mosleh et al, 2015); and (iii) uncertainty associated with ground-based yield estimates due to insufficient observations could lead to poor rice yield assessment (Mosleh & Hassan, 2014).…”

“…Despite the accuracy of these data and its ability to depict historical trends, this method has two major drawbacks: (i) time-consuming, subjective, costly, and labour-intensive (Reynolds et al, 2000;Prasad et al, 2006;Nguyen et al, 2012); and (ii) the outcomes are usually made available to the government and public after several months of the harvesting of the crop; thus not useful for food security purposes (Noureldin et al, 2013). In order to address these issues, an alternate method is the use of the remote sensing-based techniques that have already demonstrated effectiveness in forecasting the rice yield (Jing-Feng et al, 2002;Wang et al, 2010;Chen et al, 2011;Nuarsa et al, 2012;Huang et al, 2013) and assessing the yield for other crops (Bonilla et al, 2015;Fortes et al, 2015). It is being possible as remote sensing platforms are able to acquire cropping season dynamics over a large geographic extent on timely fashion in the form of images.…”

Mapping of Daily Mean Air Temperature in Agricultural Regions Using Daytime and Nighttime Land Surface Temperatures Derived from TERRA and AQUA MODIS Data

“…Air temperature is an important parameter of the climate system and useful for a wide range of agriculture applications, including crop growth simulation [1,2], yield prediction [3,4], estimation of heat accumulation during the growing season [5], assessment of high-temperature damage [6], evaluation of crop freeze injury [7,8], and crop insect development prediction [9]. Currently, near-surface temperature data is collected by meteorological stations, and although such measurements offer the advantage of high accuracy and temporal resolution, their spatial resolution may be low and they may not adequately represent surface temperatures in areas with rugged or heterogeneous surfaces [10].These limitations can bias estimates of the spatial distribution of air temperature, even when researchers use advanced spatial interpolation methods [11].With the development of remote sensing technology, it has become possible to use thermal images from satellites to obtain land surface temperatures (LSTs) over wide areas, and this data can be used to instantaneously estimate spatially contiguous air temperatures [12][13][14][15].…”

Global Crop Monitoring: A Satellite-Based Hierarchical Approach

“…Max (NDVI) and mean (NDVI) are the maximum NDVI and average NDVI of the whole growing season. Abundant literature confirms that maximum NDVI or average NDVI during the growing season is strongly related with crop yield [69][70][71][72], etc. The selection of either max (NDVIi)/max (NDVIr) or mean (NDVIi)/mean (NDVIr) is based on the impact of clouds on the NDVI signal.…”