Diverse sensitivity of winter crops over the growing season to climate and land surface temperature across the rainfed cropland-belt of eastern Australia

Abstract

The rainfed cropland belt in Australia is of great importance to the world grain market but has the highest climate variability of all such regions globally. However, the spatial-temporal impacts of climate variability on crops during different crop growth stages across broadacre farming systems are largely unknown. This study aims to quantify the contributions of climate and Land Surface Temperature (LST) variations to the variability of the Enhanced Vegetation Index (EVI) by using remote sensing methods. The datasets were analyzed at an 8-day timescale across the rainfed cropland of eastern Australia. First, we found that EVI values were more variable during the crop reproductive growth stages than at any other crop life stage within a calendar year, but nevertheless had the highest correlation with crop grain yield (t ha-1). Second, climate factors and LST during the crop reproductive growth stages showed the largest variability and followed a typical east-west gradient of rainfall and a north-south temperature gradient across the study area during the crop growing season. Last, we identified two critical 8-day periods, beginning on day of the year (DoY) 257 and 289, as the key 'windows' of crop growth variation that arose from the variability in climate and LST. Our results show that the sum of the variability of the climate components within these two 8-day 'windows' explained >88% of the variability in the EVI, with LST being the dominant factor. This study offers a fresh understanding of the spatial-temporal climate-crop relationships in rainfed cropland and can serve as an early warning system for agricultural adaptation in broadacre rainfed cropping practices in Australia and worldwide.

Remote Sensing in Studies of the Growing Season: A Bibliometric Analysis

“…Some research has focused on large areas (e.g., the Northern Hemisphere [36,63,121,134,178,265]). Some researchers have used large datasets from the entire world and undertaken phenological investigations from a global perspective [37,218,325,345]. In most European countries, there were only single papers or no studies were recorded (Figure 8).…”

“…The first of these groups comprises research conducted on a global scale. For example, Ren et al [325] analyzed the impact of climatic conditions on wheat phenophases from a global perspective; additionally, they analyzed the influence of haze on phenology in India and China.…”

The Potential of Landsat NDVI Sequences to Explain Wheat Yield Variation in Fields in Western Australia

“…The SPD model composites commonly adopted methodology for fitting curves to NDVI time sequences. The process includes data interpolation and smoothing, turning points determination, and integrated metrics calculation [9][10][11][12]. Specific steps were con-figured from the pixel-by-pixel check at field J (see Supplementary Materials File S1 for detailed examples).…”

“…For example, the normalised difference vegetation index (NDVI) measures the ratio of the difference between near-infrared (NIR) and red (RED) reflectance and their sum [7,8]. Time sequences of VIs are used to monitor crop development, fit statistical crop growth curves and identify the timing and intensity of phenological events [9][10][11][12][13]. Metrics derived from phenological growth curves, such as timing and degree of growth stages and time-integrated VIs, can then be used as predictors of yield [14][15][16][17].…”

Study the Relationship Between LULC, LST, NDVI, NDWI and NDBI in Greater Arba Minch Area, Rift Valley, Ethiopia

“…The first research on LST assessment and its relationship with LULC and NDVI, NDWI, and NDBI utilizes Landsat-8 (OLI + TIRS) imagery for the current study area. The present study results are much helpful in the analysis of environmental changes in the present study area [51][52][53][54][55].…”