Large‐area yield prediction early in the growing season is important in agricultural decision‐making. This study derived maize (Zea mays L.) leaf area index (LAI) estimates from spectral data and used these estimates with a simple LAI‐based yield model to forecast yield under irrigated conditions in large areas in Sinaloa, Mexico. Leaf area index was derived from satellite data with the use of an equation developed with LAI measurements from farmers' fields during the 2001–2002 autumn–winter growing season. These measurements were correlated with the normalized difference vegetation index values from 2002 Landsat ETM+ (enhanced thematic mapper) data. The equation was then tested with 2003 Landsat imagery data. A yield model was validated with maximum LAI and yield data measured in farmers' fields in northern and central Sinaloa during three consecutive autumn–winter growing seasons (1999–2000, 2000–2001, and 2001–2002). The yield model was further validated with 2002–2003 autumn–winter ground LAI (gLAI) and satellite‐derived LAI (sLAI) data from 71 farmers' fields in northern and central Sinaloa. Grain yield was predicted with a mean error of −9.2% with maximum gLAI and −11.2% with sLAI. Results indicate that the yield model using LAI can forecast yield in large areas in Sinaloa in the middle of the growing season with a mean absolute error of −1.2 Mg ha−1. The use of sLAI in place of ground measurements increased the mean absolute error by 0.3 Mg ha−1. Nevertheless, the use of sLAI would eliminate laborious LAI measurements for large‐area yield prediction in Sinaloa.
tion on vegetation function as well as land-use cover (Tan and Shih, 1997; Fang et al., 1998;Jiang and Islam, The large-scale monitoring and estimation of crop yield is essential Ochi and Murai, 1999). The NDVI derived from for food security in Mexico. This study developed and validated a method of monitoring and estimating corn (Zea mays L.) yield by satellite-image data has been strongly linked to vegetameans of satellite and ground-based data. In autumn-winter 1999 and tion condition and plant biomass on the land surface spring-summer 2000, eight locations under irrigated and nonirrigated (Tan and Shih, 1997; Fang et al., 1998; Jiang and Islam, conditions in corn valleys of Mexico were localized by Global Position-1999; Ochi and Murai, 1999). Values for NDVI range ing Systems (GPS) and were sampled every 15 d. Photosynthetic from Ϫ1.0 to 1.0. Larger NDVI values indicate that the active radiation (PAR), leaf area index (LAI), crop development stage land surface is covered with dense healthy vegetation, (DVS), planting dates, and grain yield data were gathered from the while negative values indicate the presence of clouds, field. The normalized difference vegetation index (NDVI) was derived snow, water, or a bright nonvegetated surface (Yin and from NOAA-Advanced Very High Resolution Radiometer (AVHRR)Williams, 1997). A typical NDVI temporal profile for images. A growth model was developed to integrate satellite and healthy green vegetation rises as plant cover increases ground data. Net primary productivity (NPP) was estimated using PAR and NDVI. Dry weight increase (kg ha Ϫ1 d Ϫ1 ) was determined in spring, reaches a peak or plateau during summer, and considering NPP and the partitioning factor. Results indicated that declines with plant senescence in fall. the model accounts for 89% of the variability in yields under irrigatedCloud contamination that appears in virtually every conditions and 76% under nonirrigated conditions. The methodology AVHRR scene decreases NDVI values; therefore, daily seems advantageous in large-scale monitoring and assessment of corn NDVI images in a continuous time series do not always yield.
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