Sino–EU Earth Observation Data to Support the Monitoring and Management of Agricultural Resources

Abstract

Novel approaches and algorithms to estimate crop physiological processes from Earth Observation (EO) data are essential to develop more sustainable management practices in agricultural systems. Within this context, this paper presents the results of different research activities carried out within the ESA-MOST Dragon 4 programme. The paper encompasses two research avenues: (a) the retrieval of biophysical variables of crops and yield prediction; and (b) food security related to different crop management strategies. Concerning the retrieval of variables, results show that LAI, derived by radiative transfer model (RTM) inversion, when assimilated into a crop growth model (i.e., SAFY) provides a way to assess yields with a higher accuracy with respect to open loop model runs: 1.14 t·ha−1 vs 4.42 t·ha−1 RMSE for assimilation and open loop, respectively. Concerning food security, results show that different pathogens could be detected by remote sensing satellite data. A k coefficient higher than 0.84 was achieved for yellow rust, thus assuring a monitoring accuracy, and for the diseased samples k was higher than 0.87. Concerning permanent crops, neural network (NN) algorithms allow classification of the Pseudomonas syringae pathogen on kiwi orchards with an overall accuracy higher than 91%.

Assimilation of Earth Observation Data for Crop Yield Estimation in Smallholder Agricultural Systems

“…Our result confirms that including water balance in the SAFY model is unnecessary as long as ELUE is calibrated with EO-derived LAI [19,36]. However, stress factors during the grain filling stage, such as high temperature, pests, and disease, could also lead to discrepancies between calibration and yield prediction accuracy [44]. Thus, joint assimilation of parameters that determine yield (e.g., soil moisture) and integration of yield in the calibration process could further improve yield prediction, which is worth further investigation.…”

“…Although the calibration accuracy of rice was good, we found a higher DAM and yield error, implying that the fixed parameters influencing biomass and yield portioning (DAM0 and HI) contributed to the error. It was also reported that SAFY performs poorly when yield is greatly limited by stress due to weeds, pests, and diseases [44]. Although weeds are a common problem, the higher sensitivity of rice to weeds than maize could be the second reason for SAFY's low performance in rice.…”

Technology and Data Fusion Methods to Enhance Site-Specific Crop Monitoring

“…Similarly, the data fusion between Landsat and MERIS-FR was performed by Sisheber et al [158], Zurita-Milla et al [159] and Zurita-Milla et al [160] to develop the spatial resolution of MERIS. Pignatti et al [19] developed these methods by analyzing the spatial data of Landsat to improve the MERIS data (Table 2). These data are acquired by classifying the Landsat images to characterize the mixed pixels.…”

“…The LAI is an important index that provides crop growth condition by regulating water and predicting yield [18]. Therefore, accurate estimation of LAI can be used to precisely forecast croprelated factors, such as crop coefficient (kc) and crop yield factor (ky) [19]. The AGB content is strongly correlated with crop yield.…”

Advanced Technologies and Artificial Intelligence in Agriculture

“…Let us briefly explore the main technologies in agriculture, progressing from the top to the bottom. Satellite imagery enables soil classification [9][10][11][12][13][14][15][16][17][18][19][20][21], field monitoring [22][23][24], planning assistance [25], and yield prediction [26][27][28][29]. Open programs are mostly limited-both spatially and temporally.…”