Inferências sobre o calendário agrícola a partir de perfis temporais de NDVI/MODIS

Abstract

A major challenge for grain yield modeling in the context of estimates made operationally for large areas is related to the identification of periods in which annual crops show greater susceptibility to environmental stress. For soybean grown in the spring-summer period in southern Brazil, the main risk factor is the occurrence of water stress during flowering and grain filling. These subperiods occur at different times across the production region due to differences in management practices of each farmer. This study aimed to relate the soybean crop calendar to the temporal profiles of normalized difference vegetation index (NDVI/MODIS), in order to present/validate a low cost technology with adequate accuracy for crop monitoring and harvest prediction. Thus, we analyzed data from soybean crop calendar (subperiods of flowering, grain filling and maturation) from EMATER (RS) regions and NDVI MODIS images. The NDVI temporal profiles allow monitoring the development of the soybean crop biomass and determining the occurrence of subperiods. Differences in NDVI values between harvests, regions and subperiods demonstrate the sensitivity of this index in detecting the responses of soybean plants to environmental conditions. Because NDVI data are generated from MODIS images, it is possible to create maps with information about the subperiods for all harvests and throughout the State, which enables greater temporal and spatial details compared to data currently available.

Normalized difference vegetation index obtained by ground-based remote sensing to characterize vine cycle in Rio Grande do Sul, Brazil

“…Ground-based remote sensing has emerged as an important source of data collected in the field in real time. In Brazil, temporal NDVI profiles, obtained by orbital or ground-based remote sensing, have been widely used to monitor biomes (Kuplich;Moreira;Fontana, 2013, Wagner et al, 2013Junges et al, 2016) and characterize vegetation growth in annual crops Fontana, 2011, Bredemeier et al, 2013Fontana et al, 2015;Klering et al, 2016;Pinto et al, 2016). However, the use of precision agriculture technologies applied to perennial crops is incipient in the country (Bassoi et al, 2014), with few studies published on the monitoring of fruit crop cycle using remote sensing techniques.…”

Mapping of corn phenological stages using NDVI from OLI and MODIS sensors

“…Previous studies with both sensors, MODIS and OLI, have shown that crop phenology can be assessed using vegetation indexes (Fontana, Pinto, Junges, & Bremm, 2015;Jayawardhana & Chathurange, 2016;Pan et al, 2015;Sakamoto et al, 2005;Zheng et al, 2016). Based on an accurate characterization of crop phenological stages for assertive decision making in the field and potential of satellite images to acquire surface space-time information, this study aimed to evaluate the potential use of the Normalized Difference Vegetation Index (NDVI), calculated from images from the OLI and MODIS sensors, to obtain phenological information from corn crops, and particularly to define the length of each phenological stage based on average NDVI values.…”

TVDI spatiotemporal pattern of a soybean growing area in humid subtropical climate

“…At blooming, soybeans reach maximum leaf area index, maximum capacity to intercept solar radiation, and photosynthesis, thus causing NDVI to peak. In the 2004 -2005 and 2009 -2010 crop seasons, the highest NDVI were 0.79 and 0.85, respectively, both in February, coinciding with blooming that usually occurs in February (Fontana et al 2015). (Figure 4) showed a similar temporal pattern for both crops seasons.…”

“…The dynamics of the soybean crop development in the study area can be evaluated looking at the pattern of NDVI over time (Figure 4) the region (Fontana et al 2015), allowing to track the differences regarding the temporal evolution of the green biomass of soybean over the 2 crop seasons. The analysis of crop development against the critical periods of the cycle allows interpreting the responses of soybean plants to environmental conditions.…”